From a7aba4f1d162e9cbf7f48fd5019d07e23ca5e7db Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Mon, 27 Mar 2023 17:27:25 +0900 Subject: [PATCH 01/25] first --- parse_coco.py | 9 +- predict.py | 282 ++++++++++++++++++++++------- predict_OPT.py | 480 +++++++++++++++++++++++++++++++++++++++++++++++++ train.py | 2 +- 4 files changed, 699 insertions(+), 74 deletions(-) create mode 100644 predict_OPT.py diff --git a/parse_coco.py b/parse_coco.py index fcd35ab..be3a2a4 100644 --- a/parse_coco.py +++ b/parse_coco.py @@ -14,17 +14,18 @@ def main(clip_model_type: str): clip_model_name = clip_model_type.replace('/', '_') out_path = f"./data/coco/oscar_split_{clip_model_name}_train.pkl" clip_model, preprocess = clip.load(clip_model_type, device=device, jit=False) - with open('./data/coco/annotations/train_caption.json', 'r') as f: + with open('/data/img_cap/coco/annotations/captions_train2017.json', 'r') as f: data = json.load(f) + data = data['annotations'] # data = data['images'] print("%0d captions loaded from json " % len(data)) all_embeddings = [] all_captions = [] for i in tqdm(range(len(data))): d = data[i] img_id = d["image_id"] - filename = f"./data/coco/train2014/COCO_train2014_{int(img_id):012d}.jpg" + filename = f"/data/img_cap/coco/images/train2017/{int(img_id):012d}.jpg" if not os.path.isfile(filename): - filename = f"./data/coco/val2014/COCO_val2014_{int(img_id):012d}.jpg" + filename = f"/data/img_cap/coco/images/train2017/{int(img_id):012d}.jpg" image = io.imread(filename) image = preprocess(Image.fromarray(image)).unsqueeze(0).to(device) with torch.no_grad(): @@ -48,4 +49,4 @@ def main(clip_model_type: str): parser = argparse.ArgumentParser() parser.add_argument('--clip_model_type', default="ViT-B/32", choices=('RN50', 'RN101', 'RN50x4', 'ViT-B/32')) args = parser.parse_args() - exit(main(args.clip_model_type)) + exit(main(args.clip_model_type)) \ No newline at end of file diff --git a/predict.py b/predict.py index 0d91c5b..4f43cf1 100644 --- a/predict.py +++ b/predict.py @@ -9,12 +9,10 @@ import torch.nn.functional as nnf import sys from typing import Tuple, List, Union, Optional -from transformers import ( - GPT2Tokenizer, - GPT2LMHeadModel, - AdamW, - get_linear_schedule_with_warmup, -) +from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup +from transformers import AutoTokenizer # OPTForCausalLM +from modeling_opt_pp import OPTForCausalLM +from configuration_opt_pp import OPTConfig import skimage.io as io import PIL.Image @@ -36,31 +34,59 @@ TSN = Optional[TS] TA = Union[T, ARRAY] -WEIGHTS_PATHS = { - "coco": "coco_weights.pt", - "conceptual-captions": "conceptual_weights.pt", -} +# WEIGHTS_PATHS = { +# "coco_gpt": "coco_train/gpt-finetuned/coco_prefix-009.pt", +# "coco_gpt008": "coco_train/gpt-finetuned/coco_prefix-008.pt", +# # "conceptual-captions": "conceptual_weights.pt", +# } + +def direct_weiht_paths(language_model): + if language_model == 'gpt2': + WEIGHTS_PATHS = { + "coco": "/data/daisy/clipcap_output/gpt2_32quries/coco_prefix-009.pt", + "coco_gpt008": "/data/daisy/clipcap_output/gpt-finetuned/coco_prefix-008.pt", + } + print('your language model is : GPT-2') + return WEIGHTS_PATHS + elif language_model == 'opt': + WEIGHTS_PATHS = { + "coco": "/data/daisy/clipcap_output/opt_32quries/coco_prefix-018.pt", + "coco_gpt008": "/data/daisy/clipcap_output/opt_32quries/coco_prefix-009.pt", + } + print('your language model is : OPT') + return WEIGHTS_PATHS + +WEIGHTS_PATHS = direct_weiht_paths('opt') + D = torch.device CPU = torch.device("cpu") - +OPT_MODEL = 'facebook/opt-125m' class Predictor(cog.Predictor): - def setup(self): + def setup(self, language_model='opt', prefix_length=32, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): """Load the model into memory to make running multiple predictions efficient""" - self.device = torch.device("cuda") + # self.device = torch.device("cuda") + self.device1 = device1 + self.device2 = device2 self.clip_model, self.preprocess = clip.load( - "ViT-B/32", device=self.device, jit=False + "ViT-B/32", device=self.device1, jit=False ) - self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + + self.language_model = language_model + if self.language_model == 'gpt2': + self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + elif self.language_model == 'opt': + self.tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL) self.models = {} - self.prefix_length = 10 + self.prefix_length = prefix_length for key, weights_path in WEIGHTS_PATHS.items(): - model = ClipCaptionModel(self.prefix_length) - model.load_state_dict(torch.load(weights_path, map_location=CPU)) + + model = ClipCaptionModel(self.prefix_length, language_model=self.language_model, device1=self.device1, device2=self.device2) + model.load_state_dict(torch.load(weights_path)) #, map_location=CPU)) model = model.eval() - model = model.to(self.device) + # model = model.to(self.device) self.models[key] = model @cog.input("image", type=cog.Path, help="Input image") @@ -68,7 +94,7 @@ def setup(self): "model", type=str, options=WEIGHTS_PATHS.keys(), - default="coco", + default="customized", help="Model to use", ) @cog.input( @@ -82,76 +108,189 @@ def predict(self, image, model, use_beam_search): image = io.imread(image) model = self.models[model] pil_image = PIL.Image.fromarray(image) - image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + image = self.preprocess(pil_image).unsqueeze(0).to(self.device1) with torch.no_grad(): prefix = self.clip_model.encode_image(image).to( - self.device, dtype=torch.float32 + self.device1, dtype=torch.float32 ) prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) + if use_beam_search: return generate_beam(model, self.tokenizer, embed=prefix_embed)[0] else: return generate2(model, self.tokenizer, embed=prefix_embed) -class MLP(nn.Module): - def forward(self, x: T) -> T: - return self.model(x) - - def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): - super(MLP, self).__init__() +class MlpTransformer(nn.Module): + def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): + super().__init__() + out_d = out_d if out_d is not None else in_dim + self.fc1 = nn.Linear(in_dim, h_dim) + self.act = act + self.fc2 = nn.Linear(h_dim, out_d) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + +class MultiHeadAttention(nn.Module): + + def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim_self // num_heads + self.scale = head_dim ** -0.5 + self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) + self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) + self.project = nn.Linear(dim_self, dim_self) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, y=None, mask=None): + y = y if y is not None else x + b, n, c = x.shape + _, m, d = y.shape + # b n h dh + queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) + # b m 2 h dh + keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) + keys, values = keys_values[:, :, 0], keys_values[:, :, 1] + attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale + if mask is not None: + if mask.dim() == 2: + mask = mask.unsqueeze(1) + attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) + attention = attention.softmax(dim=2) + out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) + out = self.project(out) + return out, attention + + +class TransformerLayer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + x_, attention = self.attn(self.norm1(x), y, mask) + x = x + x_ + x = x + self.mlp(self.norm2(x)) + return x, attention + + def forward(self, x, y=None, mask=None): + x = x + self.attn(self.norm1(x), y, mask)[0] + x = x + self.mlp(self.norm2(x)) + return x + + def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, + norm_layer: nn.Module = nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim_self) + self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) + self.norm2 = norm_layer(dim_self) + self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) + + +class Transformer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + attentions = [] + for layer in self.layers: + x, att = layer.forward_with_attention(x, y, mask) + attentions.append(att) + return x, attentions + + def forward(self, x, y=None, mask=None): + for i, layer in enumerate(self.layers): + if i % 2 == 0 and self.enc_dec: # cross + x = layer(x, y) + elif self.enc_dec: # self + x = layer(x, x, mask) + else: # self or cross + x = layer(x, y, mask) + return x + + def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, + mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): + super(Transformer, self).__init__() + dim_ref = dim_ref if dim_ref is not None else dim_self + self.enc_dec = enc_dec + if enc_dec: + num_layers = num_layers * 2 layers = [] - for i in range(len(sizes) - 1): - layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) - if i < len(sizes) - 2: - layers.append(act()) - self.model = nn.Sequential(*layers) + for i in range(num_layers): + if i % 2 == 0 and enc_dec: # cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + elif enc_dec: # self + layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + else: # self or cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + self.layers = nn.ModuleList(layers) + + +class TransformerMapper(nn.Module): + + def forward(self, x): + x = self.linear(x).view(x.shape[0], self.clip_length, -1) + prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) + prefix = torch.cat((x, prefix), dim=1) + out = self.transformer(prefix)[:, self.clip_length:] + return out + def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): + super(TransformerMapper, self).__init__() + self.clip_length = clip_length + self.transformer = Transformer(dim_embedding, 8, num_layers) + self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) + self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) -class ClipCaptionModel(nn.Module): - # @functools.lru_cache #FIXME - def get_dummy_token(self, batch_size: int, device: D) -> T: - return torch.zeros( - batch_size, self.prefix_length, dtype=torch.int64, device=device - ) +class ClipCaptionModel(nn.Module): - def forward( - self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None - ): - embedding_text = self.gpt.transformer.wte(tokens) - prefix_projections = self.clip_project(prefix).view( - -1, self.prefix_length, self.gpt_embedding_size - ) - # print(embedding_text.size()) #torch.Size([5, 67, 768]) - # print(prefix_projections.size()) #torch.Size([5, 1, 768]) - embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) + def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: + return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) + + def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None): + if self.language_model == 'gpt2': + embedding_text = self.gpt.transformer.wte(tokens) + elif self.language_model == 'opt': + embedding_text = self.gpt.model.embed_tokens(tokens) + prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) + embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) if labels is not None: dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) labels = torch.cat((dummy_token, tokens), dim=1) out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) - return out + return out.to(self.device1) - def __init__(self, prefix_length: int, prefix_size: int = 512): + def __init__(self, prefix_length: int, language_model='gpt2', clip_length: Optional[int] = 32, prefix_size: int = 512, + num_layers: int = 8, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): super(ClipCaptionModel, self).__init__() self.prefix_length = prefix_length - self.gpt = GPT2LMHeadModel.from_pretrained("gpt2") - self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] - if prefix_length > 10: # not enough memory - self.clip_project = nn.Linear( - prefix_size, self.gpt_embedding_size * prefix_length - ) - else: - self.clip_project = MLP( - ( - prefix_size, - (self.gpt_embedding_size * prefix_length) // 2, - self.gpt_embedding_size * prefix_length, - ) - ) + self.language_model = language_model + self.prefix_size=prefix_size + self.clip_length = clip_length + self.num_layers = num_layers + self.device1 = device1 + self.device2 = device2 + + if self.language_model == 'gpt2': + self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') + self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] + elif self.language_model == 'opt': + print('clipcaption - LM : OPT') + self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) + self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] + self.gpt.setting_device(device1 = self.device1, device2 = self.device2) + + self.clip_project = TransformerMapper(dim_clip=self.prefix_size, dim_embedding=self.gpt_embedding_size, + prefix_length=self.prefix_length, clip_length=self.clip_length, num_layers=self.num_layers).to(self.device1) class ClipCaptionPrefix(ClipCaptionModel): + def parameters(self, recurse: bool = True): return self.clip_project.parameters() @@ -161,6 +300,7 @@ def train(self, mode: bool = True): return self + def generate_beam( model, tokenizer, @@ -219,9 +359,11 @@ def generate_beam( generated = generated[next_tokens_source] scores = scores_sum_average * seq_lengths is_stopped = is_stopped[next_tokens_source] - next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view( - generated.shape[0], 1, -1 - ) + # next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view( + # generated.shape[0], 1, -1 + # ) # GPT-2 + next_token_embed = model.gpt.model.decoder.embed_tokens(next_tokens.squeeze()).view( + generated.shape[0], 1, -1) # OPT generated = torch.cat((generated, next_token_embed), dim=1) is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() if is_stopped.all(): @@ -286,7 +428,8 @@ def generate2( indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[:, indices_to_remove] = filter_value next_token = torch.argmax(logits, -1).unsqueeze(0) - next_token_embed = model.gpt.transformer.wte(next_token) + # next_token_embed = model.gpt.transformer.wte(next_token) # GPT-2 + next_token_embed = model.gpt.model.decoder.embed_tokens(next_token) # OPT if tokens is None: tokens = next_token else: @@ -300,3 +443,4 @@ def generate2( generated_list.append(output_text) return generated_list[0] + diff --git a/predict_OPT.py b/predict_OPT.py new file mode 100644 index 0000000..8559924 --- /dev/null +++ b/predict_OPT.py @@ -0,0 +1,480 @@ +# Prediction interface for Cog ⚙️ +# Reference: https://github.com/replicate/cog/blob/main/docs/python.md + +import clip +import os +from torch import nn +import numpy as np +import torch +import torch.nn.functional as nnf +import sys +from typing import Tuple, List, Union, Optional +# from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup +from transformers import OPTForCausalLM, AdamW, get_linear_schedule_with_warmup +from transformers import AutoTokenizer +import skimage.io as io +import PIL.Image + +import cog + + +import os +from tqdm import tqdm +import pandas as pd +import json +import torch + +# import torch + +N = type(None) +V = np.array +ARRAY = np.ndarray +ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]] +VS = Union[Tuple[V, ...], List[V]] +VN = Union[V, N] +VNS = Union[VS, N] +T = torch.Tensor +TS = Union[Tuple[T, ...], List[T]] +TN = Optional[T] +TNS = Union[Tuple[TN, ...], List[TN]] +TSN = Optional[TS] +TA = Union[T, ARRAY] + +WEIGHTS_PATHS = { + "coco_opt": "coco_train/opt-finetuned/coco_prefix-009.pt", + "coco": "coco_train/opt-finetuned/coco_prefix-008.pt", + # "conceptual-captions": "conceptual_weights.pt", +} + +D = torch.device +CPU = torch.device("cpu") + + +class Predictor(cog.Predictor): + def setup(self): + """Load the model into memory to make running multiple predictions efficient""" + self.device = torch.device("cuda") + self.clip_model, self.preprocess = clip.load( + "ViT-B/32", device=self.device, jit=False + ) + self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + + self.models = {} + self.prefix_length = 40 #10 + for key, weights_path in WEIGHTS_PATHS.items(): + model = ClipCaptionModel(self.prefix_length) + model.load_state_dict(torch.load(weights_path, map_location=CPU)) + model = model.eval() + model = model.to(self.device) + self.models[key] = model + + @cog.input("image", type=cog.Path, help="Input image") + @cog.input( + "model", + type=str, + options=WEIGHTS_PATHS.keys(), + default="customized", + help="Model to use", + ) + @cog.input( + "use_beam_search", + type=bool, + default=False, + help="Whether to apply beam search to generate the output text", + ) + def predict(self, image, model, use_beam_search): + """Run a single prediction on the model""" + image = io.imread(image) + model = self.models[model] + pil_image = PIL.Image.fromarray(image) + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + with torch.no_grad(): + prefix = self.clip_model.encode_image(image).to( + self.device, dtype=torch.float32 + ) + prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) + if use_beam_search: + return generate_beam(model, self.tokenizer, embed=prefix_embed)[0], prefix_embed + else: + return generate2(model, self.tokenizer, embed=prefix_embed), prefix_embed + + +class MLP(nn.Module): + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.model(x) + + def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): + super(MLP, self).__init__() + layers = [] + for i in range(len(sizes) - 1): + layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) + if i < len(sizes) - 2: + layers.append(act()) + self.model = nn.Sequential(*layers) + + +class MlpTransformer(nn.Module): + def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): + super().__init__() + out_d = out_d if out_d is not None else in_dim + self.fc1 = nn.Linear(in_dim, h_dim) + self.act = act + self.fc2 = nn.Linear(h_dim, out_d) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + +class MultiHeadAttention(nn.Module): + + def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim_self // num_heads + self.scale = head_dim ** -0.5 + self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) + self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) + self.project = nn.Linear(dim_self, dim_self) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, y=None, mask=None): + y = y if y is not None else x + b, n, c = x.shape + _, m, d = y.shape + # b n h dh + queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) + # b m 2 h dh + keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) + keys, values = keys_values[:, :, 0], keys_values[:, :, 1] + attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale + if mask is not None: + if mask.dim() == 2: + mask = mask.unsqueeze(1) + attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) + attention = attention.softmax(dim=2) + out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) + out = self.project(out) + return out, attention + + +class TransformerLayer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + x_, attention = self.attn(self.norm1(x), y, mask) + x = x + x_ + x = x + self.mlp(self.norm2(x)) + return x, attention + + def forward(self, x, y=None, mask=None): + x = x + self.attn(self.norm1(x), y, mask)[0] + x = x + self.mlp(self.norm2(x)) + return x + + def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, + norm_layer: nn.Module = nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim_self) + self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) + self.norm2 = norm_layer(dim_self) + self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) + + +class Transformer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + attentions = [] + for layer in self.layers: + x, att = layer.forward_with_attention(x, y, mask) + attentions.append(att) + return x, attentions + + def forward(self, x, y=None, mask=None): + for i, layer in enumerate(self.layers): + if i % 2 == 0 and self.enc_dec: # cross + x = layer(x, y) + elif self.enc_dec: # self + x = layer(x, x, mask) + else: # self or cross + x = layer(x, y, mask) + return x + + def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, + mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): + super(Transformer, self).__init__() + dim_ref = dim_ref if dim_ref is not None else dim_self + self.enc_dec = enc_dec + if enc_dec: + num_layers = num_layers * 2 + layers = [] + for i in range(num_layers): + if i % 2 == 0 and enc_dec: # cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + elif enc_dec: # self + layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + else: # self or cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + self.layers = nn.ModuleList(layers) + + +class TransformerMapper(nn.Module): + + def forward(self, x): + x = self.linear(x).view(x.shape[0], self.clip_length, -1) + prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) + prefix = torch.cat((x, prefix), dim=1) + out = self.transformer(prefix)[:, self.clip_length:] + return out + + def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): + super(TransformerMapper, self).__init__() + self.clip_length = clip_length + self.transformer = Transformer(dim_embedding, 8, num_layers) + self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) + self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) + + +class ClipCaptionModel(nn.Module): + + def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: + return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) + + def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None): + # embedding_text = self.gpt.transformer.wte(tokens) + embedding_text = self.gpt.model.decoder.embed_tokens(tokens) # 수정 + prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) + embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) + if labels is not None: + dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) + labels = torch.cat((dummy_token, tokens), dim=1) + out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) + return out + + def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512, + num_layers: int = 8): + super(ClipCaptionModel, self).__init__() + self.prefix_length = prefix_length + # self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') + print('LM Model : opt-2.7b') + self.gpt = OPTForCausalLM.from_pretrained('facebook/opt-2.7b') # edit_ + # self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] + self.gpt_embedding_size = self.gpt.lm_head.in_features + self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length, + clip_length, num_layers) + + +class ClipCaptionPrefix(ClipCaptionModel): + + def parameters(self, recurse: bool = True): + return self.clip_project.parameters() + + def train(self, mode: bool = True): + super(ClipCaptionPrefix, self).train(mode) + self.gpt.eval() + return self + + + +class ClipCaptionPrefix(ClipCaptionModel): + def parameters(self, recurse: bool = True): + return self.clip_project.parameters() + + def train(self, mode: bool = True): + super(ClipCaptionPrefix, self).train(mode) + self.gpt.eval() + return self + + +def generate_beam( + model, + tokenizer, + beam_size: int = 5, + prompt=None, + embed=None, + entry_length=67, + temperature=1.0, + stop_token: str = ".", +): + + model.eval() + stop_token_index = tokenizer.encode(stop_token)[0] + tokens = None + scores = None + device = next(model.parameters()).device + seq_lengths = torch.ones(beam_size, device=device) + is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) + with torch.no_grad(): + if embed is not None: + generated = embed + else: + if tokens is None: + tokens = torch.tensor(tokenizer.encode(prompt)) + tokens = tokens.unsqueeze(0).to(device) + generated = model.gpt.transformer.wte(tokens) + for i in range(entry_length): + outputs = model.gpt(inputs_embeds=generated) + logits = outputs.logits + logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) + logits = logits.softmax(-1).log() + if scores is None: + scores, next_tokens = logits.topk(beam_size, -1) + generated = generated.expand(beam_size, *generated.shape[1:]) + next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) + if tokens is None: + tokens = next_tokens + else: + tokens = tokens.expand(beam_size, *tokens.shape[1:]) + tokens = torch.cat((tokens, next_tokens), dim=1) + else: + logits[is_stopped] = -float(np.inf) + logits[is_stopped, 0] = 0 + scores_sum = scores[:, None] + logits + seq_lengths[~is_stopped] += 1 + scores_sum_average = scores_sum / seq_lengths[:, None] + scores_sum_average, next_tokens = scores_sum_average.view(-1).topk( + beam_size, -1 + ) + next_tokens_source = next_tokens // scores_sum.shape[1] + seq_lengths = seq_lengths[next_tokens_source] + next_tokens = next_tokens % scores_sum.shape[1] + next_tokens = next_tokens.unsqueeze(1) + tokens = tokens[next_tokens_source] + tokens = torch.cat((tokens, next_tokens), dim=1) + generated = generated[next_tokens_source] + scores = scores_sum_average * seq_lengths + is_stopped = is_stopped[next_tokens_source] + next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view( + generated.shape[0], 1, -1 + ) + generated = torch.cat((generated, next_token_embed), dim=1) + is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() + if is_stopped.all(): + break + scores = scores / seq_lengths + output_list = tokens.cpu().numpy() + output_texts = [ + tokenizer.decode(output[: int(length)]) + for output, length in zip(output_list, seq_lengths) + ] + order = scores.argsort(descending=True) + output_texts = [output_texts[i] for i in order] + return output_texts + + +def generate2( + model, + tokenizer, + tokens=None, + prompt=None, + embed=None, + entry_count=1, + entry_length=67, # maximum number of words + top_p=0.8, + temperature=1.0, + stop_token: str = ".", +): + model.eval() + generated_num = 0 + generated_list = [] + stop_token_index = tokenizer.encode(stop_token)[0] + filter_value = -float("Inf") + device = next(model.parameters()).device + + with torch.no_grad(): + + for entry_idx in range(entry_count): + if embed is not None: + generated = embed + else: + if tokens is None: + tokens = torch.tensor(tokenizer.encode(prompt)) + tokens = tokens.unsqueeze(0).to(device) + + generated = model.gpt.transformer.wte(tokens) + + for i in range(entry_length): + + outputs = model.gpt(inputs_embeds=generated) + logits = outputs.logits + logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum( + nnf.softmax(sorted_logits, dim=-1), dim=-1 + ) + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[ + ..., :-1 + ].clone() + sorted_indices_to_remove[..., 0] = 0 + + indices_to_remove = sorted_indices[sorted_indices_to_remove] + logits[:, indices_to_remove] = filter_value + next_token = torch.argmax(logits, -1).unsqueeze(0) + next_token_embed = model.gpt.transformer.wte(next_token) + if tokens is None: + tokens = next_token + else: + tokens = torch.cat((tokens, next_token), dim=1) + generated = torch.cat((generated, next_token_embed), dim=1) + if stop_token_index == next_token.item(): + break + + output_list = list(tokens.squeeze().cpu().numpy()) + output_text = tokenizer.decode(output_list) + generated_list.append(output_text) + + return generated_list[0] + +######################################################### + +import os +from tqdm import tqdm +import pandas as pd +import json + +fpath_nice = os.path.join('/data/img_cap/nice', 'NICE_val') +flist_nice = os.listdir(fpath_nice) +annot_csv = pd.read_csv(os.path.join('/data/img_cap/nice', 'nice-val-5k.csv')) + +# data = {} +# for img_nice in tqdm(flist_nice): +# inputs = {'image':open(os.path.join(fpath_nice, img_nice), 'rb'), 'model':'coco', 'use_beam_search':False} +# generated_caption = version.predict(**inputs) + +# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() + +# data[int(img_nice[:-4])] = [target_caption, generated_caption] + +# with open('NICE-clipcap_generate.json', 'w') as f_: +# json.dump(data, f_) + + +# predict = Predictor() +# predict.setup() + + +# data_coco_2 = {} +# data_coco_beam = {} +# for img_nice in tqdm(flist_nice): +# image = os.path.join(fpath_nice, img_nice) + +# # generated_caption_coco_2, prefix_embed = predict.predict(image=image, model='coco', use_beam_search=False) +# generated_caption_coco_beam, prefix_embed = predict.predict(image=image, model='coco', use_beam_search=True) + +# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() + +# # data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] +# # torch.save(prefix_embed, f'prefix_embedding/clipcap/{img_nice[:-4]}') + +# data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] + +# # with open('nice-clipcap_coco_2.json', 'w') as fp: +# # json.dump(data_coco_2, fp) +# with open('nice-clipcap_coco_beam.json', 'w') as fp: +# json.dump(data_coco_beam, fp) \ No newline at end of file diff --git a/train.py b/train.py index 61e945e..b4c082a 100644 --- a/train.py +++ b/train.py @@ -367,4 +367,4 @@ def main(): if __name__ == '__main__': - main() + main() \ No newline at end of file From a3a9cace05397d3a1d917a8add3e23dfeb0e2fe0 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Mon, 27 Mar 2023 19:40:31 +0900 Subject: [PATCH 02/25] add OPT model --- README.md | 50 +- modeling_opt_pp.py | 1278 ++++++++++++++++++++++++++++++++++++++++++++ predict.py | 36 +- predict_nice.py | 68 +++ train_OPT.py | 405 ++++++++++++++ 5 files changed, 1779 insertions(+), 58 deletions(-) create mode 100644 modeling_opt_pp.py create mode 100644 predict_nice.py create mode 100644 train_OPT.py diff --git a/README.md b/README.md index b314e05..b8b467b 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,14 @@ Image captioning is a complicated task, where usually a pretrained detection net In our work, we use the [CLIP](https://github.com/openai/CLIP) model, which was already trained over an extremely large number of images, thus is capable of generating semantic encodings for arbitrary images without additional supervision. To produce meaningful sentences we fine-tune a pretrained language model, which has been proven to be successful for other natural language tasks. The key idea is to use the CLIP encoding as a prefix to the textual captions by employing a simple mapping network over the raw encoding, and then fine-tune our language model to generate a valid caption. In addition, we present another variant, where we utilize a transformer architecture for the mapping network and avoid the fine-tuning of GPT-2. Still, our light model achieve comaparable to state-of-the-art over nocaps dataset. + + + +## Swith your language model from GPT-2 to OPT +We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). +Training code is available at train.py and inference code will be updated on predict_OPT.py, which is basically running Predictor function in predict.py. +Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both predict.py and train.py. + ## COCO Examples @@ -46,33 +54,7 @@ In our work, we use the [CLIP](https://github.com/openai/CLIP) model, which was
-## Conceptual Captions Examples - - - - - - - - - - - -
3D render of a man holding a globe.Students enjoing the cherry blossomsGreen leaf of lettuce on a white plate.
- - - - - - - - - - - - -
The hotel and casino on the waterfront. The triangle is a symbol of the soul.Cartoon boy in the bath.
## Inference Notebooks @@ -123,7 +105,7 @@ python train.py --only_prefix --data ./data/coco/oscar_split_ViT-B_32_train.pkl ``` **If you wish to use ResNet-based CLIP:** - +https://github.com/Jhryu30/cvpr2023_challenge_clipcap.git ``` python parse_coco.py --clip_model_type RN50x4 ``` @@ -131,21 +113,7 @@ python parse_coco.py --clip_model_type RN50x4 python train.py --only_prefix --data ./data/coco/oscar_split_RN50x4_train.pkl --out_dir ./coco_train/ --mapping_type transformer --num_layres 8 --prefix_length 40 --prefix_length_clip 40 --is_rn ``` -## Conceptual training - -Download the .TSV train/val files from [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/download) and place them under directory. -Download the images and extract CLIP features using (outputs are `/conceptual_clip_ViT-B_32_train.pkl` and `/conceptual_clip_ViT-B_32_val.pkl`): -``` -python parse_conceptual.py --clip_model_type ViT-B/32 --data_root --num_threads 16 -``` -Notice, downloading the images might take a few days. - -Train with fine-tuning of GPT2: -``` -python train.py --data /conceptual_clip_ViT-B_32_train.pkl --out_dir ./conceptual_train/ -``` -Similarly to the COCO training, you can train a transformer mapping network, and / or parse the images using a ResNet-based CLIP. ## Citation If you use this code for your research, please cite: diff --git a/modeling_opt_pp.py b/modeling_opt_pp.py new file mode 100644 index 0000000..a656e83 --- /dev/null +++ b/modeling_opt_pp.py @@ -0,0 +1,1278 @@ +# coding=utf-8 +# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch OPT model.""" +import random +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from transformers.models.opt.configuration_opt import OPTConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "facebook/opt-350m" +_CONFIG_FOR_DOC = "OPTConfig" + +# Base model docstring +_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] + +# SequenceClassification docstring +_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc" +_SEQ_CLASS_EXPECTED_LOSS = 1.71 +_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" + +OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "facebook/opt-125m", + "facebook/opt-350m", + "facebook/opt-1.3b", + "facebook/opt-2.7b", + "facebook/opt-6.7b", + "facebook/opt-13b", + "facebook/opt-30b", + # See all OPT models at https://huggingface.co/models?filter=opt +] + + +def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) + mask_cond = torch.arange(mask.size(-1)) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +class OPTLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): + """`input_ids_shape` is expected to be [bsz x seqlen].""" + attention_mask = attention_mask.long() + + # create positions depending on attention_mask + positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 + + # cut positions if `past_key_values_length` is > 0 + positions = positions[:, past_key_values_length:] + + return super().forward(positions + self.offset) + + +class OPTAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: Optional[torch.Tensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_states = past_key_value[0] + value_states = past_key_value[1] + elif is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, bsz) + value_states = self._shape(self.v_proj(key_value_states), -1, bsz) + elif past_key_value is not None: + # reuse k, v, self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_states, value_states) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437 + if attn_weights.dtype == torch.float16: + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16) + else: + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if layer_head_mask is not None: + if layer_head_mask.size() != (self.num_heads,): + raise ValueError( + f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" + f" {layer_head_mask.size()}" + ) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned aross GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped, past_key_value + + +class OPTDecoderLayer(nn.Module): + def __init__(self, config: OPTConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = OPTAttention( + embed_dim=self.embed_dim, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + bias=config.enable_bias, + ) + self.do_layer_norm_before = config.do_layer_norm_before + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + + self.self_attn_layer_norm = nn.LayerNorm( + self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine + ) + self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias) + self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias) + self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + layer_head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size + `(encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Fully Connected + hidden_states_shape = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + hidden_states = (residual + hidden_states).view(hidden_states_shape) + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +OPT_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`OPTConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare OPT Model outputting raw hidden-states without any specific head on top.", + OPT_START_DOCSTRING, +) +class OPTPreTrainedModel(PreTrainedModel): + config_class = OPTConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OPTDecoderLayer"] + _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] + + def _init_weights(self, module): + std = self.config.init_std + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (OPTDecoder)): + module.gradient_checkpointing = value + +OPT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class OPTDecoder(OPTPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`] + + Args: + config: OPTConfig + """ + + def __init__(self, config: OPTConfig): + super().__init__(config) + self.cfg = config + self.dropout = config.dropout + self.layerdrop = config.layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx) + self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) + + + if config.word_embed_proj_dim != config.hidden_size: + self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False) + else: + self.project_out = None + + if config.word_embed_proj_dim != config.hidden_size: + self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False) + else: + self.project_in = None + + # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility + # with checkpoints that have been fine-tuned before transformers v4.20.1 + # see https://github.com/facebookresearch/metaseq/pull/164 + if config.do_layer_norm_before and not config._remove_final_layer_norm: + self.final_layer_norm = nn.LayerNorm( + config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine + ) + else: + self.final_layer_norm = None + + + self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def setting_device(self, device1, device2, pn): + self.device1 = device1 + self.device2 = device2 + self.parallel_num = pn + self._model_to_device() + + def _model_to_device(self): + self.embed_tokens.to(self.device1) + self.embed_positions.to(self.device1) + self.final_layer_norm.to(self.device1) + for i in range(self.cfg.num_hidden_layers): + if i [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length + ).to(inputs_embeds.device) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # embed positions + if attention_mask is None: + attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device) + pos_embeds = self.embed_positions(attention_mask, past_key_values_length) + + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, input_shape, inputs_embeds, past_key_values_length + ) + + if self.project_in is not None: + inputs_embeds = self.project_in(inputs_embeds) + + hidden_states = inputs_embeds + pos_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + # check if head_mask has a correct number of layers specified if desired + for attn_mask, mask_name in zip([head_mask], ["head_mask"]): + if attn_mask is not None: + if attn_mask.size()[0] != (len(self.layers)): + raise ValueError( + f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" + f" {head_mask.size()[0]}." + ) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + + dropout_probability = random.uniform(0, 1) + if self.training and (dropout_probability < self.layerdrop): + continue + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, output_attentions, None) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(decoder_layer), + hidden_states, + attention_mask, + head_mask[idx] if head_mask is not None else None, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + layer_head_mask=(head_mask[idx] if head_mask is not None else None), + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + hidden_states = layer_outputs[0] + + if idx == self.parallel_num-1: + # layer_outputs = layer_outputs.to(self.device2) + hidden_states = hidden_states.to(self.device2) + attention_mask = attention_mask.to(self.device2) + # head_mask = head_mask.to(self.device2) + + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if self.final_layer_norm is not None: + self.final_layer_norm.to(self.device2) + hidden_states = self.final_layer_norm(hidden_states) + + if self.project_out is not None: + self.project_out.to(self.device2) + hidden_states = self.project_out(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + +@add_start_docstrings( + "The bare OPT Model outputting raw hidden-states without any specific head on top.", + OPT_START_DOCSTRING, +) +class OPTModel(OPTPreTrainedModel): + def __init__(self, config: OPTConfig): + super().__init__(config) + self.decoder = OPTDecoder(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + def get_decoder(self): + return self.decoder + + @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPast, + config_class=_CONFIG_FOR_DOC, + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + + return BaseModelOutputWithPast( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + hidden_states=decoder_outputs.hidden_states, + attentions=decoder_outputs.attentions, + ) + + +class OPTForCausalLM(OPTPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = OPTModel(config) + + # the lm_head weight is automatically tied to the embed tokens weight + self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model.decoder = decoder + + def get_decoder(self): + return self.model.decoder + + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): + Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of + shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional + tensors are only required when the model is used as a decoder in a Sequence to Sequence model. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForCausalLM + + >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + device = self.model.decoder.device1 + logits = self.lm_head(outputs[0].to(device)).contiguous() + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + ): + if past_key_values: + input_ids = input_ids[:, -1:] + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and past_key_values is None: + model_inputs = {"inputs_embeds": inputs_embeds} + else: + model_inputs = {"input_ids": input_ids} + + model_inputs.update( + { + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) + return model_inputs + + @staticmethod + def _reorder_cache(past_key_values, beam_idx): + reordered_past = () + for layer_past in past_key_values: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past + + +@add_start_docstrings( + """ + The OPT Model transformer with a sequence classification head on top (linear layer). + + [`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + OPT_START_DOCSTRING, +) +class OPTForSequenceClassification(OPTPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"lm_head.weight"] + + def __init__(self, config: OPTConfig): + super().__init__(config) + self.num_labels = config.num_labels + self.model = OPTModel(config) + self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, + output_type=SequenceClassifierOutputWithPast, + config_class=_CONFIG_FOR_DOC, + expected_output=_SEQ_CLASS_EXPECTED_OUTPUT, + expected_loss=_SEQ_CLASS_EXPECTED_LOSS, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size, sequence_length = input_ids.shape[:2] + else: + batch_size, sequence_length = inputs_embeds.shape[:2] + + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + +@add_start_docstrings( + """ + The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD + (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + OPT_START_DOCSTRING, +) +class OPTForQuestionAnswering(OPTPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"lm_head.weight"] + + def __init__(self, config: OPTConfig): + super().__init__(config) + self.model = OPTModel(config) + self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForQuestionAnswering + >>> import torch + + >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> # note: we are loading a OPTForQuestionAnswering from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random + >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") + + >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" + + >>> inputs = tokenizer(question, text, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> answer_start_index = outputs.start_logits.argmax() + >>> answer_end_index = outputs.end_logits.argmax() + + >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] + >>> predicted = tokenizer.decode(predict_answer_tokens) + >>> predicted + ' Henson?' + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + logits = self.qa_outputs(hidden_states) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + transformer_outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value \ No newline at end of file diff --git a/predict.py b/predict.py index 4f43cf1..a4559cd 100644 --- a/predict.py +++ b/predict.py @@ -12,7 +12,6 @@ from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup from transformers import AutoTokenizer # OPTForCausalLM from modeling_opt_pp import OPTForCausalLM -from configuration_opt_pp import OPTConfig import skimage.io as io import PIL.Image @@ -50,8 +49,8 @@ def direct_weiht_paths(language_model): return WEIGHTS_PATHS elif language_model == 'opt': WEIGHTS_PATHS = { - "coco": "/data/daisy/clipcap_output/opt_32quries/coco_prefix-018.pt", - "coco_gpt008": "/data/daisy/clipcap_output/opt_32quries/coco_prefix-009.pt", + "coco": "/data/daisy/clipcap_output/opt13b_32query/coco_prefix-018.pt", + "coco_gpt008": "/data/daisy/clipcap_output/opt13b_32query/coco_prefix-008.pt", } print('your language model is : OPT') return WEIGHTS_PATHS @@ -61,7 +60,7 @@ def direct_weiht_paths(language_model): D = torch.device CPU = torch.device("cpu") -OPT_MODEL = 'facebook/opt-125m' +OPT_MODEL = 'facebook/opt-1.3b' class Predictor(cog.Predictor): def setup(self, language_model='opt', prefix_length=32, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): @@ -116,9 +115,9 @@ def predict(self, image, model, use_beam_search): prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) if use_beam_search: - return generate_beam(model, self.tokenizer, embed=prefix_embed)[0] + return prefix_embed, generate_beam(model, self.tokenizer, embed=prefix_embed)[0] else: - return generate2(model, self.tokenizer, embed=prefix_embed) + return prefix_embed, generate2(model, self.tokenizer, embed=prefix_embed) class MlpTransformer(nn.Module): @@ -256,14 +255,14 @@ def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[tor if self.language_model == 'gpt2': embedding_text = self.gpt.transformer.wte(tokens) elif self.language_model == 'opt': - embedding_text = self.gpt.model.embed_tokens(tokens) + embedding_text = self.gpt.model.decoder.embed_tokens(tokens) prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) if labels is not None: dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) labels = torch.cat((dummy_token, tokens), dim=1) out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) - return out.to(self.device1) + return out def __init__(self, prefix_length: int, language_model='gpt2', clip_length: Optional[int] = 32, prefix_size: int = 512, num_layers: int = 8, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): @@ -283,7 +282,7 @@ def __init__(self, prefix_length: int, language_model='gpt2', clip_length: Optio print('clipcaption - LM : OPT') self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] - self.gpt.setting_device(device1 = self.device1, device2 = self.device2) + self.gpt.model.decoder.setting_device(device1 = self.device1, device2 = self.device2, pn=6) self.clip_project = TransformerMapper(dim_clip=self.prefix_size, dim_embedding=self.gpt_embedding_size, prefix_length=self.prefix_length, clip_length=self.clip_length, num_layers=self.num_layers).to(self.device1) @@ -305,18 +304,19 @@ def generate_beam( model, tokenizer, beam_size: int = 5, - prompt=None, + prompt="a photo of", embed=None, entry_length=67, temperature=1.0, - stop_token: str = ".", + stop_token: str = "/n", ): model.eval() stop_token_index = tokenizer.encode(stop_token)[0] tokens = None scores = None - device = next(model.parameters()).device + device = embed.device + embed = embed.type(torch.DoubleTensor) seq_lengths = torch.ones(beam_size, device=device) is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) with torch.no_grad(): @@ -326,7 +326,8 @@ def generate_beam( if tokens is None: tokens = torch.tensor(tokenizer.encode(prompt)) tokens = tokens.unsqueeze(0).to(device) - generated = model.gpt.transformer.wte(tokens) + # generated = model.gpt.transformer.wte(tokens) # GPT-2 + generated = model.gpt.decoder.embed_tokens(tokens) # OPT for i in range(entry_length): outputs = model.gpt(inputs_embeds=generated) logits = outputs.logits @@ -383,20 +384,21 @@ def generate2( model, tokenizer, tokens=None, - prompt=None, + prompt="a photo of", embed=None, entry_count=1, entry_length=67, # maximum number of words top_p=0.8, temperature=1.0, - stop_token: str = ".", + stop_token: str = "", ): model.eval() generated_num = 0 generated_list = [] stop_token_index = tokenizer.encode(stop_token)[0] filter_value = -float("Inf") - device = next(model.parameters()).device + device = embed.device + embed = embed.type(torch.DoubleTensor) with torch.no_grad(): @@ -429,7 +431,7 @@ def generate2( logits[:, indices_to_remove] = filter_value next_token = torch.argmax(logits, -1).unsqueeze(0) # next_token_embed = model.gpt.transformer.wte(next_token) # GPT-2 - next_token_embed = model.gpt.model.decoder.embed_tokens(next_token) # OPT + next_token_embed = model.gpt.model.decoder.embed_tokens(next_token).to(device) # OPT if tokens is None: tokens = next_token else: diff --git a/predict_nice.py b/predict_nice.py new file mode 100644 index 0000000..57d3cfc --- /dev/null +++ b/predict_nice.py @@ -0,0 +1,68 @@ +import os +import pandas as pd +import json +from tqdm import tqdm +from datetime import datetime +import argparse + + +from predict import * + +# options +parser = argparse.ArgumentParser() +parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') +parser.add_argument('--prefix_length', type=int, default=32, help='must match prefix_length of your trained model') +parser.add_argument('--device', default='03') +args = parser.parse_args() +# make sure your language_model is 'GPT-2', if not edit on predict.py + +def make_device(args): + device_num = len(args.device) + devices = [] + for i in range(device_num): + device = "cuda:" + args.device[i] + devices.append(torch.device(device)) + return devices + +device1, device2 = make_device(args) + +# file path : CVPR2023challenge +fpath_nice = os.path.join('/data/img_cap/nice', 'NICE_val') +flist_nice = os.listdir(fpath_nice) +annot_csv = pd.read_csv(os.path.join('/data/img_cap/nice', 'nice-val-5k.csv')) +output_file = f'./output_caption/{datetime.now().strftime("%Y%m%d-%H%M%S")}' +os.makedirs(output_file, exist_ok=True) + +# Setup predictor +predict = Predictor() +predict.setup(language_model=args.language_model, prefix_length=args.prefix_length, device1=device1, device2=device2) +print('Ready to predict captions of CVPR2023-NICE dataset') + + +img_nice = flist_nice[0] +image = os.path.join(fpath_nice, img_nice) +print('generate function') +generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=True) +print(generated_caption_coco_2) + +# start generating captions +# data_coco_2 = {} +# data_coco_beam = {} +# for img_nice in tqdm(flist_nice): +# image = os.path.join(fpath_nice, img_nice) + +# generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=False) +# generated_caption_coco_beam = predict.predict(image=image, model='coco', use_beam_search=True) + +# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() + +# # print(f'target_caption: {target_caption}') +# # print(f'generated 1 : {generated_caption_coco_beam}') +# # print(f'generated 2 : {generated_caption_coco_2}') +# data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] +# data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] + +# with open(os.path.join(output_file, f'clipcap_2_{args.language_model}.json'), 'w') as fp: +# json.dump(data_coco_2, fp) +# with open(os.path.join(output_file, f'clipcap_beam_{args.language_model}.json'), 'w') as fp: +# json.dump(data_coco_beam, fp) diff --git a/train_OPT.py b/train_OPT.py new file mode 100644 index 0000000..a5c60a9 --- /dev/null +++ b/train_OPT.py @@ -0,0 +1,405 @@ +import torch +import torch.nn as nn +from torch.nn import functional as nnf +from torch.utils.data import Dataset, DataLoader +from enum import Enum +from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup +from transformers import AdamW, get_linear_schedule_with_warmup +from modeling_opt_pp import OPTForCausalLM +from transformers import AutoTokenizer +from tqdm import tqdm +import os +import pickle +import sys +import argparse +import json +from typing import Tuple, Optional, Union + +import wandb + +OPT_MODEL = 'facebook/opt-1.3b' + +class MappingType(Enum): + MLP = 'mlp' + Transformer = 'transformer' + + +class ClipCocoDataset(Dataset): + + def __len__(self) -> int: + return len(self.captions_tokens) + + def pad_tokens(self, item: int): + tokens = self.captions_tokens[item] + padding = self.max_seq_len - tokens.shape[0] + if padding > 0: + tokens = torch.cat((tokens, torch.zeros(padding, dtype=torch.int64) - 1)) + self.captions_tokens[item] = tokens + elif padding < 0: + tokens = tokens[:self.max_seq_len] + self.captions_tokens[item] = tokens + mask = tokens.ge(0) # mask is zero where we out of sequence + tokens[~mask] = 0 + mask = mask.float() + mask = torch.cat((torch.ones(self.prefix_length), mask), dim=0) # adding prefix mask + return tokens, mask + + def __getitem__(self, item: int) -> Tuple[torch.Tensor, ...]: + tokens, mask = self.pad_tokens(item) + prefix = self.prefixes[self.caption2embedding[item]] + if self.normalize_prefix: + prefix = prefix.float() + prefix = prefix / prefix.norm(2, -1) + return tokens, mask, prefix + + def __init__(self, data_path: str, prefix_length: int, gpt2_type: str = OPT_MODEL, # edit + normalize_prefix=False): + # self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type) + self.tokenizer = AutoTokenizer.from_pretrained(gpt2_type, use_fast=True) + self.prefix_length = prefix_length + self.normalize_prefix = normalize_prefix + with open(data_path, 'rb') as f: + all_data = pickle.load(f) + print("Data size is %0d" % len(all_data["clip_embedding"])) + sys.stdout.flush() + self.prefixes = all_data["clip_embedding"] + captions_raw = all_data["captions"] + self.image_ids = [caption["image_id"] for caption in captions_raw] + self.captions = [caption['caption'] for caption in captions_raw] + if os.path.isfile(f"{data_path[:-4]}_tokens.pkl"): + with open(f"{data_path[:-4]}_tokens.pkl", 'rb') as f: + self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f) + else: + self.captions_tokens = [] + self.caption2embedding = [] + max_seq_len = 0 + for caption in captions_raw: + self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64)) + self.caption2embedding.append(caption["clip_embedding"]) + max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0]) + # self.max_seq_len = max_seq_len + with open(f"{data_path[:-4]}_tokens.pkl", 'wb') as f: + pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f) + all_len = torch.tensor([len(self.captions_tokens[i]) for i in range(len(self))]).float() + self.max_seq_len = min(int(all_len.mean() + all_len.std() * 10), int(all_len.max())) + + +class MLP(nn.Module): + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.model(x) + + def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): + super(MLP, self).__init__() + layers = [] + for i in range(len(sizes) - 1): + layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) + if i < len(sizes) - 2: + layers.append(act()) + self.model = nn.Sequential(*layers) + + +class MlpTransformer(nn.Module): + def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): + super().__init__() + out_d = out_d if out_d is not None else in_dim + self.fc1 = nn.Linear(in_dim, h_dim) + self.act = act + self.fc2 = nn.Linear(h_dim, out_d) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.dropout(x) + x = self.fc2(x) + x = self.dropout(x) + return x + +class MultiHeadAttention(nn.Module): + + def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim_self // num_heads + self.scale = head_dim ** -0.5 + self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) + self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) + self.project = nn.Linear(dim_self, dim_self) + self.dropout = nn.Dropout(dropout) + + def forward(self, x, y=None, mask=None): + y = y if y is not None else x + b, n, c = x.shape + _, m, d = y.shape + # b n h dh + queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) + # b m 2 h dh + keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) + keys, values = keys_values[:, :, 0], keys_values[:, :, 1] + attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale + if mask is not None: + if mask.dim() == 2: + mask = mask.unsqueeze(1) + attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) + attention = attention.softmax(dim=2) + out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) + out = self.project(out) + return out, attention + + +class TransformerLayer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + x_, attention = self.attn(self.norm1(x), y, mask) + x = x + x_ + x = x + self.mlp(self.norm2(x)) + return x, attention + + def forward(self, x, y=None, mask=None): + x = x + self.attn(self.norm1(x), y, mask)[0] + x = x + self.mlp(self.norm2(x)) + return x + + def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, + norm_layer: nn.Module = nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim_self) + self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) + self.norm2 = norm_layer(dim_self) + self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) + + +class Transformer(nn.Module): + + def forward_with_attention(self, x, y=None, mask=None): + attentions = [] + for layer in self.layers: + x, att = layer.forward_with_attention(x, y, mask) + attentions.append(att) + return x, attentions + + def forward(self, x, y=None, mask=None): + for i, layer in enumerate(self.layers): + if i % 2 == 0 and self.enc_dec: # cross + x = layer(x, y) + elif self.enc_dec: # self + x = layer(x, x, mask) + else: # self or cross + x = layer(x, y, mask) + return x + + def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, + mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): + super(Transformer, self).__init__() + dim_ref = dim_ref if dim_ref is not None else dim_self + self.enc_dec = enc_dec + if enc_dec: + num_layers = num_layers * 2 + layers = [] + for i in range(num_layers): + if i % 2 == 0 and enc_dec: # cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + elif enc_dec: # self + layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + else: # self or cross + layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) + self.layers = nn.ModuleList(layers) + + +class TransformerMapper(nn.Module): + + def forward(self, x): + x = self.linear(x).view(x.shape[0], self.clip_length, -1) + prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) + prefix = torch.cat((x, prefix), dim=1) + out = self.transformer(prefix)[:, self.clip_length:] + return out + + def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): + super(TransformerMapper, self).__init__() + self.clip_length = clip_length + self.transformer = Transformer(dim_embedding, 8, num_layers) + self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) + self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) + + +class ClipCaptionModel(nn.Module): + + def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: + return torch.zeros(batch_size, self.args.prefix_length, dtype=torch.int64, device=device) + + def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None): + if self.args.language_model == 'gpt2': + embedding_text = self.gpt.transformer.wte(tokens) + elif self.args.language_model == 'opt': + embedding_text = self.gpt.model.decoder.embed_tokens(tokens) + prefix_projections = self.clip_project(prefix).view(-1, self.args.prefix_length, self.gpt_embedding_size) + embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) + if labels is not None: + dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) + labels = torch.cat((dummy_token, tokens), dim=1) + out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) + return out + + def __init__(self, args, prefix_size: int = 512): + super(ClipCaptionModel, self).__init__() + devices = make_device(args) + + self.device1 = devices[0] + self.device2 = devices[1] + self.args = args + + if self.args.language_model == 'gpt2': + self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') + self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] + elif self.args.language_model == 'opt': + print('clipcaption - LM : OPT') + self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) + self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] + self.gpt.model.decoder.setting_device(device1 = self.device1, device2 = self.device2, pn = args.parallel_num) + + if args.mapping_type == MappingType.MLP: + self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * self.args.prefix_length) // 2, + self.gpt_embedding_size * self.args.prefix_length)) + else: + self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, self.args.prefix_length, + self.args.prefix_length_clip, self.args.num_layers).to(self.device1) + + +class ClipCaptionPrefix(ClipCaptionModel): + + def parameters(self, recurse: bool = True): + return self.clip_project.parameters() + + def train(self, mode: bool = True): + super(ClipCaptionPrefix, self).train(mode) + self.gpt.eval() + return self + +def make_device(args): + device_num = len(args.device) + devices = [] + for i in range(device_num): + device = "cuda:" + args.device[i] + devices.append(torch.device(device)) + return devices + +def save_config(args: argparse.Namespace): + config = {} + for key, item in args._get_kwargs(): + config[key] = item + out_path = os.path.join(args.out_dir, f"{args.prefix}.json") + with open(out_path, 'w') as outfile: + json.dump(config, outfile) + + +def load_model(config_path: str, epoch_or_latest: Union[str, int] = '_latest'): + with open(config_path) as f: + config = json.load(f) + parser = argparse.ArgumentParser() + parser.set_defaults(**config) + args = parser.parse_args() + if type(epoch_or_latest) is int: + epoch_or_latest = f"-{epoch_or_latest:03d}" + model_path = os.path.join(args.out_dir, f"{args.prefix}{epoch_or_latest}.pt") + if args.only_prefix: + model = ClipCaptionPrefix(args.prefix_length) + else: + model = ClipCaptionModel(args.prefix_length) + if os.path.isfile(model_path): + print(f"loading model from {model_path}") + model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) + else: + print(f"{model_path} is not exist") + return model, parser + + +def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, + lr: float = 2e-5, warmup_steps: int = 5000, output_dir: str = ".", output_prefix: str = ""): + + device = torch.device(f'cuda:{args.device[0]}') + batch_size = args.bs + epochs = args.epochs + if not os.path.exists(output_dir): + os.makedirs(output_dir) + # model = model.to(device) + model.train() + optimizer = AdamW(model.parameters(), lr=lr) + train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True) + scheduler = get_linear_schedule_with_warmup( + optimizer, num_warmup_steps=warmup_steps, num_training_steps=epochs * len(train_dataloader) + ) + # save_config(args) + for epoch in range(epochs): + print(f">>> Training epoch {epoch}") + sys.stdout.flush() + progress = tqdm(total=len(train_dataloader), desc=output_prefix) + for idx, (tokens, mask, prefix) in enumerate(train_dataloader): + model.zero_grad() + tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32) + outputs = model(tokens, prefix, mask) + logits = outputs.logits[:, dataset.prefix_length - 1: -1].to(device) + loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0) + loss.backward() + optimizer.step() + scheduler.step() + optimizer.zero_grad() + + wandb.log({'loss':loss.item()}) + progress.set_postfix({"loss": loss.item()}) + progress.update() + if (idx + 1) % 10000 == 0: + torch.save( + model.state_dict(), + os.path.join(output_dir, f"{output_prefix}_latest.pt"), + ) + progress.close() + if epoch % args.save_every == 0 or epoch == epochs - 1: + torch.save( + model.state_dict(), + os.path.join(output_dir, f"{output_prefix}-{epoch:03d}.pt"), + ) + return model + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', default='./data/coco/oscar_split_train.pkl') + parser.add_argument('--out_dir', default='./checkpoints') + parser.add_argument('--prefix', default='coco_prefix', help='prefix for saved filenames') + parser.add_argument('--epochs', type=int, default=10) + parser.add_argument('--save_every', type=int, default=1) + parser.add_argument('--prefix_length', type=int, default=10) + parser.add_argument('--prefix_length_clip', type=int, default=10) + parser.add_argument('--bs', type=int, default=40) + parser.add_argument('--only_prefix', dest='only_prefix', action='store_true') + parser.add_argument('--mapping_type', type=str, default='mlp', help='mlp/transformer') + parser.add_argument('--num_layers', type=int, default=8) + parser.add_argument('--is_rn', dest='is_rn', action='store_true') + parser.add_argument('--normalize_prefix', dest='normalize_prefix', action='store_true') + parser.add_argument('--device', default='23') + parser.add_argument('--language_model', type=str, default='gpt2', help='gpt2/opt') + parser.add_argument('--parallel_num', type=int, default=6, help='0 Date: Mon, 27 Mar 2023 20:12:23 +0900 Subject: [PATCH 03/25] readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b8b467b..da83fdf 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,8 @@ In our work, we use the [CLIP](https://github.com/openai/CLIP) model, which was + + ## Swith your language model from GPT-2 to OPT We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). Training code is available at train.py and inference code will be updated on predict_OPT.py, which is basically running Predictor function in predict.py. From 9902b2ef089653c91c6bd51c843d5f9976b89fc0 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Tue, 28 Mar 2023 15:33:10 +0900 Subject: [PATCH 04/25] readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index da83fdf..e82596a 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ Inference Notebook: Date: Tue, 28 Mar 2023 15:34:25 +0900 Subject: [PATCH 05/25] readme --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index e82596a..da83fdf 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,6 @@ Inference Notebook: Date: Tue, 28 Mar 2023 15:39:57 +0900 Subject: [PATCH 06/25] changed readme --- README.md | 33 ++------------------------------- 1 file changed, 2 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index da83fdf..268461a 100644 --- a/README.md +++ b/README.md @@ -24,37 +24,8 @@ In our work, we use the [CLIP](https://github.com/openai/CLIP) model, which was ## Swith your language model from GPT-2 to OPT We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). -Training code is available at train.py and inference code will be updated on predict_OPT.py, which is basically running Predictor function in predict.py. -Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both predict.py and train.py. - -## COCO Examples - - - - - - - - - - - - -
A couple of people standing next to an elephant. A wooden table sitting in front of a window.A bunch of bananas sitting on top of a table.
- - - - - - - - - - - - -
A woman holding a plate with a piece of cake in front of her face. A wooden table topped with lots of wooden utensils.A red motorcycle parked on top of a dirt field.
- +Training code is available at `train.py` and inference code will be updated on `predict_OPT.py`, which is basically running Predictor function in predict.py. +Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both `predict.py` and `train.py`. From 30ab46417ca464a6c6216301ad8811de50a7d6b0 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Tue, 28 Mar 2023 16:07:13 +0900 Subject: [PATCH 07/25] fix default option --- README.md | 18 +++++++++++++++--- predict.py | 2 +- predict_nice.py | 41 ++++++++++++++++++----------------------- train_OPT.py | 10 +++++----- 4 files changed, 39 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 268461a..4e4a696 100644 --- a/README.md +++ b/README.md @@ -7,15 +7,18 @@ Inference Notebook: Date: Tue, 28 Mar 2023 16:30:48 +0900 Subject: [PATCH 08/25] . --- README.md | 75 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 38 insertions(+), 37 deletions(-) diff --git a/README.md b/README.md index 4e4a696..688c595 100644 --- a/README.md +++ b/README.md @@ -21,43 +21,6 @@ code references - [BLIP2](https://github.com/salesforce/BLIP.git) - - - - -## Swith your language model from GPT-2 to OPT -We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). -Training code is available at `train.py` and inference code will be updated on `predict_OPT.py`, which is basically running Predictor function in predict.py. -Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both `predict.py` and `train.py`. - -``` -python train_OPT.py --data ./data/coco/oscar_split_ViT-B_32_train.pkl --out_dir /data/daisy/clipcap_output/coco_train/ --only_prefix --device -``` -``` -python predict_nice.py -``` - -### model parallelization -- OPT-1.3b : 2-GPU, 16GB (per GPU), 1h13m per epoch - - -## Inference Notebooks -To help visualize the results we provide a Colab notebook found in `notebooks/clip_prefix_captioning_inference.ipynb`. -The notebook will download the pretrained models and run inference on a sample images or -on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing). -Inference notebook for the **transformer mapping network (without fine-tune GPT-2)** can be found [here](https://colab.research.google.com/drive/180L3rMFmGujudwO1EJNF-lHIpAsAZ5xq?usp=sharing) for the COCO model (also in `notebooks/transformer_inference.ipynb`). - - - -Both [COCO](https://drive.google.com/file/d/1IdaBtMSvtyzF0ByVaBHtvM0JYSXRExRX/view?usp=sharing) and [Conceptual Captions](https://drive.google.com/file/d/14pXWwB4Zm82rsDdvbGguLfx9F8aM7ovT/view?usp=sharing) pretrained models are available for mlp mapping network. For the transformer (without fine-tuning GPT-2) we provide [COCO](https://drive.google.com/file/d/1GYPToCqFREwi285wPLhuVExlz7DDUDfJ/view?usp=sharing) pretrained model. - - - -## Inference GUI -1. Run it [in the browser](https://replicate.ai/rmokady/clip_prefix_caption) using replicate.ai UI. -2. Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/CLIP_prefix_captioning) (currently not supporting beam search) - - ## Training prerequisites [comment]: <> (Dependencies can be found at the [Inference notebook](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing) ) @@ -83,6 +46,7 @@ Train with fine-tuning of GPT2: python train.py --data ./data/coco/oscar_split_ViT-B_32_train.pkl --out_dir ./coco_train/ ``` +__In case you want to train model with OPT, please look directly "Swith your language model from GPT-2 to OPT"__ Train only transformer mapping network: ``` python train.py --only_prefix --data ./data/coco/oscar_split_ViT-B_32_train.pkl --out_dir ./coco_train/ --mapping_type transformer --num_layres 8 --prefix_length 40 --prefix_length_clip 40 @@ -98,6 +62,43 @@ python train.py --only_prefix --data ./data/coco/oscar_split_RN50x4_train.pkl -- ``` + + +## Swith your language model from GPT-2 to OPT +We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). +Training code is available at `train.py` and inference code will be updated on `predict_OPT.py`, which is basically running Predictor function in predict.py. +Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both `predict.py` and `train.py`. + +``` +python train_OPT.py --data ./data/coco/oscar_split_ViT-B_32_train.pkl --out_dir /data/daisy/clipcap_output/coco_train/ --only_prefix --device +``` +``` +python predict_nice.py +``` + +### model parallelization +- OPT-1.3b : 2-GPU, 16GB (per GPU), 1h13m per epoch + + +## Inference Notebooks +To help visualize the results we provide a Colab notebook found in `notebooks/clip_prefix_captioning_inference.ipynb`. +The notebook will download the pretrained models and run inference on a sample images or +on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing). +Inference notebook for the **transformer mapping network (without fine-tune GPT-2)** can be found [here](https://colab.research.google.com/drive/180L3rMFmGujudwO1EJNF-lHIpAsAZ5xq?usp=sharing) for the COCO model (also in `notebooks/transformer_inference.ipynb`). + + + +Both [COCO](https://drive.google.com/file/d/1IdaBtMSvtyzF0ByVaBHtvM0JYSXRExRX/view?usp=sharing) and [Conceptual Captions](https://drive.google.com/file/d/14pXWwB4Zm82rsDdvbGguLfx9F8aM7ovT/view?usp=sharing) pretrained models are available for mlp mapping network. For the transformer (without fine-tuning GPT-2) we provide [COCO](https://drive.google.com/file/d/1GYPToCqFREwi285wPLhuVExlz7DDUDfJ/view?usp=sharing) pretrained model. + + + +## Inference GUI +1. Run it [in the browser](https://replicate.ai/rmokady/clip_prefix_caption) using replicate.ai UI. +2. Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/CLIP_prefix_captioning) (currently not supporting beam search) + + + + *latest update : 2023-03-28* ## Citation From 1165cd4b657a88daafaecd96ea28ef136d80f6a3 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Wed, 29 Mar 2023 20:51:25 +0900 Subject: [PATCH 09/25] Fix OPT bug (1.1) --- modeling_opt_pp.py | 619 ++++++++++++++++++--------------------------- predict.py | 66 +++-- predict_nice.py | 113 ++++++--- train_OPT.py | 78 +++--- 4 files changed, 409 insertions(+), 467 deletions(-) diff --git a/modeling_opt_pp.py b/modeling_opt_pp.py index a656e83..ab2c0e0 100644 --- a/modeling_opt_pp.py +++ b/modeling_opt_pp.py @@ -25,8 +25,6 @@ from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, - QuestionAnsweringModelOutput, - SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import ( @@ -43,6 +41,7 @@ _CHECKPOINT_FOR_DOC = "facebook/opt-350m" _CONFIG_FOR_DOC = "OPTConfig" +_TOKENIZER_FOR_DOC = "GPT2Tokenizer" # Base model docstring _EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] @@ -52,6 +51,12 @@ _SEQ_CLASS_EXPECTED_LOSS = 1.71 _SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" +# QuestionAnswering docstring +_QA_EXPECTED_OUTPUT = "'a nice puppet'" +_QA_EXPECTED_LOSS = 7.41 +_QA_TARGET_START_INDEX = 14 +_QA_TARGET_END_INDEX = 15 + OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/opt-125m", "facebook/opt-350m", @@ -64,7 +69,9 @@ ] -def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0 +): """ Make causal mask used for bi-directional self-attention. """ @@ -75,8 +82,12 @@ def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_ mask = mask.to(dtype) if past_key_values_length > 0: - mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) - return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + mask = torch.cat( + [torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1 + ) + return mask[None, None, :, :].expand( + bsz, 1, tgt_len, tgt_len + past_key_values_length + ) def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): @@ -90,7 +101,9 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] inverted_mask = 1.0 - expanded_mask - return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + return inverted_mask.masked_fill( + inverted_mask.to(torch.bool), torch.finfo(dtype).min + ) class OPTLearnedPositionalEmbedding(nn.Embedding): @@ -104,12 +117,16 @@ def __init__(self, num_embeddings: int, embedding_dim: int): self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) - def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): + def forward( + self, attention_mask: torch.LongTensor, past_key_values_length: int = 0 + ): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() # create positions depending on attention_mask - positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 + positions = ( + torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask + ).long() - 1 # cut positions if `past_key_values_length` is > 0 positions = positions[:, past_key_values_length:] @@ -148,7 +165,11 @@ def __init__( self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): - return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + return ( + tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + .transpose(1, 2) + .contiguous() + ) def forward( self, @@ -218,13 +239,20 @@ def forward( raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask - attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + attn_weights = ( + attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + + attention_mask + ) + attn_weights = torch.max( + attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min) + ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437 if attn_weights.dtype == torch.float16: - attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16) + attn_weights = nn.functional.softmax( + attn_weights, dim=-1, dtype=torch.float32 + ).to(torch.float16) else: attn_weights = nn.functional.softmax(attn_weights, dim=-1) @@ -234,7 +262,9 @@ def forward( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) - attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view( + bsz, self.num_heads, tgt_len, src_len + ) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: @@ -242,12 +272,18 @@ def forward( # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following - attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + attn_weights_reshaped = attn_weights.view( + bsz, self.num_heads, tgt_len, src_len + ) + attn_weights = attn_weights_reshaped.view( + bsz * self.num_heads, tgt_len, src_len + ) else: attn_weights_reshaped = None - attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + attn_probs = nn.functional.dropout( + attn_weights, p=self.dropout, training=self.training + ) attn_output = torch.bmm(attn_probs, value_states) @@ -278,18 +314,15 @@ def __init__(self, config: OPTConfig): num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=True, - bias=config.enable_bias, ) self.do_layer_norm_before = config.do_layer_norm_before self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] - self.self_attn_layer_norm = nn.LayerNorm( - self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine - ) - self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias) - self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias) - self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim) + self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, @@ -299,7 +332,9 @@ def forward( output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, past_key_value: Optional[Tuple[torch.Tensor]] = None, - ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + ) -> Tuple[ + torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] + ]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` @@ -330,7 +365,9 @@ def forward( layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = nn.functional.dropout( + hidden_states, p=self.dropout, training=self.training + ) hidden_states = residual + hidden_states # 350m applies layer norm AFTER attention @@ -350,7 +387,9 @@ def forward( hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = nn.functional.dropout( + hidden_states, p=self.dropout, training=self.training + ) hidden_states = (residual + hidden_states).view(hidden_states_shape) @@ -391,6 +430,7 @@ def forward( OPT_START_DOCSTRING, ) class OPTPreTrainedModel(PreTrainedModel): + config_class = OPTConfig base_model_prefix = "model" supports_gradient_checkpointing = True @@ -412,13 +452,14 @@ def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (OPTDecoder)): module.gradient_checkpointing = value + OPT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) @@ -430,7 +471,7 @@ def _set_gradient_checkpointing(self, module, value=False): [What are attention masks?](../glossary#attention-mask) - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see @@ -484,24 +525,31 @@ class OPTDecoder(OPTPreTrainedModel): def __init__(self, config: OPTConfig): super().__init__(config) - self.cfg = config + self.num_hidden_layers = config.num_hidden_layers self.dropout = config.dropout self.layerdrop = config.layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.vocab_size = config.vocab_size - self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx) - self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) - + self.embed_tokens = nn.Embedding( + config.vocab_size, config.word_embed_proj_dim, self.padding_idx + ) + self.embed_positions = OPTLearnedPositionalEmbedding( + config.max_position_embeddings, config.hidden_size + ) if config.word_embed_proj_dim != config.hidden_size: - self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False) + self.project_out = nn.Linear( + config.hidden_size, config.word_embed_proj_dim, bias=False + ) else: self.project_out = None if config.word_embed_proj_dim != config.hidden_size: - self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False) + self.project_in = nn.Linear( + config.word_embed_proj_dim, config.hidden_size, bias=False + ) else: self.project_in = None @@ -509,36 +557,38 @@ def __init__(self, config: OPTConfig): # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 if config.do_layer_norm_before and not config._remove_final_layer_norm: - self.final_layer_norm = nn.LayerNorm( - config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine - ) + self.final_layer_norm = nn.LayerNorm(config.hidden_size) else: self.final_layer_norm = None - - - self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]) - + + self.layers = nn.ModuleList( + [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False - # Initialize weights and apply final processing self.post_init() - - def setting_device(self, device1, device2, pn): + + def setting_device(self, device1, device2, device3, pn1, pn2): self.device1 = device1 self.device2 = device2 - self.parallel_num = pn + self.device3 = device3 + self.parallel_num1 = pn1 + self.parallel_num2 = pn2 self._model_to_device() def _model_to_device(self): self.embed_tokens.to(self.device1) self.embed_positions.to(self.device1) self.final_layer_norm.to(self.device1) - for i in range(self.cfg.num_hidden_layers): - if i [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( - input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length + input_shape, + inputs_embeds.dtype, + past_key_values_length=past_key_values_length, ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( - inputs_embeds.device - ) + expanded_attn_mask = _expand_mask( + attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ).to(inputs_embeds.device) + combined_attention_mask = ( - expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + expanded_attn_mask + if combined_attention_mask is None + else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask @@ -573,6 +630,7 @@ def forward( head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, + query_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, @@ -584,7 +642,7 @@ def forward( Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) @@ -625,33 +683,53 @@ def forward( return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + raise ValueError( + "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time" + ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: - raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + raise ValueError( + "You have to specify either decoder_input_ids or decoder_inputs_embeds" + ) - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) + if query_embeds is not None: + inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1) + input_shape = inputs_embeds.size()[:-1] + # embed positions if attention_mask is None: - attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device) + attention_mask = torch.ones( + inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device + ) pos_embeds = self.embed_positions(attention_mask, past_key_values_length) attention_mask = self._prepare_decoder_attention_mask( @@ -663,13 +741,6 @@ def forward( hidden_states = inputs_embeds + pos_embeds - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None @@ -686,6 +757,13 @@ def forward( for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if idx == self.parallel_num1: + hidden_states = hidden_states.to(self.device2) + attention_mask = attention_mask.to(self.device2) + elif idx == self.parallel_num2: + hidden_states = hidden_states.to(self.device3) + attention_mask = attention_mask.to(self.device3) + if output_hidden_states: all_hidden_states += (hidden_states,) @@ -693,10 +771,18 @@ def forward( if self.training and (dropout_probability < self.layerdrop): continue - past_key_value = past_key_values[idx] if past_key_values is not None else None + past_key_value = ( + past_key_values[idx] if past_key_values is not None else None + ) if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value @@ -712,6 +798,7 @@ def custom_forward(*inputs): None, ) else: + layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, @@ -722,20 +809,14 @@ def custom_forward(*inputs): ) hidden_states = layer_outputs[0] - - if idx == self.parallel_num-1: - # layer_outputs = layer_outputs.to(self.device2) - hidden_states = hidden_states.to(self.device2) - attention_mask = attention_mask.to(self.device2) - # head_mask = head_mask.to(self.device2) - if use_cache: - next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + tmp = layer_outputs[2 if output_attentions else 1][0].to(self.device1) + next_decoder_cache += ((tmp,),) if output_attentions: all_self_attns += (layer_outputs[1],) - + if self.final_layer_norm is not None: self.final_layer_norm.to(self.device2) hidden_states = self.final_layer_norm(hidden_states) @@ -750,7 +831,11 @@ def custom_forward(*inputs): next_cache = next_decoder_cache if use_cache else None if not return_dict: - return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] + if v is not None + ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, @@ -781,6 +866,7 @@ def get_decoder(self): @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, @@ -793,17 +879,27 @@ def forward( head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, + query_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( @@ -812,6 +908,7 @@ def forward( head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, + query_embeds=query_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, @@ -835,9 +932,11 @@ class OPTForCausalLM(OPTPreTrainedModel): def __init__(self, config): super().__init__(config) self.model = OPTModel(config) - + # the lm_head weight is automatically tied to the embed tokens weight - self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) + self.lm_head = nn.Linear( + config.word_embed_proj_dim, config.vocab_size, bias=False + ) # Initialize weights and apply final processing self.post_init() @@ -860,7 +959,9 @@ def set_decoder(self, decoder): def get_decoder(self): return self.model.decoder - @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + @replace_return_docstrings( + output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC + ) def forward( self, input_ids: torch.LongTensor = None, @@ -868,11 +969,13 @@ def forward( head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, + query_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, + reduction: Optional[str] = "mean", ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: @@ -880,7 +983,7 @@ def forward( Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) @@ -934,10 +1037,10 @@ def forward( Example: ```python - >>> from transformers import AutoTokenizer, OPTForCausalLM + >>> from transformers import GPT2Tokenizer, OPTForCausalLM >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") - >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m") >>> prompt = "Hey, are you consciours? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") @@ -948,12 +1051,20 @@ def forward( "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ```""" - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, @@ -961,6 +1072,7 @@ def forward( head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, + query_embeds=query_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, @@ -971,12 +1083,18 @@ def forward( loss = None if labels is not None: + logits = logits[:, -labels.size(1) :, :] + # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens - loss_fct = CrossEntropyLoss() - loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) + loss_fct = CrossEntropyLoss(reduction=reduction) + loss = loss_fct( + shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1) + ) + if reduction == "none": + loss = loss.view(shift_logits.size(0), -1).sum(1) if not return_dict: output = (logits,) + outputs[1:] @@ -991,288 +1109,37 @@ def forward( ) def prepare_inputs_for_generation( - self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs + self, + input_ids=None, + query_embeds=None, + past=None, + attention_mask=None, + use_cache=None, + **kwargs, ): - if past_key_values: + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + if input_ids is not None: + attention_mask = input_ids.new_ones(input_ids.shape) + if past: input_ids = input_ids[:, -1:] - - # if `inputs_embeds` are passed, we only want to use them in the 1st generation step - if inputs_embeds is not None and past_key_values is None: - model_inputs = {"inputs_embeds": inputs_embeds} - else: - model_inputs = {"input_ids": input_ids} - - model_inputs.update( - { - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), - "attention_mask": attention_mask, - } - ) - return model_inputs + query_embeds = None + # first step, decoder_cached_states are empty + return { + "input_ids": input_ids, + "query_embeds": query_embeds, + "attention_mask": attention_mask, + "past_key_values": past, + "use_cache": use_cache, + } @staticmethod - def _reorder_cache(past_key_values, beam_idx): + def _reorder_cache(past, beam_idx): reordered_past = () - for layer_past in past_key_values: - reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) - return reordered_past - - -@add_start_docstrings( - """ - The OPT Model transformer with a sequence classification head on top (linear layer). - - [`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models - (e.g. GPT-2) do. - - Since it does classification on the last token, it requires to know the position of the last token. If a - `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If - no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the - padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in - each row of the batch). - """, - OPT_START_DOCSTRING, -) -class OPTForSequenceClassification(OPTPreTrainedModel): - _keys_to_ignore_on_load_missing = [r"lm_head.weight"] - - def __init__(self, config: OPTConfig): - super().__init__(config) - self.num_labels = config.num_labels - self.model = OPTModel(config) - self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) - @add_code_sample_docstrings( - checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION, - output_type=SequenceClassifierOutputWithPast, - config_class=_CONFIG_FOR_DOC, - expected_output=_SEQ_CLASS_EXPECTED_OUTPUT, - expected_loss=_SEQ_CLASS_EXPECTED_LOSS, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, SequenceClassifierOutputWithPast]: - r""" - labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - transformer_outputs = self.model( - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = transformer_outputs[0] - logits = self.score(hidden_states) - - if input_ids is not None: - batch_size, sequence_length = input_ids.shape[:2] - else: - batch_size, sequence_length = inputs_embeds.shape[:2] - - if self.config.pad_token_id is None: - sequence_lengths = -1 - else: - if input_ids is not None: - sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) - else: - sequence_lengths = -1 - logger.warning( - f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " - "unexpected if using padding tokens in conjunction with `inputs_embeds.`" - ) - - pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] - - loss = None - if labels is not None: - if self.config.problem_type is None: - if self.num_labels == 1: - self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): - self.config.problem_type = "single_label_classification" - else: - self.config.problem_type = "multi_label_classification" - - if self.config.problem_type == "regression": - loss_fct = MSELoss() - if self.num_labels == 1: - loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) - else: - loss = loss_fct(pooled_logits, labels) - elif self.config.problem_type == "single_label_classification": - loss_fct = CrossEntropyLoss() - loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) - elif self.config.problem_type == "multi_label_classification": - loss_fct = BCEWithLogitsLoss() - loss = loss_fct(pooled_logits, labels) - if not return_dict: - output = (pooled_logits,) + transformer_outputs[1:] - return ((loss,) + output) if loss is not None else output - - return SequenceClassifierOutputWithPast( - loss=loss, - logits=pooled_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - def get_input_embeddings(self): - return self.model.decoder.embed_tokens - - def set_input_embeddings(self, value): - self.model.decoder.embed_tokens = value - - -@add_start_docstrings( - """ - The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD - (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). - """, - OPT_START_DOCSTRING, -) -class OPTForQuestionAnswering(OPTPreTrainedModel): - _keys_to_ignore_on_load_missing = [r"lm_head.weight"] - - def __init__(self, config: OPTConfig): - super().__init__(config) - self.model = OPTModel(config) - self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2) - - # Initialize weights and apply final processing - self.post_init() - - @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - start_positions: Optional[torch.LongTensor] = None, - end_positions: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, QuestionAnsweringModelOutput]: - r""" - start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, OPTForQuestionAnswering - >>> import torch - - >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT - >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") - - >>> # note: we are loading a OPTForQuestionAnswering from the hub here, - >>> # so the head will be randomly initialized, hence the predictions will be random - >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") - - >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" - - >>> inputs = tokenizer(question, text, return_tensors="pt") - >>> with torch.no_grad(): - ... outputs = model(**inputs) - - >>> answer_start_index = outputs.start_logits.argmax() - >>> answer_end_index = outputs.end_logits.argmax() - - >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] - >>> predicted = tokenizer.decode(predict_answer_tokens) - >>> predicted - ' Henson?' - ```""" - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - transformer_outputs = self.model( - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = transformer_outputs[0] - - logits = self.qa_outputs(hidden_states) - start_logits, end_logits = logits.split(1, dim=-1) - start_logits = start_logits.squeeze(-1).contiguous() - end_logits = end_logits.squeeze(-1).contiguous() - - total_loss = None - if start_positions is not None and end_positions is not None: - # If we are on multi-GPU, split add a dimension - if len(start_positions.size()) > 1: - start_positions = start_positions.squeeze(-1) - if len(end_positions.size()) > 1: - end_positions = end_positions.squeeze(-1) - # sometimes the start/end positions are outside our model inputs, we ignore these terms - ignored_index = start_logits.size(1) - start_positions = start_positions.clamp(0, ignored_index) - end_positions = end_positions.clamp(0, ignored_index) - - loss_fct = CrossEntropyLoss(ignore_index=ignored_index) - start_loss = loss_fct(start_logits, start_positions) - end_loss = loss_fct(end_logits, end_positions) - total_loss = (start_loss + end_loss) / 2 - - if not return_dict: - output = (start_logits, end_logits) + transformer_outputs[2:] - return ((total_loss,) + output) if total_loss is not None else output - - return QuestionAnsweringModelOutput( - loss=total_loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - def get_input_embeddings(self): - return self.model.decoder.embed_tokens - - def set_input_embeddings(self, value): - self.model.decoder.embed_tokens = value \ No newline at end of file + for layer_past in past: + reordered_past += ( + tuple( + past_state.index_select(0, beam_idx) for past_state in layer_past + ), + ) + return reordered_past \ No newline at end of file diff --git a/predict.py b/predict.py index 91e60e7..e6d3cfd 100644 --- a/predict.py +++ b/predict.py @@ -63,27 +63,26 @@ def direct_weiht_paths(language_model): OPT_MODEL = 'facebook/opt-1.3b' class Predictor(cog.Predictor): - def setup(self, language_model='opt', prefix_length=32, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): + def setup(self, args): """Load the model into memory to make running multiple predictions efficient""" # self.device = torch.device("cuda") - self.device1 = device1 - self.device2 = device2 + self.device1 = make_device(args)[0] self.clip_model, self.preprocess = clip.load( "ViT-B/32", device=self.device1, jit=False ) + self.args = args - self.language_model = language_model - if self.language_model == 'gpt2': + if self.args.language_model == 'gpt2': self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") - elif self.language_model == 'opt': + elif self.args.language_model == 'opt': self.tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL) self.models = {} - self.prefix_length = prefix_length + self.prefix_length = args.prefix_length for key, weights_path in WEIGHTS_PATHS.items(): - model = ClipCaptionModel(self.prefix_length, language_model=self.language_model, device1=self.device1, device2=self.device2) - model.load_state_dict(torch.load(weights_path)) #, map_location=CPU)) + model = ClipCaptionModel(args) + model.load_state_dict(torch.load(weights_path, map_location=CPU)) model = model.eval() # model = model.to(self.device) self.models[key] = model @@ -113,7 +112,6 @@ def predict(self, image, model, use_beam_search): self.device1, dtype=torch.float32 ) prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) - if use_beam_search: return prefix_embed, generate_beam(model, self.tokenizer, embed=prefix_embed)[0] else: @@ -248,15 +246,15 @@ def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_l class ClipCaptionModel(nn.Module): def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: - return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) + return torch.zeros(batch_size, self.args.prefix_length, dtype=torch.int64, device=device) def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None): - if self.language_model == 'gpt2': + if self.args.language_model == 'gpt2': embedding_text = self.gpt.transformer.wte(tokens) - elif self.language_model == 'opt': + elif self.args.language_model == 'opt': embedding_text = self.gpt.model.decoder.embed_tokens(tokens) - prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) + prefix_projections = self.clip_project(prefix).view(-1, self.args.prefix_length, self.gpt_embedding_size) embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) if labels is not None: dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) @@ -264,28 +262,26 @@ def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[tor out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) return out - def __init__(self, prefix_length: int, language_model='gpt2', clip_length: Optional[int] = 32, prefix_size: int = 512, - num_layers: int = 8, device1=torch.device("cuda:2"), device2=torch.device("cuda:3")): + def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512, num_layers: int = 8): super(ClipCaptionModel, self).__init__() - self.prefix_length = prefix_length - self.language_model = language_model - self.prefix_size=prefix_size + self.args = args + self.prefix_size = prefix_size self.clip_length = clip_length self.num_layers = num_layers - self.device1 = device1 - self.device2 = device2 + self.device1, device2, device3 = make_device(args) + pn1, pn2 = int(args.pn[0]), int(args.pn[1]) - if self.language_model == 'gpt2': + if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] - elif self.language_model == 'opt': + elif self.args.language_model == 'opt': print('clipcaption - LM : OPT') self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] - self.gpt.model.decoder.setting_device(device1 = self.device1, device2 = self.device2, pn=6) - + self.gpt.model.decoder.setting_device(device1=self.device1, device2=device2, device3=device3, pn1=pn1, pn2=pn2) + self.clip_project = TransformerMapper(dim_clip=self.prefix_size, dim_embedding=self.gpt_embedding_size, - prefix_length=self.prefix_length, clip_length=self.clip_length, num_layers=self.num_layers).to(self.device1) + prefix_length=self.args.prefix_length, clip_length=self.clip_length, num_layers=self.num_layers).to(self.device1) class ClipCaptionPrefix(ClipCaptionModel): @@ -298,6 +294,24 @@ def train(self, mode: bool = True): self.gpt.eval() return self +def make_device(args): + device_num = len(args.device) + devices = [] + for i in range(device_num): + device = "cuda:" + args.device[i] + devices.append(torch.device(device)) + + assert len(devices) < 4 + if len(devices) == 1: + devices *= 3 + device1, device2, device3 = devices + elif len(devices) == 2: + device1 = devices[0] + device2 = devices[1] + device3 = devices[1] + else: + device1, device2, device3 = devices + return device1, device2, device3 def generate_beam( diff --git a/predict_nice.py b/predict_nice.py index 5ad02fc..a8db277 100644 --- a/predict_nice.py +++ b/predict_nice.py @@ -1,10 +1,10 @@ -import os -import pandas as pd +import argparse import json -from tqdm import tqdm +import os from datetime import datetime -import argparse +import pandas as pd +from tqdm import tqdm from predict import * @@ -12,19 +12,10 @@ parser = argparse.ArgumentParser() parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') parser.add_argument('--prefix_length', type=int, default=32, help='must match prefix_length of your trained model') -parser.add_argument('--device', default='03') +parser.add_argument('--device', default='12') +parser.add_argument('--pn', default='47') args = parser.parse_args() -def make_device(args): - device_num = len(args.device) - devices = [] - for i in range(device_num): - device = "cuda:" + args.device[i] - devices.append(torch.device(device)) - return devices - -device1, device2 = make_device(args) - # file path : CVPR2023challenge fpath_nice = os.path.join('/data/img_cap/nice', 'NICE_val') flist_nice = os.listdir(fpath_nice) @@ -34,30 +25,82 @@ def make_device(args): # Setup predictor predict = Predictor() -predict.setup(language_model=args.language_model, prefix_length=args.prefix_length, device1=device1, device2=device2) +predict.setup(args) print('Ready to predict captions of CVPR2023-NICE dataset') # example -image = os.path.join(fpath_nice, flist_nice[0]) -generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=True) -print("Exammple Caption :", generated_caption_coco_2) - -# start generating captions -data_coco_2 = {} -data_coco_beam = {} -for img_nice in tqdm(flist_nice): - image = os.path.join(fpath_nice, img_nice) +for i in [0, 1, 2, 3]: + print(i) + image = os.path.join(fpath_nice, flist_nice[i]) + + image = io.imread(image) + model = predict.models['coco']; tokenizer = predict.tokenizer + pil_image = PIL.Image.fromarray(image) + image = predict.preprocess(pil_image).unsqueeze(0).to(predict.device1) + with torch.no_grad(): + prefix = predict.clip_model.encode_image(image).to( + predict.device1, dtype=torch.float32 + ) + prefix_embed = model.clip_project(prefix).reshape(1, predict.prefix_length, -1) + + use_nucleus_sampling=False + num_beams=5 + max_length=30 + min_length=1 + top_p=0.9 + repetition_penalty=1.5 + length_penalty=1.0 + num_captions=1 + temperature=1 + + atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(predict.device1) + opt_tokens = tokenizer([""], return_tensors='pt').to(predict.device1) + input_ids = opt_tokens.input_ids + query_embeds = prefix_embed #.repeat_interleave(num_beams, dim=0) + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = model.gpt.generate( + input_ids=input_ids, + query_embeds=query_embeds, + attention_mask=attention_mask, + do_sample=use_nucleus_sampling, + top_p=top_p, + temperature=temperature, + num_beams=num_beams, + max_new_tokens=max_length, + min_length=min_length, + eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + num_return_sequences=num_captions, + ) + + prompt_length = input_ids.shape[1] + output_text = tokenizer.batch_decode( + outputs[:, prompt_length:], skip_special_tokens=True + ) + output_text = [text.strip() for text in output_text] + print(output_text) + +# generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=True) +# print("Exammple Caption :", generated_caption_coco_2) + +# # start generating captions +# data_coco_2 = {} +# data_coco_beam = {} +# for img_nice in tqdm(flist_nice): +# image = os.path.join(fpath_nice, img_nice) - generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=False) - generated_caption_coco_beam = predict.predict(image=image, model='coco', use_beam_search=True) +# generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=False) +# generated_caption_coco_beam = predict.predict(image=image, model='coco', use_beam_search=True) - target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() +# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() - data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] - data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] +# data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] +# data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] -# save generated caption -with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}.json'), 'w') as fp: - json.dump(data_coco_2, fp) -with open(os.path.join(output_file, f'clipcap_beam_opt13b_{args.language_model}.json'), 'w') as fp: - json.dump(data_coco_beam, fp) +# # save generated caption +# with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}.json'), 'w') as fp: +# json.dump(data_coco_2, fp) +# with open(os.path.join(output_file, f'clipcap_beam_opt13b_{args.language_model}.json'), 'w') as fp: +# json.dump(data_coco_beam, fp) diff --git a/train_OPT.py b/train_OPT.py index c9febbf..1dd8b64 100644 --- a/train_OPT.py +++ b/train_OPT.py @@ -66,20 +66,20 @@ def __init__(self, data_path: str, prefix_length: int, gpt2_type: str = OPT_MOD captions_raw = all_data["captions"] self.image_ids = [caption["image_id"] for caption in captions_raw] self.captions = [caption['caption'] for caption in captions_raw] - if os.path.isfile(f"{data_path[:-4]}_tokens.pkl"): - with open(f"{data_path[:-4]}_tokens.pkl", 'rb') as f: - self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f) - else: - self.captions_tokens = [] - self.caption2embedding = [] - max_seq_len = 0 - for caption in captions_raw: - self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64)) - self.caption2embedding.append(caption["clip_embedding"]) - max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0]) - # self.max_seq_len = max_seq_len - with open(f"{data_path[:-4]}_tokens.pkl", 'wb') as f: - pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f) + # if os.path.isfile(f"{data_path[:-4]}_tokens.pkl"): + # with open(f"{data_path[:-4]}_tokens.pkl", 'rb') as f: + # self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f) + # else: + self.captions_tokens = [] + self.caption2embedding = [] + max_seq_len = 0 + for caption in captions_raw: + self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64)) + self.caption2embedding.append(caption["clip_embedding"]) + max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0]) + # self.max_seq_len = max_seq_len + with open(f"{data_path[:-4]}_OPT_tokens.pkl", 'wb') as f: + pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f) all_len = torch.tensor([len(self.captions_tokens[i]) for i in range(len(self))]).float() self.max_seq_len = min(int(all_len.mean() + all_len.std() * 10), int(all_len.max())) @@ -243,13 +243,14 @@ def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[tor out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) return out - def __init__(self, args, prefix_size: int = 512): + def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512, num_layers: int = 8): super(ClipCaptionModel, self).__init__() - devices = make_device(args) - - self.device1 = devices[0] - self.device2 = devices[1] self.args = args + self.prefix_size = prefix_size + self.clip_length = clip_length + self.num_layers = num_layers + self.device1, device2, device3 = make_device(args) + pn1, pn2 = int(args.pn[0]), int(args.pn[1]) if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') @@ -258,14 +259,10 @@ def __init__(self, args, prefix_size: int = 512): print('clipcaption - LM : OPT') self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] - self.gpt.model.decoder.setting_device(device1 = self.device1, device2 = self.device2, pn = args.parallel_num) - - if args.mapping_type == MappingType.MLP: - self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * self.args.prefix_length) // 2, - self.gpt_embedding_size * self.args.prefix_length)) - else: - self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, self.args.prefix_length, - self.args.prefix_length_clip, self.args.num_layers).to(self.device1) + self.gpt.model.decoder.setting_device(device1=self.device1, device2=device2, device3=device3, pn1=pn1, pn2=pn2) + + self.clip_project = TransformerMapper(dim_clip=self.prefix_size, dim_embedding=self.gpt_embedding_size, + prefix_length=self.args.prefix_length, clip_length=self.clip_length, num_layers=self.num_layers).to(self.device1) class ClipCaptionPrefix(ClipCaptionModel): @@ -341,6 +338,7 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32) outputs = model(tokens, prefix, mask) logits = outputs.logits[:, dataset.prefix_length - 1: -1].to(device) + breakpoint() loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0) loss.backward() optimizer.step() @@ -363,10 +361,30 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, ) return model +def make_device(args): + device_num = len(args.device) + devices = [] + for i in range(device_num): + device = "cuda:" + args.device[i] + devices.append(torch.device(device)) + + assert len(devices) < 4 + if len(devices) == 1: + devices *= 3 + device1, device2, device3 = devices + elif len(devices) == 2: + device1 = devices[0] + device2 = devices[1] + device3 = devices[1] + else: + device1, device2, device3 = devices + return device1, device2, device3 + + def main(): parser = argparse.ArgumentParser() - parser.add_argument('--data', default='./data/coco/oscar_split_train.pkl') + parser.add_argument('--data', default='./data/coco/oscar_split_ViT-B_32_train.pkl') parser.add_argument('--out_dir', default='./checkpoints') parser.add_argument('--prefix', default='coco_prefix', help='prefix for saved filenames') parser.add_argument('--epochs', type=int, default=10) @@ -379,9 +397,9 @@ def main(): parser.add_argument('--num_layers', type=int, default=8) parser.add_argument('--is_rn', dest='is_rn', action='store_true') parser.add_argument('--normalize_prefix', dest='normalize_prefix', action='store_true') - parser.add_argument('--device', default='23') + parser.add_argument('--device', default='12') parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') - parser.add_argument('--parallel_num', type=int, default=6, help='0 Date: Fri, 31 Mar 2023 16:57:28 +0900 Subject: [PATCH 10/25] Fix generate --- modeling_opt_pp.py | 4 ++-- predict.py | 4 ++-- train_OPT.py | 15 +++++++++++---- 3 files changed, 15 insertions(+), 8 deletions(-) diff --git a/modeling_opt_pp.py b/modeling_opt_pp.py index ab2c0e0..014ebe3 100644 --- a/modeling_opt_pp.py +++ b/modeling_opt_pp.py @@ -818,11 +818,11 @@ def custom_forward(*inputs): all_self_attns += (layer_outputs[1],) if self.final_layer_norm is not None: - self.final_layer_norm.to(self.device2) + self.final_layer_norm.to(self.device3) hidden_states = self.final_layer_norm(hidden_states) if self.project_out is not None: - self.project_out.to(self.device2) + self.project_out.to(self.device3) hidden_states = self.project_out(hidden_states) # add hidden states from the last decoder layer diff --git a/predict.py b/predict.py index e6d3cfd..0cfe645 100644 --- a/predict.py +++ b/predict.py @@ -60,7 +60,7 @@ def direct_weiht_paths(language_model): D = torch.device CPU = torch.device("cpu") -OPT_MODEL = 'facebook/opt-1.3b' +OPT_MODEL = 'facebook/opt-2.7b' class Predictor(cog.Predictor): def setup(self, args): @@ -269,7 +269,7 @@ def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512 self.clip_length = clip_length self.num_layers = num_layers self.device1, device2, device3 = make_device(args) - pn1, pn2 = int(args.pn[0]), int(args.pn[1]) + pn1, pn2 = int(args.pn[0]), int(args.pn[1:]) if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') diff --git a/train_OPT.py b/train_OPT.py index 1dd8b64..1ef588a 100644 --- a/train_OPT.py +++ b/train_OPT.py @@ -17,7 +17,7 @@ import wandb -OPT_MODEL = 'facebook/opt-1.3b' +OPT_MODEL = 'facebook/opt-2.7b' class MappingType(Enum): MLP = 'mlp' @@ -250,7 +250,7 @@ def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512 self.clip_length = clip_length self.num_layers = num_layers self.device1, device2, device3 = make_device(args) - pn1, pn2 = int(args.pn[0]), int(args.pn[1]) + pn1, pn2 = int(args.pn[0]), int(args.pn[1:]) if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') @@ -338,7 +338,6 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32) outputs = model(tokens, prefix, mask) logits = outputs.logits[:, dataset.prefix_length - 1: -1].to(device) - breakpoint() loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0) loss.backward() optimizer.step() @@ -357,7 +356,15 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, if epoch % args.save_every == 0 or epoch == epochs - 1: torch.save( model.state_dict(), - os.path.join(output_dir, f"{output_prefix}-{epoch:03d}.pt"), + os.path.join(output_dir, f"model_{output_prefix}-{epoch:03d}.pt"), + ) + torch.save( + scheduler.state_dict(), + os.path.join(output_dir, f"schedular_{output_prefix}-{epoch:03d}.pt"), + ) + torch.save( + optimizer.state_dict(), + os.path.join(output_dir, f"optimizer_{output_prefix}-{epoch:03d}.pt"), ) return model From c988102b18cea107ab70a5331ac452fd18d78bc8 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Fri, 31 Mar 2023 17:00:32 +0900 Subject: [PATCH 11/25] fix generation while in prediction --- predict.py | 209 +++++++++++------------------------------------- predict_nice.py | 150 ++++++++++++++++++---------------- 2 files changed, 129 insertions(+), 230 deletions(-) diff --git a/predict.py b/predict.py index 0cfe645..4f4bdb7 100644 --- a/predict.py +++ b/predict.py @@ -33,29 +33,23 @@ TSN = Optional[TS] TA = Union[T, ARRAY] -# WEIGHTS_PATHS = { -# "coco_gpt": "coco_train/gpt-finetuned/coco_prefix-009.pt", -# "coco_gpt008": "coco_train/gpt-finetuned/coco_prefix-008.pt", -# # "conceptual-captions": "conceptual_weights.pt", -# } - -def direct_weiht_paths(language_model): +def direct_weight_paths(language_model): if language_model == 'gpt2': WEIGHTS_PATHS = { - "coco": "/data/daisy/clipcap_output/gpt2_32quries/coco_prefix-009.pt", - "coco_gpt008": "/data/daisy/clipcap_output/gpt-finetuned/coco_prefix-008.pt", + "coco": "/data/IC/clipcap/coco_prefix-000.pt", + "coco_gpt008": "/data/IC/clipcap/coco_prefix-000.pt", } print('your language model is : GPT-2') return WEIGHTS_PATHS elif language_model == 'opt': WEIGHTS_PATHS = { - "coco": "/data/daisy/clipcap_output/opt13b_32query/coco_prefix-018.pt", - "coco_gpt008": "/data/daisy/clipcap_output/opt13b_32query/coco_prefix-008.pt", + "opt_000": "/data/IC/clipcap/model_coco_prefix-000.pt", + "opt_001": "/data/IC/clipcap/model_coco_prefix-001.pt", } print('your language model is : OPT') return WEIGHTS_PATHS -WEIGHTS_PATHS = direct_weiht_paths('opt') +WEIGHTS_PATHS = direct_weight_paths('opt') D = torch.device @@ -112,10 +106,8 @@ def predict(self, image, model, use_beam_search): self.device1, dtype=torch.float32 ) prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) - if use_beam_search: - return prefix_embed, generate_beam(model, self.tokenizer, embed=prefix_embed)[0] - else: - return prefix_embed, generate2(model, self.tokenizer, embed=prefix_embed) + + return generate(model, self.tokenizer, prefix_embed, self.device1) class MlpTransformer(nn.Module): @@ -314,149 +306,44 @@ def make_device(args): return device1, device2, device3 -def generate_beam( - model, - tokenizer, - beam_size: int = 5, - prompt="a photo of", - embed=None, - entry_length=67, - temperature=1.0, - stop_token: str = "/n", -): - - model.eval() - stop_token_index = tokenizer.encode(stop_token)[0] - tokens = None - scores = None - device = embed.device - embed = embed.type(torch.DoubleTensor) - seq_lengths = torch.ones(beam_size, device=device) - is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) - with torch.no_grad(): - if embed is not None: - generated = embed - else: - if tokens is None: - tokens = torch.tensor(tokenizer.encode(prompt)) - tokens = tokens.unsqueeze(0).to(device) - # generated = model.gpt.transformer.wte(tokens) # GPT-2 - generated = model.gpt.decoder.embed_tokens(tokens) - for i in range(entry_length): - outputs = model.gpt(inputs_embeds=generated) - logits = outputs.logits - logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) - logits = logits.softmax(-1).log() - if scores is None: - scores, next_tokens = logits.topk(beam_size, -1) - generated = generated.expand(beam_size, *generated.shape[1:]) - next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) - if tokens is None: - tokens = next_tokens - else: - tokens = tokens.expand(beam_size, *tokens.shape[1:]) - tokens = torch.cat((tokens, next_tokens), dim=1) - else: - logits[is_stopped] = -float(np.inf) - logits[is_stopped, 0] = 0 - scores_sum = scores[:, None] + logits - seq_lengths[~is_stopped] += 1 - scores_sum_average = scores_sum / seq_lengths[:, None] - scores_sum_average, next_tokens = scores_sum_average.view(-1).topk( - beam_size, -1 - ) - next_tokens_source = next_tokens // scores_sum.shape[1] - seq_lengths = seq_lengths[next_tokens_source] - next_tokens = next_tokens % scores_sum.shape[1] - next_tokens = next_tokens.unsqueeze(1) - tokens = tokens[next_tokens_source] - tokens = torch.cat((tokens, next_tokens), dim=1) - generated = generated[next_tokens_source] - scores = scores_sum_average * seq_lengths - is_stopped = is_stopped[next_tokens_source] - # next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view( - # generated.shape[0], 1, -1 - # ) # GPT-2 - next_token_embed = model.gpt.model.decoder.embed_tokens(next_tokens.squeeze()).view( - generated.shape[0], 1, -1) # OPT - generated = torch.cat((generated, next_token_embed), dim=1) - is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() - if is_stopped.all(): - break - scores = scores / seq_lengths - output_list = tokens.cpu().numpy() - output_texts = [ - tokenizer.decode(output[: int(length)]) - for output, length in zip(output_list, seq_lengths) - ] - order = scores.argsort(descending=True) - output_texts = [output_texts[i] for i in order] - return output_texts - - -def generate2( - model, - tokenizer, - tokens=None, - prompt="a photo of", - embed=None, - entry_count=1, - entry_length=67, # maximum number of words - top_p=0.8, - temperature=1.0, - stop_token: str = "", -): - model.eval() - generated_num = 0 - generated_list = [] - stop_token_index = tokenizer.encode(stop_token)[0] - filter_value = -float("Inf") - device = embed.device - embed = embed.type(torch.DoubleTensor) - - with torch.no_grad(): - - for entry_idx in range(entry_count): - if embed is not None: - generated = embed - else: - if tokens is None: - tokens = torch.tensor(tokenizer.encode(prompt)) - tokens = tokens.unsqueeze(0).to(device) - - generated = model.gpt.transformer.wte(tokens) - - for i in range(entry_length): - - outputs = model.gpt(inputs_embeds=generated) - logits = outputs.logits - logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum( - nnf.softmax(sorted_logits, dim=-1), dim=-1 +def generate(model, tokenizer, prefix_embed, device1, + use_nucleus_sampling=False, + num_beams=5, + max_length=30, + min_length=1, + top_p=0.9, + repetition_penalty=1.5, + length_penalty=1.0, + num_captions=1, + temperature=1, + prompt=""): + + atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device1) + opt_tokens = tokenizer([prompt], return_tensors='pt').to(device1) + input_ids = opt_tokens.input_ids + query_embeds = prefix_embed + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = model.gpt.generate( + input_ids=input_ids, + query_embeds=query_embeds, + attention_mask=attention_mask, + do_sample=use_nucleus_sampling, + top_p=top_p, + temperature=temperature, + num_beams=num_beams, + max_new_tokens=max_length, + min_length=min_length, + eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + num_return_sequences=num_captions, ) - sorted_indices_to_remove = cumulative_probs > top_p - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[ - ..., :-1 - ].clone() - sorted_indices_to_remove[..., 0] = 0 - - indices_to_remove = sorted_indices[sorted_indices_to_remove] - logits[:, indices_to_remove] = filter_value - next_token = torch.argmax(logits, -1).unsqueeze(0) - # next_token_embed = model.gpt.transformer.wte(next_token) # GPT-2 - next_token_embed = model.gpt.model.decoder.embed_tokens(next_token).to(device) # OPT - if tokens is None: - tokens = next_token - else: - tokens = torch.cat((tokens, next_token), dim=1) - generated = torch.cat((generated, next_token_embed), dim=1) - if stop_token_index == next_token.item(): - break - - output_list = list(tokens.squeeze().cpu().numpy()) - output_text = tokenizer.decode(output_list) - generated_list.append(output_text) - - return generated_list[0] - + + prompt_length = input_ids.shape[1] + output_text = tokenizer.batch_decode( + outputs[:, prompt_length:], skip_special_tokens=True + ) + output_text = [text.strip() for text in output_text] + + return output_text[0] \ No newline at end of file diff --git a/predict_nice.py b/predict_nice.py index a8db277..0a43b5a 100644 --- a/predict_nice.py +++ b/predict_nice.py @@ -12,95 +12,107 @@ parser = argparse.ArgumentParser() parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') parser.add_argument('--prefix_length', type=int, default=32, help='must match prefix_length of your trained model') -parser.add_argument('--device', default='12') -parser.add_argument('--pn', default='47') +parser.add_argument('--checkpoint', type=int, default='001', help='checkpoint weight path') +parser.add_argument('--device', default='123') +parser.add_argument('--pn', default='111') args = parser.parse_args() # file path : CVPR2023challenge -fpath_nice = os.path.join('/data/img_cap/nice', 'NICE_val') +fpath_nice = os.path.join('/data/IC/nice-eval', 'images') flist_nice = os.listdir(fpath_nice) -annot_csv = pd.read_csv(os.path.join('/data/img_cap/nice', 'nice-val-5k.csv')) +annot_csv = pd.read_csv(os.path.join('/data/IC/nice-eval', 'nice-val-5k.csv')) output_file = f'./output_caption/{datetime.now().strftime("%Y%m%d-%H%M%S")}' os.makedirs(output_file, exist_ok=True) +OPT_MODEL = 'facebook/opt-2.7b' + # Setup predictor predict = Predictor() predict.setup(args) print('Ready to predict captions of CVPR2023-NICE dataset') +# p_model = ClipCaptionModel(args) +# p_model.load_state_dict(torch.load("/data/IC/clipcap/model_coco_prefix-000.pt", map_location=CPU)) +# p_tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL) +# p_model = p_model.eval() +# p_device1 = make_device(args)[0] +# p_prefix_length = args.prefix_length +# p_clip_model, p_preprocess = clip.load("ViT-B/32", device=p_device1, jit=False) + # example -for i in [0, 1, 2, 3]: - print(i) - image = os.path.join(fpath_nice, flist_nice[i]) - - image = io.imread(image) - model = predict.models['coco']; tokenizer = predict.tokenizer - pil_image = PIL.Image.fromarray(image) - image = predict.preprocess(pil_image).unsqueeze(0).to(predict.device1) - with torch.no_grad(): - prefix = predict.clip_model.encode_image(image).to( - predict.device1, dtype=torch.float32 - ) - prefix_embed = model.clip_project(prefix).reshape(1, predict.prefix_length, -1) - - use_nucleus_sampling=False - num_beams=5 - max_length=30 - min_length=1 - top_p=0.9 - repetition_penalty=1.5 - length_penalty=1.0 - num_captions=1 - temperature=1 - - atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(predict.device1) - opt_tokens = tokenizer([""], return_tensors='pt').to(predict.device1) - input_ids = opt_tokens.input_ids - query_embeds = prefix_embed #.repeat_interleave(num_beams, dim=0) - attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) - - outputs = model.gpt.generate( - input_ids=input_ids, - query_embeds=query_embeds, - attention_mask=attention_mask, - do_sample=use_nucleus_sampling, - top_p=top_p, - temperature=temperature, - num_beams=num_beams, - max_new_tokens=max_length, - min_length=min_length, - eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], - repetition_penalty=repetition_penalty, - length_penalty=length_penalty, - num_return_sequences=num_captions, - ) - - prompt_length = input_ids.shape[1] - output_text = tokenizer.batch_decode( - outputs[:, prompt_length:], skip_special_tokens=True - ) - output_text = [text.strip() for text in output_text] - print(output_text) - -# generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=True) +# for i in [0, 1, 2, 3]: +# print(i) +# image = os.path.join(fpath_nice, flist_nice[i]) + +# image = io.imread(image) +# model = p_model; tokenizer = p_tokenizer +# pil_image = PIL.Image.fromarray(image) +# image = p_preprocess(pil_image).unsqueeze(0).to(p_device1) +# with torch.no_grad(): +# prefix = p_clip_model.encode_image(image).to( +# p_device1, dtype=torch.float32 +# ) +# prefix_embed = model.clip_project(prefix).reshape(1, p_prefix_length, -1) + +# use_nucleus_sampling=False +# num_beams=5 +# max_length=30 +# min_length=1 +# top_p=0.9 +# repetition_penalty=1.5 +# length_penalty=1.0 +# num_captions=1 +# temperature=1 + +# atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(p_device1) +# opt_tokens = tokenizer([""], return_tensors='pt').to(p_device1) +# input_ids = opt_tokens.input_ids +# query_embeds = prefix_embed #.repeat_interleave(num_beams, dim=0) +# attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + +# outputs = model.gpt.generate( +# input_ids=input_ids, +# query_embeds=query_embeds, +# attention_mask=attention_mask, +# do_sample=use_nucleus_sampling, +# top_p=top_p, +# temperature=temperature, +# num_beams=num_beams, +# max_new_tokens=max_length, +# min_length=min_length, +# eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], +# repetition_penalty=repetition_penalty, +# length_penalty=length_penalty, +# num_return_sequences=num_captions, +# ) + +# prompt_length = input_ids.shape[1] +# output_text = tokenizer.batch_decode( +# outputs[:, prompt_length:], skip_special_tokens=True +# ) +# output_text = [text.strip() for text in output_text] +# print(output_text) + +# generated_caption_coco_2 = p_predict(image=image, model='coco', use_beam_search=True) # print("Exammple Caption :", generated_caption_coco_2) -# # start generating captions -# data_coco_2 = {} -# data_coco_beam = {} -# for img_nice in tqdm(flist_nice): -# image = os.path.join(fpath_nice, img_nice) +# start generating captions +data= {} +for img_nice in tqdm(flist_nice): + image = os.path.join(fpath_nice, img_nice) -# generated_caption_coco_2 = predict.predict(image=image, model='coco', use_beam_search=False) -# generated_caption_coco_beam = predict.predict(image=image, model='coco', use_beam_search=True) + generated_caption= predict.predict(image=image, model=f'opt_{args.checkpoint}', use_beam_search=False) -# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() + target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() -# data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] -# data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] + data[int(img_nice[:-4])] = [target_caption, generated_caption] -# # save generated caption +# save generated caption # with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}.json'), 'w') as fp: # json.dump(data_coco_2, fp) # with open(os.path.join(output_file, f'clipcap_beam_opt13b_{args.language_model}.json'), 'w') as fp: # json.dump(data_coco_beam, fp) + +# save generated caption +with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}_{args.checkpoint}.json'), 'w') as fp: + json.dump(data, fp) \ No newline at end of file From 5071812abf0214e2bd6e8d0cb9293e89087528b8 Mon Sep 17 00:00:00 2001 From: Jhryu30 Date: Fri, 31 Mar 2023 17:01:35 +0900 Subject: [PATCH 12/25] add requirements --- requirements.txt | 137 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..f56b24b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,137 @@ +anyio==3.6.2 +appdirs==1.4.4 +asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work +async-timeout==4.0.2 +attrs==22.2.0 +backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work +backoff==2.2.1 +brotlipy==0.7.0 +certifi @ file:///croot/certifi_1671487769961/work/certifi +cffi @ file:///croot/cffi_1670423208954/work +charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work +click==8.1.3 +clip @ git+https://github.com/openai/CLIP.git@a9b1bf5920416aaeaec965c25dd9e8f98c864f16 +cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work +-e git+https://github.com/replicate/cog.git@6348238ef8ffe8faff11c84c305a54deb051b690#egg=cog&subdirectory=python +comm @ file:///croot/comm_1671231121260/work +contourpy @ file:///opt/conda/conda-bld/contourpy_1663827406301/work +cryptography @ file:///croot/cryptography_1677533068310/work +cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work +cytoolz @ file:///croot/cytoolz_1667465931118/work +dask @ file:///tmp/abs_994957d9-ec12-411f-b953-c010f9d489d10hj3gz4k/croots/recipe/dask-core_1658513209934/work +debugpy @ file:///tmp/build/80754af9/debugpy_1637091799509/work +decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work +Deprecated==1.2.13 +docker-pycreds==0.4.0 +entrypoints @ file:///tmp/build/80754af9/entrypoints_1649926439650/work +executing @ file:///opt/conda/conda-bld/executing_1646925071911/work +fastapi==0.92.0 +filelock==3.9.0 +Flask==2.2.3 +fonttools==4.25.0 +fsspec @ file:///croot/fsspec_1670336592807/work +ftfy==6.1.1 +gitdb==4.0.10 +GitPython==3.1.31 +googleapis-common-protos==1.58.0 +grpcio==1.51.3 +h11==0.14.0 +httptools==0.5.0 +huggingface-hub==0.12.1 +idna @ file:///croot/idna_1666125576474/work +imagecodecs @ file:///croot/imagecodecs_1677576717595/work +imageio @ file:///tmp/abs_cd920173-f360-47c5-97b0-bf4d1076d5d4dvic0oys/croots/recipe/imageio_1658785036907/work +importlib-metadata==6.0.0 +importlib-resources @ file:///tmp/build/80754af9/importlib_resources_1625135880749/work +ipykernel @ file:///croot/ipykernel_1671488378391/work +ipython @ file:///croot/ipython_1676582224036/work +itsdangerous==2.1.2 +jedi @ file:///tmp/build/80754af9/jedi_1644297102865/work +Jinja2==3.1.2 +joblib==1.2.0 +jupyter_client @ file:///croot/jupyter_client_1676329080601/work +jupyter_core @ file:///croot/jupyter_core_1676538566912/work +kiwisolver @ file:///croot/kiwisolver_1672387140495/work +locket @ file:///opt/conda/conda-bld/locket_1652903118915/work +MarkupSafe==2.1.2 +matplotlib @ file:///croot/matplotlib-suite_1677674301264/work +matplotlib-inline @ file:///opt/conda/conda-bld/matplotlib-inline_1662014470464/work +mkl-fft==1.3.1 +mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186066731/work +mkl-service==2.4.0 +munkres==1.1.4 +nest-asyncio @ file:///croot/nest-asyncio_1672387112409/work +networkx @ file:///opt/conda/conda-bld/networkx_1657784097507/work +numpy @ file:///croot/numpy_and_numpy_base_1672336185480/work +nvidia-cublas-cu11==11.10.3.66 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cudnn-cu11==8.5.0.96 +opentelemetry-api==1.16.0 +opentelemetry-exporter-otlp==1.16.0 +opentelemetry-exporter-otlp-proto-grpc==1.16.0 +opentelemetry-exporter-otlp-proto-http==1.16.0 +opentelemetry-proto==1.16.0 +opentelemetry-sdk==1.16.0 +opentelemetry-semantic-conventions==0.37b0 +packaging @ file:///croot/packaging_1671697413597/work +pandas==1.5.3 +parso @ file:///opt/conda/conda-bld/parso_1641458642106/work +partd @ file:///opt/conda/conda-bld/partd_1647245470509/work +pathtools==0.1.2 +pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work +pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work +Pillow==9.4.0 +platformdirs @ file:///opt/conda/conda-bld/platformdirs_1662711380096/work +pooch @ file:///tmp/build/80754af9/pooch_1623324770023/work +prompt-toolkit @ file:///croot/prompt-toolkit_1672387306916/work +protobuf==3.20.3 +psutil @ file:///opt/conda/conda-bld/psutil_1656431268089/work +ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work +pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work +pydantic==1.10.5 +Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work +pyOpenSSL @ file:///croot/pyopenssl_1677607685877/work +pyparsing @ file:///opt/conda/conda-bld/pyparsing_1661452539315/work +PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work +python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work +python-dotenv==1.0.0 +pytz==2022.7.1 +PyWavelets @ file:///croot/pywavelets_1670425177960/work +PyYAML @ file:///croot/pyyaml_1670514731622/work +pyzmq @ file:///opt/conda/conda-bld/pyzmq_1657724186960/work +redis==3.5.3 +regex==2022.10.31 +replicate==0.5.1 +requests @ file:///opt/conda/conda-bld/requests_1657734628632/work +sacremoses==0.0.53 +scikit-image==0.18.1 +scipy==1.10.0 +sentry-sdk==1.17.0 +setproctitle==1.3.2 +six @ file:///tmp/build/80754af9/six_1644875935023/work +smmap==5.0.0 +sniffio==1.3.0 +stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work +starlette==0.25.0 +tifffile @ file:///tmp/build/80754af9/tifffile_1627275862826/work +tokenizers==0.13.2 +toolz @ file:///croot/toolz_1667464077321/work +torch==1.13.1 +torchvision==0.14.1 +tornado @ file:///opt/conda/conda-bld/tornado_1662061693373/work +tqdm==4.64.1 +traitlets @ file:///croot/traitlets_1671143879854/work +transformers==4.27.1 +typing_extensions==4.5.0 +urllib3 @ file:///croot/urllib3_1673575502006/work +uvicorn==0.20.0 +uvloop==0.17.0 +wandb==0.14.0 +watchfiles==0.18.1 +wcwidth==0.2.6 +websockets==10.4 +Werkzeug==2.2.3 +wrapt==1.15.0 +zipp @ file:///croot/zipp_1672387121353/work From 5f5895911ac3f40b0c72f221f1e94da72e2884ef Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Fri, 31 Mar 2023 16:56:53 +0900 Subject: [PATCH 13/25] Delete predict_OPT.py --- predict_OPT.py | 480 ------------------------------------------------- 1 file changed, 480 deletions(-) delete mode 100644 predict_OPT.py diff --git a/predict_OPT.py b/predict_OPT.py deleted file mode 100644 index 8559924..0000000 --- a/predict_OPT.py +++ /dev/null @@ -1,480 +0,0 @@ -# Prediction interface for Cog ⚙️ -# Reference: https://github.com/replicate/cog/blob/main/docs/python.md - -import clip -import os -from torch import nn -import numpy as np -import torch -import torch.nn.functional as nnf -import sys -from typing import Tuple, List, Union, Optional -# from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup -from transformers import OPTForCausalLM, AdamW, get_linear_schedule_with_warmup -from transformers import AutoTokenizer -import skimage.io as io -import PIL.Image - -import cog - - -import os -from tqdm import tqdm -import pandas as pd -import json -import torch - -# import torch - -N = type(None) -V = np.array -ARRAY = np.ndarray -ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]] -VS = Union[Tuple[V, ...], List[V]] -VN = Union[V, N] -VNS = Union[VS, N] -T = torch.Tensor -TS = Union[Tuple[T, ...], List[T]] -TN = Optional[T] -TNS = Union[Tuple[TN, ...], List[TN]] -TSN = Optional[TS] -TA = Union[T, ARRAY] - -WEIGHTS_PATHS = { - "coco_opt": "coco_train/opt-finetuned/coco_prefix-009.pt", - "coco": "coco_train/opt-finetuned/coco_prefix-008.pt", - # "conceptual-captions": "conceptual_weights.pt", -} - -D = torch.device -CPU = torch.device("cpu") - - -class Predictor(cog.Predictor): - def setup(self): - """Load the model into memory to make running multiple predictions efficient""" - self.device = torch.device("cuda") - self.clip_model, self.preprocess = clip.load( - "ViT-B/32", device=self.device, jit=False - ) - self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") - - self.models = {} - self.prefix_length = 40 #10 - for key, weights_path in WEIGHTS_PATHS.items(): - model = ClipCaptionModel(self.prefix_length) - model.load_state_dict(torch.load(weights_path, map_location=CPU)) - model = model.eval() - model = model.to(self.device) - self.models[key] = model - - @cog.input("image", type=cog.Path, help="Input image") - @cog.input( - "model", - type=str, - options=WEIGHTS_PATHS.keys(), - default="customized", - help="Model to use", - ) - @cog.input( - "use_beam_search", - type=bool, - default=False, - help="Whether to apply beam search to generate the output text", - ) - def predict(self, image, model, use_beam_search): - """Run a single prediction on the model""" - image = io.imread(image) - model = self.models[model] - pil_image = PIL.Image.fromarray(image) - image = self.preprocess(pil_image).unsqueeze(0).to(self.device) - with torch.no_grad(): - prefix = self.clip_model.encode_image(image).to( - self.device, dtype=torch.float32 - ) - prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) - if use_beam_search: - return generate_beam(model, self.tokenizer, embed=prefix_embed)[0], prefix_embed - else: - return generate2(model, self.tokenizer, embed=prefix_embed), prefix_embed - - -class MLP(nn.Module): - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.model(x) - - def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): - super(MLP, self).__init__() - layers = [] - for i in range(len(sizes) - 1): - layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) - if i < len(sizes) - 2: - layers.append(act()) - self.model = nn.Sequential(*layers) - - -class MlpTransformer(nn.Module): - def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): - super().__init__() - out_d = out_d if out_d is not None else in_dim - self.fc1 = nn.Linear(in_dim, h_dim) - self.act = act - self.fc2 = nn.Linear(h_dim, out_d) - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.dropout(x) - x = self.fc2(x) - x = self.dropout(x) - return x - -class MultiHeadAttention(nn.Module): - - def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim_self // num_heads - self.scale = head_dim ** -0.5 - self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) - self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) - self.project = nn.Linear(dim_self, dim_self) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, y=None, mask=None): - y = y if y is not None else x - b, n, c = x.shape - _, m, d = y.shape - # b n h dh - queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) - # b m 2 h dh - keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) - keys, values = keys_values[:, :, 0], keys_values[:, :, 1] - attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale - if mask is not None: - if mask.dim() == 2: - mask = mask.unsqueeze(1) - attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) - attention = attention.softmax(dim=2) - out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) - out = self.project(out) - return out, attention - - -class TransformerLayer(nn.Module): - - def forward_with_attention(self, x, y=None, mask=None): - x_, attention = self.attn(self.norm1(x), y, mask) - x = x + x_ - x = x + self.mlp(self.norm2(x)) - return x, attention - - def forward(self, x, y=None, mask=None): - x = x + self.attn(self.norm1(x), y, mask)[0] - x = x + self.mlp(self.norm2(x)) - return x - - def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, - norm_layer: nn.Module = nn.LayerNorm): - super().__init__() - self.norm1 = norm_layer(dim_self) - self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) - self.norm2 = norm_layer(dim_self) - self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) - - -class Transformer(nn.Module): - - def forward_with_attention(self, x, y=None, mask=None): - attentions = [] - for layer in self.layers: - x, att = layer.forward_with_attention(x, y, mask) - attentions.append(att) - return x, attentions - - def forward(self, x, y=None, mask=None): - for i, layer in enumerate(self.layers): - if i % 2 == 0 and self.enc_dec: # cross - x = layer(x, y) - elif self.enc_dec: # self - x = layer(x, x, mask) - else: # self or cross - x = layer(x, y, mask) - return x - - def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, - mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): - super(Transformer, self).__init__() - dim_ref = dim_ref if dim_ref is not None else dim_self - self.enc_dec = enc_dec - if enc_dec: - num_layers = num_layers * 2 - layers = [] - for i in range(num_layers): - if i % 2 == 0 and enc_dec: # cross - layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - elif enc_dec: # self - layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - else: # self or cross - layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - self.layers = nn.ModuleList(layers) - - -class TransformerMapper(nn.Module): - - def forward(self, x): - x = self.linear(x).view(x.shape[0], self.clip_length, -1) - prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) - prefix = torch.cat((x, prefix), dim=1) - out = self.transformer(prefix)[:, self.clip_length:] - return out - - def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): - super(TransformerMapper, self).__init__() - self.clip_length = clip_length - self.transformer = Transformer(dim_embedding, 8, num_layers) - self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) - self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) - - -class ClipCaptionModel(nn.Module): - - def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: - return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) - - def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None): - # embedding_text = self.gpt.transformer.wte(tokens) - embedding_text = self.gpt.model.decoder.embed_tokens(tokens) # 수정 - prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) - embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) - if labels is not None: - dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) - labels = torch.cat((dummy_token, tokens), dim=1) - out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) - return out - - def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512, - num_layers: int = 8): - super(ClipCaptionModel, self).__init__() - self.prefix_length = prefix_length - # self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') - print('LM Model : opt-2.7b') - self.gpt = OPTForCausalLM.from_pretrained('facebook/opt-2.7b') # edit_ - # self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] - self.gpt_embedding_size = self.gpt.lm_head.in_features - self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length, - clip_length, num_layers) - - -class ClipCaptionPrefix(ClipCaptionModel): - - def parameters(self, recurse: bool = True): - return self.clip_project.parameters() - - def train(self, mode: bool = True): - super(ClipCaptionPrefix, self).train(mode) - self.gpt.eval() - return self - - - -class ClipCaptionPrefix(ClipCaptionModel): - def parameters(self, recurse: bool = True): - return self.clip_project.parameters() - - def train(self, mode: bool = True): - super(ClipCaptionPrefix, self).train(mode) - self.gpt.eval() - return self - - -def generate_beam( - model, - tokenizer, - beam_size: int = 5, - prompt=None, - embed=None, - entry_length=67, - temperature=1.0, - stop_token: str = ".", -): - - model.eval() - stop_token_index = tokenizer.encode(stop_token)[0] - tokens = None - scores = None - device = next(model.parameters()).device - seq_lengths = torch.ones(beam_size, device=device) - is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) - with torch.no_grad(): - if embed is not None: - generated = embed - else: - if tokens is None: - tokens = torch.tensor(tokenizer.encode(prompt)) - tokens = tokens.unsqueeze(0).to(device) - generated = model.gpt.transformer.wte(tokens) - for i in range(entry_length): - outputs = model.gpt(inputs_embeds=generated) - logits = outputs.logits - logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) - logits = logits.softmax(-1).log() - if scores is None: - scores, next_tokens = logits.topk(beam_size, -1) - generated = generated.expand(beam_size, *generated.shape[1:]) - next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) - if tokens is None: - tokens = next_tokens - else: - tokens = tokens.expand(beam_size, *tokens.shape[1:]) - tokens = torch.cat((tokens, next_tokens), dim=1) - else: - logits[is_stopped] = -float(np.inf) - logits[is_stopped, 0] = 0 - scores_sum = scores[:, None] + logits - seq_lengths[~is_stopped] += 1 - scores_sum_average = scores_sum / seq_lengths[:, None] - scores_sum_average, next_tokens = scores_sum_average.view(-1).topk( - beam_size, -1 - ) - next_tokens_source = next_tokens // scores_sum.shape[1] - seq_lengths = seq_lengths[next_tokens_source] - next_tokens = next_tokens % scores_sum.shape[1] - next_tokens = next_tokens.unsqueeze(1) - tokens = tokens[next_tokens_source] - tokens = torch.cat((tokens, next_tokens), dim=1) - generated = generated[next_tokens_source] - scores = scores_sum_average * seq_lengths - is_stopped = is_stopped[next_tokens_source] - next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view( - generated.shape[0], 1, -1 - ) - generated = torch.cat((generated, next_token_embed), dim=1) - is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() - if is_stopped.all(): - break - scores = scores / seq_lengths - output_list = tokens.cpu().numpy() - output_texts = [ - tokenizer.decode(output[: int(length)]) - for output, length in zip(output_list, seq_lengths) - ] - order = scores.argsort(descending=True) - output_texts = [output_texts[i] for i in order] - return output_texts - - -def generate2( - model, - tokenizer, - tokens=None, - prompt=None, - embed=None, - entry_count=1, - entry_length=67, # maximum number of words - top_p=0.8, - temperature=1.0, - stop_token: str = ".", -): - model.eval() - generated_num = 0 - generated_list = [] - stop_token_index = tokenizer.encode(stop_token)[0] - filter_value = -float("Inf") - device = next(model.parameters()).device - - with torch.no_grad(): - - for entry_idx in range(entry_count): - if embed is not None: - generated = embed - else: - if tokens is None: - tokens = torch.tensor(tokenizer.encode(prompt)) - tokens = tokens.unsqueeze(0).to(device) - - generated = model.gpt.transformer.wte(tokens) - - for i in range(entry_length): - - outputs = model.gpt(inputs_embeds=generated) - logits = outputs.logits - logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) - sorted_logits, sorted_indices = torch.sort(logits, descending=True) - cumulative_probs = torch.cumsum( - nnf.softmax(sorted_logits, dim=-1), dim=-1 - ) - sorted_indices_to_remove = cumulative_probs > top_p - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[ - ..., :-1 - ].clone() - sorted_indices_to_remove[..., 0] = 0 - - indices_to_remove = sorted_indices[sorted_indices_to_remove] - logits[:, indices_to_remove] = filter_value - next_token = torch.argmax(logits, -1).unsqueeze(0) - next_token_embed = model.gpt.transformer.wte(next_token) - if tokens is None: - tokens = next_token - else: - tokens = torch.cat((tokens, next_token), dim=1) - generated = torch.cat((generated, next_token_embed), dim=1) - if stop_token_index == next_token.item(): - break - - output_list = list(tokens.squeeze().cpu().numpy()) - output_text = tokenizer.decode(output_list) - generated_list.append(output_text) - - return generated_list[0] - -######################################################### - -import os -from tqdm import tqdm -import pandas as pd -import json - -fpath_nice = os.path.join('/data/img_cap/nice', 'NICE_val') -flist_nice = os.listdir(fpath_nice) -annot_csv = pd.read_csv(os.path.join('/data/img_cap/nice', 'nice-val-5k.csv')) - -# data = {} -# for img_nice in tqdm(flist_nice): -# inputs = {'image':open(os.path.join(fpath_nice, img_nice), 'rb'), 'model':'coco', 'use_beam_search':False} -# generated_caption = version.predict(**inputs) - -# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() - -# data[int(img_nice[:-4])] = [target_caption, generated_caption] - -# with open('NICE-clipcap_generate.json', 'w') as f_: -# json.dump(data, f_) - - -# predict = Predictor() -# predict.setup() - - -# data_coco_2 = {} -# data_coco_beam = {} -# for img_nice in tqdm(flist_nice): -# image = os.path.join(fpath_nice, img_nice) - -# # generated_caption_coco_2, prefix_embed = predict.predict(image=image, model='coco', use_beam_search=False) -# generated_caption_coco_beam, prefix_embed = predict.predict(image=image, model='coco', use_beam_search=True) - -# target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() - -# # data_coco_2[int(img_nice[:-4])] = [target_caption, generated_caption_coco_2] -# # torch.save(prefix_embed, f'prefix_embedding/clipcap/{img_nice[:-4]}') - -# data_coco_beam[int(img_nice[:-4])] = [target_caption, generated_caption_coco_beam] - -# # with open('nice-clipcap_coco_2.json', 'w') as fp: -# # json.dump(data_coco_2, fp) -# with open('nice-clipcap_coco_beam.json', 'w') as fp: -# json.dump(data_coco_beam, fp) \ No newline at end of file From cdbe82042b07fe78c80b21328628165cecb51a11 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Fri, 31 Mar 2023 16:57:33 +0900 Subject: [PATCH 14/25] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 688c595..f1ecdae 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,8 @@ Clone, create environment and install dependencies: git clone https://github.com/rmokady/CLIP_prefix_caption && cd CLIP_prefix_caption conda env create -f environment.yml conda activate clip_prefix_caption +pip install -e "git+https://github.com/replicate/cog.git@v0.0.20#egg=cog&subdirectory=python/" +pip install transformers --upgrade ``` ## COCO training From e7db264fc6d3840ed12a0dab2965c75bae0fcf36 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:13:30 +0900 Subject: [PATCH 15/25] Create evaluate.py --- evaluate.py | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 evaluate.py diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..7581292 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,42 @@ +import json + +from pycocoevalcap.meteor.meteor import Meteor +from pycocoevalcap.rouge.rouge import Rouge +from pycocoevalcap.cider.cider import Cider +from pycocoevalcap.spice.spice import Spice +from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer +from pycocoevalcap.eval import COCOEvalCap +from pycocotools.coco import COCO + + +# your file path +file_path = ## YOUR FILE PATH (.json) +# split your file with GroundTruth & Prediction +gt_file_name = f'./clipcap_opt27_gt.json' +gr_file_name = f'./clipcap_opt27_gr.json' + +gt = {}; gr = {} +with open(file_path,'r') as f: + json_data = json.load(f) + +gt["annotations"] = []; gt["images"] = [] +gr["annotations"] = []; gr["images"] = [] +for key, value in json_data.items(): + temp_1, temp_2, temp_3 = {}, {}, {} + temp_1["image_id"] = key; temp_2["image_id"] = key + temp_1["caption"] = value[0]; temp_2["caption"] = value[1] + temp_1["id"] = key; temp_2["id"] = key; temp_3["id"] = key + gt["annotations"].append(temp_1); gt["images"].append(temp_3) + gr["annotations"].append(temp_2); gr["images"].append(temp_3) + +with open(gt_file_name, 'w') as f_gt, open(gr_file_name, 'w') as f_gr: + json.dump(gt, f_gt) + json.dump(gr, f_gr) + + +# evaluate CIDEr, SPICE, METEOR, BLEU-4, ROUGE, BLEU-3, BLEU-2, BLEU-1 score +coco_gt = COCO(gt_file_name) +coco_pred = COCO(gr_file_name) +coco_eval = COCOEvalCap(coco_gt, coco_pred) + +coco_eval.evaluate() From e52d054a6d0dfb2cec925ccd6c82b9814d149454 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:13:48 +0900 Subject: [PATCH 16/25] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index f1ecdae..c8169c8 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,6 @@ code references [comment]: <> (Dependencies can be found at the [Inference notebook](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing) ) Clone, create environment and install dependencies: ``` -git clone https://github.com/rmokady/CLIP_prefix_caption && cd CLIP_prefix_caption conda env create -f environment.yml conda activate clip_prefix_caption pip install -e "git+https://github.com/replicate/cog.git@v0.0.20#egg=cog&subdirectory=python/" From 5374670df346a012116b01f2f61356871d557957 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:14:13 +0900 Subject: [PATCH 17/25] Delete notebooks directory --- .../clip_prefix_captioning_inference.ipynb | 1152 ------- notebooks/transformer_inference.ipynb | 2677 ----------------- 2 files changed, 3829 deletions(-) delete mode 100644 notebooks/clip_prefix_captioning_inference.ipynb delete mode 100644 notebooks/transformer_inference.ipynb diff --git a/notebooks/clip_prefix_captioning_inference.ipynb b/notebooks/clip_prefix_captioning_inference.ipynb deleted file mode 100644 index a951dff..0000000 --- a/notebooks/clip_prefix_captioning_inference.ipynb +++ /dev/null @@ -1,1152 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "clip_prefix_captioning_inference.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "1eb939f6352f4063808350cc4a97beae": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_56c9a56fb276438ab3f6b120193deb42", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_873ac85e2658404ea6b284707824a07d", - "IPY_MODEL_bcd00181175a4b739afc7a5eb30a235b", - "IPY_MODEL_2094ef62bd9547fcb133f565818495fa" - ] - } - }, - "56c9a56fb276438ab3f6b120193deb42": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "873ac85e2658404ea6b284707824a07d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_5a2eb4632f5d477182fcbf0ebcd7eb5b", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_93c09ca8d2ab40d381163b41c3bd345d" - } - }, - "bcd00181175a4b739afc7a5eb30a235b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_f0585589cf7b49098b0905ed10d96ecf", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 548118077, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 548118077, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_90bddc0c0d164e93996d0340f45bb9b5" - } - }, - "2094ef62bd9547fcb133f565818495fa": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_5dc2c242f1594631a573bdbe4ace5a97", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 523M/523M [00:20<00:00, 25.5MB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_7ab34ffb29124fe9a2bf8b2aadf8c514" - } - }, - "5a2eb4632f5d477182fcbf0ebcd7eb5b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "93c09ca8d2ab40d381163b41c3bd345d": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "f0585589cf7b49098b0905ed10d96ecf": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "90bddc0c0d164e93996d0340f45bb9b5": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "5dc2c242f1594631a573bdbe4ace5a97": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "7ab34ffb29124fe9a2bf8b2aadf8c514": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "sdBjRnWqLwWP" - }, - "source": [ - "# Inference notenook for [CLIP prefix captioning](https://github.com/rmokady/CLIP_prefix_caption/)\n", - "\n", - "Disclaimer: the authors do not own any rights for the code or data." - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GRfpGaz27IWs", - "outputId": "ebf43909-76e1-4c4a-e387-3501e9df9c4c" - }, - "source": [ - "#@title Install\n", - "!pip install transformers\n", - "! pip install git+https://github.com/openai/CLIP.git\n" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting transformers\n", - " Downloading transformers-4.11.3-py3-none-any.whl (2.9 MB)\n", - "\u001b[K |████████████████████████████████| 2.9 MB 5.4 MB/s \n", - "\u001b[?25hCollecting pyyaml>=5.1\n", - " Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n", - "\u001b[K |████████████████████████████████| 636 kB 39.2 MB/s \n", - "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.1)\n", - "Collecting huggingface-hub>=0.0.17\n", - " Downloading huggingface_hub-0.0.19-py3-none-any.whl (56 kB)\n", - "\u001b[K |████████████████████████████████| 56 kB 5.0 MB/s \n", - "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n", - " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n", - "\u001b[K |████████████████████████████████| 3.3 MB 34.6 MB/s \n", - "\u001b[?25hCollecting sacremoses\n", - " Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n", - "\u001b[K |████████████████████████████████| 895 kB 38.7 MB/s \n", - "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.3.0)\n", - "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n", - "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.17->transformers) (3.7.4.3)\n", - "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (2.4.7)\n", - "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n", - "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n", - "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n", - "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n", - "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n", - "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n", - "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n", - " Attempting uninstall: pyyaml\n", - " Found existing installation: PyYAML 3.13\n", - " Uninstalling PyYAML-3.13:\n", - " Successfully uninstalled PyYAML-3.13\n", - "Successfully installed huggingface-hub-0.0.19 pyyaml-5.4.1 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.11.3\n", - "Collecting git+https://github.com/openai/CLIP.git\n", - " Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-e9pft9oj\n", - " Running command git clone -q https://github.com/openai/CLIP.git /tmp/pip-req-build-e9pft9oj\n", - "Collecting ftfy\n", - " Downloading ftfy-6.0.3.tar.gz (64 kB)\n", - "\u001b[K |████████████████████████████████| 64 kB 2.0 MB/s \n", - "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (2019.12.20)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (4.62.3)\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (1.9.0+cu111)\n", - "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (0.10.0+cu111)\n", - "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy->clip==1.0) (0.2.5)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->clip==1.0) (3.7.4.3)\n", - "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->clip==1.0) (7.1.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->clip==1.0) (1.19.5)\n", - "Building wheels for collected packages: clip, ftfy\n", - " Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369090 sha256=48b7ac167d3631347368e3ba02d8cf5c319ed9414532467ba8eb1e2ee5af3786\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-_wqq4nr7/wheels/fd/b9/c3/5b4470e35ed76e174bff77c92f91da82098d5e35fd5bc8cdac\n", - " Building wheel for ftfy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for ftfy: filename=ftfy-6.0.3-py3-none-any.whl size=41933 sha256=9ce1d67ee2d04149250e7246335dad1a3f1c8b9bd41d07182aa7f04a66c48abb\n", - " Stored in directory: /root/.cache/pip/wheels/19/f5/38/273eb3b5e76dfd850619312f693716ac4518b498f5ffb6f56d\n", - "Successfully built clip ftfy\n", - "Installing collected packages: ftfy, clip\n", - "Successfully installed clip-1.0 ftfy-6.0.3\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "iqE3Fj5-uYSR", - "cellView": "form" - }, - "source": [ - "#@title Drive Downloader\n", - "\n", - "from pydrive.auth import GoogleAuth\n", - "from pydrive.drive import GoogleDrive\n", - "from google.colab import auth\n", - "from oauth2client.client import GoogleCredentials\n", - "\n", - "download_with_pydrive = True #@param {type:\"boolean\"} \n", - "\n", - "class Downloader(object):\n", - " def __init__(self, use_pydrive):\n", - " self.use_pydrive = use_pydrive\n", - "\n", - " if self.use_pydrive:\n", - " self.authenticate()\n", - " \n", - " def authenticate(self):\n", - " auth.authenticate_user()\n", - " gauth = GoogleAuth()\n", - " gauth.credentials = GoogleCredentials.get_application_default()\n", - " self.drive = GoogleDrive(gauth)\n", - " \n", - " def download_file(self, file_id, file_dst):\n", - " if self.use_pydrive:\n", - " downloaded = self.drive.CreateFile({'id':file_id})\n", - " downloaded.FetchMetadata(fetch_all=True)\n", - " downloaded.GetContentFile(file_dst)\n", - " else:\n", - " !gdown --id $file_id -O $file_dst\n", - "\n", - "downloader = Downloader(download_with_pydrive)" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "OArDkm_24w4L", - "cellView": "form" - }, - "source": [ - "#@title Imports\n", - "\n", - "import clip\n", - "import os\n", - "from torch import nn\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn.functional as nnf\n", - "import sys\n", - "from typing import Tuple, List, Union, Optional\n", - "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n", - "from tqdm import tqdm, trange\n", - "from google.colab import files\n", - "import skimage.io as io\n", - "import PIL.Image\n", - "from IPython.display import Image \n", - "\n", - "\n", - "N = type(None)\n", - "V = np.array\n", - "ARRAY = np.ndarray\n", - "ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]]\n", - "VS = Union[Tuple[V, ...], List[V]]\n", - "VN = Union[V, N]\n", - "VNS = Union[VS, N]\n", - "T = torch.Tensor\n", - "TS = Union[Tuple[T, ...], List[T]]\n", - "TN = Optional[T]\n", - "TNS = Union[Tuple[TN, ...], List[TN]]\n", - "TSN = Optional[TS]\n", - "TA = Union[T, ARRAY]\n", - "\n", - "\n", - "D = torch.device\n", - "CPU = torch.device('cpu')\n", - "\n", - "\n", - "def get_device(device_id: int) -> D:\n", - " if not torch.cuda.is_available():\n", - " return CPU\n", - " device_id = min(torch.cuda.device_count() - 1, device_id)\n", - " return torch.device(f'cuda:{device_id}')\n", - "\n", - "\n", - "CUDA = get_device\n", - "\n", - "current_directory = os.getcwd()\n", - "save_path = os.path.join(os.path.dirname(current_directory), \"pretrained_models\")\n", - "os.makedirs(save_path, exist_ok=True)\n", - "model_path = os.path.join(save_path, 'model_wieghts.pt')\n" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "4ClW2ebek8DK", - "cellView": "form" - }, - "source": [ - "#@title Model\n", - "\n", - "class MLP(nn.Module):\n", - "\n", - " def forward(self, x: T) -> T:\n", - " return self.model(x)\n", - "\n", - " def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):\n", - " super(MLP, self).__init__()\n", - " layers = []\n", - " for i in range(len(sizes) -1):\n", - " layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))\n", - " if i < len(sizes) - 2:\n", - " layers.append(act())\n", - " self.model = nn.Sequential(*layers)\n", - "\n", - "\n", - "class ClipCaptionModel(nn.Module):\n", - "\n", - " #@functools.lru_cache #FIXME\n", - " def get_dummy_token(self, batch_size: int, device: D) -> T:\n", - " return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)\n", - "\n", - " def forward(self, tokens: T, prefix: T, mask: Optional[T] = None, labels: Optional[T] = None):\n", - " embedding_text = self.gpt.transformer.wte(tokens)\n", - " prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)\n", - " #print(embedding_text.size()) #torch.Size([5, 67, 768])\n", - " #print(prefix_projections.size()) #torch.Size([5, 1, 768])\n", - " embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)\n", - " if labels is not None:\n", - " dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)\n", - " labels = torch.cat((dummy_token, tokens), dim=1)\n", - " out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)\n", - " return out\n", - "\n", - " def __init__(self, prefix_length: int, prefix_size: int = 512):\n", - " super(ClipCaptionModel, self).__init__()\n", - " self.prefix_length = prefix_length\n", - " self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')\n", - " self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]\n", - " if prefix_length > 10: # not enough memory\n", - " self.clip_project = nn.Linear(prefix_size, self.gpt_embedding_size * prefix_length)\n", - " else:\n", - " self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, self.gpt_embedding_size * prefix_length))\n", - "\n", - "\n", - "class ClipCaptionPrefix(ClipCaptionModel):\n", - "\n", - " def parameters(self, recurse: bool = True):\n", - " return self.clip_project.parameters()\n", - "\n", - " def train(self, mode: bool = True):\n", - " super(ClipCaptionPrefix, self).train(mode)\n", - " self.gpt.eval()\n", - " return self" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "V7xocT3TUgey", - "cellView": "form" - }, - "source": [ - "#@title Caption prediction\n", - "\n", - "def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None,\n", - " entry_length=67, temperature=1., stop_token: str = '.'):\n", - "\n", - " model.eval()\n", - " stop_token_index = tokenizer.encode(stop_token)[0]\n", - " tokens = None\n", - " scores = None\n", - " device = next(model.parameters()).device\n", - " seq_lengths = torch.ones(beam_size, device=device)\n", - " is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)\n", - " with torch.no_grad():\n", - " if embed is not None:\n", - " generated = embed\n", - " else:\n", - " if tokens is None:\n", - " tokens = torch.tensor(tokenizer.encode(prompt))\n", - " tokens = tokens.unsqueeze(0).to(device)\n", - " generated = model.gpt.transformer.wte(tokens)\n", - " for i in range(entry_length):\n", - " outputs = model.gpt(inputs_embeds=generated)\n", - " logits = outputs.logits\n", - " logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n", - " logits = logits.softmax(-1).log()\n", - " if scores is None:\n", - " scores, next_tokens = logits.topk(beam_size, -1)\n", - " generated = generated.expand(beam_size, *generated.shape[1:])\n", - " next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)\n", - " if tokens is None:\n", - " tokens = next_tokens\n", - " else:\n", - " tokens = tokens.expand(beam_size, *tokens.shape[1:])\n", - " tokens = torch.cat((tokens, next_tokens), dim=1)\n", - " else:\n", - " logits[is_stopped] = -float(np.inf)\n", - " logits[is_stopped, 0] = 0\n", - " scores_sum = scores[:, None] + logits\n", - " seq_lengths[~is_stopped] += 1\n", - " scores_sum_average = scores_sum / seq_lengths[:, None]\n", - " scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)\n", - " next_tokens_source = next_tokens // scores_sum.shape[1]\n", - " seq_lengths = seq_lengths[next_tokens_source]\n", - " next_tokens = next_tokens % scores_sum.shape[1]\n", - " next_tokens = next_tokens.unsqueeze(1)\n", - " tokens = tokens[next_tokens_source]\n", - " tokens = torch.cat((tokens, next_tokens), dim=1)\n", - " generated = generated[next_tokens_source]\n", - " scores = scores_sum_average * seq_lengths\n", - " is_stopped = is_stopped[next_tokens_source]\n", - " next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)\n", - " generated = torch.cat((generated, next_token_embed), dim=1)\n", - " is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()\n", - " if is_stopped.all():\n", - " break\n", - " scores = scores / seq_lengths\n", - " output_list = tokens.cpu().numpy()\n", - " output_texts = [tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)]\n", - " order = scores.argsort(descending=True)\n", - " output_texts = [output_texts[i] for i in order]\n", - " return output_texts\n", - "\n", - "\n", - "def generate2(\n", - " model,\n", - " tokenizer,\n", - " tokens=None,\n", - " prompt=None,\n", - " embed=None,\n", - " entry_count=1,\n", - " entry_length=67, # maximum number of words\n", - " top_p=0.8,\n", - " temperature=1.,\n", - " stop_token: str = '.',\n", - "):\n", - " model.eval()\n", - " generated_num = 0\n", - " generated_list = []\n", - " stop_token_index = tokenizer.encode(stop_token)[0]\n", - " filter_value = -float(\"Inf\")\n", - " device = next(model.parameters()).device\n", - "\n", - " with torch.no_grad():\n", - "\n", - " for entry_idx in trange(entry_count):\n", - " if embed is not None:\n", - " generated = embed\n", - " else:\n", - " if tokens is None:\n", - " tokens = torch.tensor(tokenizer.encode(prompt))\n", - " tokens = tokens.unsqueeze(0).to(device)\n", - "\n", - " generated = model.gpt.transformer.wte(tokens)\n", - "\n", - " for i in range(entry_length):\n", - "\n", - " outputs = model.gpt(inputs_embeds=generated)\n", - " logits = outputs.logits\n", - " logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n", - " sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n", - " cumulative_probs = torch.cumsum(nnf.softmax(sorted_logits, dim=-1), dim=-1)\n", - " sorted_indices_to_remove = cumulative_probs > top_p\n", - " sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[\n", - " ..., :-1\n", - " ].clone()\n", - " sorted_indices_to_remove[..., 0] = 0\n", - "\n", - " indices_to_remove = sorted_indices[sorted_indices_to_remove]\n", - " logits[:, indices_to_remove] = filter_value\n", - " next_token = torch.argmax(logits, -1).unsqueeze(0)\n", - " next_token_embed = model.gpt.transformer.wte(next_token)\n", - " if tokens is None:\n", - " tokens = next_token\n", - " else:\n", - " tokens = torch.cat((tokens, next_token), dim=1)\n", - " generated = torch.cat((generated, next_token_embed), dim=1)\n", - " if stop_token_index == next_token.item():\n", - " break\n", - "\n", - " output_list = list(tokens.squeeze().cpu().numpy())\n", - " output_text = tokenizer.decode(output_list)\n", - " generated_list.append(output_text)\n", - "\n", - " return generated_list[0]" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "xE-uUStuv1Nl", - "cellView": "form" - }, - "source": [ - "#@title Choose pretrained model - COCO or Coneptual captions\n", - "\n", - "\n", - "pretrained_model = 'Conceptual captions' # @param ['COCO', 'Conceptual captions']\n", - "\n", - "if pretrained_model == 'Conceptual captions':\n", - " downloader.download_file(\"14pXWwB4Zm82rsDdvbGguLfx9F8aM7ovT\", model_path)\n", - "else:\n", - " downloader.download_file(\"1IdaBtMSvtyzF0ByVaBHtvM0JYSXRExRX\", model_path)" - ], - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "7lCgFHSgr_ny" - }, - "source": [ - "#@title GPU/CPU\n", - "\n", - "\n", - "is_gpu = True #@param {type:\"boolean\"} \n" - ], - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "6bi_2zQ3QD57", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162 - }, - "cellView": "form", - "outputId": "47afad0d-a76c-4316-8f6d-682dd3e49587" - }, - "source": [ - "#@title CLIP model + GPT2 tokenizer\n", - "\n", - "device = CUDA(0) if is_gpu else \"cpu\"\n", - "clip_model, preprocess = clip.load(\"ViT-B/32\", device=device, jit=False)\n", - "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|████████████████████████████████████████| 338M/338M [00:02<00:00, 120MiB/s]\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "349a53ebb2ca49da9a9da9f0c0a3160b", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/0.99M [00:00\n", - " \n", - " Upload widget is only available when the cell has been executed in the\n", - " current browser session. Please rerun this cell to enable.\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saving COCO_val2014_000000354533.jpg to COCO_val2014_000000354533.jpg\n", - "COCO_val2014_000000354533.jpg\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "pohtQ8AfWNk_", - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5116f31c-1e23-4e2a-9e05-5d8ed1ab835e" - }, - "source": [ - "#@title Or download random samples form COCO test set (Karpathy et al. split)\n", - "\n", - "IMAGE_NAME = '354533' # @param ['562207', '579664', '060623', '165547', '334321', '483108', '386164', '354533']\n", - "\n", - "name_ = \"COCO_val2014_000000\" + IMAGE_NAME + \".jpg\"\n", - "images_path = os.path.join(os.path.dirname(current_directory), \"images\")\n", - "os.makedirs(images_path, exist_ok=True)\n", - "UPLOADED_FILE = os.path.join(images_path, name_)\n", - "\n", - "if not os.path.isfile(UPLOADED_FILE):\n", - " download_path = os.path.join(images_path, \"images.zip\")\n", - " downloader.download_file(\"1BwJeBME-dpwcCT8IXYeWz7uaPkbexjNB\", download_path)\n", - "\n", - " !unzip {download_path} -d {images_path}\n", - "\n" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Archive: /images/images.zip\n", - " inflating: /images/COCO_val2014_000000060623.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000060623.jpg \n", - " inflating: /images/COCO_val2014_000000165547.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000165547.jpg \n", - " inflating: /images/COCO_val2014_000000334321.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000334321.jpg \n", - " inflating: /images/COCO_val2014_000000354533.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000354533.jpg \n", - " inflating: /images/COCO_val2014_000000386164.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000386164.jpg \n", - " inflating: /images/COCO_val2014_000000483108.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000483108.jpg \n", - " inflating: /images/COCO_val2014_000000562207.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000562207.jpg \n", - " inflating: /images/COCO_val2014_000000579664.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000579664.jpg \n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XyVkuZ07llSC" - }, - "source": [ - "Conceptual captions examples:\n", - "https://drive.google.com/file/d/1mzH3b0LQrGEWjEva4hI6HE_fIYRIgtBT/view?usp=sharing" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 395 - }, - "id": "rRmcYnEfSMc_", - "cellView": "form", - "outputId": "9f169ac4-ae39-4e51-b8ca-7955a212813e" - }, - "source": [ - "#@title Inference\n", - "use_beam_search = False #@param {type:\"boolean\"} \n", - "\n", - "image = io.imread(UPLOADED_FILE)\n", - "pil_image = PIL.Image.fromarray(image)\n", - "#pil_img = Image(filename=UPLOADED_FILE)\n", - "display(pil_image)\n", - "\n", - "image = preprocess(pil_image).unsqueeze(0).to(device)\n", - "with torch.no_grad():\n", - " # if type(model) is ClipCaptionE2E:\n", - " # prefix_embed = model.forward_image(image)\n", - " # else:\n", - " prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)\n", - " prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)\n", - "if use_beam_search:\n", - " generated_text_prefix = generate_beam(model, tokenizer, embed=prefix_embed)[0]\n", - "else:\n", - " generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)\n", - "\n", - "\n", - "print('\\n')\n", - "print(generated_text_prefix)" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 1/1 [00:00<00:00, 3.59it/s]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "\n", - "a motorcycle parked in the desert.\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/notebooks/transformer_inference.ipynb b/notebooks/transformer_inference.ipynb deleted file mode 100644 index 2134e5b..0000000 --- a/notebooks/transformer_inference.ipynb +++ /dev/null @@ -1,2677 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "transformer_inference.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "display_name": "PyCharm (cvpr22)", - "language": "python", - "name": "pycharm-98db7c03" - }, - "language_info": { - "name": "python" - }, - "stem_cell": { - "cell_type": "raw", - "metadata": { - "pycharm": { - "metadata": false - } - }, - "source": "" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "52519cb969284d99838713141e698764": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_467d658002da46818e077b1006010947", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_6c368cf3c9ea4ace9d95028e413801d1", - "IPY_MODEL_edcfa38725d44106a8ff6a1e8e5a589b", - "IPY_MODEL_7c88423573cb4ddebba2ea96e618f002" - ] - } - }, - "467d658002da46818e077b1006010947": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "6c368cf3c9ea4ace9d95028e413801d1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_22b2ff1b7398419389aa375ab4c72847", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_654b184369294d7d926cae30721d0afc" - } - }, - "edcfa38725d44106a8ff6a1e8e5a589b": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_0157f7f0326244d19d79ed48fa253a63", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 1042301, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 1042301, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_89bce76c1e974b5abe73aed0f5ae4c12" - } - }, - "7c88423573cb4ddebba2ea96e618f002": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_6d76486b19254269be5cebd45a5592ca", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 0.99M/0.99M [00:00<00:00, 4.88MB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_857c65639fbc4519894fa34fe9cb7da7" - } - }, - "22b2ff1b7398419389aa375ab4c72847": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "654b184369294d7d926cae30721d0afc": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "0157f7f0326244d19d79ed48fa253a63": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "89bce76c1e974b5abe73aed0f5ae4c12": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "6d76486b19254269be5cebd45a5592ca": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "857c65639fbc4519894fa34fe9cb7da7": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "4c9919afae3a4ae087e8c62d48b29f5f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_4efea4d2c08d4d22929f3ce0ae446d56", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_b21345175d3e4cbb91cd31edaa13d7c0", - "IPY_MODEL_165004f4cfc0484aa7244acff7a8c162", - "IPY_MODEL_897f93df21fb4384a791d84deb49f297" - ] - } - }, - "4efea4d2c08d4d22929f3ce0ae446d56": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "b21345175d3e4cbb91cd31edaa13d7c0": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_9a14c2939449403295faa921dab6b8b6", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_47ba23445257468e965f8694097bef38" - } - }, - "165004f4cfc0484aa7244acff7a8c162": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_41e003814ccc4f518969de12a3853233", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 456318, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 456318, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_24322fecd55d4cefb8e32b8ed46f1002" - } - }, - "897f93df21fb4384a791d84deb49f297": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_42af2ad7ec0446c982ebca16ff0e35fc", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 446k/446k [00:00<00:00, 679kB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_fe1998b4ae414ff5a57ec4d020df074d" - } - }, - "9a14c2939449403295faa921dab6b8b6": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "47ba23445257468e965f8694097bef38": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "41e003814ccc4f518969de12a3853233": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "24322fecd55d4cefb8e32b8ed46f1002": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "42af2ad7ec0446c982ebca16ff0e35fc": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "fe1998b4ae414ff5a57ec4d020df074d": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "16a3955ae2e441d3b5094df2858e71f4": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_d310a1224b494ef4b6348966edaf7237", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_c36a04b157a74a9a90413145b6479ecd", - "IPY_MODEL_97b5688fa3ec488aa3025fc015cc9c3e", - "IPY_MODEL_717dd1ea735a4154aca034070d1e186c" - ] - } - }, - "d310a1224b494ef4b6348966edaf7237": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "c36a04b157a74a9a90413145b6479ecd": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_60f44114f512453aa71306ce079e0bf2", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_befbd0f377194d24a993e37ebe4c1955" - } - }, - "97b5688fa3ec488aa3025fc015cc9c3e": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_141e03dcfb0d4b74b9b5bb2496c0d1b2", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 1355256, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 1355256, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_167c06f309914ebb95052bdd9fda6b55" - } - }, - "717dd1ea735a4154aca034070d1e186c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_01e6a9e24d9941debe21dc3096ff4660", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 1.29M/1.29M [00:00<00:00, 7.73MB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_668b3b2d1e5a4d69ab6782359fd9c137" - } - }, - "60f44114f512453aa71306ce079e0bf2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "befbd0f377194d24a993e37ebe4c1955": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "141e03dcfb0d4b74b9b5bb2496c0d1b2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "167c06f309914ebb95052bdd9fda6b55": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "01e6a9e24d9941debe21dc3096ff4660": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "668b3b2d1e5a4d69ab6782359fd9c137": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "7322a4f712c24903a7587fac39968490": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_e5d922b6a26046c484626ec95febfc40", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_15aaf9e61ad047ba91716b6d4d290563", - "IPY_MODEL_79f020b8f2bb472a84d4fecbdbdc3e86", - "IPY_MODEL_99d10ad9f213415388426ab65344a066" - ] - } - }, - "e5d922b6a26046c484626ec95febfc40": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "15aaf9e61ad047ba91716b6d4d290563": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_1608135299924faf975dd3e2f1e190c2", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_f2ef2398b5fc4da0a2f4d4a6b2805765" - } - }, - "79f020b8f2bb472a84d4fecbdbdc3e86": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_4a95d045c5964b0eb3ca948c6ba96e91", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 665, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 665, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_0d73b179c31544f5829948754a74220a" - } - }, - "99d10ad9f213415388426ab65344a066": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_6ccd65f0c36e4d238eabadc613a1426c", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 665/665 [00:00<00:00, 14.2kB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_348ac40c8d164cc1bb585c773ac66b71" - } - }, - "1608135299924faf975dd3e2f1e190c2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "f2ef2398b5fc4da0a2f4d4a6b2805765": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "4a95d045c5964b0eb3ca948c6ba96e91": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "0d73b179c31544f5829948754a74220a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "6ccd65f0c36e4d238eabadc613a1426c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "348ac40c8d164cc1bb585c773ac66b71": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "f61ccef508674b1288eea6bdee9a477d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.5.0", - "box_style": "", - "layout": "IPY_MODEL_6e17133a08e44f568715bd23e1318273", - "_model_module": "@jupyter-widgets/controls", - "children": [ - "IPY_MODEL_47b0e62063dc4c038192c9b93be805ef", - "IPY_MODEL_51e5b763cb33450eb4b5b7bea62750da", - "IPY_MODEL_d3e3af32894d42bb889efac82d85ffaf" - ] - } - }, - "6e17133a08e44f568715bd23e1318273": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "47b0e62063dc4c038192c9b93be805ef": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_216d2847afbe4275b15f61d286bcc828", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": "Downloading: 100%", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_c8897686b56d48338fcd3791f4dd636b" - } - }, - "51e5b763cb33450eb4b5b7bea62750da": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "ProgressView", - "style": "IPY_MODEL_41175b8f7bb645168832478193594713", - "_dom_classes": [], - "description": "", - "_model_name": "FloatProgressModel", - "bar_style": "success", - "max": 548118077, - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": 548118077, - "_view_count": null, - "_view_module_version": "1.5.0", - "orientation": "horizontal", - "min": 0, - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_cd34322f38054565b8b051748d80657e" - } - }, - "d3e3af32894d42bb889efac82d85ffaf": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HTMLView", - "style": "IPY_MODEL_40c5c4a8bb4c45978c99cf17c1e4f451", - "_dom_classes": [], - "description": "", - "_model_name": "HTMLModel", - "placeholder": "​", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "value": " 523M/523M [00:19<00:00, 28.2MB/s]", - "_view_count": null, - "_view_module_version": "1.5.0", - "description_tooltip": null, - "_model_module": "@jupyter-widgets/controls", - "layout": "IPY_MODEL_73b8eb5794574fc1b90e8204f93bccad" - } - }, - "216d2847afbe4275b15f61d286bcc828": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "c8897686b56d48338fcd3791f4dd636b": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "41175b8f7bb645168832478193594713": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "ProgressStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "bar_color": null, - "_model_module": "@jupyter-widgets/controls" - } - }, - "cd34322f38054565b8b051748d80657e": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - }, - "40c5c4a8bb4c45978c99cf17c1e4f451": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "73b8eb5794574fc1b90e8204f93bccad": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - } - }, - "cells": [ - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GRfpGaz27IWs", - "outputId": "1f5bce07-8708-4026-c44e-5ff34267d652" - }, - "source": [ - "#@title Install\n", - "!pip install transformers\n", - "! pip install git+https://github.com/openai/CLIP.git\n" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting transformers\n", - " Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\n", - "\u001b[K |████████████████████████████████| 3.1 MB 5.3 MB/s \n", - "\u001b[?25hCollecting sacremoses\n", - " Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n", - "\u001b[K |████████████████████████████████| 895 kB 45.6 MB/s \n", - "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n", - "Collecting pyyaml>=5.1\n", - " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n", - "\u001b[K |████████████████████████████████| 596 kB 41.6 MB/s \n", - "\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n", - " Downloading huggingface_hub-0.1.2-py3-none-any.whl (59 kB)\n", - "\u001b[K |████████████████████████████████| 59 kB 6.7 MB/s \n", - "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n", - "Collecting tokenizers<0.11,>=0.10.1\n", - " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n", - "\u001b[K |████████████████████████████████| 3.3 MB 33.8 MB/s \n", - "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n", - "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n", - "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n", - "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n", - "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n", - "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n", - "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n", - "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n", - "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n", - "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n", - "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n", - "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n", - " Attempting uninstall: pyyaml\n", - " Found existing installation: PyYAML 3.13\n", - " Uninstalling PyYAML-3.13:\n", - " Successfully uninstalled PyYAML-3.13\n", - "Successfully installed huggingface-hub-0.1.2 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.12.5\n", - "Collecting git+https://github.com/openai/CLIP.git\n", - " Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-l4omvyqi\n", - " Running command git clone -q https://github.com/openai/CLIP.git /tmp/pip-req-build-l4omvyqi\n", - "Collecting ftfy\n", - " Downloading ftfy-6.0.3.tar.gz (64 kB)\n", - "\u001b[K |████████████████████████████████| 64 kB 1.9 MB/s \n", - "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (2019.12.20)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (4.62.3)\n", - "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (1.10.0+cu111)\n", - "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from clip==1.0) (0.11.1+cu111)\n", - "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from ftfy->clip==1.0) (0.2.5)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->clip==1.0) (3.10.0.2)\n", - "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->clip==1.0) (7.1.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->clip==1.0) (1.19.5)\n", - "Building wheels for collected packages: clip, ftfy\n", - " Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369106 sha256=479d8f18317ce05be0f002c5b70f1e0d0e02ccdf5b43072ad02e01af0be92f1e\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-0rk2idwe/wheels/fd/b9/c3/5b4470e35ed76e174bff77c92f91da82098d5e35fd5bc8cdac\n", - " Building wheel for ftfy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for ftfy: filename=ftfy-6.0.3-py3-none-any.whl size=41933 sha256=01302e53ff5cdbda58d8bc4a948f52db5e755ab7f2f5929d10a7e500b66ad36a\n", - " Stored in directory: /root/.cache/pip/wheels/19/f5/38/273eb3b5e76dfd850619312f693716ac4518b498f5ffb6f56d\n", - "Successfully built clip ftfy\n", - "Installing collected packages: ftfy, clip\n", - "Successfully installed clip-1.0 ftfy-6.0.3\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "iqE3Fj5-uYSR" - }, - "source": [ - "#@title Drive Downloader\n", - "\n", - "from pydrive.auth import GoogleAuth\n", - "from pydrive.drive import GoogleDrive\n", - "from google.colab import auth\n", - "from oauth2client.client import GoogleCredentials\n", - "\n", - "download_with_pydrive = True #@param {type:\"boolean\"} \n", - "\n", - "class Downloader(object):\n", - " def __init__(self, use_pydrive):\n", - " self.use_pydrive = use_pydrive\n", - "\n", - " if self.use_pydrive:\n", - " self.authenticate()\n", - " \n", - " def authenticate(self):\n", - " auth.authenticate_user()\n", - " gauth = GoogleAuth()\n", - " gauth.credentials = GoogleCredentials.get_application_default()\n", - " self.drive = GoogleDrive(gauth)\n", - " \n", - " def download_file(self, file_id, file_dst):\n", - " if self.use_pydrive:\n", - " downloaded = self.drive.CreateFile({'id':file_id})\n", - " downloaded.FetchMetadata(fetch_all=True)\n", - " downloaded.GetContentFile(file_dst)\n", - " else:\n", - " !gdown --id $file_id -O $file_dst\n", - "\n", - "downloader = Downloader(download_with_pydrive)" - ], - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "OArDkm_24w4L" - }, - "source": [ - "#@title Imports\n", - "\n", - "import clip\n", - "import os\n", - "from torch import nn\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn.functional as nnf\n", - "import sys\n", - "from typing import Tuple, List, Union, Optional\n", - "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n", - "from tqdm import tqdm, trange\n", - "from google.colab import files\n", - "import skimage.io as io\n", - "import PIL.Image\n", - "from IPython.display import Image \n", - "from enum import Enum\n", - "\n", - "\n", - "\n", - "N = type(None)\n", - "V = np.array\n", - "ARRAY = np.ndarray\n", - "ARRAYS = Union[Tuple[ARRAY, ...], List[ARRAY]]\n", - "VS = Union[Tuple[V, ...], List[V]]\n", - "VN = Union[V, N]\n", - "VNS = Union[VS, N]\n", - "T = torch.Tensor\n", - "TS = Union[Tuple[T, ...], List[T]]\n", - "TN = Optional[T]\n", - "TNS = Union[Tuple[TN, ...], List[TN]]\n", - "TSN = Optional[TS]\n", - "TA = Union[T, ARRAY]\n", - "\n", - "\n", - "D = torch.device\n", - "CPU = torch.device('cpu')\n", - "\n", - "\n", - "def get_device(device_id: int) -> D:\n", - " if not torch.cuda.is_available():\n", - " return CPU\n", - " device_id = min(torch.cuda.device_count() - 1, device_id)\n", - " return torch.device(f'cuda:{device_id}')\n", - "\n", - "\n", - "CUDA = get_device\n", - "\n", - "current_directory = os.getcwd()\n", - "save_path = os.path.join(os.path.dirname(current_directory), \"pretrained_models\")\n", - "os.makedirs(save_path, exist_ok=True)\n", - "model_path = os.path.join(save_path, 'model_wieghts.pt')\n" - ], - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "4ClW2ebek8DK" - }, - "source": [ - "#@title Model\n", - "\n", - "\n", - "class MappingType(Enum):\n", - " MLP = 'mlp'\n", - " Transformer = 'transformer'\n", - "\n", - "\n", - "class MlpTransformer(nn.Module):\n", - " def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):\n", - " super().__init__()\n", - " out_d = out_d if out_d is not None else in_dim\n", - " self.fc1 = nn.Linear(in_dim, h_dim)\n", - " self.act = act\n", - " self.fc2 = nn.Linear(h_dim, out_d)\n", - " self.dropout = nn.Dropout(dropout)\n", - "\n", - " def forward(self, x):\n", - " x = self.fc1(x)\n", - " x = self.act(x)\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - " x = self.dropout(x)\n", - " return x\n", - "\n", - "class MLP(nn.Module):\n", - "\n", - " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", - " return self.model(x)\n", - "\n", - " def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):\n", - " super(MLP, self).__init__()\n", - " layers = []\n", - " for i in range(len(sizes) - 1):\n", - " layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))\n", - " if i < len(sizes) - 2:\n", - " layers.append(act())\n", - " self.model = nn.Sequential(*layers)\n", - "\n", - "\n", - "class MultiHeadAttention(nn.Module):\n", - "\n", - " def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):\n", - " super().__init__()\n", - " self.num_heads = num_heads\n", - " head_dim = dim_self // num_heads\n", - " self.scale = head_dim ** -0.5\n", - " self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)\n", - " self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)\n", - " self.project = nn.Linear(dim_self, dim_self)\n", - " self.dropout = nn.Dropout(dropout)\n", - "\n", - " def forward(self, x, y=None, mask=None):\n", - " y = y if y is not None else x\n", - " b, n, c = x.shape\n", - " _, m, d = y.shape\n", - " # b n h dh\n", - " queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)\n", - " # b m 2 h dh\n", - " keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)\n", - " keys, values = keys_values[:, :, 0], keys_values[:, :, 1]\n", - " attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale\n", - " if mask is not None:\n", - " if mask.dim() == 2:\n", - " mask = mask.unsqueeze(1)\n", - " attention = attention.masked_fill(mask.unsqueeze(3), float(\"-inf\"))\n", - " attention = attention.softmax(dim=2)\n", - " out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)\n", - " out = self.project(out)\n", - " return out, attention\n", - "\n", - "\n", - "class TransformerLayer(nn.Module):\n", - "\n", - " def forward_with_attention(self, x, y=None, mask=None):\n", - " x_, attention = self.attn(self.norm1(x), y, mask)\n", - " x = x + x_\n", - " x = x + self.mlp(self.norm2(x))\n", - " return x, attention\n", - "\n", - " def forward(self, x, y=None, mask=None):\n", - " x = x + self.attn(self.norm1(x), y, mask)[0]\n", - " x = x + self.mlp(self.norm2(x))\n", - " return x\n", - "\n", - " def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,\n", - " norm_layer: nn.Module = nn.LayerNorm):\n", - " super().__init__()\n", - " self.norm1 = norm_layer(dim_self)\n", - " self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)\n", - " self.norm2 = norm_layer(dim_self)\n", - " self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)\n", - "\n", - "\n", - "class Transformer(nn.Module):\n", - "\n", - " def forward_with_attention(self, x, y=None, mask=None):\n", - " attentions = []\n", - " for layer in self.layers:\n", - " x, att = layer.forward_with_attention(x, y, mask)\n", - " attentions.append(att)\n", - " return x, attentions\n", - "\n", - " def forward(self, x, y=None, mask=None):\n", - " for i, layer in enumerate(self.layers):\n", - " if i % 2 == 0 and self.enc_dec: # cross\n", - " x = layer(x, y)\n", - " elif self.enc_dec: # self\n", - " x = layer(x, x, mask)\n", - " else: # self or cross\n", - " x = layer(x, y, mask)\n", - " return x\n", - "\n", - " def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,\n", - " mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):\n", - " super(Transformer, self).__init__()\n", - " dim_ref = dim_ref if dim_ref is not None else dim_self\n", - " self.enc_dec = enc_dec\n", - " if enc_dec:\n", - " num_layers = num_layers * 2\n", - " layers = []\n", - " for i in range(num_layers):\n", - " if i % 2 == 0 and enc_dec: # cross\n", - " layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", - " elif enc_dec: # self\n", - " layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", - " else: # self or cross\n", - " layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", - " self.layers = nn.ModuleList(layers)\n", - "\n", - "\n", - "class TransformerMapper(nn.Module):\n", - "\n", - " def forward(self, x):\n", - " x = self.linear(x).view(x.shape[0], self.clip_length, -1)\n", - " prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)\n", - " prefix = torch.cat((x, prefix), dim=1)\n", - " out = self.transformer(prefix)[:, self.clip_length:]\n", - " return out\n", - "\n", - " def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):\n", - " super(TransformerMapper, self).__init__()\n", - " self.clip_length = clip_length\n", - " self.transformer = Transformer(dim_embedding, 8, num_layers)\n", - " self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)\n", - " self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)\n", - "\n", - "\n", - "class ClipCaptionModel(nn.Module):\n", - "\n", - " def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:\n", - " return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)\n", - "\n", - " def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None,\n", - " labels: Optional[torch.Tensor] = None):\n", - " embedding_text = self.gpt.transformer.wte(tokens)\n", - " prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)\n", - " embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)\n", - " if labels is not None:\n", - " dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)\n", - " labels = torch.cat((dummy_token, tokens), dim=1)\n", - " out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)\n", - " return out\n", - "\n", - " def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,\n", - " num_layers: int = 8, mapping_type: MappingType = MappingType.MLP):\n", - " super(ClipCaptionModel, self).__init__()\n", - " self.prefix_length = prefix_length\n", - " self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')\n", - " self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]\n", - " if mapping_type == MappingType.MLP:\n", - " self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,\n", - " self.gpt_embedding_size * prefix_length))\n", - " else:\n", - " self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,\n", - " clip_length, num_layers)\n", - "\n", - "\n", - "class ClipCaptionPrefix(ClipCaptionModel):\n", - "\n", - " def parameters(self, recurse: bool = True):\n", - " return self.clip_project.parameters()\n", - "\n", - " def train(self, mode: bool = True):\n", - " super(ClipCaptionPrefix, self).train(mode)\n", - " self.gpt.eval()\n", - " return self" - ], - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "V7xocT3TUgey" - }, - "source": [ - "#@title Caption prediction\n", - "\n", - "def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None,\n", - " entry_length=67, temperature=1., stop_token: str = '.'):\n", - "\n", - " model.eval()\n", - " stop_token_index = tokenizer.encode(stop_token)[0]\n", - " tokens = None\n", - " scores = None\n", - " device = next(model.parameters()).device\n", - " seq_lengths = torch.ones(beam_size, device=device)\n", - " is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)\n", - " with torch.no_grad():\n", - " if embed is not None:\n", - " generated = embed\n", - " else:\n", - " if tokens is None:\n", - " tokens = torch.tensor(tokenizer.encode(prompt))\n", - " tokens = tokens.unsqueeze(0).to(device)\n", - " generated = model.gpt.transformer.wte(tokens)\n", - " for i in range(entry_length):\n", - " outputs = model.gpt(inputs_embeds=generated)\n", - " logits = outputs.logits\n", - " logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n", - " logits = logits.softmax(-1).log()\n", - " if scores is None:\n", - " scores, next_tokens = logits.topk(beam_size, -1)\n", - " generated = generated.expand(beam_size, *generated.shape[1:])\n", - " next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)\n", - " if tokens is None:\n", - " tokens = next_tokens\n", - " else:\n", - " tokens = tokens.expand(beam_size, *tokens.shape[1:])\n", - " tokens = torch.cat((tokens, next_tokens), dim=1)\n", - " else:\n", - " logits[is_stopped] = -float(np.inf)\n", - " logits[is_stopped, 0] = 0\n", - " scores_sum = scores[:, None] + logits\n", - " seq_lengths[~is_stopped] += 1\n", - " scores_sum_average = scores_sum / seq_lengths[:, None]\n", - " scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)\n", - " next_tokens_source = next_tokens // scores_sum.shape[1]\n", - " seq_lengths = seq_lengths[next_tokens_source]\n", - " next_tokens = next_tokens % scores_sum.shape[1]\n", - " next_tokens = next_tokens.unsqueeze(1)\n", - " tokens = tokens[next_tokens_source]\n", - " tokens = torch.cat((tokens, next_tokens), dim=1)\n", - " generated = generated[next_tokens_source]\n", - " scores = scores_sum_average * seq_lengths\n", - " is_stopped = is_stopped[next_tokens_source]\n", - " next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)\n", - " generated = torch.cat((generated, next_token_embed), dim=1)\n", - " is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()\n", - " if is_stopped.all():\n", - " break\n", - " scores = scores / seq_lengths\n", - " output_list = tokens.cpu().numpy()\n", - " output_texts = [tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)]\n", - " order = scores.argsort(descending=True)\n", - " output_texts = [output_texts[i] for i in order]\n", - " return output_texts\n", - "\n", - "\n", - "def generate2(\n", - " model,\n", - " tokenizer,\n", - " tokens=None,\n", - " prompt=None,\n", - " embed=None,\n", - " entry_count=1,\n", - " entry_length=67, # maximum number of words\n", - " top_p=0.8,\n", - " temperature=1.,\n", - " stop_token: str = '.',\n", - "):\n", - " model.eval()\n", - " generated_num = 0\n", - " generated_list = []\n", - " stop_token_index = tokenizer.encode(stop_token)[0]\n", - " filter_value = -float(\"Inf\")\n", - " device = next(model.parameters()).device\n", - "\n", - " with torch.no_grad():\n", - "\n", - " for entry_idx in trange(entry_count):\n", - " if embed is not None:\n", - " generated = embed\n", - " else:\n", - " if tokens is None:\n", - " tokens = torch.tensor(tokenizer.encode(prompt))\n", - " tokens = tokens.unsqueeze(0).to(device)\n", - "\n", - " generated = model.gpt.transformer.wte(tokens)\n", - "\n", - " for i in range(entry_length):\n", - "\n", - " outputs = model.gpt(inputs_embeds=generated)\n", - " logits = outputs.logits\n", - " logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n", - " sorted_logits, sorted_indices = torch.sort(logits, descending=True)\n", - " cumulative_probs = torch.cumsum(nnf.softmax(sorted_logits, dim=-1), dim=-1)\n", - " sorted_indices_to_remove = cumulative_probs > top_p\n", - " sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[\n", - " ..., :-1\n", - " ].clone()\n", - " sorted_indices_to_remove[..., 0] = 0\n", - "\n", - " indices_to_remove = sorted_indices[sorted_indices_to_remove]\n", - " logits[:, indices_to_remove] = filter_value\n", - " next_token = torch.argmax(logits, -1).unsqueeze(0)\n", - " next_token_embed = model.gpt.transformer.wte(next_token)\n", - " if tokens is None:\n", - " tokens = next_token\n", - " else:\n", - " tokens = torch.cat((tokens, next_token), dim=1)\n", - " generated = torch.cat((generated, next_token_embed), dim=1)\n", - " if stop_token_index == next_token.item():\n", - " break\n", - "\n", - " output_list = list(tokens.squeeze().cpu().numpy())\n", - " output_text = tokenizer.decode(output_list)\n", - " generated_list.append(output_text)\n", - "\n", - " return generated_list[0]" - ], - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "xE-uUStuv1Nl", - "cellView": "form" - }, - "source": [ - "#@title Download pretrained model over COCO\n", - "\n", - "\n", - "pretrained_model = 'COCO' \n", - "\n", - "\n", - "downloader.download_file(\"1GYPToCqFREwi285wPLhuVExlz7DDUDfJ\", model_path)" - ], - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "7lCgFHSgr_ny" - }, - "source": [ - "#@title GPU/CPU\n", - "\n", - "\n", - "is_gpu = True #@param {type:\"boolean\"} \n" - ], - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "6bi_2zQ3QD57", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 162, - "referenced_widgets": [ - "52519cb969284d99838713141e698764", - "467d658002da46818e077b1006010947", - "6c368cf3c9ea4ace9d95028e413801d1", - "edcfa38725d44106a8ff6a1e8e5a589b", - "7c88423573cb4ddebba2ea96e618f002", - "22b2ff1b7398419389aa375ab4c72847", - "654b184369294d7d926cae30721d0afc", - "0157f7f0326244d19d79ed48fa253a63", - "89bce76c1e974b5abe73aed0f5ae4c12", - "6d76486b19254269be5cebd45a5592ca", - "857c65639fbc4519894fa34fe9cb7da7", - "4c9919afae3a4ae087e8c62d48b29f5f", - "4efea4d2c08d4d22929f3ce0ae446d56", - "b21345175d3e4cbb91cd31edaa13d7c0", - "165004f4cfc0484aa7244acff7a8c162", - "897f93df21fb4384a791d84deb49f297", - "9a14c2939449403295faa921dab6b8b6", - "47ba23445257468e965f8694097bef38", - "41e003814ccc4f518969de12a3853233", - "24322fecd55d4cefb8e32b8ed46f1002", - "42af2ad7ec0446c982ebca16ff0e35fc", - "fe1998b4ae414ff5a57ec4d020df074d", - "16a3955ae2e441d3b5094df2858e71f4", - "d310a1224b494ef4b6348966edaf7237", - "c36a04b157a74a9a90413145b6479ecd", - "97b5688fa3ec488aa3025fc015cc9c3e", - "717dd1ea735a4154aca034070d1e186c", - "60f44114f512453aa71306ce079e0bf2", - "befbd0f377194d24a993e37ebe4c1955", - "141e03dcfb0d4b74b9b5bb2496c0d1b2", - "167c06f309914ebb95052bdd9fda6b55", - "01e6a9e24d9941debe21dc3096ff4660", - "668b3b2d1e5a4d69ab6782359fd9c137", - "7322a4f712c24903a7587fac39968490", - "e5d922b6a26046c484626ec95febfc40", - "15aaf9e61ad047ba91716b6d4d290563", - "79f020b8f2bb472a84d4fecbdbdc3e86", - "99d10ad9f213415388426ab65344a066", - "1608135299924faf975dd3e2f1e190c2", - "f2ef2398b5fc4da0a2f4d4a6b2805765", - "4a95d045c5964b0eb3ca948c6ba96e91", - "0d73b179c31544f5829948754a74220a", - "6ccd65f0c36e4d238eabadc613a1426c", - "348ac40c8d164cc1bb585c773ac66b71" - ] - }, - "outputId": "a339ffed-52a5-425a-bfc6-7ba603d5cb6d" - }, - "source": [ - "#@title CLIP model + GPT2 tokenizer\n", - "\n", - "device = CUDA(0) if is_gpu else \"cpu\"\n", - "clip_model, preprocess = clip.load(\"RN50x4\", device=device, jit=False)\n", - "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|███████████████████████████████████████| 402M/402M [00:48<00:00, 8.64MiB/s]\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "52519cb969284d99838713141e698764", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/0.99M [00:00\n", - " \n", - " Upload widget is only available when the cell has been executed in the\n", - " current browser session. Please rerun this cell to enable.\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "pohtQ8AfWNk_", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fcbef604-1383-472e-ca2d-9e3f9c7c386b" - }, - "source": [ - "#@title Or download random samples form COCO test set (Karpathy et al. split)\n", - "\n", - "IMAGE_NAME = '334321' # @param ['562207', '579664', '060623', '165547', '334321', '483108', '386164', '354533']\n", - "\n", - "name_ = \"COCO_val2014_000000\" + IMAGE_NAME + \".jpg\"\n", - "images_path = os.path.join(os.path.dirname(current_directory), \"images\")\n", - "os.makedirs(images_path, exist_ok=True)\n", - "UPLOADED_FILE = os.path.join(images_path, name_)\n", - "\n", - "if not os.path.isfile(UPLOADED_FILE):\n", - " download_path = os.path.join(images_path, \"images.zip\")\n", - " downloader.download_file(\"1l6J9WFYxpF-1HFr3A5Oq1eoObTxzbPgs\", download_path)\n", - "\n", - " !unzip {download_path} -d {images_path}\n", - "\n" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Archive: /images/images.zip\n", - " inflating: /images/COCO_val2014_000000060623.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000060623.jpg \n", - " inflating: /images/COCO_val2014_000000165547.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000165547.jpg \n", - " inflating: /images/COCO_val2014_000000334321.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000334321.jpg \n", - " inflating: /images/COCO_val2014_000000354533.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000354533.jpg \n", - " inflating: /images/COCO_val2014_000000386164.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000386164.jpg \n", - " inflating: /images/COCO_val2014_000000483108.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000483108.jpg \n", - " inflating: /images/COCO_val2014_000000562207.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000562207.jpg \n", - " inflating: /images/COCO_val2014_000000579664.jpg \n", - " inflating: /images/__MACOSX/._COCO_val2014_000000579664.jpg \n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "cellView": "form", - "id": "xQC2FQPtKLbz", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 465 - }, - "outputId": "3c765ed2-ad0b-478d-dbc8-131aa428d378" - }, - "source": [ - "#@title Inference\n", - "use_beam_search = True #@param {type:\"boolean\"} \n", - "\n", - "image = io.imread(UPLOADED_FILE)\n", - "pil_image = PIL.Image.fromarray(image)\n", - "#pil_img = Image(filename=UPLOADED_FILE)\n", - "display(pil_image)\n", - "\n", - "image = preprocess(pil_image).unsqueeze(0).to(device)\n", - "with torch.no_grad():\n", - " prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)\n", - " prefix = prefix / prefix.norm(2, -1).item()\n", - " prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)\n", - "if use_beam_search:\n", - " generated_text_prefix = generate_beam(model, tokenizer, embed=prefix_embed)[0]\n", - "else:\n", - " generated_text_prefix = generate2(model, tokenizer, embed=prefix_embed)\n", - "\n", - "\n", - "print('\\n')\n", - "print(generated_text_prefix)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "\n", - "A man and a dog sitting on a bench in a park.\n" - ] - } - ] - } - ] -} \ No newline at end of file From 48567871bceb1134ded335454eb9349bb1d0bb3e Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:14:30 +0900 Subject: [PATCH 18/25] Delete parse_conceptual.py --- parse_conceptual.py | 215 -------------------------------------------- 1 file changed, 215 deletions(-) delete mode 100644 parse_conceptual.py diff --git a/parse_conceptual.py b/parse_conceptual.py deleted file mode 100644 index 72278c7..0000000 --- a/parse_conceptual.py +++ /dev/null @@ -1,215 +0,0 @@ -import torch -import clip -from torch.utils.data import DataLoader, Dataset -from PIL import Image -import pickle -from tqdm import tqdm -import os -import csv -import threading -import requests -import shutil -import PIL -import json -from typing import List, Tuple, Optional -import argparse - - -class ConceptualDS(Dataset): - - @staticmethod - def get_all_data(data_root: str, suffix: str): - data = [] - for i in range(16): - out_data_path = f"{data_root}/conceptual_{suffix}_{i:02d}.pkl" - if os.path.isfile(out_data_path): - with open(out_data_path, 'rb') as f: - raw_data = pickle.load(f)["info"] - data.append(raw_data) - - return data - - @staticmethod - def collect(data_root: str, suffix: str): - raw_data = ConceptualDS.get_all_data(data_root, suffix) - data = [] - for thread_data in raw_data: - for item in thread_data: - data.append((item, thread_data[item]["caption"])) - return data - - def __len__(self): - return len(self.data) - - def __getitem__(self, item: int): - image_name, caption = self.data[item] - image_path = f"{self.data_root}/{self.suffix}/{image_name}.jpg" - is_error = False - image = self.dummy - try: - image = self.preprocess(Image.open(image_path)) - except PIL.UnidentifiedImageError: - is_error = True - except OSError: - is_error = True - except BaseException: - is_error = True - if is_error: - return image, "", image_name - return image, caption, image_name - - def __init__(self, data_root: str, preprocess, suffix: str): - self.suffix = suffix - self.data_root = data_root - self.data = self.collect(data_root, suffix) - self.preprocess = preprocess - self.dummy = torch.zeros(3, 288, 288) - - -def save_pickle(data, out_path: str, recover_index: Optional[int] = None): - if os.path.isfile(out_path) and recover_index is not None: - recover_path = f'{out_path[:-4]}_{recover_index:02d}.pkl' - shutil.copyfile(out_path, recover_path) - with open(out_path, 'wb') as f: - pickle.dump(data, f) - - -def get_image(url: str, out_path: str, timeout=10): - try: - r = requests.get(url, stream=True, timeout=timeout) - if r.status_code == 200: - with open(out_path, 'wb') as f: - r.raw.decode_content = True - shutil.copyfileobj(r.raw, f) - return True - return False - except BaseException: - return False - - -def thread(urls: List[Tuple[List[str], int]], thread_id: int, progress: tqdm, lock: Optional[threading.Lock], - suffix: str, conceptual_root: str): - out_root = f"{conceptual_root}/{suffix}" - out_data_path = f"{conceptual_root}/conceptual_{suffix}_{thread_id:02d}.pkl" - recover_index = 0 - if os.path.isfile(out_data_path): - with open(out_data_path, 'rb') as f: - data = pickle.load(f) - parsed = data['parsed'] - info = data['info'] - else: - parsed = set() - info = {} - for i in range(0, len(urls)): - (caption, url), ind = urls[i] - name = f"{ind:08d}" - out_path = f"{out_root}/{name}.jpg" - if url not in parsed and not os.path.isfile(out_path) and get_image(url, out_path): - parsed.add(url) - info[name] = {"url": url, "caption": caption} - if lock is not None: - lock.acquire() - try: - progress.update() - finally: - lock.release() - else: - progress.update() - if (i + 1) % 1000 == 0: - save_pickle({'parsed': parsed, 'info': info}, out_data_path, recover_index) - recover_index = 1 - recover_index - save_pickle({'parsed': parsed, 'info': info}, out_data_path, 2) - return 0 - - -def download_conceptual(conceptual_root: str, num_threads: int): - urls = [] - for suffix in ("val", "train"): - if suffix == "train": - tsv_path = f"{conceptual_root}/Train_GCC-training.tsv" - else: - tsv_path = f"{conceptual_root}/Validation_GCC-1.1.0-Validation.tsv" - with open(tsv_path) as f: - read_tsv = csv.reader(f, delimiter="\t") - for i, row in enumerate(read_tsv): - urls.append((row, i)) - progress = tqdm(total=len(urls)) - if num_threads == 1: - thread(urls, 0, progress, None, suffix, conceptual_root) - else: - groups = [] - threads = [] - lock = threading.Lock() - split_size = len(urls) // num_threads - for i in range(num_threads): - if i < num_threads - 1: - groups.append(urls[i * split_size: (i + 1) * split_size]) - else: - groups.append(urls[i * split_size:]) - for i in range(num_threads): - threads.append(threading.Thread(target=thread, args=(groups[i], i, progress, lock, suffix, conceptual_root))) - for i in range(num_threads): - threads[i].start() - for i in range(num_threads): - threads[i].join() - progress.close() - - -def add_period(caption: str): - caption = caption.strip() - if caption[-1] != '.': - caption = caption + '.' - elif caption[-2] == ' ': - caption = caption[:-2] + '.' - return caption - - -def create_clip_embeddings(conceptual_root: str, clip_model_type: str): - all_embeddings = [] - all_captions = [] - for suffix in ("val", "train"): - device = torch.device("cuda:0") - clip_model, preprocess = clip.load(clip_model_type, device=device, jit=False) - clip_model = clip_model.eval() - ds = ConceptualDS(conceptual_root, preprocess, suffix) - dl = DataLoader(ds, batch_size=200, shuffle=False, num_workers=8, drop_last=False) - progress = tqdm(total=len(dl)) - counter = 0 - clip_model_name = clip_model_type.replace('/', '_') - out_data_path = f"{conceptual_root}/conceptual_clip_{clip_model_name}_{suffix}.pkl" - recover_index = 0 - for i, data in enumerate(dl): - images, captions, image_names = data - images = images.to(device) - with torch.no_grad(): - prefix = clip_model.encode_image(images).cpu() - is_valid = list(map(lambda x: x != "", captions)) - mask = torch.tensor(is_valid) - all_embeddings.append(prefix[mask]) - captions = [caption for j, caption in enumerate(captions) if is_valid[j]] - image_names = [image_name for j, image_name in enumerate(image_names) if is_valid[j]] - all_captions.extend([{"caption": add_period(caption), "clip_embedding": counter + j, "image_id": image_name} - for j, (caption, image_name) in enumerate(zip(captions, image_names))]) - progress.update() - counter += len(captions) - if (i + 1) % 1000 == 0: - save_pickle({"clip_embedding": torch.cat(all_embeddings, dim=0), "captions": all_captions}, out_data_path, recover_index) - recover_index = 1 - recover_index - save_pickle({"clip_embedding": torch.cat(all_embeddings, dim=0), "captions": all_captions}, out_data_path, 2) - progress.close() - - return 0 - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--data_root', default='./data/conceptual') - parser.add_argument('--clip_model_type', default="ViT-B/32", choices=('RN50', 'RN101', 'RN50x4', 'ViT-B/32')) - parser.add_argument('--num_threads', type=int, default=16) - args = parser.parse_args() - download_conceptual(args.data_root, args.num_threads) - create_clip_embeddings(args.data_root, args.clip_model_type) - - -if __name__ == '__main__': - main() From 2c9e06c1e1a46e10dcf188521613ab6b5f91fbbd Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:14:46 +0900 Subject: [PATCH 19/25] Delete requirements.txt --- requirements.txt | 137 ----------------------------------------------- 1 file changed, 137 deletions(-) delete mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f56b24b..0000000 --- a/requirements.txt +++ /dev/null @@ -1,137 +0,0 @@ -anyio==3.6.2 -appdirs==1.4.4 -asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work -async-timeout==4.0.2 -attrs==22.2.0 -backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work -backoff==2.2.1 -brotlipy==0.7.0 -certifi @ file:///croot/certifi_1671487769961/work/certifi -cffi @ file:///croot/cffi_1670423208954/work -charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work -click==8.1.3 -clip @ git+https://github.com/openai/CLIP.git@a9b1bf5920416aaeaec965c25dd9e8f98c864f16 -cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work --e git+https://github.com/replicate/cog.git@6348238ef8ffe8faff11c84c305a54deb051b690#egg=cog&subdirectory=python -comm @ file:///croot/comm_1671231121260/work -contourpy @ file:///opt/conda/conda-bld/contourpy_1663827406301/work -cryptography @ file:///croot/cryptography_1677533068310/work -cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work -cytoolz @ file:///croot/cytoolz_1667465931118/work -dask @ file:///tmp/abs_994957d9-ec12-411f-b953-c010f9d489d10hj3gz4k/croots/recipe/dask-core_1658513209934/work -debugpy @ file:///tmp/build/80754af9/debugpy_1637091799509/work -decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work -Deprecated==1.2.13 -docker-pycreds==0.4.0 -entrypoints @ file:///tmp/build/80754af9/entrypoints_1649926439650/work -executing @ file:///opt/conda/conda-bld/executing_1646925071911/work -fastapi==0.92.0 -filelock==3.9.0 -Flask==2.2.3 -fonttools==4.25.0 -fsspec @ file:///croot/fsspec_1670336592807/work -ftfy==6.1.1 -gitdb==4.0.10 -GitPython==3.1.31 -googleapis-common-protos==1.58.0 -grpcio==1.51.3 -h11==0.14.0 -httptools==0.5.0 -huggingface-hub==0.12.1 -idna @ file:///croot/idna_1666125576474/work -imagecodecs @ file:///croot/imagecodecs_1677576717595/work -imageio @ file:///tmp/abs_cd920173-f360-47c5-97b0-bf4d1076d5d4dvic0oys/croots/recipe/imageio_1658785036907/work -importlib-metadata==6.0.0 -importlib-resources @ file:///tmp/build/80754af9/importlib_resources_1625135880749/work -ipykernel @ file:///croot/ipykernel_1671488378391/work -ipython @ file:///croot/ipython_1676582224036/work -itsdangerous==2.1.2 -jedi @ file:///tmp/build/80754af9/jedi_1644297102865/work -Jinja2==3.1.2 -joblib==1.2.0 -jupyter_client @ file:///croot/jupyter_client_1676329080601/work -jupyter_core @ file:///croot/jupyter_core_1676538566912/work -kiwisolver @ file:///croot/kiwisolver_1672387140495/work -locket @ file:///opt/conda/conda-bld/locket_1652903118915/work -MarkupSafe==2.1.2 -matplotlib @ file:///croot/matplotlib-suite_1677674301264/work -matplotlib-inline @ file:///opt/conda/conda-bld/matplotlib-inline_1662014470464/work -mkl-fft==1.3.1 -mkl-random @ file:///tmp/build/80754af9/mkl_random_1626186066731/work -mkl-service==2.4.0 -munkres==1.1.4 -nest-asyncio @ file:///croot/nest-asyncio_1672387112409/work -networkx @ file:///opt/conda/conda-bld/networkx_1657784097507/work -numpy @ file:///croot/numpy_and_numpy_base_1672336185480/work -nvidia-cublas-cu11==11.10.3.66 -nvidia-cuda-nvrtc-cu11==11.7.99 -nvidia-cuda-runtime-cu11==11.7.99 -nvidia-cudnn-cu11==8.5.0.96 -opentelemetry-api==1.16.0 -opentelemetry-exporter-otlp==1.16.0 -opentelemetry-exporter-otlp-proto-grpc==1.16.0 -opentelemetry-exporter-otlp-proto-http==1.16.0 -opentelemetry-proto==1.16.0 -opentelemetry-sdk==1.16.0 -opentelemetry-semantic-conventions==0.37b0 -packaging @ file:///croot/packaging_1671697413597/work -pandas==1.5.3 -parso @ file:///opt/conda/conda-bld/parso_1641458642106/work -partd @ file:///opt/conda/conda-bld/partd_1647245470509/work -pathtools==0.1.2 -pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work -pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work -Pillow==9.4.0 -platformdirs @ file:///opt/conda/conda-bld/platformdirs_1662711380096/work -pooch @ file:///tmp/build/80754af9/pooch_1623324770023/work -prompt-toolkit @ file:///croot/prompt-toolkit_1672387306916/work -protobuf==3.20.3 -psutil @ file:///opt/conda/conda-bld/psutil_1656431268089/work -ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl -pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work -pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work -pydantic==1.10.5 -Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work -pyOpenSSL @ file:///croot/pyopenssl_1677607685877/work -pyparsing @ file:///opt/conda/conda-bld/pyparsing_1661452539315/work -PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work -python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work -python-dotenv==1.0.0 -pytz==2022.7.1 -PyWavelets @ file:///croot/pywavelets_1670425177960/work -PyYAML @ file:///croot/pyyaml_1670514731622/work -pyzmq @ file:///opt/conda/conda-bld/pyzmq_1657724186960/work -redis==3.5.3 -regex==2022.10.31 -replicate==0.5.1 -requests @ file:///opt/conda/conda-bld/requests_1657734628632/work -sacremoses==0.0.53 -scikit-image==0.18.1 -scipy==1.10.0 -sentry-sdk==1.17.0 -setproctitle==1.3.2 -six @ file:///tmp/build/80754af9/six_1644875935023/work -smmap==5.0.0 -sniffio==1.3.0 -stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work -starlette==0.25.0 -tifffile @ file:///tmp/build/80754af9/tifffile_1627275862826/work -tokenizers==0.13.2 -toolz @ file:///croot/toolz_1667464077321/work -torch==1.13.1 -torchvision==0.14.1 -tornado @ file:///opt/conda/conda-bld/tornado_1662061693373/work -tqdm==4.64.1 -traitlets @ file:///croot/traitlets_1671143879854/work -transformers==4.27.1 -typing_extensions==4.5.0 -urllib3 @ file:///croot/urllib3_1673575502006/work -uvicorn==0.20.0 -uvloop==0.17.0 -wandb==0.14.0 -watchfiles==0.18.1 -wcwidth==0.2.6 -websockets==10.4 -Werkzeug==2.2.3 -wrapt==1.15.0 -zipp @ file:///croot/zipp_1672387121353/work From bbaed33f531628281ba45a3ea5228490be3a1b43 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:15:01 +0900 Subject: [PATCH 20/25] Delete train.py --- train.py | 370 ------------------------------------------------------- 1 file changed, 370 deletions(-) delete mode 100644 train.py diff --git a/train.py b/train.py deleted file mode 100644 index b4c082a..0000000 --- a/train.py +++ /dev/null @@ -1,370 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn import functional as nnf -from torch.utils.data import Dataset, DataLoader -from enum import Enum -from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup -from tqdm import tqdm -import os -import pickle -import sys -import argparse -import json -from typing import Tuple, Optional, Union - - -class MappingType(Enum): - MLP = 'mlp' - Transformer = 'transformer' - - -class ClipCocoDataset(Dataset): - - def __len__(self) -> int: - return len(self.captions_tokens) - - def pad_tokens(self, item: int): - tokens = self.captions_tokens[item] - padding = self.max_seq_len - tokens.shape[0] - if padding > 0: - tokens = torch.cat((tokens, torch.zeros(padding, dtype=torch.int64) - 1)) - self.captions_tokens[item] = tokens - elif padding < 0: - tokens = tokens[:self.max_seq_len] - self.captions_tokens[item] = tokens - mask = tokens.ge(0) # mask is zero where we out of sequence - tokens[~mask] = 0 - mask = mask.float() - mask = torch.cat((torch.ones(self.prefix_length), mask), dim=0) # adding prefix mask - return tokens, mask - - def __getitem__(self, item: int) -> Tuple[torch.Tensor, ...]: - tokens, mask = self.pad_tokens(item) - prefix = self.prefixes[self.caption2embedding[item]] - if self.normalize_prefix: - prefix = prefix.float() - prefix = prefix / prefix.norm(2, -1) - return tokens, mask, prefix - - def __init__(self, data_path: str, prefix_length: int, gpt2_type: str = "gpt2", - normalize_prefix=False): - self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type) - self.prefix_length = prefix_length - self.normalize_prefix = normalize_prefix - with open(data_path, 'rb') as f: - all_data = pickle.load(f) - print("Data size is %0d" % len(all_data["clip_embedding"])) - sys.stdout.flush() - self.prefixes = all_data["clip_embedding"] - captions_raw = all_data["captions"] - self.image_ids = [caption["image_id"] for caption in captions_raw] - self.captions = [caption['caption'] for caption in captions_raw] - if os.path.isfile(f"{data_path[:-4]}_tokens.pkl"): - with open(f"{data_path[:-4]}_tokens.pkl", 'rb') as f: - self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f) - else: - self.captions_tokens = [] - self.caption2embedding = [] - max_seq_len = 0 - for caption in captions_raw: - self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64)) - self.caption2embedding.append(caption["clip_embedding"]) - max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0]) - # self.max_seq_len = max_seq_len - with open(f"{data_path[:-4]}_tokens.pkl", 'wb') as f: - pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f) - all_len = torch.tensor([len(self.captions_tokens[i]) for i in range(len(self))]).float() - self.max_seq_len = min(int(all_len.mean() + all_len.std() * 10), int(all_len.max())) - - -class MLP(nn.Module): - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.model(x) - - def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh): - super(MLP, self).__init__() - layers = [] - for i in range(len(sizes) - 1): - layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) - if i < len(sizes) - 2: - layers.append(act()) - self.model = nn.Sequential(*layers) - - -class MlpTransformer(nn.Module): - def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.): - super().__init__() - out_d = out_d if out_d is not None else in_dim - self.fc1 = nn.Linear(in_dim, h_dim) - self.act = act - self.fc2 = nn.Linear(h_dim, out_d) - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.dropout(x) - x = self.fc2(x) - x = self.dropout(x) - return x - -class MultiHeadAttention(nn.Module): - - def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim_self // num_heads - self.scale = head_dim ** -0.5 - self.to_queries = nn.Linear(dim_self, dim_self, bias=bias) - self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias) - self.project = nn.Linear(dim_self, dim_self) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, y=None, mask=None): - y = y if y is not None else x - b, n, c = x.shape - _, m, d = y.shape - # b n h dh - queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads) - # b m 2 h dh - keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads) - keys, values = keys_values[:, :, 0], keys_values[:, :, 1] - attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale - if mask is not None: - if mask.dim() == 2: - mask = mask.unsqueeze(1) - attention = attention.masked_fill(mask.unsqueeze(3), float("-inf")) - attention = attention.softmax(dim=2) - out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c) - out = self.project(out) - return out, attention - - -class TransformerLayer(nn.Module): - - def forward_with_attention(self, x, y=None, mask=None): - x_, attention = self.attn(self.norm1(x), y, mask) - x = x + x_ - x = x + self.mlp(self.norm2(x)) - return x, attention - - def forward(self, x, y=None, mask=None): - x = x + self.attn(self.norm1(x), y, mask)[0] - x = x + self.mlp(self.norm2(x)) - return x - - def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu, - norm_layer: nn.Module = nn.LayerNorm): - super().__init__() - self.norm1 = norm_layer(dim_self) - self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout) - self.norm2 = norm_layer(dim_self) - self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout) - - -class Transformer(nn.Module): - - def forward_with_attention(self, x, y=None, mask=None): - attentions = [] - for layer in self.layers: - x, att = layer.forward_with_attention(x, y, mask) - attentions.append(att) - return x, attentions - - def forward(self, x, y=None, mask=None): - for i, layer in enumerate(self.layers): - if i % 2 == 0 and self.enc_dec: # cross - x = layer(x, y) - elif self.enc_dec: # self - x = layer(x, x, mask) - else: # self or cross - x = layer(x, y, mask) - return x - - def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None, - mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False): - super(Transformer, self).__init__() - dim_ref = dim_ref if dim_ref is not None else dim_self - self.enc_dec = enc_dec - if enc_dec: - num_layers = num_layers * 2 - layers = [] - for i in range(num_layers): - if i % 2 == 0 and enc_dec: # cross - layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - elif enc_dec: # self - layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - else: # self or cross - layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer)) - self.layers = nn.ModuleList(layers) - - -class TransformerMapper(nn.Module): - - def forward(self, x): - x = self.linear(x).view(x.shape[0], self.clip_length, -1) - prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape) - prefix = torch.cat((x, prefix), dim=1) - out = self.transformer(prefix)[:, self.clip_length:] - return out - - def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8): - super(TransformerMapper, self).__init__() - self.clip_length = clip_length - self.transformer = Transformer(dim_embedding, 8, num_layers) - self.linear = nn.Linear(dim_clip, clip_length * dim_embedding) - self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True) - - -class ClipCaptionModel(nn.Module): - - def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: - return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) - - def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None): - embedding_text = self.gpt.transformer.wte(tokens) - prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size) - embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1) - if labels is not None: - dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) - labels = torch.cat((dummy_token, tokens), dim=1) - out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) - return out - - def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512, - num_layers: int = 8, mapping_type: MappingType = MappingType.MLP): - super(ClipCaptionModel, self).__init__() - self.prefix_length = prefix_length - self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') - self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] - if mapping_type == MappingType.MLP: - self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2, - self.gpt_embedding_size * prefix_length)) - else: - self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length, - clip_length, num_layers) - - -class ClipCaptionPrefix(ClipCaptionModel): - - def parameters(self, recurse: bool = True): - return self.clip_project.parameters() - - def train(self, mode: bool = True): - super(ClipCaptionPrefix, self).train(mode) - self.gpt.eval() - return self - - -def save_config(args: argparse.Namespace): - config = {} - for key, item in args._get_kwargs(): - config[key] = item - out_path = os.path.join(args.out_dir, f"{args.prefix}.json") - with open(out_path, 'w') as outfile: - json.dump(config, outfile) - - -def load_model(config_path: str, epoch_or_latest: Union[str, int] = '_latest'): - with open(config_path) as f: - config = json.load(f) - parser = argparse.ArgumentParser() - parser.set_defaults(**config) - args = parser.parse_args() - if type(epoch_or_latest) is int: - epoch_or_latest = f"-{epoch_or_latest:03d}" - model_path = os.path.join(args.out_dir, f"{args.prefix}{epoch_or_latest}.pt") - if args.only_prefix: - model = ClipCaptionPrefix(args.prefix_length) - else: - model = ClipCaptionModel(args.prefix_length) - if os.path.isfile(model_path): - print(f"loading model from {model_path}") - model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) - else: - print(f"{model_path} is not exist") - return model, parser - - -def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, - lr: float = 2e-5, warmup_steps: int = 5000, output_dir: str = ".", output_prefix: str = ""): - - device = torch.device('cuda:0') - batch_size = args.bs - epochs = args.epochs - if not os.path.exists(output_dir): - os.makedirs(output_dir) - model = model.to(device) - model.train() - optimizer = AdamW(model.parameters(), lr=lr) - train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True) - scheduler = get_linear_schedule_with_warmup( - optimizer, num_warmup_steps=warmup_steps, num_training_steps=epochs * len(train_dataloader) - ) - # save_config(args) - for epoch in range(epochs): - print(f">>> Training epoch {epoch}") - sys.stdout.flush() - progress = tqdm(total=len(train_dataloader), desc=output_prefix) - for idx, (tokens, mask, prefix) in enumerate(train_dataloader): - model.zero_grad() - tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32) - outputs = model(tokens, prefix, mask) - logits = outputs.logits[:, dataset.prefix_length - 1: -1] - loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0) - loss.backward() - optimizer.step() - scheduler.step() - optimizer.zero_grad() - progress.set_postfix({"loss": loss.item()}) - progress.update() - if (idx + 1) % 10000 == 0: - torch.save( - model.state_dict(), - os.path.join(output_dir, f"{output_prefix}_latest.pt"), - ) - progress.close() - if epoch % args.save_every == 0 or epoch == epochs - 1: - torch.save( - model.state_dict(), - os.path.join(output_dir, f"{output_prefix}-{epoch:03d}.pt"), - ) - return model - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--data', default='./data/coco/oscar_split_train.pkl') - parser.add_argument('--out_dir', default='./checkpoints') - parser.add_argument('--prefix', default='coco_prefix', help='prefix for saved filenames') - parser.add_argument('--epochs', type=int, default=10) - parser.add_argument('--save_every', type=int, default=1) - parser.add_argument('--prefix_length', type=int, default=10) - parser.add_argument('--prefix_length_clip', type=int, default=10) - parser.add_argument('--bs', type=int, default=40) - parser.add_argument('--only_prefix', dest='only_prefix', action='store_true') - parser.add_argument('--mapping_type', type=str, default='mlp', help='mlp/transformer') - parser.add_argument('--num_layers', type=int, default=8) - parser.add_argument('--is_rn', dest='is_rn', action='store_true') - parser.add_argument('--normalize_prefix', dest='normalize_prefix', action='store_true') - args = parser.parse_args() - prefix_length = args.prefix_length - dataset = ClipCocoDataset(args.data, prefix_length, normalize_prefix=args.normalize_prefix) - prefix_dim = 640 if args.is_rn else 512 - args.mapping_type = {'mlp': MappingType.MLP, 'transformer': MappingType.Transformer}[args.mapping_type] - if args.only_prefix: - model = ClipCaptionPrefix(prefix_length, clip_length=args.prefix_length_clip, prefix_size=prefix_dim, - num_layers=args.num_layers, mapping_type=args.mapping_type) - print("Train only prefix") - else: - model = ClipCaptionModel(prefix_length, clip_length=args.prefix_length_clip, prefix_size=prefix_dim, - num_layers=args.num_layers, mapping_type=args.mapping_type) - print("Train both prefix and GPT") - sys.stdout.flush() - train(dataset, model, args, output_dir=args.out_dir, output_prefix=args.prefix) - - -if __name__ == '__main__': - main() \ No newline at end of file From d4f2cf56101f54f41be5a08afeeb4c65452a2c80 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:25:32 +0900 Subject: [PATCH 21/25] Update README.md --- README.md | 20 ++------------------ 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index c8169c8..56efd1e 100644 --- a/README.md +++ b/README.md @@ -67,7 +67,7 @@ python train.py --only_prefix --data ./data/coco/oscar_split_RN50x4_train.pkl -- ## Swith your language model from GPT-2 to OPT We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). -Training code is available at `train.py` and inference code will be updated on `predict_OPT.py`, which is basically running Predictor function in predict.py. +Training code is available at `train_OPT.py` and inference code will be updated on `predict_OPT.py`, which is basically running Predictor function in predict.py. Please note that you manullay have to make sure your desired language model is 'facebook/opt-125m' (variable named as OPT_MODEL) on both `predict.py` and `train.py`. ``` @@ -79,23 +79,7 @@ python predict_nice.py ### model parallelization - OPT-1.3b : 2-GPU, 16GB (per GPU), 1h13m per epoch - - -## Inference Notebooks -To help visualize the results we provide a Colab notebook found in `notebooks/clip_prefix_captioning_inference.ipynb`. -The notebook will download the pretrained models and run inference on a sample images or -on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/drive/1tuoAC5F4sC7qid56Z0ap-stR3rwdk0ZV?usp=sharing). -Inference notebook for the **transformer mapping network (without fine-tune GPT-2)** can be found [here](https://colab.research.google.com/drive/180L3rMFmGujudwO1EJNF-lHIpAsAZ5xq?usp=sharing) for the COCO model (also in `notebooks/transformer_inference.ipynb`). - - - -Both [COCO](https://drive.google.com/file/d/1IdaBtMSvtyzF0ByVaBHtvM0JYSXRExRX/view?usp=sharing) and [Conceptual Captions](https://drive.google.com/file/d/14pXWwB4Zm82rsDdvbGguLfx9F8aM7ovT/view?usp=sharing) pretrained models are available for mlp mapping network. For the transformer (without fine-tuning GPT-2) we provide [COCO](https://drive.google.com/file/d/1GYPToCqFREwi285wPLhuVExlz7DDUDfJ/view?usp=sharing) pretrained model. - - - -## Inference GUI -1. Run it [in the browser](https://replicate.ai/rmokady/clip_prefix_caption) using replicate.ai UI. -2. Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/CLIP_prefix_captioning) (currently not supporting beam search) +- OPT-2.7b : 3-GPU, 18GB (per GPU), 11h per epoch From 8ce8c8e9df50ccad86464e085fe7f079ea19f62c Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:26:07 +0900 Subject: [PATCH 22/25] Update README.md --- README.md | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/README.md b/README.md index 56efd1e..8120b6e 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,5 @@ # CLIP prefix captioning. - -Inference Notebook: - - - ## implementation for the paper ["ClipCap: CLIP Prefix for Image Captioning"](https://arxiv.org/abs/2111.09734) @@ -84,7 +79,7 @@ python predict_nice.py -*latest update : 2023-03-28* +*latest update : 2023-04-04* ## Citation If you use this code for your research, please cite: From 384637135accc82b395ffea6c98e1208a8fc0818 Mon Sep 17 00:00:00 2001 From: Jhryu30 <100555769+Jhryu30@users.noreply.github.com> Date: Tue, 4 Apr 2023 13:30:44 +0900 Subject: [PATCH 23/25] Update README.md --- README.md | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/README.md b/README.md index 8120b6e..6070c1e 100644 --- a/README.md +++ b/README.md @@ -48,17 +48,6 @@ Train only transformer mapping network: python train.py --only_prefix --data ./data/coco/oscar_split_ViT-B_32_train.pkl --out_dir ./coco_train/ --mapping_type transformer --num_layres 8 --prefix_length 40 --prefix_length_clip 40 ``` -**If you wish to use ResNet-based CLIP:** -https://github.com/Jhryu30/cvpr2023_challenge_clipcap.git -``` -python parse_coco.py --clip_model_type RN50x4 -``` -``` -python train.py --only_prefix --data ./data/coco/oscar_split_RN50x4_train.pkl --out_dir ./coco_train/ --mapping_type transformer --num_layres 8 --prefix_length 40 --prefix_length_clip 40 --is_rn -``` - - - ## Swith your language model from GPT-2 to OPT We enabled to train your ClipCap model with OPT. We are looking forward to make this code work well with [BLIP model](https://github.com/salesforce/BLIP.git). From 57ddf3144a8b4d6e08e53bfe83b1b0574ff71c9a Mon Sep 17 00:00:00 2001 From: snow-parkis Date: Wed, 12 Apr 2023 14:09:37 +0900 Subject: [PATCH 24/25] first commit --- for_inference/nice_gt.json | 1 + inference.ipynb | 251 ++++++++++++++++++++++++++++++++++ inference.py | 102 ++++++++++++++ modeling_opt_pp.py | 9 +- predict.py | 140 ++++++++++--------- predict_nice.py | 127 +++++++---------- train.py | 209 ++++++++++++++++++++++++++++ train_OPT.py | 112 ++++++++------- train_models.py | 270 +++++++++++++++++++++++++++++++++++++ 9 files changed, 1028 insertions(+), 193 deletions(-) create mode 100755 for_inference/nice_gt.json create mode 100755 inference.ipynb create mode 100755 inference.py mode change 100644 => 100755 modeling_opt_pp.py mode change 100644 => 100755 predict.py mode change 100644 => 100755 predict_nice.py create mode 100644 train.py mode change 100644 => 100755 train_OPT.py create mode 100755 train_models.py diff --git a/for_inference/nice_gt.json b/for_inference/nice_gt.json new file mode 100755 index 0000000..b6b8aa9 --- /dev/null +++ b/for_inference/nice_gt.json @@ -0,0 +1 @@ +{"annotations": [{"image_id": "1587140930", "caption": "Portrait of a young man applying sunscreen", "id": "1587140930"}, {"image_id": "1297794716", "caption": "cutout of smiling doctor wearing lab coat and stethoscope at camera", "id": "1297794716"}, {"image_id": "1873405220", "caption": "Vertical three quarter length shot of a smiling woman in an armchair using a digital tablet", "id": "1873405220"}, {"image_id": "1811160950", "caption": "A horizontal shot of young broad bean plants", "id": "1811160950"}, {"image_id": "1572486425", "caption": "Young mother multi tasking holding baby cooking dinner and looking at laptop", "id": "1572486425"}, {"image_id": "1583609237", "caption": "Woman drilling wood on sawhorse", "id": "1583609237"}, {"image_id": "1297776779", "caption": "Colorful field of beautiful wild summer flowers", "id": "1297776779"}, {"image_id": "1866123341", "caption": "Portrait shot of a team of pharmacists standing next to the shelves at a pharmacy and smiling at the camera", "id": "1866123341"}, {"image_id": "1297792622", "caption": "Stressed businesswoman with head in hands working at outdoor cafe", "id": "1297792622"}, {"image_id": "1868703086", "caption": "A portrait of a serious blonde businesswoman sitting at the edge of a table in the conference room", "id": "1868703086"}, {"image_id": "1766819739", "caption": "A young couple waving at Santa Claus and his team of Husky dogs", "id": "1766819739"}, {"image_id": "1277230520", "caption": "Girl sitting in open boot of car packed with luggage for vacation", "id": "1277230520"}, {"image_id": "1865999636", "caption": "Horizontal low angle shot of a seated mature couple hiking on a mountain trail with the woman taking a self portrait with a camera", "id": "1865999636"}, {"image_id": "1277237108", "caption": "Businessman holding folder standing by office wall at camera", "id": "1277237108"}, {"image_id": "1869314615", "caption": "Silhouette of an elderly couple sitting on a bench with their arms around each other against the sunset over the ocean", "id": "1869314615"}, {"image_id": "1586659883", "caption": "Portrait of businessman and businesswoman standing outside office building", "id": "1586659883"}, {"image_id": "1586648897", "caption": "Sign Marking Hiking Trail Through Countryside In Western Cape Region Of South Africa", "id": "1586648897"}, {"image_id": "1844190626", "caption": "Low angle portrait of a male gymnast performing on gymnastic rings", "id": "1844190626"}, {"image_id": "1572538823", "caption": "A portrait of an attractive middle aged woman", "id": "1572538823"}, {"image_id": "1657956392", "caption": "Woman with Christmas gift ringing doorbell", "id": "1657956392"}, {"image_id": "1839578876", "caption": "Full length portrait of a senior man sitting on floor drinking water from a bottle and looking up happily indoors", "id": "1839578876"}, {"image_id": "1578208685", "caption": "A young woman wearing a winter coat with a fur hood", "id": "1578208685"}, {"image_id": "1813181234", "caption": "Vertical shot of a female at her office desk with a pen in her hand looking at the camera", "id": "1813181234"}, {"image_id": "1587056594", "caption": "Three people sitting on orange seats", "id": "1587056594"}, {"image_id": "1844724134", "caption": "Vertical wide shot of a worker with clipboard standing next to a pallet of cardboard boxes wrapped in plastic at a distribution warehouse", "id": "1844724134"}, {"image_id": "1590351173", "caption": "Hairpin curve at the winding road to Port de Valldemossa Mallorca Spain", "id": "1590351173"}, {"image_id": "1571576855", "caption": "A woman sitting by a waterfall", "id": "1571576855"}, {"image_id": "1572381272", "caption": "Johnston Canyon Banff National Park Alberta Canada", "id": "1572381272"}, {"image_id": "1586681510", "caption": "cutout Of Male Teenage Student Studying Fashion", "id": "1586681510"}, {"image_id": "1572482078", "caption": "A family walking the dog on a beach", "id": "1572482078"}, {"image_id": "1587144479", "caption": "View of a moai statue against blue sky Chile Easter Island Rapa Nui", "id": "1587144479"}, {"image_id": "1578225338", "caption": "A woman holding a bag full of mixed nuts", "id": "1578225338"}, {"image_id": "1709219594", "caption": "Portrait of a caucasian senior woman crying", "id": "1709219594"}, {"image_id": "1578947231", "caption": "A businessman standing in a garden", "id": "1578947231"}, {"image_id": "1590209309", "caption": "Scenic view of Peitlerkofel in autumn Dolomite Alps South Tyrol Italy", "id": "1590209309"}, {"image_id": "1873350869", "caption": "Close up of a detailed golden horse on the edge of a gondola in the canal in Venice Italy", "id": "1873350869"}, {"image_id": "1859233562", "caption": "An electrician using a screwdriver for fitting the energy saving light bulb on the ceiling", "id": "1859233562"}, {"image_id": "1277240015", "caption": "Portrait of young woman running water for bath at home", "id": "1277240015"}, {"image_id": "1859237084", "caption": "A pensive architect holding blueprints at a housing construction site while looking at the surrounding location", "id": "1859237084"}, {"image_id": "1590056570", "caption": "Young man telling tales of the fish that got away", "id": "1590056570"}, {"image_id": "216582038", "caption": "View of snowy mountain range", "id": "216582038"}, {"image_id": "1866109439", "caption": "Portrait of a confident technician nurse at MRI scanner in the hospital", "id": "1866109439"}, {"image_id": "1586728034", "caption": "Music Fan Taking Picture On Mobile Phone At Rock Concert", "id": "1586728034"}, {"image_id": "1576745393", "caption": "A young couple at home", "id": "1576745393"}, {"image_id": "1869278936", "caption": "Wide shot of a worker examining fresh harvested potato kept in a wooden crate", "id": "1869278936"}, {"image_id": "1709207771", "caption": "A young woman boxer getting ready to practice", "id": "1709207771"}, {"image_id": "1590337340", "caption": "Church of Parschins with view to Merano Alto Adige Italy", "id": "1590337340"}, {"image_id": "1571337356", "caption": "Raftsund Vesteralen Lofoten Nordland Norway", "id": "1571337356"}, {"image_id": "1578954437", "caption": "Male and female business colleagues chatting in office building", "id": "1578954437"}, {"image_id": "1839588557", "caption": "Vertical waist up portrait of a woman making fruit salad and smiling at the camera in kitchen", "id": "1839588557"}, {"image_id": "1590024095", "caption": "A young woman in a bath", "id": "1590024095"}, {"image_id": "1816212777", "caption": "Woman carrying boxes down staircase as man seals boxes with duct tape", "id": "1816212777"}, {"image_id": "1297798532", "caption": "Loving senior couple lying in countryside field holding hands", "id": "1297798532"}, {"image_id": "1590314084", "caption": "Man with snow on head Luesener Alm Dolomite Alps South Tyrol Italy", "id": "1590314084"}, {"image_id": "1297799963", "caption": "Portrait of smiling family standing in field of spring daffodils", "id": "1297799963"}, {"image_id": "1576741895", "caption": "Group of teenage friends at a bowling alley laughing and joking", "id": "1576741895"}, {"image_id": "1839589178", "caption": "Three quarter length portrait of a happy mature couple walking with bicycles outdoors", "id": "1839589178"}, {"image_id": "1570154705", "caption": "Place de l Europe and Friday Mosque Moroni Grand Comore Island Ngazidja Comores Africa", "id": "1570154705"}, {"image_id": "1869041345", "caption": "Vertical shot of a senior couple standing beside a parked car and taking a selfie with a digital camera", "id": "1869041345"}, {"image_id": "1556812026", "caption": "Virtual neurons firing in an artificial intelligence deep learning neural network concept", "id": "1556812026"}, {"image_id": "1578922343", "caption": "A girl and a boy holding Easter eggs", "id": "1578922343"}, {"image_id": "1840650371", "caption": "View of a hand touching a bright window of an airplane", "id": "1840650371"}, {"image_id": "1844723999", "caption": "Wide shot of a robotic machinery lifting steel gates in a factory", "id": "1844723999"}, {"image_id": "1590160415", "caption": "Close up of bubble in level tool", "id": "1590160415"}, {"image_id": "1297778825", "caption": "Woman in protective glasses lying in surgery chair having dental exam", "id": "1297778825"}, {"image_id": "1844731844", "caption": "Medium shot of a happy elderly couple smiling at the camera while riding a bicycle together in a meadow full of wildflowers", "id": "1844731844"}, {"image_id": "1846038902", "caption": "A portrait shot of a smiling saleswoman standing at a counter and wrapping a wedge of cheese in cheese shop", "id": "1846038902"}, {"image_id": "1586684384", "caption": "Woman Paying For Shopping At Supermarket Checkout", "id": "1586684384"}, {"image_id": "1587030983", "caption": "View of young couple holding heart shaped balloons while others float away", "id": "1587030983"}, {"image_id": "1277229257", "caption": "Baker by oven baking fresh baguettes at camera", "id": "1277229257"}, {"image_id": "1586673356", "caption": "Snow capped mountains in Fimbatal the border between Switzerland and Austria Eschol", "id": "1586673356"}, {"image_id": "1570162253", "caption": "Church of La Digue Island Seychelles", "id": "1570162253"}, {"image_id": "1878688547", "caption": "Surfer With a prosthetic Leg Standing On the Beach", "id": "1878688547"}, {"image_id": "1570568135", "caption": "Aerial view of the confluence of the Rio Negro s water and the Solimoes River's water", "id": "1570568135"}, {"image_id": "1876650716", "caption": "Close up of students using microscopes", "id": "1876650716"}, {"image_id": "1855878236", "caption": "Group of young male athletes with arms raised overhead in celebration on a bright sunny day at an athletics competition out at the track", "id": "1855878236"}, {"image_id": "1590317678", "caption": "Stack of One Euro coins next to bull figurine", "id": "1590317678"}, {"image_id": "1839580559", "caption": "Smiling woman standing on a ladder with one leg off it and leaning on her husband's shoulder next to a wall", "id": "1839580559"}, {"image_id": "1586672642", "caption": "Mother and daughter drinking hot chocolate on winter day", "id": "1586672642"}, {"image_id": "1878681089", "caption": "Horizontal Close up shot of a businessman sitting in the backseat of a car wearing a seatbelt and using a laptop", "id": "1878681089"}, {"image_id": "1590164825", "caption": "High angle view of male teenage student studying with laptop in library", "id": "1590164825"}, {"image_id": "1572524360", "caption": "Family wrapped in blanket on beach", "id": "1572524360"}, {"image_id": "1587855641", "caption": "Close up of woman turning her head", "id": "1587855641"}, {"image_id": "1587845312", "caption": "Coffee grinds being stirred into cup", "id": "1587845312"}, {"image_id": "1297725746", "caption": "Detail of male hairdresser in salon cutting female clients long hair", "id": "1297725746"}, {"image_id": "217368656", "caption": "Finnish Flag on Top of Soccer Ball", "id": "217368656"}, {"image_id": "1578207233", "caption": "A young woman laying on a sun lounger smiling", "id": "1578207233"}, {"image_id": "1590201239", "caption": "Fern in the Garden of the Lighthouse at Key West Florida USA", "id": "1590201239"}, {"image_id": "216586940", "caption": "Growing barley blowing in the wind", "id": "216586940"}, {"image_id": "1844729123", "caption": "Vertical shot of an elderly couple having lunch and toasting wine glasses at the patio table", "id": "1844729123"}, {"image_id": "1588003460", "caption": "Scenic view of meadow and mountains Kaisergebirge Tyrol Austria", "id": "1588003460"}, {"image_id": "1587144287", "caption": "Low angle view of hot air balloons against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587144287"}, {"image_id": "1851485540", "caption": "Full shot of the backside of a happy couple walking along the shore of a sunny beach", "id": "1851485540"}, {"image_id": "1725713111", "caption": "Twin brothers climbing on ropes in playground", "id": "1725713111"}, {"image_id": "1846059800", "caption": "A woman sitting among plants and smiling at the camera in a forest", "id": "1846059800"}, {"image_id": "1845993980", "caption": "A father and son holding solar panels on a globe covered in grass with windmills on it", "id": "1845993980"}, {"image_id": "1587857354", "caption": "View of a young woman flamenco dancing", "id": "1587857354"}, {"image_id": "1847335727", "caption": "A medium shot of a young businessman wearing headset and holding laptop giving a presentation on a projection screen", "id": "1847335727"}, {"image_id": "1587998411", "caption": "Man opening wine bottle with corkscrew", "id": "1587998411"}, {"image_id": "1844731739", "caption": "Wide shot of a happy elderly couple sitting in a rowboat on a lake surrounded by tall grass", "id": "1844731739"}, {"image_id": "1571338088", "caption": "Landscape at Gala Gudbrandsdalen Oppland Norway", "id": "1571338088"}, {"image_id": "1578924920", "caption": "A young couple pushing a bicycle in autumn time", "id": "1578924920"}, {"image_id": "1844766173", "caption": "Young couple sitting on a sofa and reading messages on a digital tablet", "id": "1844766173"}, {"image_id": "1846752473", "caption": "A team of surgeons performing an operation with precision by wearing surgical binoculars in the operating room", "id": "1846752473"}, {"image_id": "1572528599", "caption": "Coast of Isla Santa Margerita Baja California Sur Mexico", "id": "1572528599"}, {"image_id": "1590361103", "caption": "Close up of assorted US paper currency", "id": "1590361103"}, {"image_id": "1570154798", "caption": "Ostrich burying head in the sand Tsavo East National Park Kenya Africa", "id": "1570154798"}, {"image_id": "216387086", "caption": "Tree in snowy field", "id": "216387086"}, {"image_id": "1590213614", "caption": "Woman receiving spa facial treatment", "id": "1590213614"}, {"image_id": "1572508223", "caption": "Beauty portrait of a woman in a tropical setting touching the foliage", "id": "1572508223"}, {"image_id": "1859237117", "caption": "A front view of two smart architects reviewing blueprints under the brightly glowing sun at a construction site", "id": "1859237117"}, {"image_id": "1873346399", "caption": "Middle schoolboys and teacher running while playing soccer on the field in physical education", "id": "1873346399"}, {"image_id": "1878682946", "caption": "Front view portrait of a smiling young man leaning against a red motorbike on the driveway", "id": "1878682946"}, {"image_id": "1277365355", "caption": "Office portrait of young businessman in start up new business working at desk and smiling at camera", "id": "1277365355"}, {"image_id": "1576774415", "caption": "Argument between teenage boys in a bowling alley", "id": "1576774415"}, {"image_id": "1766928966", "caption": "Woman petting cow Kleinwalsertal Allgau Germany", "id": "1766928966"}, {"image_id": "1572486347", "caption": "A young man inserting a contact lens", "id": "1572486347"}, {"image_id": "1663818179", "caption": "Teenage friends performing press ups together in gym", "id": "1663818179"}, {"image_id": "1847201882", "caption": "A vertically wide view of a large factory with a mature adult man working on a sheet metal press", "id": "1847201882"}, {"image_id": "1586729777", "caption": "Worker In Solar Panel Manufacturing Warehouse", "id": "1586729777"}, {"image_id": "1590179393", "caption": "Historical Penal Colony along water Kingston Norfolk Island External Territory of Australia", "id": "1590179393"}, {"image_id": "1590224714", "caption": "Group of children playing with pinwheels outdoors", "id": "1590224714"}, {"image_id": "1873296866", "caption": "Art teacher taking a painting from a middle school student during an art class", "id": "1873296866"}, {"image_id": "216580118", "caption": "Close up of tranquil field of blooming buttercups", "id": "216580118"}, {"image_id": "216587720", "caption": "Ornate pillar in Plaza de Espana Seville Spain", "id": "216587720"}, {"image_id": "1873296701", "caption": "Gym teacher teaching a high school student badminton at the gymnasium", "id": "1873296701"}, {"image_id": "1277239433", "caption": "Portrait of loving couple in pajamas lying on bed at home hugging", "id": "1277239433"}, {"image_id": "1578924710", "caption": "A young family sitting on the grass in autumn time", "id": "1578924710"}, {"image_id": "1297741703", "caption": "Close up of man in vegetable garden picking fresh Brussels sprouts", "id": "1297741703"}, {"image_id": "1864656257", "caption": "Vertical shot of a couple moving house with the woman besides a van leaning over a hand dolly and her husband with a pot plant in the background", "id": "1864656257"}, {"image_id": "1859342048", "caption": "A low angle portrait shot of a senior man smiling at camera while standing in a park under autumn leaves", "id": "1859342048"}, {"image_id": "1572463142", "caption": "A young man using a mobile phone at a picnic", "id": "1572463142"}, {"image_id": "1576774421", "caption": "Father and daughter bowling daughter crossing her fingers for good luck", "id": "1576774421"}, {"image_id": "1587148154", "caption": "Detail view of pumpkins in autumn", "id": "1587148154"}, {"image_id": "1873296719", "caption": "Low angle shot of a high school girl and her teacher cooking pasta during a home economics class", "id": "1873296719"}, {"image_id": "1576741802", "caption": "Young girl writing in chalk on the playground", "id": "1576741802"}, {"image_id": "1872077435", "caption": "Scientists in clean suits discussing paperwork in silicon wafer manufacturing laboratory", "id": "1872077435"}, {"image_id": "1587642974", "caption": "Close up of infant girl sleeping", "id": "1587642974"}, {"image_id": "216582452", "caption": "Hay being harvested into straw bales in farm field", "id": "216582452"}, {"image_id": "1572538727", "caption": "A young woman applying moisturiser", "id": "1572538727"}, {"image_id": "1581271640", "caption": "Smiling senior man in scarf and hooded top in autumn countryside", "id": "1581271640"}, {"image_id": "1869316937", "caption": "Portrait shot of a technician using a laptop in a server room", "id": "1869316937"}, {"image_id": "1869316970", "caption": "Silhouette shot of a technician using a laptop in the aisle of a server room", "id": "1869316970"}, {"image_id": "1590210725", "caption": "Young girl drinking from can with straw", "id": "1590210725"}, {"image_id": "1586648876", "caption": "Straight Road Running Through Arid Agricultural Landscape In Western Cape Region Of South Africa", "id": "1586648876"}, {"image_id": "1859331860", "caption": "A wide shot of a boy running past a girl unlocking her school locker", "id": "1859331860"}, {"image_id": "1578207290", "caption": "A young woman crying wiping her tears away with a tissue", "id": "1578207290"}, {"image_id": "217375592", "caption": "Cup of coffee", "id": "217375592"}, {"image_id": "1843607165", "caption": "Vertical portrait of two generations of family sitting with a map by the roadside in front of a car smile at the camera on a sunny day", "id": "1843607165"}, {"image_id": "1866109304", "caption": "Farmer examining the yield in the sunny rural barley crop field in summer", "id": "1866109304"}, {"image_id": "1869314603", "caption": "Wide shot of the silhouette of a couple sitting on a bench and raising their wine glasses against the sunset over the ocean", "id": "1869314603"}, {"image_id": "1297725806", "caption": "Studio beauty shot with young smiling woman touching mouth", "id": "1297725806"}, {"image_id": "1588011569", "caption": "Two businessmen talking in front of computer monitors", "id": "1588011569"}, {"image_id": "1590179312", "caption": "Exterior view of The Mission House Kerikeri North Island New Zealand", "id": "1590179312"}, {"image_id": "1670342054", "caption": "Detail of baby girls face with Close up on eye and eyelashes", "id": "1670342054"}, {"image_id": "1878695720", "caption": "Teacher and female high school students assembling a bicycle in a woodworking class", "id": "1878695720"}, {"image_id": "1839589106", "caption": "Vertical shot of a girl with a monkey rucksack leaning and looking through a large window of an airport lounge seen from rear", "id": "1839589106"}, {"image_id": "1587038600", "caption": "Close up of one head of romaine lettuce", "id": "1587038600"}, {"image_id": "1844196359", "caption": "Horizontal shot of woman holding celery and tape measure a nutrition concept", "id": "1844196359"}, {"image_id": "1297799906", "caption": "Cattle grazing in pasture on livestock farm against blue sky", "id": "1297799906"}, {"image_id": "1572512522", "caption": "Girl using icing bag to decorate cookies", "id": "1572512522"}, {"image_id": "1590214457", "caption": "Close up of wine leaves in autumn", "id": "1590214457"}, {"image_id": "1572535112", "caption": "Portrait of a young woman", "id": "1572535112"}, {"image_id": "1843607000", "caption": "Horizontal portrait of a businesswoman in striped white shirt meditating sitting cross legged on desk in front of the window in office", "id": "1843607000"}, {"image_id": "1862126084", "caption": "A portrait shot of a young blonde boy in school uniform lying his head down on the books kept on his desk in a classroom", "id": "1862126084"}, {"image_id": "1590314159", "caption": "Man looking through frosted window", "id": "1590314159"}, {"image_id": "1859337146", "caption": "A medium shot of a young African American woman smiling at camera while studying in a library", "id": "1859337146"}, {"image_id": "1869314585", "caption": "Vertical shot of a woman in swimwear standing in water at the shore of a sunny beach", "id": "1869314585"}, {"image_id": "1839582923", "caption": "Two white eggs placed on top of yellow sunflowers which are placed in egg cups on a bright sunny spring day in the park with selective focus on the egg in the front", "id": "1839582923"}, {"image_id": "1587849773", "caption": "Close up of tar on road", "id": "1587849773"}, {"image_id": "1846749455", "caption": "An experienced radiologist taking car of a senior adult patient while helping him with the MRI scanning test", "id": "1846749455"}, {"image_id": "1857286055", "caption": "Young businessman with short hair smiling at the camera during a conference while his colleagues are having a discussion in the background", "id": "1857286055"}, {"image_id": "1572478493", "caption": "Portrait of a senior woman at the beach", "id": "1572478493"}, {"image_id": "1277260814", "caption": "Portrait of girl with pumpkin growing in vegetable garden smiling at camera", "id": "1277260814"}, {"image_id": "1844731667", "caption": "Wide shot of two engineers in a control room with multiple monitors at a nuclear power station", "id": "1844731667"}, {"image_id": "1572661478", "caption": "A young woman taking a photo of herself with a mobile phone", "id": "1572661478"}, {"image_id": "1859176814", "caption": "Wide shot of senior engineers inspecting the tail of a passenger jet at a hangar", "id": "1859176814"}, {"image_id": "1586685053", "caption": "Woman With Flat Tyre On car Resting Feet On Suitcase", "id": "1586685053"}, {"image_id": "1587982730", "caption": "Rear view of students sitting at graduation ceremony", "id": "1587982730"}, {"image_id": "1576780013", "caption": "Portrait of a girl leaning against a blue painted wall", "id": "1576780013"}, {"image_id": "1878695747", "caption": "High school student in uniform using a digital tablet in the classroom", "id": "1878695747"}, {"image_id": "1590059978", "caption": "A portrait of a young woman looking to camera", "id": "1590059978"}, {"image_id": "1766928975", "caption": "Group of people practicing yoga Kleinwalsertal Allgau Germany", "id": "1766928975"}, {"image_id": "1587850169", "caption": "View of church in Prague", "id": "1587850169"}, {"image_id": "1766923872", "caption": "Worried father giving daughter car keys and money", "id": "1766923872"}, {"image_id": "216584114", "caption": "Trees and field in snow covered winter landscape", "id": "216584114"}, {"image_id": "1587838187", "caption": "Couple on sofa with man using laptop and woman filing her nails", "id": "1587838187"}, {"image_id": "1578902381", "caption": "A bride sitting on a chair", "id": "1578902381"}, {"image_id": "1590352277", "caption": "Lighthouse at Cap de Ses Salines Mallorca Spain", "id": "1590352277"}, {"image_id": "1711214267", "caption": "You man jumping in the air with his hands up", "id": "1711214267"}, {"image_id": "1590209144", "caption": "Close up of sweet chestnuts", "id": "1590209144"}, {"image_id": "1840559624", "caption": "Steel tubes and safety barriers in warehouse stacked neatly", "id": "1840559624"}, {"image_id": "1590337334", "caption": "Prokulus church Prokuluskirche in Naturns Province of Bolzano Trentino AltoAdige Italy", "id": "1590337334"}, {"image_id": "1277230598", "caption": "Female customer being shown car repair bill by mechanic in garage", "id": "1277230598"}, {"image_id": "1862063180", "caption": "Multi generation family running over a wooden jetty to jump into the lake", "id": "1862063180"}, {"image_id": "1277231528", "caption": "Butler in country house polishing silver spoon", "id": "1277231528"}, {"image_id": "1590315398", "caption": "Rolls of assorted Euro coins with businessmen figurines", "id": "1590315398"}, {"image_id": "1859181176", "caption": "Engineer testing an engine of a passenger jet at a hangar", "id": "1859181176"}, {"image_id": "1587144335", "caption": "Silhouette of a man standing in the snow", "id": "1587144335"}, {"image_id": "1572528818", "caption": "Sand Dollar Beach Isla Magdalena Baja California Sur Mexico", "id": "1572528818"}, {"image_id": "1590209141", "caption": "Illuminated Church of the dear Lady in winter Frauenkirch near Davos Grisons Switzerland", "id": "1590209141"}, {"image_id": "1844194310", "caption": "The sun shines bright in the daytime in summer with fluffy clouds in the blue sky", "id": "1844194310"}, {"image_id": "1576774220", "caption": "A senior man riding a motorbike", "id": "1576774220"}, {"image_id": "1587033782", "caption": "Couple yelling at each other at close range red tile wall in background", "id": "1587033782"}, {"image_id": "1297732715", "caption": "Young man drinking from bottle of water in summer park", "id": "1297732715"}, {"image_id": "216395267", "caption": "Low angle view of autumn leaves", "id": "216395267"}, {"image_id": "1766920365", "caption": "Teenage girls wearing socks with legs up", "id": "1766920365"}, {"image_id": "1578947168", "caption": "A couple relaxing with a drink on a boat", "id": "1578947168"}, {"image_id": "1590363419", "caption": "Close up of woman smoking cigarette", "id": "1590363419"}, {"image_id": "1297781540", "caption": "Loving senior couple holding hands on walk through autumn countryside", "id": "1297781540"}, {"image_id": "1869316979", "caption": "Young technician speaking on a walkie talkie while working on a computer connected to a cabinet in the server room", "id": "1869316979"}, {"image_id": "1586684477", "caption": "Farmer Inspecting Oat Crop In Field", "id": "1586684477"}, {"image_id": "1578924779", "caption": "A family group standing together in autumn time", "id": "1578924779"}, {"image_id": "1852967162", "caption": "Portrait of a woman drinking wine in a wine shop", "id": "1852967162"}, {"image_id": "1297781495", "caption": "Female high school students performing experiment in chemistry lab", "id": "1297781495"}, {"image_id": "1874804267", "caption": "High angle view of engineers assembling passenger jets in hangar", "id": "1874804267"}, {"image_id": "1766925084", "caption": "Woman relaxing on palm tree at beach", "id": "1766925084"}, {"image_id": "1571664311", "caption": "Woman drinking a glass of water", "id": "1571664311"}, {"image_id": "217362053", "caption": "Carnival", "id": "217362053"}, {"image_id": "1590177932", "caption": "Low angle view of bungee jumper on Sky Tower Auckland New Zealand", "id": "1590177932"}, {"image_id": "1816749579", "caption": "Portrait of a happy couple in pajamas sitting at kitchen table with man drinking mug of coffee", "id": "1816749579"}, {"image_id": "1587031418", "caption": "View of many Easter ornaments hanging off of a bare tree Pfullendorf Sigmaringen Baden Wuerttemberg Germany", "id": "1587031418"}, {"image_id": "1586723015", "caption": "Mother With Children Eating Meal Outdoors At Home In Garden", "id": "1586723015"}, {"image_id": "1570350353", "caption": "A mountain hut and church above Monstein Davos Grisons Switzerland", "id": "1570350353"}, {"image_id": "1878778730", "caption": "Happy senior couple lying on the grass in the field on a beautiful spring day", "id": "1878778730"}, {"image_id": "1586684492", "caption": "Farmer With Tablet Computer Inspecting Oat Crop In Field", "id": "1586684492"}, {"image_id": "1572542282", "caption": "A young female singer performing", "id": "1572542282"}, {"image_id": "1590216746", "caption": "Couple riding bicycle on path in autumn", "id": "1590216746"}, {"image_id": "1590163853", "caption": "Group of people whitewater rafting", "id": "1590163853"}, {"image_id": "1590070607", "caption": "A young boy loading the washing machine", "id": "1590070607"}, {"image_id": "1571663771", "caption": "A bride and groom in a car", "id": "1571663771"}, {"image_id": "1813179749", "caption": "Close up of businessman wearing telephone headset holding yellow file", "id": "1813179749"}, {"image_id": "1852925516", "caption": "Horizontal shot of a young smiling pregnant woman with hands on stomach in a park", "id": "1852925516"}, {"image_id": "216108530", "caption": "Clouds in blue sky over wheat field", "id": "216108530"}, {"image_id": "1587121172", "caption": "Overworked housewife looking at a reflection of herself as wealthy and privileged", "id": "1587121172"}, {"image_id": "1586681936", "caption": "School children having lunch in school canteen", "id": "1586681936"}, {"image_id": "1878871124", "caption": "A vertical shot of a smart smiling teacher explaining an interesting topic written on the whiteboard to a group of students in a classroom", "id": "1878871124"}, {"image_id": "1583881124", "caption": "Father Christmas Santa Claus with a mother and daughter", "id": "1583881124"}, {"image_id": "1587031040", "caption": "View of a woman jumping with a red umbrella in her hand on the beach", "id": "1587031040"}, {"image_id": "1868722163", "caption": "A portrait shot of a happy senior man lying on his wife's lap while reading a book and having picnic in a field of wildflowers", "id": "1868722163"}, {"image_id": "1844724236", "caption": "Vertical shot of a worker smiling at the camera while inspecting boxes at the production line of a distribution warehouse", "id": "1844724236"}, {"image_id": "1297757135", "caption": "Loving couple embracing in garden outside home", "id": "1297757135"}, {"image_id": "1570236710", "caption": "Lake Davos Davoser See in early morning light Davos Grisons Switzerland", "id": "1570236710"}, {"image_id": "1576745915", "caption": "Couple moving into their new home carrying a packing box", "id": "1576745915"}, {"image_id": "1878692735", "caption": "Portrait shot of a high school boy looking at the camera while testing the electronics of a drone in a shop class", "id": "1878692735"}, {"image_id": "1590056627", "caption": "Senior man with snorkel gear and flippers at the beach", "id": "1590056627"}, {"image_id": "1865895140", "caption": "Friends celebrating and toasting wine glasses over a lunch table and a woman smiling with focus on the wine glasses", "id": "1865895140"}, {"image_id": "1277230358", "caption": "Competitive male athletes in starting blocks for sprint running race", "id": "1277230358"}, {"image_id": "1650271805", "caption": "Teenage boy 17 19 posing on the beach side view", "id": "1650271805"}, {"image_id": "1297725704", "caption": "Young woman at home sitting on bench in garden with digital tablet", "id": "1297725704"}, {"image_id": "1571610938", "caption": "Young man standing on bathroom scales", "id": "1571610938"}, {"image_id": "1862131307", "caption": "A medium shot of an African American businessman smiling while talking with a businessman and a businesswoman while using a laptop in an airport terminal", "id": "1862131307"}, {"image_id": "216587690", "caption": "Snowy mountain range and blue sky", "id": "216587690"}, {"image_id": "1297786298", "caption": "Couple on winter vacation with man giving woman piggyback in snow", "id": "1297786298"}, {"image_id": "1590222326", "caption": "Close up of senior woman washing hands", "id": "1590222326"}, {"image_id": "1869911636", "caption": "A medium shot of a businessman standing in office doorway with businesswomen walking in corridor in background", "id": "1869911636"}, {"image_id": "1813180613", "caption": "Vertical shot of two female teenagers in the trial room hand picking an outfit", "id": "1813180613"}, {"image_id": "1844769152", "caption": "Businessmen talking in the lobby of a modern office", "id": "1844769152"}, {"image_id": "1277238296", "caption": "Portrait of two teenage girls holding shopping bags in store", "id": "1277238296"}, {"image_id": "1816211349", "caption": "Portrait shot of a young woman smiling and holding a glue gun doing DIY at home", "id": "1816211349"}, {"image_id": "1590061796", "caption": "A middle aged woman holding a pot of moisturising cream", "id": "1590061796"}, {"image_id": "1576738283", "caption": "Young man styling his hair", "id": "1576738283"}, {"image_id": "1586685137", "caption": "Farmer Watching Tractor And Trailer In Field Of Oats", "id": "1586685137"}, {"image_id": "1570230875", "caption": "View from mountain restaurant Grisons Switzerland", "id": "1570230875"}, {"image_id": "1710280361", "caption": "Portrait of a couple Early 40 s", "id": "1710280361"}, {"image_id": "1587992660", "caption": "Boy jumping over man lying on grass", "id": "1587992660"}, {"image_id": "1590352079", "caption": "Cala Figuera with boats Mallorca Spain", "id": "1590352079"}, {"image_id": "1857288803", "caption": "Young woman collecting pile of gambling chips at the roulette table after winning a game at the casino", "id": "1857288803"}, {"image_id": "1587031427", "caption": "Low angle view of windmill against blue sky", "id": "1587031427"}, {"image_id": "1567889369", "caption": "Plage de N'gouja N gouja beach Mayotte French Overseas Department Union of the Comoros Africa", "id": "1567889369"}, {"image_id": "1859144537", "caption": "Side view of an elderly couple with a fishing rod sitting on a sunny beach and enjoying coffee", "id": "1859144537"}, {"image_id": "1670340041", "caption": "Multi generation family collecting autumn leaves in garden at camera", "id": "1670340041"}, {"image_id": "1572390290", "caption": "Ronco to the Lake Maggiore Ticino Switzerland", "id": "1572390290"}, {"image_id": "1878688580", "caption": "Close up of the prosthetic Leg of a man Running Along the Beach", "id": "1878688580"}, {"image_id": "1866123407", "caption": "Vertical shot of pharmacists in aprons inspecting medicines at a pharmacy counter", "id": "1866123407"}, {"image_id": "1277233040", "caption": "Mature couple relaxing on motorhome vacation at beach", "id": "1277233040"}, {"image_id": "1587830111", "caption": "Young woman smiling on beach", "id": "1587830111"}, {"image_id": "1839580517", "caption": "Smiling man with one hand on his hip and the other one holding a paint roller", "id": "1839580517"}, {"image_id": "1847335655", "caption": "A side profile portrait shot of a smiling young saleswoman talking on telephone", "id": "1847335655"}, {"image_id": "1844769308", "caption": "Portrait of smiling young men sitting on stairs and using a digital tablet", "id": "1844769308"}, {"image_id": "1578920300", "caption": "A young woman holding a bunch of pink tulips Close up", "id": "1578920300"}, {"image_id": "1590181361", "caption": "Couple smiling at each other at beach", "id": "1590181361"}, {"image_id": "1586735573", "caption": "Portrait Of Businessman On Floor Of Engineering Factory", "id": "1586735573"}, {"image_id": "1846060094", "caption": "A portrait beautiful shot of an orange sunset in a blue sky with clouds", "id": "1846060094"}, {"image_id": "1869911600", "caption": "A portrait shot of a happy businesswoman talking on a telephone while standing in her cubicle with people talking in background", "id": "1869911600"}, {"image_id": "1586683907", "caption": "Senior Man Asleep In Vegetable Garden", "id": "1586683907"}, {"image_id": "1571576939", "caption": "Portrait of a young woman", "id": "1571576939"}, {"image_id": "1277240855", "caption": "Detail of baby girls feet against blanket", "id": "1277240855"}, {"image_id": "1572388625", "caption": "Highland cattle on farm Seefeld Bavaria Germany", "id": "1572388625"}, {"image_id": "1572396362", "caption": "Snow covered house Davos Grisons Kanton Graubuenden Switzerland", "id": "1572396362"}, {"image_id": "1862081210", "caption": "Vertical shot of a highland cow standing in an open field", "id": "1862081210"}, {"image_id": "1852646675", "caption": "A low angle close up of surgeon's hands covered with rubber gloves while passing surgical scissor over a background of an electric lamp", "id": "1852646675"}, {"image_id": "1864656356", "caption": "Horizontal shot of a joyous mother and daughter pushing a shopping trolley in a supermarket car park", "id": "1864656356"}, {"image_id": "1813179758", "caption": "Group of children sprint up a grassy hill in park leaving bicycles behind", "id": "1813179758"}, {"image_id": "1586693111", "caption": "High school student drilling wood in shop class", "id": "1586693111"}, {"image_id": "1590344036", "caption": "Small fishing boats at low tide North Coast of Guernsey Channel Islands UK", "id": "1590344036"}, {"image_id": "1297775138", "caption": "Young businessman sitting at boardroom table with flipchart looking at camera", "id": "1297775138"}, {"image_id": "1586684123", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586684123"}, {"image_id": "1570354679", "caption": "Bouquets at Binh Tay Market Ho Chi Minh City Saigon Vietnam", "id": "1570354679"}, {"image_id": "1851481535", "caption": "Smiling woman got soap on her nose while washing her blue car", "id": "1851481535"}, {"image_id": "1878869711", "caption": "Strong handsome man carrying his girlfriend for a piggyback ride through the snow while she happily outstretches her arms", "id": "1878869711"}, {"image_id": "1578935504", "caption": "little girl sitting on the floor looking up at her bunch of balloons", "id": "1578935504"}, {"image_id": "1590338072", "caption": "View to the boat harbor of Diessen with Marienmuenster in background Ammersee Bavaria Germany", "id": "1590338072"}, {"image_id": "216387074", "caption": "Tree in snowy field", "id": "216387074"}, {"image_id": "1586664884", "caption": "Diver in wetsuit waist deep in ocean cove after scuba dive", "id": "1586664884"}, {"image_id": "1587984401", "caption": "Close up of a young man's face focusing on the eye", "id": "1587984401"}, {"image_id": "1570300724", "caption": "Pfarrkirche St Sebastian with Reiteralm in background Ramsau bei Berchtesgaden Bavaria Germany", "id": "1570300724"}, {"image_id": "1669107905", "caption": "Businessman eating breakfast in waiting area", "id": "1669107905"}, {"image_id": "1304265911", "caption": "Active senior man drinking water after outdoor exercise at camera", "id": "1304265911"}, {"image_id": "1578927377", "caption": "Two business colleagues talking at a table having a break", "id": "1578927377"}, {"image_id": "1572460034", "caption": "Portrait of a young girl", "id": "1572460034"}, {"image_id": "1586724557", "caption": "Young Couples Sitting On Grass With Arms In The Air", "id": "1586724557"}, {"image_id": "1578224426", "caption": "A mid adult woman holding an apple", "id": "1578224426"}, {"image_id": "1840555061", "caption": "Salesman showing customer the hatchback of car in a car dealership showroom while the customer is holding a brochure", "id": "1840555061"}, {"image_id": "1277235527", "caption": "Business colleagues with laptop collaborating in office meeting", "id": "1277235527"}, {"image_id": "1843605716", "caption": "Kids sitting on parents shoulders throwing autumn leaves in autumn park", "id": "1843605716"}, {"image_id": "1570162070", "caption": "Corals at Aldabra Atoll Seychelles", "id": "1570162070"}, {"image_id": "1846749176", "caption": "A couple happily piggybacking on the top of a remote mountain while the wife stretching her arms out with joy", "id": "1846749176"}, {"image_id": "1839586319", "caption": "Close up of shimmering water in a clear swimming pool on a bright sunny day", "id": "1839586319"}, {"image_id": "1590216704", "caption": "Father helping young daughter fly kite outdoors", "id": "1590216704"}, {"image_id": "1277234600", "caption": "Active man exercising on cycling machine in gym", "id": "1277234600"}, {"image_id": "1873351076", "caption": "Gondoliers paddling tourists in gondola among architectural buildings in the sunny Grand Canal in Venice Italy", "id": "1873351076"}, {"image_id": "1578942938", "caption": "Portrait of a senior couple hand in hand walking in the sea", "id": "1578942938"}, {"image_id": "1587149465", "caption": "Detail view of a mug of tea with dried orange slices and tea candles on a hardwood surface", "id": "1587149465"}, {"image_id": "1586693144", "caption": "Portrait confident gym teacher in school gym", "id": "1586693144"}, {"image_id": "1578225890", "caption": "A mid adult woman holding a cup of soup", "id": "1578225890"}, {"image_id": "1578921338", "caption": "Left side of a young woman's face", "id": "1578921338"}, {"image_id": "1710028655", "caption": "mother and children gardening in their yard", "id": "1710028655"}, {"image_id": "1590214532", "caption": "Close up of hazel leaf", "id": "1590214532"}, {"image_id": "1581286463", "caption": "Multi generation family relaxing in countryside on motorhome vacation", "id": "1581286463"}, {"image_id": "1587149051", "caption": "High angle view of tea leaves being strained into tea", "id": "1587149051"}, {"image_id": "1590067880", "caption": "A businessman packing a suitcase", "id": "1590067880"}, {"image_id": "1578921332", "caption": "A portrait of a young blonde woman thinking", "id": "1578921332"}, {"image_id": "1587642941", "caption": "Summer Residence of Napoleon Bonaparte in Elba Tuscany Italy", "id": "1587642941"}, {"image_id": "1844196023", "caption": "Vertical shot of summer bright blue sky with fluffy clouds", "id": "1844196023"}, {"image_id": "1670341739", "caption": "car mechanic in auto repair garage with car diagnostic computer", "id": "1670341739"}, {"image_id": "1766918385", "caption": "Girl and boy sitting on hay in countryside", "id": "1766918385"}, {"image_id": "1590163829", "caption": "Parabolic antenna satellite dishes in field Raisting Bavaria Germany", "id": "1590163829"}, {"image_id": "217367033", "caption": "Waterfall in Lauterbrunnen Switzerland", "id": "217367033"}, {"image_id": "1663817651", "caption": "Portrait of teenage girls lying on bed", "id": "1663817651"}, {"image_id": "1590360539", "caption": "Man performing handstand on beach", "id": "1590360539"}, {"image_id": "1862067386", "caption": "Close up shot of bright yellow rapeseeds in a sunny meadow", "id": "1862067386"}, {"image_id": "1572546803", "caption": "A young couple in a forest", "id": "1572546803"}, {"image_id": "1844765963", "caption": "Young couple talking and using a digital tablet in an urban park", "id": "1844765963"}, {"image_id": "1567877678", "caption": "Mountain scenery near Fussen Allgau Bavaria Germany", "id": "1567877678"}, {"image_id": "1304266772", "caption": "Senior man lying on sofa listening to music on headphones reading book", "id": "1304266772"}, {"image_id": "1576772051", "caption": "A couple and their young daughter opening Christmas presents", "id": "1576772051"}, {"image_id": "1588002839", "caption": "A diver standing on a diving board", "id": "1588002839"}, {"image_id": "1576774451", "caption": "Portrait of happy little girl smiling", "id": "1576774451"}, {"image_id": "1590149384", "caption": "Close up of bull figurine", "id": "1590149384"}, {"image_id": "1571518013", "caption": "A senior man driving a sports car", "id": "1571518013"}, {"image_id": "1670339453", "caption": "Close up of businessman cycling to work on folding commuter bicycle on pavement", "id": "1670339453"}, {"image_id": "1572527729", "caption": "California sea lion Zalophus californianus Sea of Cortez Los Islotes Baja California Sur Mexico", "id": "1572527729"}, {"image_id": "1852935032", "caption": "Vertical overhead shot of a young woman on a step ladder with paint pot and brush smiles at the camera", "id": "1852935032"}, {"image_id": "1590327425", "caption": "Fortified tower and gate made of mudbricks at Al Kamil Oman", "id": "1590327425"}, {"image_id": "1878694193", "caption": "Full shot of high school students playing basketball during a gym class", "id": "1878694193"}, {"image_id": "1840553813", "caption": "Salesman and couple with brochures next to car in a car dealership showroom", "id": "1840553813"}, {"image_id": "1857297359", "caption": "Horizontal shot of a young businesswoman talking on mobile phone in the car on a runway", "id": "1857297359"}, {"image_id": "1570154768", "caption": "Helmeted Guinea fowl at Tsavo East National Park Kenya Africa", "id": "1570154768"}, {"image_id": "1572537347", "caption": "A couple on a beach", "id": "1572537347"}, {"image_id": "1663817615", "caption": "Portrait of three teenage friends lying on floor", "id": "1663817615"}, {"image_id": "1845993911", "caption": "A portrait shot of a happy female baker showing a loaf of bread to camera while putting a pan of bread loaves in a rack", "id": "1845993911"}, {"image_id": "1852961738", "caption": "Woman shouting at her boyfriend while sitting at a sea side caf table", "id": "1852961738"}, {"image_id": "1866109394", "caption": "Wide portrait of a Farmer examining the yield in the sunny rural barley crop field in summer", "id": "1866109394"}, {"image_id": "1855916000", "caption": "Store assistants are standing in a computer store while customers are browsing through the products", "id": "1855916000"}, {"image_id": "1859334752", "caption": "A portrait shot of a senior woman with gray hair writing a test with teacher helping a senior student in background", "id": "1859334752"}, {"image_id": "1839585464", "caption": "Close up of shimmering water in a clear swimming pool on a bright sunny day", "id": "1839585464"}, {"image_id": "1590210734", "caption": "Close up of woman smiling", "id": "1590210734"}, {"image_id": "1878695792", "caption": "Middle school students studying with digital tablets in a classroom", "id": "1878695792"}, {"image_id": "1852961639", "caption": "Close up of a boy blowing pinwheel while sitting on monkey bars at a playground", "id": "1852961639"}, {"image_id": "1852924409", "caption": "Young female mechanic leaning against the open hood of a broken down car and smiling at the camera in her garage", "id": "1852924409"}, {"image_id": "1846752437", "caption": "A vertical view of a modern operating room with experienced team of surgeons performing a serious operation with use of latest surgical tools", "id": "1846752437"}, {"image_id": "1588008341", "caption": "Couple walking on snowy mountain", "id": "1588008341"}, {"image_id": "1839587396", "caption": "Horizontal shot of a young smiling receptionist answering a phone call behind the counter in reception area with copy space", "id": "1839587396"}, {"image_id": "1588002716", "caption": "A diver standing on a diving board", "id": "1588002716"}, {"image_id": "1868714402", "caption": "A medium Close up view of an engineer in a clean suit taking notes in the silicon wafer manufacturing laboratory", "id": "1868714402"}, {"image_id": "1556812050", "caption": "Concept view of neurons firing in an artificial intelligence deep learning neural network", "id": "1556812050"}, {"image_id": "1862081204", "caption": "Close up portrait of a highland cow with long horns standing in an open field", "id": "1862081204"}, {"image_id": "1585879976", "caption": "Calved icebergs from Monacobreen glacier floating in Liefdefjorden Haakon VII Land Spitsbergen Svalbard Norway Europe", "id": "1585879976"}, {"image_id": "1570365635", "caption": "Golden Jubilee Bridge River Thames London UK", "id": "1570365635"}, {"image_id": "1567879982", "caption": "Woman with flower between toes", "id": "1567879982"}, {"image_id": "1650267014", "caption": "Foreman co workers and boy looking at blueprints on construction site", "id": "1650267014"}, {"image_id": "1865856611", "caption": "A couple of middle aged businessmen walking and pulling suitcases in the airport lobby with a businesswoman standing in the foreground", "id": "1865856611"}, {"image_id": "1590215483", "caption": "Close up of wine leaves in autumn", "id": "1590215483"}, {"image_id": "1817410962", "caption": "Close up shot of a group of friends in swimwear sitting on a beach with the sky in the background", "id": "1817410962"}, {"image_id": "1578927281", "caption": "A businessman and woman shaking hands", "id": "1578927281"}, {"image_id": "1865993540", "caption": "Horizontal profile shot of a couple with their two kids and their grandparents walk in a line holding hands during hiking in the mountains", "id": "1865993540"}, {"image_id": "1895443064", "caption": "Skier on mountain top looking at mountains", "id": "1895443064"}, {"image_id": "1571366138", "caption": "Doctor and nurse looking at patient chart in hospital room", "id": "1571366138"}, {"image_id": "1587139034", "caption": "Close up detail of barley crop", "id": "1587139034"}, {"image_id": "1876650545", "caption": "Senior man reading newspaper Close up", "id": "1876650545"}, {"image_id": "1304264360", "caption": "Girl on chair as family shop for furniture in store at camera", "id": "1304264360"}, {"image_id": "1865993435", "caption": "Horizontal shot of a couple with their daughter and grandparents sitting in a tent on a camping trip with the man holding a map smile at the camera", "id": "1865993435"}, {"image_id": "1587642908", "caption": "View from a rry to Portorraio Elba Tuscany Italy", "id": "1587642908"}, {"image_id": "1277235731", "caption": "Man holding solar panel against clear blue sky", "id": "1277235731"}, {"image_id": "1578927338", "caption": "A group of work colleagues sitting at a table having a meeting", "id": "1578927338"}, {"image_id": "1578906938", "caption": "A businesswoman talking on a mobile phone using a laptop computer", "id": "1578906938"}, {"image_id": "1657966331", "caption": "Mother and son playing with soccer ball in grass", "id": "1657966331"}, {"image_id": "216348173", "caption": "Colorful clouds in sky", "id": "216348173"}, {"image_id": "1572387446", "caption": "View over Swimming pool to Monte Bre Lugano Ticino Switzerland", "id": "1572387446"}, {"image_id": "1570552790", "caption": "Raincloud near Balaio Amazon River Brazil", "id": "1570552790"}, {"image_id": "1859178629", "caption": "Wide shot of an engineer in uniforms repairing a passenger jet at a hangar", "id": "1859178629"}, {"image_id": "1587032009", "caption": "View of a frame around a crack on the wall", "id": "1587032009"}, {"image_id": "1862131412", "caption": "A medium shot of a surveyor from backside looking at a co worker through a theodolite at a construction site", "id": "1862131412"}, {"image_id": "216105776", "caption": "Field of canola and cloudy sky", "id": "216105776"}, {"image_id": "1873340963", "caption": "Tranquil river surrounded by green trees and grass", "id": "1873340963"}, {"image_id": "1844731772", "caption": "Medium shot of a happy elderly couple sitting in a rowboat on a lake surrounded by tall grass", "id": "1844731772"}, {"image_id": "1869314846", "caption": "Wide shot through an office window with business people shaking hands with each other", "id": "1869314846"}, {"image_id": "1865986619", "caption": "Horizontal profile shot of a young boy in swimming shorts letting go of a rope swing above the lake with copy space", "id": "1865986619"}, {"image_id": "1586682860", "caption": "Senior Male Judging Fuschia Plant At Flower Show", "id": "1586682860"}, {"image_id": "1869041636", "caption": "Elevated rear view of a smiling senior couple driving in a convertible car along coastal road at the western cape in south Africa", "id": "1869041636"}, {"image_id": "1840561337", "caption": "High angle shot of workers with a clipboard and digital tablet having a discussion among boxes laid on conveyor belts at a distribution warehouse", "id": "1840561337"}, {"image_id": "1587997037", "caption": "Close up of wine glasses touching", "id": "1587997037"}, {"image_id": "1578946934", "caption": "Portrait of a young man", "id": "1578946934"}, {"image_id": "1588020107", "caption": "Sun shining over tree in snow Bavaria Germany", "id": "1588020107"}, {"image_id": "1590359501", "caption": "Scenic view of Garachico Tenerife Canary Islands Spain", "id": "1590359501"}, {"image_id": "1869314531", "caption": "Woman in swimwear sitting at the shore of a sunny beach with her feet in the water", "id": "1869314531"}, {"image_id": "1590206117", "caption": "Close up of electric meter", "id": "1590206117"}, {"image_id": "1859176985", "caption": "Vertical shot of engineers in multicolored uniforms assembling a passenger jet at a hangar", "id": "1859176985"}, {"image_id": "1277230664", "caption": "Doctor and nurse wearing scrubs looking at notes in hospital corridor", "id": "1277230664"}, {"image_id": "1839588356", "caption": "Portrait of a joyous family of four walking in waves on a beach with hand in hand on a sunny day with copy space", "id": "1839588356"}, {"image_id": "1571580875", "caption": "A man meditating by a pool", "id": "1571580875"}, {"image_id": "1710181409", "caption": "Business people feet under a desk", "id": "1710181409"}, {"image_id": "1859177120", "caption": "Engineers with a toolbox using a digital tablet next to the engine of a passenger jet at a hangar", "id": "1859177120"}, {"image_id": "1813180589", "caption": "Two female teenagers in the trial room discussing over a bunch of outfits", "id": "1813180589"}, {"image_id": "1571687333", "caption": "Town Hall Ravensburg Baden Wurttemberg Germany", "id": "1571687333"}, {"image_id": "1297790012", "caption": "Close up of a girl holding head of cabbage in vegetable garden", "id": "1297790012"}, {"image_id": "1852927010", "caption": "Horizontal profile shot of a baby girl looking at a toddler boy besides on sofa with toys", "id": "1852927010"}, {"image_id": "1572536705", "caption": "A bride and groom dancing", "id": "1572536705"}, {"image_id": "1297775231", "caption": "Businessman and businesswoman having informal meeting in office", "id": "1297775231"}, {"image_id": "1588014299", "caption": "Businessman leaning on wall under world time zone clocks", "id": "1588014299"}, {"image_id": "1852926914", "caption": "Vertical head and shoulder shot of a smiling baby boy with hand to the mouth", "id": "1852926914"}, {"image_id": "1570405532", "caption": "Roots of Tetrameles Nudiflora in Ta Prohm temple Angkor Siem Reap Cambodia", "id": "1570405532"}, {"image_id": "1590358562", "caption": "Boy magician on tight rope", "id": "1590358562"}, {"image_id": "1852938179", "caption": "Horizontal head and shoulder portrait of a joyous brother and sister embracing in driveway with parents sitting in back of car in background", "id": "1852938179"}, {"image_id": "1277247644", "caption": "Teenage girl in reclining leather armchair at home using mobile phone", "id": "1277247644"}, {"image_id": "405737462", "caption": "Waste paper scattered around a bin", "id": "405737462"}, {"image_id": "1572536708", "caption": "A young girl holding a bunch of balloons", "id": "1572536708"}, {"image_id": "1878695879", "caption": "Medium shot of a high school student dribbling a basketball in a gym glass", "id": "1878695879"}, {"image_id": "1590360704", "caption": "Close up of couple hugging", "id": "1590360704"}, {"image_id": "1587983375", "caption": "A young woman applying face cream", "id": "1587983375"}, {"image_id": "1859178434", "caption": "Engineer with a tool box walking alongside a passenger jet at a hangar", "id": "1859178434"}, {"image_id": "1864651058", "caption": "Young man sitting on a deck chair and working on his laptop on the balcony with his wife standing behind him in front of the sea", "id": "1864651058"}, {"image_id": "1586736506", "caption": "Portrait Of Bricklayer Working On Construction Site", "id": "1586736506"}, {"image_id": "1840648082", "caption": "A wide horizontal shot of a Truck driver standing near a semi truck holding a delivery package on a background of a factory warehouse at a loading dock", "id": "1840648082"}, {"image_id": "1587839444", "caption": "Close up of broken dish on floor", "id": "1587839444"}, {"image_id": "1576777112", "caption": "A teenage boy working on a laptop in a park", "id": "1576777112"}, {"image_id": "1581275180", "caption": "Smiling man on road trip with motorbike holding pannier bags at camera", "id": "1581275180"}, {"image_id": "1840559663", "caption": "Portrait of smiling worker in front of steel tubes in warehouse", "id": "1840559663"}, {"image_id": "1590220544", "caption": "Boy lying on rattan sofa", "id": "1590220544"}, {"image_id": "1878688646", "caption": "Back shot of a Technician Working on a Laptop In the secured data center", "id": "1878688646"}, {"image_id": "1859353367", "caption": "Spanish flag fluttering with cathedral in background Seville Spain", "id": "1859353367"}, {"image_id": "1587114347", "caption": "Portrait woman leaning on folded arms Havana Cuba", "id": "1587114347"}, {"image_id": "1908116630", "caption": "Surfer carrying surfboard wading out to sea", "id": "1908116630"}, {"image_id": "1857301733", "caption": "Vertical shot of two businesswomen by car and airplane on a runway at the airport", "id": "1857301733"}, {"image_id": "1868714408", "caption": "A frontal Close up portrait of an expert scientist in a clean suit", "id": "1868714408"}, {"image_id": "1588025624", "caption": "Four business colleagues holding a meeting one talking on a mobile phone", "id": "1588025624"}, {"image_id": "1571660534", "caption": "Portrait of a young woman", "id": "1571660534"}, {"image_id": "1587137231", "caption": "A group of doctor's conversing in office setting", "id": "1587137231"}, {"image_id": "1843610627", "caption": "Vertical full length portrait of a joyous couple with a trolley full of stuff while shopping in an hardware store", "id": "1843610627"}, {"image_id": "1869314786", "caption": "Father lying on the ground while holding his daughter up like a plane in an autumn park", "id": "1869314786"}, {"image_id": "1570154891", "caption": "Tropical garden near Zanzibar City Zanzibar Tanzania Africa", "id": "1570154891"}, {"image_id": "1588010654", "caption": "Businesspeople working with businessman cheering in background", "id": "1588010654"}, {"image_id": "1852646636", "caption": "Modern medical equipment and advanced computer monitors installed in a hospital room", "id": "1852646636"}, {"image_id": "1866088610", "caption": "Female customer making a purchase at the pharmacy counter", "id": "1866088610"}, {"image_id": "1840649672", "caption": "A smiling electrician holding a drill and cable spool with an electrician on a ladder in the background", "id": "1840649672"}, {"image_id": "1869278909", "caption": "Vertical shot of worker with clipboard standing on a platform examining potatoes on a conveyor belt in the factory", "id": "1869278909"}, {"image_id": "1297776590", "caption": "Loving senior couple hugging outdoors with focus on hands", "id": "1297776590"}, {"image_id": "1587056555", "caption": "Three young people in office setting", "id": "1587056555"}, {"image_id": "1722076466", "caption": "Teenagers enjoying the sun on skiing holiday Tirol Austria Europe", "id": "1722076466"}, {"image_id": "1570357052", "caption": "The Smile of Angkor Bayon Temple Angkor Thom Siem Reap Cambodia", "id": "1570357052"}, {"image_id": "216587576", "caption": "Trees and field in snow covered winter landscape", "id": "216587576"}, {"image_id": "1844194106", "caption": "Vertical shot of a wheat field on a bright sunny day with a background of blue sky with clouds", "id": "1844194106"}, {"image_id": "1587982706", "caption": "Low angle view of pipes on organ", "id": "1587982706"}, {"image_id": "1859339825", "caption": "A Close up shot of a young blonde girl's hand performing experiment in a beaker in a school chemistry laboratory", "id": "1859339825"}, {"image_id": "1277252000", "caption": "Businesswoman on trip pulling suitcase and using mobile phone", "id": "1277252000"}, {"image_id": "1852959650", "caption": "Senior couple hiking and talking to each other on a hill", "id": "1852959650"}, {"image_id": "1576746137", "caption": "mother and son messing about sitting on jetty", "id": "1576746137"}, {"image_id": "1670346359", "caption": "Businesswoman working outdoors on laptop checking text messages", "id": "1670346359"}, {"image_id": "1869907973", "caption": "Overhead shot of businesspeople holding large jigsaw puzzle pieces in their hand denoting teamwork", "id": "1869907973"}, {"image_id": "1843609253", "caption": "Vertical shot of a salesman s hand handing the keys to a joyous man of a new red convertible in a showroom", "id": "1843609253"}, {"image_id": "1859334704", "caption": "A wide shot of a sunrise in a blue sky over a beautiful valley covered with fog", "id": "1859334704"}, {"image_id": "1844190614", "caption": "Close up shot of a male gymnast kissing gold medal around his neck", "id": "1844190614"}, {"image_id": "216348149", "caption": "Sun setting in sky with clouds", "id": "216348149"}, {"image_id": "1572509090", "caption": "A man splashing in the sea", "id": "1572509090"}, {"image_id": "1670340668", "caption": "Portrait of parents at wedding with woman holding roses", "id": "1670340668"}, {"image_id": "1590161795", "caption": "Boat on water with buildings in background Marciana Marina Elba Tuscany Italy", "id": "1590161795"}, {"image_id": "1590353468", "caption": "Portrait of young woman in hat", "id": "1590353468"}, {"image_id": "1586684429", "caption": "Farmer Watching As Field Is Harrowed By Tractor", "id": "1586684429"}, {"image_id": "1587849824", "caption": "High angle view of beach at Bay of Biodola Island of Elba Tuscany Italy", "id": "1587849824"}, {"image_id": "1590220499", "caption": "High angle view of boy eating chocolate marshmallow on rattan sofa", "id": "1590220499"}, {"image_id": "1868714348", "caption": "An expert engineer working at the electrical test bench next to an oscilloscope", "id": "1868714348"}, {"image_id": "1277240063", "caption": "Young man eating fresh green apple as healthy snack outdoors at camera", "id": "1277240063"}, {"image_id": "1586683733", "caption": "Close up Of Senior Man Watering Flowers In Beautiful Cottage Garden", "id": "1586683733"}, {"image_id": "1855886183", "caption": "Horizontal profile shot of a bridesmaid adjusting a bride's dress smiling besides the window", "id": "1855886183"}, {"image_id": "1578239030", "caption": "A mid adult woman relaxing in an armchair", "id": "1578239030"}, {"image_id": "1588019078", "caption": "Cars in underground parking lot", "id": "1588019078"}, {"image_id": "1581271655", "caption": "Father pushing son in wheelbarrow in garden sitting on autumn leaves", "id": "1581271655"}, {"image_id": "1590347597", "caption": "Businessmen looking through binoculars outdoors", "id": "1590347597"}, {"image_id": "1874805098", "caption": "Girl 9 11 playing on trampoline in garden low section", "id": "1874805098"}, {"image_id": "1590358559", "caption": "Young angel girl in mid air", "id": "1590358559"}, {"image_id": "1570537148", "caption": "Boat on Breves Channels Brazil", "id": "1570537148"}, {"image_id": "1864651007", "caption": "Young man sitting on the couch with his son and daughter and making a sketch with a pencil in hand", "id": "1864651007"}, {"image_id": "1862131448", "caption": "A portrait shot of a senior businesswoman in a black suit standing with her arms crossed and smiling at camera with a man standing in background", "id": "1862131448"}, {"image_id": "1586672651", "caption": "Mature couple walking through snow in Flevoland The Netherlands", "id": "1586672651"}, {"image_id": "1590359507", "caption": "View to Garachico at sunset Tenerife Canary Islands Spain", "id": "1590359507"}, {"image_id": "1586736467", "caption": "Worker Supervising Loading Of Containers At Port", "id": "1586736467"}, {"image_id": "1590327437", "caption": "Fortress of Nizwa with minaret in background Ad Dakhiliyah Oman", "id": "1590327437"}, {"image_id": "1578930485", "caption": "Four business colleagues holding a business meeting", "id": "1578930485"}, {"image_id": "1846708154", "caption": "A vertical view of a mid adult surgeon under selective focus wearing surgical loupe in the operating room", "id": "1846708154"}, {"image_id": "1766915472", "caption": "Woman helping boyfriend do handstand in park", "id": "1766915472"}, {"image_id": "1817411673", "caption": "Vertical shot of a young male with a backpack helping a female climb a rock with the sky in the background", "id": "1817411673"}, {"image_id": "1297790066", "caption": "Smiling family lying in tent on camping trip at camera", "id": "1297790066"}, {"image_id": "1581299096", "caption": "cutout of baseball team in blue uniforms celebrating", "id": "1581299096"}, {"image_id": "1665810884", "caption": "Grandfather and grandson doing diy decorating in the home", "id": "1665810884"}, {"image_id": "1587032267", "caption": "High angle view of Swiss franc", "id": "1587032267"}, {"image_id": "1811160932", "caption": "A landscape shot of sunset at horizon over ocean", "id": "1811160932"}, {"image_id": "1813172246", "caption": "Smiling girl inside a pit at the beach playing with sand looks at the camera", "id": "1813172246"}, {"image_id": "1716617837", "caption": "Family with dog on beach", "id": "1716617837"}, {"image_id": "1846771055", "caption": "Aerial view of tourists sitting under umbrellas on a sunny beach in Cape Town South Africa", "id": "1846771055"}, {"image_id": "1572512429", "caption": "Young girl holding box portrait", "id": "1572512429"}, {"image_id": "1590314078", "caption": "Close up of hiker s feet Kleinwalsertal Allgau Germany", "id": "1590314078"}, {"image_id": "1567876805", "caption": "Fishing boat in harbor of Marsaxlokk Malta", "id": "1567876805"}, {"image_id": "1567878509", "caption": "Wooden house with garden in Davos Grisons Switzerland", "id": "1567878509"}, {"image_id": "1864637657", "caption": "Horizontal profile shot of a teenage couple sitting over a rock by the water's edge on the beach", "id": "1864637657"}, {"image_id": "1664820515", "caption": "Man interviewing a young businesswoman", "id": "1664820515"}, {"image_id": "1878871115", "caption": "A full length vertical view of school children standing and observantly listening to biology teacher as he explains the model of a human skeleton", "id": "1878871115"}, {"image_id": "1844731643", "caption": "Senior engineer speaking on the telephone at the control desk with a young engineer working in the background in the control room of a nuclear power station", "id": "1844731643"}, {"image_id": "1587990323", "caption": "Family with estate agent outside property for sale", "id": "1587990323"}, {"image_id": "1588006856", "caption": "A woman diving into a pool", "id": "1588006856"}, {"image_id": "1813172240", "caption": "Close up of two girls listening to MP3 player with shared earphones and laughing away", "id": "1813172240"}, {"image_id": "1590053357", "caption": "A woman relaxing in a pool", "id": "1590053357"}, {"image_id": "1590079862", "caption": "Baton passing between relay runners", "id": "1590079862"}, {"image_id": "1297725731", "caption": "Active mature man standing in woods on hike through countryside", "id": "1297725731"}, {"image_id": "1578226835", "caption": "A young woman standing in a gym", "id": "1578226835"}, {"image_id": "1844766209", "caption": "Portrait of a happy family sitting on a small wooden bridge over the stream", "id": "1844766209"}, {"image_id": "1766920434", "caption": "Old sister applying lip gloss on younger sister", "id": "1766920434"}, {"image_id": "1587642932", "caption": "View to the garden of the Summer Residence of Napoleon Bonaparte in Elba Tuscany Italy", "id": "1587642932"}, {"image_id": "1576777307", "caption": "A climber climbing a rock face", "id": "1576777307"}, {"image_id": "1590076856", "caption": "Two business colleagues looking at laptop smiling", "id": "1590076856"}, {"image_id": "1844724179", "caption": "Medium wide shot of a worker smiling at the camera while scanning a box at the production line of a distribution warehouse", "id": "1844724179"}, {"image_id": "1590361106", "caption": "Close up of assorted US paper currency", "id": "1590361106"}, {"image_id": "1590201227", "caption": "Colonial age residential houses Key West Florida United States", "id": "1590201227"}, {"image_id": "1586725778", "caption": "Businesswoman Mother On Phone With Children Outside Office", "id": "1586725778"}, {"image_id": "1587032270", "caption": "High angle view of Swiss currency", "id": "1587032270"}, {"image_id": "1865986811", "caption": "View through the bushes of a father and son jumping off a jetty into the lake at sunset as the daughter and mother cheers on", "id": "1865986811"}, {"image_id": "1665809573", "caption": "Woman eating chips on sofa", "id": "1665809573"}, {"image_id": "1725907826", "caption": "A young woman on a bicycle speaking on a mobile phone", "id": "1725907826"}, {"image_id": "1670340671", "caption": "Bride and groom hugging at wedding as guests throw confetti", "id": "1670340671"}, {"image_id": "1588002842", "caption": "A woman holding a flower", "id": "1588002842"}, {"image_id": "1578225329", "caption": "A mid adult woman holding a bag full of mixed nuts", "id": "1578225329"}, {"image_id": "1874366402", "caption": "Blonde girl walking beside mother side view midsection Close up", "id": "1874366402"}, {"image_id": "1570559111", "caption": "Pavement in front of Monumento dos Continentes Manaus Amazonas Amazon River Brazil", "id": "1570559111"}, {"image_id": "1851481529", "caption": "Close up of a woman spraying water on the bonnet to wash off the soap", "id": "1851481529"}, {"image_id": "1590065399", "caption": "Portrait of a young girl leaning against a metallic wall", "id": "1590065399"}, {"image_id": "1846771040", "caption": "Aerial view of Noordhoek Beach Cape Town South Africa", "id": "1846771040"}, {"image_id": "1852965386", "caption": "Close up shot of the hands of a nail technician applying polish to a woman's fingernails", "id": "1852965386"}, {"image_id": "1839580637", "caption": "Businesspeople posing in suits with selective focus", "id": "1839580637"}, {"image_id": "1590317615", "caption": "Palm Tree with Hammock on beach Mahe Seychelles", "id": "1590317615"}, {"image_id": "1590341225", "caption": "Young woman holding soccer balls", "id": "1590341225"}, {"image_id": "1590183779", "caption": "Tropical plants on Lifou Island Loyalty Islands New Caledonia Overseas Territory of France", "id": "1590183779"}, {"image_id": "1590329693", "caption": "Capitol and flag of Cuba Capitolio Havana Cuba", "id": "1590329693"}, {"image_id": "1878695750", "caption": "High school student cheating over a classmate s shoulder", "id": "1878695750"}, {"image_id": "1590323561", "caption": "Landscape at Jebel Harim mountain Musandam peninsula exclave of Oman", "id": "1590323561"}, {"image_id": "1585874003", "caption": "View to Doerflein z Mutt village Zmutt Zermatt Valais Switzerland Europe", "id": "1585874003"}, {"image_id": "1859181137", "caption": "Wide shot of a man helping a female rock climber ascend a rock face", "id": "1859181137"}, {"image_id": "1585868897", "caption": "Seaside with Cruiser in the background Hornsund Spitsbergen Svalbard Norway Europe", "id": "1585868897"}, {"image_id": "1578205946", "caption": "A young woman hula hooping in a rape seed field", "id": "1578205946"}, {"image_id": "1817411529", "caption": "Portrait of a father reading book on sofa at home with a boy changing TV channel and girl playing a video game", "id": "1817411529"}, {"image_id": "1297781582", "caption": "Engineers discussing plans standing next to large solar panels", "id": "1297781582"}, {"image_id": "1571337425", "caption": "Rundown house in Henningsvaer Lofoten Nordland Norway", "id": "1571337425"}, {"image_id": "1590216794", "caption": "Senior man lying in grass with motorcycle and mountains in background", "id": "1590216794"}, {"image_id": "1586684582", "caption": "Fishmonger In Fresh Fish Department Of Supermarket", "id": "1586684582"}, {"image_id": "1297790018", "caption": "Grandfather and granddaughter measuring marrow in vegetable garden", "id": "1297790018"}, {"image_id": "1277234690", "caption": "Active senior man with gym bag over shoulder by stairs at camera", "id": "1277234690"}, {"image_id": "1843605758", "caption": "Vertical shot of two workers in hard hats holding solar panel beneath a wind turbine on a bright sunny day", "id": "1843605758"}, {"image_id": "1587140855", "caption": "Portrait of a young woman playing a violin violin built in 1770 by Paulus Castello Genova", "id": "1587140855"}, {"image_id": "1587990356", "caption": "Woman writing a diary or journal relaxing at home", "id": "1587990356"}, {"image_id": "1571337404", "caption": "The Seven Sisters Alsten Island Helgeland Nordland Norway", "id": "1571337404"}, {"image_id": "1585876535", "caption": "View from beach to the historic town of Lindos with its acropolis Lindos Rhodes Dodecanese Greece Europe", "id": "1585876535"}, {"image_id": "1766915469", "caption": "Man helping girlfriend do handstand in park", "id": "1766915469"}, {"image_id": "216587585", "caption": "Trees and field in snow covered winter landscape", "id": "216587585"}, {"image_id": "1297792514", "caption": "Farmer with combine harvester in harvested farm field smiling at camera", "id": "1297792514"}, {"image_id": "1865943857", "caption": "Vertical shot of a senior woman loading a golf bag into the boot of a parked car on the driveway", "id": "1865943857"}, {"image_id": "1590363329", "caption": "Beach at sunset Anse Intendance Mahe Seychelles", "id": "1590363329"}, {"image_id": "1588008386", "caption": "Couple lying on snow looking at tiny snowman with cap", "id": "1588008386"}, {"image_id": "1590338969", "caption": "Young girls sitting outdoors portrait", "id": "1590338969"}, {"image_id": "1571687297", "caption": "View to Ronne Bornholm Island Denmark", "id": "1571687297"}, {"image_id": "1588014656", "caption": "Close up of a woman's mouth smiling", "id": "1588014656"}, {"image_id": "1586666900", "caption": "Sunset and clouds view through window over the wing and jet engine of a passenger plane", "id": "1586666900"}, {"image_id": "1590361196", "caption": "Woman practicing Pilates on ball", "id": "1590361196"}, {"image_id": "1567881305", "caption": "Diverse group of people standing together and cupping ears", "id": "1567881305"}, {"image_id": "1878688574", "caption": "Surfer With prosthetic Leg standing on the Beach holding the surfboard", "id": "1878688574"}, {"image_id": "1851487421", "caption": "Overhead shot of tourists at the shore of a sunny beach with waves", "id": "1851487421"}, {"image_id": "216582812", "caption": "Rooftops and river in idyllic village Bourdeilles Dordogne France", "id": "216582812"}, {"image_id": "1297780919", "caption": "Female teacher in adult education computer lesson smiling at camera", "id": "1297780919"}, {"image_id": "1843605509", "caption": "Young couple in sportswear standing with mountain bicycle preparing for the bicycle ride in the forest", "id": "1843605509"}, {"image_id": "1839589145", "caption": "Full length portrait of a young man running outdoor on street looking at the camera", "id": "1839589145"}, {"image_id": "1586736530", "caption": "Portrait Of Business Team Working In Modern Office", "id": "1586736530"}, {"image_id": "1865943548", "caption": "Vertical shot of a young couple standing at the helm of sailing boat out at sea", "id": "1865943548"}, {"image_id": "1571333384", "caption": "View to Engabreen with glacier Svartisen Nordland Norway", "id": "1571333384"}, {"image_id": "1304265932", "caption": "Mature man playing golf driving buggy on course at camera", "id": "1304265932"}, {"image_id": "216351173", "caption": "Clouds in sunset sky", "id": "216351173"}, {"image_id": "1866112205", "caption": "Surfer riding over the crest of a large wave at the sea", "id": "1866112205"}, {"image_id": "1587856106", "caption": "View of a couple dancing", "id": "1587856106"}, {"image_id": "1586684942", "caption": "Farmer Standing Next To Lorry Loaded With Straw Bales", "id": "1586684942"}, {"image_id": "216573359", "caption": "Tree in sunny field", "id": "216573359"}, {"image_id": "1878871253", "caption": "A school teacher with blonde hair working on a microscope while teaching her students in science laboratory", "id": "1878871253"}, {"image_id": "1840561301", "caption": "Vertical top shot of a group of workers holding a digital tablet clipboard and box and smiling at the camera while standing among boxes laid on conveyor belts at a distribution warehouse", "id": "1840561301"}, {"image_id": "1576780175", "caption": "A businessman waiting in an office lobby or airport concourse with a suitcase", "id": "1576780175"}, {"image_id": "1578216593", "caption": "A young woman standing in the falling snow smiling", "id": "1578216593"}, {"image_id": "1571335163", "caption": "Stockfish dried cod hanging on wooden racks in Gjesvaer Nordkapp Norway", "id": "1571335163"}, {"image_id": "1864613927", "caption": "Little girl applying toothpaste on her toothbrush in the bathroom while smiling at the camera", "id": "1864613927"}, {"image_id": "1710319457", "caption": "A closed woman's eye and eyelashes", "id": "1710319457"}, {"image_id": "1571330813", "caption": "Mid adult couple in hammock kissing", "id": "1571330813"}, {"image_id": "1859144684", "caption": "Vertical shot of a woman leaning on a mountain bike in the woods and smiling at the camera", "id": "1859144684"}, {"image_id": "1588000721", "caption": "Female pharmacist standing next to shelves of medication", "id": "1588000721"}, {"image_id": "1578942776", "caption": "A woman relaxing in a deck chair", "id": "1578942776"}, {"image_id": "1587110885", "caption": "Close up of one head of romaine lettuce", "id": "1587110885"}, {"image_id": "1766923860", "caption": "Detail of father giving daughter car keys and money", "id": "1766923860"}, {"image_id": "1816749549", "caption": "Wide shot of a senior couple in cycling helmets walking side by side through wood with their bicycles", "id": "1816749549"}, {"image_id": "1725720734", "caption": "Two young girls holding hands on street in Den Haag The Netherlands", "id": "1725720734"}, {"image_id": "1297776776", "caption": "Colorful field of beautiful wild summer flowers", "id": "1297776776"}, {"image_id": "1570311758", "caption": "Ice hockey players on Lake Woerthsee near Steinebach Bavaria Germany", "id": "1570311758"}, {"image_id": "216777662", "caption": "Close up of water splashing into swimming pool", "id": "216777662"}, {"image_id": "1297787465", "caption": "Active senior couple exercising with resistance bands looking at camera", "id": "1297787465"}, {"image_id": "1586689166", "caption": "Portrait of Technician worker looking at camera in solar panel factory", "id": "1586689166"}, {"image_id": "1840650098", "caption": "A close up shot of a man in coveralls casually standing on a ladder and looking up", "id": "1840650098"}, {"image_id": "1855878203", "caption": "Rear view of a female athlete running on the race track at an athletics competition on a bright sunny day", "id": "1855878203"}, {"image_id": "1862081342", "caption": "Businesswoman and Businessman with digital tablet standing in front of a private jet", "id": "1862081342"}, {"image_id": "1588002794", "caption": "Woman on her mobile phone at a supermarket", "id": "1588002794"}, {"image_id": "1868716631", "caption": "A medium shot of an engineer using a tablet on a big solar panel", "id": "1868716631"}, {"image_id": "1590339005", "caption": "Young happy girl playing outdoors", "id": "1590339005"}, {"image_id": "1878694235", "caption": "Vertical shot of a gym teacher standing next to a whiteboard and teaching badminton to high school students in a gym", "id": "1878694235"}, {"image_id": "1839589319", "caption": "Overhead portrait of a family of four asleep in bed under a sheet", "id": "1839589319"}, {"image_id": "1586672552", "caption": "Young girl flying kite on winter day near wind turbines", "id": "1586672552"}, {"image_id": "1813172156", "caption": "Close up shot of a businesswoman sitting at pavement cafe table with laptop and using a mobile phone", "id": "1813172156"}, {"image_id": "1722076475", "caption": "Teenage boy on slope on skiing holiday Tirol Austria Europe", "id": "1722076475"}, {"image_id": "1571663795", "caption": "A businesswoman sitting in a gym using a mobile phone", "id": "1571663795"}, {"image_id": "1586683493", "caption": "Senior Man Working In Vegetable Garden", "id": "1586683493"}, {"image_id": "1663816031", "caption": "Woman shopping surrounded by shoes", "id": "1663816031"}, {"image_id": "1590216545", "caption": "Low angle view of man on motorcycle", "id": "1590216545"}, {"image_id": "1862121647", "caption": "A vertical portrait of a smiling businessman in a suit with selective focus using a cell phone for messaging", "id": "1862121647"}, {"image_id": "1586684042", "caption": "Senior Couple Planting Flowers In Cottage Garden", "id": "1586684042"}, {"image_id": "1571688776", "caption": "View to the Asian part of Istanbul and the illuminated Bosphorus Bridge Turkey", "id": "1571688776"}, {"image_id": "1847201678", "caption": "A vertical view of a small boy during a lunch break at school eating a doughnut with cream all over his face", "id": "1847201678"}, {"image_id": "1843588208", "caption": "Smiling woman with straight brown hair wearing a black coat and black stockings is carrying a black bag and a green handbag over her shoulders", "id": "1843588208"}, {"image_id": "1766903742", "caption": "Father and son cooking fish on campfire near remote lake", "id": "1766903742"}, {"image_id": "1578930494", "caption": "Four business colleagues holding a business meeting", "id": "1578930494"}, {"image_id": "1590363905", "caption": "View over palm trees to Anse Intendance Mahe Seychelles", "id": "1590363905"}, {"image_id": "1710181358", "caption": "mother and daughter lying on a blanket", "id": "1710181358"}, {"image_id": "1878694184", "caption": "Wide shot of a gym teacher teaching basketball to a group of high school students in a gym", "id": "1878694184"}, {"image_id": "1859233541", "caption": "An electrician using a screwdriver for fitting the energy saving light bulb on the ceiling", "id": "1859233541"}, {"image_id": "1844767097", "caption": "Portrait businesswoman talking on phone at an airport while pulling her travel suitcase along", "id": "1844767097"}, {"image_id": "1590044063", "caption": "Two teenage girls in a bedroom", "id": "1590044063"}, {"image_id": "1664813837", "caption": "View of young boy holding his knees looking away", "id": "1664813837"}, {"image_id": "1576739279", "caption": "Little girl knocking at the front door of a house", "id": "1576739279"}, {"image_id": "1277230511", "caption": "Mature couple carrying windmill and basket on walk through countryside", "id": "1277230511"}, {"image_id": "1864637696", "caption": "Horizontal shot of a group of teenagers sitting on a sand dune at the beach on a sunny day", "id": "1864637696"}, {"image_id": "1587996017", "caption": "Senior woman in a swimsuit and swimming hat at the beach looking out to sea", "id": "1587996017"}, {"image_id": "1590056663", "caption": "A mother applying sunscreen on her daughter", "id": "1590056663"}, {"image_id": "1588017305", "caption": "Rows of chairs in Saint Peters Square Rome Italy", "id": "1588017305"}, {"image_id": "1590329636", "caption": "Flag of Kingdom of Bahrain blowing in wind", "id": "1590329636"}, {"image_id": "1725720938", "caption": "Woman leaning chin on hand", "id": "1725720938"}, {"image_id": "1586723807", "caption": "Romantic Young Couple Kissing In Countryside Together", "id": "1586723807"}, {"image_id": "1278037145", "caption": "Family winter beach holiday with parents swinging daughter between them as they walk along sand", "id": "1278037145"}, {"image_id": "1571358260", "caption": "View to St Martin Latsch Vinschgau Trentino Alto Adige South Tyrol Italy", "id": "1571358260"}, {"image_id": "1859143166", "caption": "Vertical shot of an elderly couple in winter jackets fishing at the shore of a sunny beach", "id": "1859143166"}, {"image_id": "1766923923", "caption": "Father and son crossing stream", "id": "1766923923"}, {"image_id": "1586683718", "caption": "Close up Of Senior Man Watering Flowers In Beautiful Cottage Garden", "id": "1586683718"}, {"image_id": "1297792583", "caption": "Man relaxing fly fishing at on bank of river in countryside", "id": "1297792583"}, {"image_id": "1578207224", "caption": "A young woman sitting at a garden bench drinking a hot beverage smiling", "id": "1578207224"}, {"image_id": "1844764208", "caption": "Close up of a cargo box against the background of a modern building", "id": "1844764208"}, {"image_id": "1587994169", "caption": "A couple relaxing by a pool", "id": "1587994169"}, {"image_id": "1844764439", "caption": "Little boy lying on his father over a wooden bridge and they are trying to catch fish from the stream below", "id": "1844764439"}, {"image_id": "1840562471", "caption": "Tilted Close up shot of a technician in safety glasses using measurement probe on the assembly line of a steel bearing manufacturing plant", "id": "1840562471"}, {"image_id": "1865943617", "caption": "Vertical shot of father and son standing at the helm of a sailing boat with boy looking through binoculars out at sea on a sunny day", "id": "1865943617"}, {"image_id": "1587142622", "caption": "Detail view of the dollar sign on a keyboard", "id": "1587142622"}, {"image_id": "1578915653", "caption": "Portrait of young woman looking fed up", "id": "1578915653"}, {"image_id": "1590217499", "caption": "Man covering ears with hands", "id": "1590217499"}, {"image_id": "1873343708", "caption": "Brewery Workers Checking Fermentation Process In Steel Vat", "id": "1873343708"}, {"image_id": "1813172102", "caption": "An angled shot of a woman in crash helmet riding moped in city street in Spain Barcelona", "id": "1813172102"}, {"image_id": "1587128807", "caption": "Young friends having snowball fight in forest", "id": "1587128807"}, {"image_id": "1846400639", "caption": "A full length vertical view of a vast tranquil field of yellow buttercup flowers under the bright blue sky and a massive white cloud diffusing the harsh sunlight", "id": "1846400639"}, {"image_id": "1586671511", "caption": "Rear view of two deckchairs beside a lake with a straw hat", "id": "1586671511"}, {"image_id": "1860726794", "caption": "Close up portrait of a woman in wetsuit with surfboard posing for the camera", "id": "1860726794"}, {"image_id": "1864640822", "caption": "Male teacher pointing at a student with raised hand in a classroom with selective focus on the hand in the foreground", "id": "1864640822"}, {"image_id": "1590047591", "caption": "Four teenage friends playing a computer game", "id": "1590047591"}, {"image_id": "1878688604", "caption": "Man With prosthetic Leg Running Along the Beach", "id": "1878688604"}, {"image_id": "1570388285", "caption": "Pre Rup Angkor Siem Reap Cambodia", "id": "1570388285"}, {"image_id": "1840555073", "caption": "Man with brochure looking into hatchback of car in a car dealership showroom", "id": "1840555073"}, {"image_id": "1590031937", "caption": "Portrait of a man using a laptop computer", "id": "1590031937"}, {"image_id": "1576782539", "caption": "A male nude hands over face", "id": "1576782539"}, {"image_id": "1297750277", "caption": "Studio shot of young man looking away from camera against grey background", "id": "1297750277"}, {"image_id": "1859347433", "caption": "A Close up portrait shot of a young blonde girl planning wood in a vocational school", "id": "1859347433"}, {"image_id": "1587141254", "caption": "View of the ocean seen through a splashing lens", "id": "1587141254"}, {"image_id": "1585870481", "caption": "Mature woman relaxing in chair on veranda", "id": "1585870481"}, {"image_id": "1586682941", "caption": "Exhibitor With Prize Winning Flower At Agricultural Show", "id": "1586682941"}, {"image_id": "1570161005", "caption": "Aldabra giant tortoise Aldabra Atoll Seychelles", "id": "1570161005"}, {"image_id": "1576745237", "caption": "Young man styling his hair", "id": "1576745237"}, {"image_id": "1571580080", "caption": "A young woman sitting on a sun lounger", "id": "1571580080"}, {"image_id": "1297789817", "caption": "Combine harvester emptying harvested wheat grain into tractor trailer", "id": "1297789817"}, {"image_id": "1572542231", "caption": "A man about to go scuba diving", "id": "1572542231"}, {"image_id": "1576771997", "caption": "A groom kissing his bride", "id": "1576771997"}, {"image_id": "1578252488", "caption": "A young couple sitting on the grass embracing", "id": "1578252488"}, {"image_id": "1588016063", "caption": "Office workers walking around modern office building", "id": "1588016063"}, {"image_id": "216355505", "caption": "Clouds in blue sky over countryside", "id": "216355505"}, {"image_id": "1590357695", "caption": "Crane on a construction site", "id": "1590357695"}, {"image_id": "1844764262", "caption": "Vertical portrait of a happy joint family enjoying on a vacation", "id": "1844764262"}, {"image_id": "1844765960", "caption": "Smiling young woman sitting on a railing and using a digital tablet", "id": "1844765960"}, {"image_id": "1571338076", "caption": "View of Nes Vega Archipelago Sor Helgeland Helegland Nordland Norway", "id": "1571338076"}, {"image_id": "1587833804", "caption": "Scenic view of ruins Rome Italy", "id": "1587833804"}, {"image_id": "1590214550", "caption": "Close up of frozen beech leaf", "id": "1590214550"}, {"image_id": "1578225314", "caption": "A woman holding a handful of conkers", "id": "1578225314"}, {"image_id": "1840649675", "caption": "A Close up portrait shot of an electrician holding a drill and cable spool with an electrician on a ladder in the background", "id": "1840649675"}, {"image_id": "1840559828", "caption": "Worker controlling robotic machinery lifting steel fencing in manufacturing plant", "id": "1840559828"}, {"image_id": "1277278862", "caption": "Multi generation family relaxing in countryside on motorhome vacation", "id": "1277278862"}, {"image_id": "1878690233", "caption": "Chemistry teacher with a molecule model helping high school students conduct a scientific experiment", "id": "1878690233"}, {"image_id": "1586724593", "caption": "Portrait Of Young Couples Standing On Bridge Drinking Beer", "id": "1586724593"}, {"image_id": "1571664791", "caption": "A woman with a suitcase hitching a lift", "id": "1571664791"}, {"image_id": "1570340039", "caption": "View over Ramsau to Reiter Alpe near Berchtesgaden Bavaria Germany", "id": "1570340039"}, {"image_id": "1586724719", "caption": "Businessman Writing On Notes Attached To Glass Office Wall", "id": "1586724719"}, {"image_id": "1852924400", "caption": "Young female mechanic leaning against the open hood of a broken down car and looking down at diagnostic equipment and scratching her head in her garage", "id": "1852924400"}, {"image_id": "1859347268", "caption": "A portrait shot of a happy family standing in front of a large solar panel", "id": "1859347268"}, {"image_id": "1868703314", "caption": "A horizontal view of two businessmen using a laptop while working in the conference room", "id": "1868703314"}, {"image_id": "1277221451", "caption": "Smiling family relaxing in sand dunes on beach in autumn at camera", "id": "1277221451"}, {"image_id": "1572387347", "caption": "House with meadow in foreground in Val Lavizzara Ticino Switzerland", "id": "1572387347"}, {"image_id": "1590352148", "caption": "Bathing Beach at Cabo de Formentor Mallorca Spain", "id": "1590352148"}, {"image_id": "1567876850", "caption": "Male peacock Pavo cristatus displaying tail feathers", "id": "1567876850"}, {"image_id": "1297787669", "caption": "Studio cutout of happy senior man with home grown produce smiling at camera", "id": "1297787669"}, {"image_id": "1874256497", "caption": "Crane loading cargo containers on container ship at commercial dock", "id": "1874256497"}, {"image_id": "1572486329", "caption": "A senior woman standing by a sports car", "id": "1572486329"}, {"image_id": "1868714447", "caption": "A horizontal view of a scientist in lab suit examining a silicon wafer under the microscope in a clean room laboratory with selective focus", "id": "1868714447"}, {"image_id": "1860742205", "caption": "Vertical shot of a female surfer in a wetsuit on the beach with a surfboard", "id": "1860742205"}, {"image_id": "1571353247", "caption": "Ramosch Lower Engadine Grisons Switzerland", "id": "1571353247"}, {"image_id": "1873346102", "caption": "Silhouetted reflection of a woman standing on the wet portion of the beach during low tide", "id": "1873346102"}, {"image_id": "1297775348", "caption": "Man carrying smiling woman through dunes on summer beach vacation", "id": "1297775348"}, {"image_id": "1859328512", "caption": "A medium shot of a teacher watching a young boy working on a wooden airplane model in a class", "id": "1859328512"}, {"image_id": "1864651091", "caption": "Close up of a young man standing in the bathroom with shaving foam on his face and his wife hugging him from behind in the background", "id": "1864651091"}, {"image_id": "1585850105", "caption": "Cookies in form of pieces of a puzzle against white background", "id": "1585850105"}, {"image_id": "1586683775", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586683775"}, {"image_id": "1586735687", "caption": "Two Engineers Quality Checking Components In Factory", "id": "1586735687"}, {"image_id": "1590352241", "caption": "Marina and Cathedral of Palma de Mallorca at sunrise Mallorca Spain", "id": "1590352241"}, {"image_id": "1878869585", "caption": "A vertical view of a science class with Teacher practically explaining students about Solar panels on a soft background of whiteboard and colorful charts", "id": "1878869585"}, {"image_id": "1297777556", "caption": "Female high school student drawing working at desk in art lesson", "id": "1297777556"}, {"image_id": "1588012403", "caption": "Businessman standing at office window drinking a cup of coffee", "id": "1588012403"}, {"image_id": "1851401546", "caption": "Horizontal shot of a standing businessman and seated businesswoman looking at paperwork in lift on stool working on a laptop with copy space", "id": "1851401546"}, {"image_id": "1586666138", "caption": "An abstract image of yellow lilac and pink lights", "id": "1586666138"}, {"image_id": "1588015142", "caption": "Overhead view of informal business meeting at table of staff canteen", "id": "1588015142"}, {"image_id": "1844196815", "caption": "Close up shot of woman holding fresh meat and frozen broccoli in kitchen", "id": "1844196815"}, {"image_id": "1586691776", "caption": "Growers talking and inspecting ripe red vine tomatoes in greenhouse", "id": "1586691776"}, {"image_id": "1590361265", "caption": "Close up of one hundred Euro banknotes", "id": "1590361265"}, {"image_id": "1813172162", "caption": "An angled mid length shot of businesswoman in polo neck jumper and white coat walking in plaza carrying briefcase", "id": "1813172162"}, {"image_id": "1840560551", "caption": "Vertical shot of happy elderly couple looking at flowers with their granddaughter in a meadow full of wildflowers", "id": "1840560551"}, {"image_id": "1572530273", "caption": "Portrait of young couple cheek to cheek", "id": "1572530273"}, {"image_id": "1844724128", "caption": "Vertical wide shot of a worker scanning a pallet of cardboard boxes wrapped in plastic at a distribution warehouse", "id": "1844724128"}, {"image_id": "1671841082", "caption": "A young blonde woman sleeping", "id": "1671841082"}, {"image_id": "1297798640", "caption": "Two smiling young women at home looking at a computer", "id": "1297798640"}, {"image_id": "1859176898", "caption": "Engineers in uniforms inspecting the engine casing of a passenger jet at a hangar", "id": "1859176898"}, {"image_id": "1587048545", "caption": "Grandfather and grandson using a laptop outdoors", "id": "1587048545"}, {"image_id": "1297774418", "caption": "Young couple having piggyback ride on winter beach smiling at camera", "id": "1297774418"}, {"image_id": "1571668397", "caption": "A young woman jumping for joy", "id": "1571668397"}, {"image_id": "1587991700", "caption": "Female scientist examining liquid in large vial", "id": "1587991700"}, {"image_id": "1277239451", "caption": "Designers by drawing board looking at book together at camera", "id": "1277239451"}, {"image_id": "1297781579", "caption": "Engineers discussing plans standing next to large solar panels", "id": "1297781579"}, {"image_id": "1297800467", "caption": "Portrait of children pushing wheelbarrow full of autumn leaves", "id": "1297800467"}, {"image_id": "1859182757", "caption": "Wide shot of a male and female rock climber standing on top of a rock", "id": "1859182757"}, {"image_id": "1859331785", "caption": "A close up shot of an Asian nurse smiling at a patient while listening to her heartbeat with a stethoscope", "id": "1859331785"}, {"image_id": "1846038956", "caption": "A happy young couple looking at each other while selecting frames in a frame shop", "id": "1846038956"}, {"image_id": "1878695849", "caption": "Low angle shot of high school students touching hands in a huddle before a volleyball game", "id": "1878695849"}, {"image_id": "1572527786", "caption": "View from Cerro del Gallego to Urique Canyon Mexico", "id": "1572527786"}, {"image_id": "1878688562", "caption": "Surfer With a prosthetic Leg Walking On the Beach carrying a surfboard", "id": "1878688562"}, {"image_id": "1859144675", "caption": "Full shot of a woman hiking with a backpack in the woods", "id": "1859144675"}, {"image_id": "1277235458", "caption": "Young woman eating cream cake at camera", "id": "1277235458"}, {"image_id": "1586682896", "caption": "Female Baker On Stall At Country Fair", "id": "1586682896"}, {"image_id": "1866001004", "caption": "Horizontal rear view of a joyous mature couple on a road trip with the woman driving on a sunny day", "id": "1866001004"}, {"image_id": "1587654119", "caption": "Detail view of a Christmas ornament and a tea candle", "id": "1587654119"}, {"image_id": "1570160951", "caption": "Young woman dressed as angel standing by sea", "id": "1570160951"}, {"image_id": "1590053348", "caption": "Senior couple relaxing at the beach", "id": "1590053348"}, {"image_id": "1587851459", "caption": "Close up of lit candles with woman and child praying", "id": "1587851459"}, {"image_id": "1587110903", "caption": "Teenage girl with cell phone", "id": "1587110903"}, {"image_id": "1576745486", "caption": "A confident and attractive middle aged woman in white smiling", "id": "1576745486"}, {"image_id": "1844194409", "caption": "Evening sky scene with golden light from the setting sun", "id": "1844194409"}, {"image_id": "1571663798", "caption": "Portrait of a young woman", "id": "1571663798"}, {"image_id": "1859204663", "caption": "Wide shot of a happy family having a picnic in the countryside", "id": "1859204663"}, {"image_id": "1590164234", "caption": "Group of people whitewater rafting", "id": "1590164234"}, {"image_id": "1864656248", "caption": "Horizontal shot of a couple moving house with the woman besides a van leaning over a hand dolly and her husband with a pot plant in the background", "id": "1864656248"}, {"image_id": "1572381332", "caption": "Maligne Lake Jasper National Park Alberta Canada", "id": "1572381332"}, {"image_id": "1766904489", "caption": "Sports fans cheering with arms raised", "id": "1766904489"}, {"image_id": "1859349074", "caption": "A portrait shot of a teacher looking at a young boy working with a wrench on a prototype in a vocational school", "id": "1859349074"}, {"image_id": "1570298798", "caption": "A young woman wearing a woolen hat smiling", "id": "1570298798"}, {"image_id": "1588007585", "caption": "Senior couple hugging in snowy landscape", "id": "1588007585"}, {"image_id": "216108605", "caption": "Clouds in blue sky over barley field", "id": "216108605"}, {"image_id": "1571353220", "caption": "View to Ardez Lower Engadine Grisons Switzerland", "id": "1571353220"}, {"image_id": "1655771105", "caption": "A young woman yawning in front of a blue wall", "id": "1655771105"}, {"image_id": "1572530309", "caption": "Young couple on sofa looking at laptop", "id": "1572530309"}, {"image_id": "1844194400", "caption": "Cumulus sunset clouds with the sun setting down", "id": "1844194400"}, {"image_id": "1680424568", "caption": "Highly detailed 3D concept of a stock market crash monitored on a tablet showing bar graphs numbers and arrows", "id": "1680424568"}, {"image_id": "1716611657", "caption": "Illustration of bull bear and upward arrows", "id": "1716611657"}, {"image_id": "1869318149", "caption": "Combine harvester reaping wheat at a rural field alongside a tractor", "id": "1869318149"}, {"image_id": "1586685143", "caption": "Farmer In Field As Oat Crop Is Harvested", "id": "1586685143"}, {"image_id": "1844769257", "caption": "Portrait of smiling young man wearing headphones while his friends are standing behind him", "id": "1844769257"}, {"image_id": "216581234", "caption": "Meadow of grass and blooming summer flowers under blue sky", "id": "216581234"}, {"image_id": "1866091451", "caption": "Low angle shot of a hooded man operating a surveillance drone in the blue sky", "id": "1866091451"}, {"image_id": "1840562315", "caption": "Wide shot of business people and engineers with a machine part and toolbox standing in the aisle of a manufacturing plant and looking at the camera", "id": "1840562315"}, {"image_id": "1868705204", "caption": "A high angle portrait of smart businessmen and businesswomen standing together near the office desk", "id": "1868705204"}, {"image_id": "1862067377", "caption": "Close up shot of a poppy flower in a meadow full of wildflowers with the sky in the background", "id": "1862067377"}, {"image_id": "1851483824", "caption": "Wide shot of elderly couple holding hands and flying kite on a sunny beach", "id": "1851483824"}, {"image_id": "1585878731", "caption": "calving glacier in Magdalenafjorden Spitsbergen Svalbard Norway Europe arctic summer", "id": "1585878731"}, {"image_id": "1586693288", "caption": "High school student using digital tablet in classroom", "id": "1586693288"}, {"image_id": "1857297389", "caption": "Low angle view of a young female student at the desk looking out of the window", "id": "1857297389"}, {"image_id": "1857301709", "caption": "Vertical shot of a smiling businessman with hand on an airplane in the hangar", "id": "1857301709"}, {"image_id": "1590206162", "caption": "Santa Claus figurine in snow globe", "id": "1590206162"}, {"image_id": "1572528611", "caption": "Long Beaked Common Dolphin Bahia Magdalena Baja California Sur Mexico", "id": "1572528611"}, {"image_id": "1588014647", "caption": "A senior woman plucking hairs from her chin with tweezers", "id": "1588014647"}, {"image_id": "1297781651", "caption": "Large solar panels providing alternative energy supply outdoors", "id": "1297781651"}, {"image_id": "1576772045", "caption": "A senior man riding a motorbike", "id": "1576772045"}, {"image_id": "1590317528", "caption": "Scenic view of west coast of Mahe Seychelles", "id": "1590317528"}, {"image_id": "1571335133", "caption": "Lighthouse on Hornoya Island Vardo Finnmark county Norway", "id": "1571335133"}, {"image_id": "1578939221", "caption": "A couple lying in a bath", "id": "1578939221"}, {"image_id": "1578922427", "caption": "A young girl holding an Easter egg", "id": "1578922427"}, {"image_id": "1570160996", "caption": "Young man in black suit standing in barren landscape eyes closed", "id": "1570160996"}, {"image_id": "1711241162", "caption": "A Christmas place setting on a nicely set dining table", "id": "1711241162"}, {"image_id": "1811163218", "caption": "A mature woman wrap presents at a table", "id": "1811163218"}, {"image_id": "1590315305", "caption": "Stacks of assorted Euro coins", "id": "1590315305"}, {"image_id": "1590164864", "caption": "Teenage couple studying with laptop in library", "id": "1590164864"}, {"image_id": "1864660103", "caption": "Vertical shot of a senior and a young couple looking for directions in a road map seated in a field behind their car smile at the camera", "id": "1864660103"}, {"image_id": "1859349170", "caption": "A high angle portrait shot of happy teacher and students sorting recyclable objects in a classroom", "id": "1859349170"}, {"image_id": "1650246044", "caption": "Boy 7 9 and girl 8 10 sitting on doorstep smiling", "id": "1650246044"}, {"image_id": "1587839042", "caption": "Close up of wallet with Euros sticking out", "id": "1587839042"}, {"image_id": "1572537425", "caption": "Mother and son in a bowling alley", "id": "1572537425"}, {"image_id": "1711021526", "caption": "A Christmas still life of food", "id": "1711021526"}, {"image_id": "1839588581", "caption": "Three quarter length portrait of a young man running and talking over a mobile phone outdoors with copy space", "id": "1839588581"}, {"image_id": "1586727959", "caption": "Mother Washing Baby Son In Plastic Bath On Nursery Table", "id": "1586727959"}, {"image_id": "1860761720", "caption": "A full length vertical view of a smiling shepherd holding a lamb in pasture next to a flock of sheep behind the fence", "id": "1860761720"}, {"image_id": "1572521447", "caption": "Man playing the sitar Agra India South Asia", "id": "1572521447"}, {"image_id": "1586681570", "caption": "cutout Of Family With Organic Produce And Chicken", "id": "1586681570"}, {"image_id": "1590027443", "caption": "A teenage girl comforting her friend", "id": "1590027443"}, {"image_id": "1817410956", "caption": "Backside of the lower body of a female in swimwear with the sea in the background", "id": "1817410956"}, {"image_id": "1572495314", "caption": "A woman holding a shell", "id": "1572495314"}, {"image_id": "1585878797", "caption": "Atlantic Walruses Odobenus rosmarus on shore of Moffen Island Spitsbergen Svalbard Norway Europe", "id": "1585878797"}, {"image_id": "1576779983", "caption": "A woman relaxing by a pool", "id": "1576779983"}, {"image_id": "1665809801", "caption": "High angle view of woman pulling petals off flower", "id": "1665809801"}, {"image_id": "216108977", "caption": "Clouds in blue sky", "id": "216108977"}, {"image_id": "1859331758", "caption": "A medium shot of a nurse checking a patient's blood pressure in a hospital room", "id": "1859331758"}, {"image_id": "1578946052", "caption": "Three teenage friends using a computer", "id": "1578946052"}, {"image_id": "1297775372", "caption": "Low angle view of wind turbine through grass in field", "id": "1297775372"}, {"image_id": "1588015193", "caption": "A businessman in a smart suit talking on a mobile phone", "id": "1588015193"}, {"image_id": "1813178675", "caption": "Close up of a smiling woman standing on beach and looking over the shoulder", "id": "1813178675"}, {"image_id": "1844769341", "caption": "Young guy piggybacking a beautiful girl in the city", "id": "1844769341"}, {"image_id": "1868720555", "caption": "A Close up portrait shot of a young blonde woman lying upside down on a sofa and smiling at camera while talking on a phone", "id": "1868720555"}, {"image_id": "1862067344", "caption": "Vertical shot of a field full of red poppy flowers", "id": "1862067344"}, {"image_id": "1572515972", "caption": "Chive broomstick and drawing of house against white background", "id": "1572515972"}, {"image_id": "1869911579", "caption": "A portrait shot of a smiling businesswoman holding a book while standing in a corridor", "id": "1869911579"}, {"image_id": "1590361133", "caption": "Man doing push ups on beach", "id": "1590361133"}, {"image_id": "1571519090", "caption": "A young woman standing on a wooden bridge in a forest", "id": "1571519090"}, {"image_id": "1813181345", "caption": "Low angled vertical shot of candidates waiting for their interviews", "id": "1813181345"}, {"image_id": "1813180616", "caption": "Two female teenagers in the trial room hand picking an outfit", "id": "1813180616"}, {"image_id": "1874804492", "caption": "Combine harvester harvesting wheat in sunny rural field", "id": "1874804492"}, {"image_id": "1839581555", "caption": "Middle aged woman sitting on a fence and playing with her dog out in the field on cold snowy day", "id": "1839581555"}, {"image_id": "1766921538", "caption": "Girl jumping in air holding pink hat with books below", "id": "1766921538"}, {"image_id": "1766929023", "caption": "Woman sitting on outcropping over valley Kleinwalsertal Allgau Germany", "id": "1766929023"}, {"image_id": "1710413957", "caption": "Birds eye view of businessman leaving office", "id": "1710413957"}, {"image_id": "1878692699", "caption": "High school students observing a plant during a scientific experiment in a biology class", "id": "1878692699"}, {"image_id": "1840560677", "caption": "Vertical shot of a happy woman touching a flower and sitting in a meadow full of wildflowers", "id": "1840560677"}, {"image_id": "1578932849", "caption": "Woman holding a pot of moisturising cream", "id": "1578932849"}, {"image_id": "1851483962", "caption": "Happy family enjoying an outing in nature", "id": "1851483962"}, {"image_id": "1840560515", "caption": "Low angle vertical shot of a happy family with the father carrying his daughter on the shoulders while she holds onto a sunflower in a meadow full of wildflowers", "id": "1840560515"}, {"image_id": "1839588359", "caption": "Portrait of a joyous family of four walking on beach with hand in hand looking at the camera on a sunny day with copy space", "id": "1839588359"}, {"image_id": "1865894900", "caption": "Middle aged man passing a glass of orange juice to his wife who is riding a stationary bicycle in the living room", "id": "1865894900"}, {"image_id": "1859181425", "caption": "Vertical wide shot of a female rock climber rappelling down a rock face", "id": "1859181425"}, {"image_id": "1590341198", "caption": "Young woman holding Italian flag", "id": "1590341198"}, {"image_id": "1590065480", "caption": "Portrait of a female athlete resting and sitting on the running track", "id": "1590065480"}, {"image_id": "1590336119", "caption": "20 Mule Wagon at Harmony Borax Works Death Valley National Park Nevada USA", "id": "1590336119"}, {"image_id": "1588015490", "caption": "Figurines of businessmen standing on US Dollars", "id": "1588015490"}, {"image_id": "216581957", "caption": "View of snowy mountain range and blue sky", "id": "216581957"}, {"image_id": "1571327438", "caption": "Close up of young blonde girl parents in the background", "id": "1571327438"}, {"image_id": "1859182823", "caption": "Low angle vertical shot of a male rock climber holding a rappelling rope while standing on a rock", "id": "1859182823"}, {"image_id": "1859347298", "caption": "A high view wide shot of circular patterns in a farm field in Wiltshire United Kingdom", "id": "1859347298"}, {"image_id": "1844733092", "caption": "A group of girls with backpacks looking at a map and compass over a wooden fence in a green field", "id": "1844733092"}, {"image_id": "1581286460", "caption": "Children playing leapfrog in countryside on motorhome vacation", "id": "1581286460"}, {"image_id": "1817411565", "caption": "Side view of university students in graduation gowns and mortarboards walking in colonnade", "id": "1817411565"}, {"image_id": "1570363811", "caption": "View over Boston in the snow towards Back Bay Charles River and Cambridge Massachusetts USA", "id": "1570363811"}, {"image_id": "1572549815", "caption": "Businessman holding a leather case with briefing papers Close up of hands", "id": "1572549815"}, {"image_id": "1864640723", "caption": "Horizontal tilted shot of a father adjusting the strap of his Son's cycling helmet outdoors", "id": "1864640723"}, {"image_id": "1590222959", "caption": "Surface shot of man paddling in kayak", "id": "1590222959"}, {"image_id": "1843607069", "caption": "Horizontal elevated rear view of a joyous couple driving a convertible with the woman spreading arms on an highway besides coast at a high speed with motion blur on a sunny day", "id": "1843607069"}, {"image_id": "1590317537", "caption": "Rocks and trees along shore Police Bay Mahe Seychelles", "id": "1590317537"}, {"image_id": "1664820671", "caption": "Portrait of a young woman holding a sarong against blue sky", "id": "1664820671"}, {"image_id": "1570160765", "caption": "Beach of Aldabra Atoll Seychelles", "id": "1570160765"}, {"image_id": "1590363392", "caption": "Boats on the beach of the northwest coast of Mahe Seychelles", "id": "1590363392"}, {"image_id": "1766931678", "caption": "Couple holding gift Luesener Alm Dolomite Alps South Tyrol Italy", "id": "1766931678"}, {"image_id": "1725719324", "caption": "Young girl doing homework mother in kitchen in background", "id": "1725719324"}, {"image_id": "1670341637", "caption": "Businessmen sitting in backseat of car with laptop at camera", "id": "1670341637"}, {"image_id": "1578217556", "caption": "A mid adult woman eating a bowl of chocolate coated cereal", "id": "1578217556"}, {"image_id": "1590352094", "caption": "Marina and Cathedral of Palma de Mallorca at night Mallorca Spain", "id": "1590352094"}, {"image_id": "1843605782", "caption": "Close up of carpenter's tool belt with hammers at the construction site on a wooden background", "id": "1843605782"}, {"image_id": "1587996977", "caption": "Low angle view of wine bottle and wine glass", "id": "1587996977"}, {"image_id": "1578922517", "caption": "A portrait of a young boy holding a chocolate Easter bunny", "id": "1578922517"}, {"image_id": "1852922645", "caption": "Vertical shot of a young male sprinter setting off from his starting blocks at an athletics event on a bright sunny day out at the track", "id": "1852922645"}, {"image_id": "1576779974", "caption": "For sale sign outside a family house", "id": "1576779974"}, {"image_id": "1587148994", "caption": "View of a candle in an oil burner", "id": "1587148994"}, {"image_id": "217372079", "caption": "Rolling waves Gold Coast Australia", "id": "217372079"}, {"image_id": "1277230658", "caption": "Detail of laboratory technician wearing white coat by microscope", "id": "1277230658"}, {"image_id": "1570552859", "caption": "Part of a ship and anchor chain Maraii River Amazon River Brazil", "id": "1570552859"}, {"image_id": "1572529418", "caption": "View to Urique and Urique River Urique Canyon Copper Canyon Chihuahua Mexico", "id": "1572529418"}, {"image_id": "1587032276", "caption": "High angle view of Swiss franc", "id": "1587032276"}, {"image_id": "1590323567", "caption": "Fossilizations at Jebel Harim mountain Musandam peninsula exclave of Oman", "id": "1590323567"}, {"image_id": "1587831080", "caption": "Pillow scarf and slippers on boardwalk", "id": "1587831080"}, {"image_id": "1878688691", "caption": "Low angle shot of a technician checking wires in the data center", "id": "1878688691"}, {"image_id": "216586736", "caption": "Rooftops of idyllic village Bourdeilles Dordogne France", "id": "216586736"}, {"image_id": "1304266766", "caption": "Senior man relaxing on sofa at home listening to music on headphones", "id": "1304266766"}, {"image_id": "216109472", "caption": "Close up of ram looking at camera", "id": "216109472"}, {"image_id": "1766904435", "caption": "Male sports fan wearing sunglasses", "id": "1766904435"}, {"image_id": "1586722013", "caption": "Sun Shining Through Branches Of Autumn Tree By Lake", "id": "1586722013"}, {"image_id": "1839580775", "caption": "A family of four enjoying their time together on a day out in the field", "id": "1839580775"}, {"image_id": "1570541396", "caption": "Riverside near Alter do Chao Rio Tapajos Amazon River Brazil", "id": "1570541396"}, {"image_id": "1864651172", "caption": "A couple with two kids and their grandparents collect autumn leaves in the garden as the kids throw up the leaves in the air", "id": "1864651172"}, {"image_id": "1587032975", "caption": "View of Monastery in winter Andechs Starnberg District Upper Bavaria Germany", "id": "1587032975"}, {"image_id": "1869314711", "caption": "Vertical shot of a father lying on the ground while holding his son over his knees in an autumn park", "id": "1869314711"}, {"image_id": "1578946964", "caption": "A young woman lying on a beach", "id": "1578946964"}, {"image_id": "1578922406", "caption": "A young girl holding a daffodil", "id": "1578922406"}, {"image_id": "1578932987", "caption": "Woman smelling a bottle of essential oil", "id": "1578932987"}, {"image_id": "1571660516", "caption": "A young man playing beach volleyball", "id": "1571660516"}, {"image_id": "1859334905", "caption": "A medium shot of a happy senior man with gray hair looking at a monitor while using a computer", "id": "1859334905"}, {"image_id": "1587649253", "caption": "View of Landsberg medieval town at the river Lech winter time Bavaria Germany", "id": "1587649253"}, {"image_id": "216108536", "caption": "Clouds in blue sky over barley field", "id": "216108536"}, {"image_id": "1586682404", "caption": "cutout Of Middle Aged Male Executive Wearing Suit", "id": "1586682404"}, {"image_id": "216581945", "caption": "View of snowy mountain range and blue sky", "id": "216581945"}, {"image_id": "1588012256", "caption": "Close up of businessman cheering with telephone", "id": "1588012256"}, {"image_id": "1840553876", "caption": "Salesman showing customer a car in a car dealership showroom", "id": "1840553876"}, {"image_id": "1864643660", "caption": "Rear View of a young boy playing with a toy airplane in the garden", "id": "1864643660"}, {"image_id": "1587992627", "caption": "A couple relaxing by a pool", "id": "1587992627"}, {"image_id": "1844194196", "caption": "Beautiful sky painted by the sun above horizon leaving bright golden shades", "id": "1844194196"}, {"image_id": "1587132728", "caption": "Teenage couple watching sunset from paddle board on Lake Starnberg Bavaria Germany Europe", "id": "1587132728"}, {"image_id": "1663818032", "caption": "Teenage girls in yoga pose in gym", "id": "1663818032"}, {"image_id": "1570154843", "caption": "Off road vehicle on dirt road at Tsavo East National Park Kenya Africa", "id": "1570154843"}, {"image_id": "1866091295", "caption": "Wide aerial shot of a farmer standing in a sunny golden barley field", "id": "1866091295"}, {"image_id": "1862131478", "caption": "A medium shot a happy senior couple smiling at each other while entering an airport through automatic doors with a luggage trolley bag", "id": "1862131478"}, {"image_id": "1840650479", "caption": "A young man almost done with the shearing of his white sheep for wool", "id": "1840650479"}, {"image_id": "1654577039", "caption": "Four young friends relaxing on sandy beach and taking a photo together", "id": "1654577039"}, {"image_id": "1587646382", "caption": "Combine in a wheat field", "id": "1587646382"}, {"image_id": "1844769155", "caption": "Business employees talking in the lobby of a modern office", "id": "1844769155"}, {"image_id": "1576777178", "caption": "A female snorkeler holding a starfish", "id": "1576777178"}, {"image_id": "1586684648", "caption": "Cashier Serving Customer At Supermarket Checkout", "id": "1586684648"}, {"image_id": "1590181373", "caption": "Woman leaning out of limousine window", "id": "1590181373"}, {"image_id": "1868718527", "caption": "A Close up shot of a young blonde woman smiling at camera while holding steering wheel in a car", "id": "1868718527"}, {"image_id": "1844196317", "caption": "Tight shot of middle age woman holding peppers and parsley", "id": "1844196317"}, {"image_id": "1587032339", "caption": "View of a man sitting in stairway", "id": "1587032339"}, {"image_id": "1670342624", "caption": "Female elementary school teacher reading book to children on grass", "id": "1670342624"}, {"image_id": "1710363722", "caption": "A dressed up couple in love", "id": "1710363722"}, {"image_id": "1590329510", "caption": "Sunset in desert Al Ain Emirate of Abu Dhabi United Arab Emirates", "id": "1590329510"}, {"image_id": "1571579156", "caption": "Couple standing on the garden path outside their new home", "id": "1571579156"}, {"image_id": "1590102629", "caption": "A businessman ringing a bell for attention", "id": "1590102629"}, {"image_id": "1587982937", "caption": "Nest of duck eggs in potted plant outdoors", "id": "1587982937"}, {"image_id": "216580475", "caption": "Tranquil field of blooming buttercups", "id": "216580475"}, {"image_id": "1844196239", "caption": "Vertical shot of a farmer standing in wheat field under the cloudy sky", "id": "1844196239"}, {"image_id": "1587144266", "caption": "View of glazed frost on a tree Upper Bavaria Germany", "id": "1587144266"}, {"image_id": "1590317651", "caption": "Palm tree crown with coconuts", "id": "1590317651"}, {"image_id": "1670246444", "caption": "Portrait of elegant young woman in a white silk dress or wrap", "id": "1670246444"}, {"image_id": "1578922382", "caption": "A young girl gathering daffodils in a basket", "id": "1578922382"}, {"image_id": "1570154807", "caption": "Termite mound at Tsavo East National Park Kenya Africa", "id": "1570154807"}, {"image_id": "1878782306", "caption": "Vertical shot of a young girl in a swimming suit standing under a beach umbrella on a sunny day with the ocean in the background", "id": "1878782306"}, {"image_id": "1590355016", "caption": "Statue of David and Hercules and Cacus in front of the main entrance of Palazzo Vecchio Florence Italy", "id": "1590355016"}, {"image_id": "1807085765", "caption": "Active senior couple standing with bicycles in park cheek to cheek smiling portrait tilt", "id": "1807085765"}, {"image_id": "1865999723", "caption": "Vertical shot of a mother pushing a shopping trolley with her daughter standing over it in a supermarket s car park", "id": "1865999723"}, {"image_id": "1857285956", "caption": "Senior couple standing in the middle of an electronics appliances store while the wife is looking at options for buying a new iron", "id": "1857285956"}, {"image_id": "1297750457", "caption": "Male vocalist performing on stage with band in concert", "id": "1297750457"}, {"image_id": "1852961912", "caption": "Close up of children sitting on climbing rods and making soap bubbles", "id": "1852961912"}, {"image_id": "1578225902", "caption": "A mid adult woman holding a cup of soup", "id": "1578225902"}, {"image_id": "1590053474", "caption": "Side view of young woman with dark hair in a bun", "id": "1590053474"}, {"image_id": "1297775183", "caption": "Young businessman in busy office looking at camera", "id": "1297775183"}, {"image_id": "1766925108", "caption": "Friends drinking together at beach", "id": "1766925108"}, {"image_id": "1868709398", "caption": "A horizontal close up of a smiling businesswoman wearing headset in the office with a colleague working in the background", "id": "1868709398"}, {"image_id": "1571580872", "caption": "A man relaxing with a drink on holiday", "id": "1571580872"}, {"image_id": "1578947120", "caption": "Three teenage friends using a computer", "id": "1578947120"}, {"image_id": "1590323627", "caption": "Minaret of Mosque of Sohar Al Batinah Region Oman", "id": "1590323627"}, {"image_id": "1650253274", "caption": "A groom drinking champagne before his big day", "id": "1650253274"}, {"image_id": "1571347802", "caption": "Brandenburg Gate with Quadriga in backlight Berlin Germany", "id": "1571347802"}, {"image_id": "1766929032", "caption": "Two women practicing yoga Kleinwalsertal Allgau Germany", "id": "1766929032"}, {"image_id": "1590027392", "caption": "Young businessman in modern office building walking along with briefcase", "id": "1590027392"}, {"image_id": "1590347606", "caption": "Man using laptop in kitchen", "id": "1590347606"}, {"image_id": "1277225282", "caption": "Detail of businessman with suitcase tying shoes", "id": "1277225282"}, {"image_id": "1587845321", "caption": "Close up of high heel and cork", "id": "1587845321"}, {"image_id": "1578947102", "caption": "A mother checking her graduate daughter's gown", "id": "1578947102"}, {"image_id": "1586664137", "caption": "Baby smiling portrait cutout", "id": "1586664137"}, {"image_id": "1578915716", "caption": "A young man standing in a field near a tent", "id": "1578915716"}, {"image_id": "1590352109", "caption": "Statue of the founder of the Monastery Santuari de Lluc near Escorca Mallorca Spain", "id": "1590352109"}, {"image_id": "1588016972", "caption": "Close up of stack of US one hundred dollar bills", "id": "1588016972"}, {"image_id": "1590163835", "caption": "Reflection of Seeon Abbey in Lake Seeon Klostersee Chiemgau Bavaria Germany", "id": "1590163835"}, {"image_id": "1869317015", "caption": "Medium shot of a young technician smiling at the camera while working on a computer connected to a cabinet in the server room", "id": "1869317015"}, {"image_id": "1839585569", "caption": "Father and son making snowballs on their trip to a ski resort in the snowy mountains with the son preparing to throw a snowball", "id": "1839585569"}, {"image_id": "1855878308", "caption": "Horizontal shot of a young man using exercise equipment in the gym", "id": "1855878308"}, {"image_id": "1868705180", "caption": "Smiling doctors in lab coats using a digital tablet while standing in the hospital corridor", "id": "1868705180"}, {"image_id": "1664815124", "caption": "View of young boy concentrating on writing", "id": "1664815124"}, {"image_id": "1839580601", "caption": "Close up photo of a confident businesswoman with brown hair at her workplace holding some documents in her hand", "id": "1839580601"}, {"image_id": "1587046664", "caption": "Two young girls watering plants in outdoors", "id": "1587046664"}, {"image_id": "1663818551", "caption": "Portrait of teenage girls touching toes in gym", "id": "1663818551"}, {"image_id": "1590060182", "caption": "Four teenage friends watching television", "id": "1590060182"}, {"image_id": "1277235470", "caption": "Businessman using telephone headset by wall in office", "id": "1277235470"}, {"image_id": "1711241957", "caption": "Young woman's smile and nose Close up", "id": "1711241957"}, {"image_id": "1851481526", "caption": "Vertical shot of a woman spraying water on her car using a hose to wash off the soap suds", "id": "1851481526"}, {"image_id": "1654652111", "caption": "Couple standing beside shelf in shop carrying orange cushions", "id": "1654652111"}, {"image_id": "1866109295", "caption": "A doctor touching the monitor displaying the scan results", "id": "1866109295"}, {"image_id": "1570572920", "caption": "Sunrise at Cuxiu Muni Amazon River Brazil", "id": "1570572920"}, {"image_id": "1581281498", "caption": "Bridal party waving to bride and groom in vintage car on wedding day", "id": "1581281498"}, {"image_id": "1297775360", "caption": "Man on walk through snowy winter landscape with backpack and flask", "id": "1297775360"}, {"image_id": "1297741688", "caption": "Close up of man in vegetable garden picking fresh Brussels sprouts", "id": "1297741688"}, {"image_id": "1587849752", "caption": "High angle view of construction in Munich", "id": "1587849752"}, {"image_id": "216388871", "caption": "Low angle view of autumn leaves", "id": "216388871"}, {"image_id": "1571353241", "caption": "White Stork Ciconia ciconia in nest", "id": "1571353241"}, {"image_id": "1571602997", "caption": "A businesswoman carrying a laptop", "id": "1571602997"}, {"image_id": "1572496661", "caption": "A young woman in a red dress", "id": "1572496661"}, {"image_id": "1590070328", "caption": "A portrait of an attractive young woman", "id": "1590070328"}, {"image_id": "1859353421", "caption": "A wide shot of a young boy keeping his head down while playing with a model wind turbine in classroom with diagrams on whiteboards in the background", "id": "1859353421"}, {"image_id": "1297775204", "caption": "Businessmen on trip with suitcases in airport building", "id": "1297775204"}, {"image_id": "1588012250", "caption": "Two businessmen cheering in office", "id": "1588012250"}, {"image_id": "1571356397", "caption": "View over Verona Veneto Italy", "id": "1571356397"}, {"image_id": "1590202241", "caption": "Figurines of bride and groom standing in ribbon", "id": "1590202241"}, {"image_id": "217368329", "caption": "Gentoo Penguins Antarctica", "id": "217368329"}, {"image_id": "1590034421", "caption": "Woman putting her finger to her lips", "id": "1590034421"}, {"image_id": "1876239929", "caption": "Surfer With Artificial Leg Standing On Beach", "id": "1876239929"}, {"image_id": "1878871268", "caption": "A vertical front view of a Smiling school teacher with blonde hair holding a pencil while watching and supervising her students in school laboratory", "id": "1878871268"}, {"image_id": "1581271658", "caption": "Smiling mother with daughter on shoulders in park in autumn at camera", "id": "1581271658"}, {"image_id": "1576774250", "caption": "Two residents in a retirement home playing cards", "id": "1576774250"}, {"image_id": "1586681534", "caption": "cutout Of Foreman On Building Site", "id": "1586681534"}, {"image_id": "1845993758", "caption": "A medium shot of a senior worker inspecting aluminum light fittings behind a monitor in a factory", "id": "1845993758"}, {"image_id": "1586722967", "caption": "Business Meeting Around Table In Modern Office", "id": "1586722967"}, {"image_id": "1586652284", "caption": "Senior women at home relaxing and pampering themselves", "id": "1586652284"}, {"image_id": "1587843029", "caption": "City hall of Salzburg Austria", "id": "1587843029"}, {"image_id": "1864637789", "caption": "Horizontal shot of a father chasing his daughter in water at the beach on a sunny day", "id": "1864637789"}, {"image_id": "1868718347", "caption": "A portrait shot of a young blonde woman standing on a kitchen counter and attaching thread to a sewing machine", "id": "1868718347"}, {"image_id": "1766903802", "caption": "Trees on shore near lake", "id": "1766903802"}, {"image_id": "1578225272", "caption": "Portrait of a mid adult woman wearing a red gilet", "id": "1578225272"}, {"image_id": "1873425311", "caption": "A vertical shot of a businessman and businesswoman talking at a desk in front of a computer in office", "id": "1873425311"}, {"image_id": "1297793675", "caption": "Detail of cable spool on ladder with electrician working in background", "id": "1297793675"}, {"image_id": "1859347232", "caption": "A medium shot of a happy couple smiling at each other and standing together in front of a large solar panel", "id": "1859347232"}, {"image_id": "1583611289", "caption": "Girl 5 7 with shell on beach smiling portrait differential focus", "id": "1583611289"}, {"image_id": "1590329666", "caption": "Airplane wing above London England Great Britain", "id": "1590329666"}, {"image_id": "1590349904", "caption": "Young boy balancing upside down on football", "id": "1590349904"}, {"image_id": "1844194451", "caption": "Close up of person holding three brown eggs and whisk with a pan on gas in background", "id": "1844194451"}, {"image_id": "1588012274", "caption": "Close up of businessman laughing with telephone", "id": "1588012274"}, {"image_id": "1571335145", "caption": "The harbour of Vardo Finnmark county Norway", "id": "1571335145"}, {"image_id": "1571333327", "caption": "Cruise Ships and boats in Geirangerfjord More og Romsdal Sunnmore region Norway", "id": "1571333327"}, {"image_id": "1671843695", "caption": "A businesswoman walking in garden", "id": "1671843695"}, {"image_id": "1868705375", "caption": "A view of a doctor in a scrub suit comforting and helping the patient with an MRI scan in a clinic", "id": "1868705375"}, {"image_id": "1587039884", "caption": "Close up man running on water", "id": "1587039884"}, {"image_id": "1869911615", "caption": "A side profile shot of two happy businesswomen talking with each other in office corridor", "id": "1869911615"}, {"image_id": "1710181361", "caption": "Portrait of a woman Close up", "id": "1710181361"}, {"image_id": "1277365364", "caption": "Teenage girl sitting on bed painting toenails whilst listening to music on MP3 player at home", "id": "1277365364"}, {"image_id": "1588012607", "caption": "Portrait of young woman yawning", "id": "1588012607"}, {"image_id": "1722076472", "caption": "Teenagers enjoying soft drinks and lunch on skiing holiday Tirol Austria Europe", "id": "1722076472"}, {"image_id": "1860727016", "caption": "Mid shot of a female surfer in a wetsuit on the beach", "id": "1860727016"}, {"image_id": "1817411589", "caption": "Father son and daughter sitting on a grassy ground with the son holding a rugby ball in his hand", "id": "1817411589"}, {"image_id": "1663818017", "caption": "Teenage boy and girl in gym on cross trainer", "id": "1663818017"}, {"image_id": "1813181198", "caption": "Vertical shot of a female sitting on her office table with books and rolls beside her", "id": "1813181198"}, {"image_id": "1590359933", "caption": "Close up of Bald Eagle", "id": "1590359933"}, {"image_id": "1576746005", "caption": "A climber climbing a rock face", "id": "1576746005"}, {"image_id": "1587647735", "caption": "Low angle view of a young woman posing in the beach near the water against blue sky", "id": "1587647735"}, {"image_id": "1572549899", "caption": "Portrait of a young man", "id": "1572549899"}, {"image_id": "1277235653", "caption": "Businessman in sales team making phone call wearing headset at camera", "id": "1277235653"}, {"image_id": "1571330861", "caption": "Grandmother and grandson playing together at beach", "id": "1571330861"}, {"image_id": "1664820650", "caption": "Portrait of a man dressed up as Santa Claus giving presents to two young children", "id": "1664820650"}, {"image_id": "1661486624", "caption": "Slowly rotating LED light bulb created in 3D 3D illustration Background used for presentation on energy climate change and new technologies", "id": "1661486624"}, {"image_id": "1587987797", "caption": "A young woman standing in an empty room", "id": "1587987797"}, {"image_id": "1297787624", "caption": "Studio cutout of car mechanic with air hammer on white background smiling at camera", "id": "1297787624"}, {"image_id": "1813178642", "caption": "An angled view of father carrying daughter on shoulders and walking on beach", "id": "1813178642"}, {"image_id": "1571520359", "caption": "A young woman sitting on a sun lounger close up of legs", "id": "1571520359"}, {"image_id": "1847350133", "caption": "A side profile medium shot of a happy young female worker in casuals operating machinery on a production line in a factory", "id": "1847350133"}, {"image_id": "1878778658", "caption": "Young woman in her 20's is holding up a globe in her outstretched arms", "id": "1878778658"}, {"image_id": "1859339603", "caption": "A Close up shot of two young boys inspecting test tubes in a school chemistry laboratory", "id": "1859339603"}, {"image_id": "1869911459", "caption": "A portrait shot of a businesswoman thinking while sitting at her desk in front of a computer", "id": "1869911459"}, {"image_id": "1277231429", "caption": "Young woman wearing underwear and gown with mug in kitchen at camera", "id": "1277231429"}, {"image_id": "1590363518", "caption": "Palm tree over beach Anse A La Mouche Mahe Seychelles", "id": "1590363518"}, {"image_id": "1277214128", "caption": "Mature woman standing in organic garden with basket of vegetables", "id": "1277214128"}, {"image_id": "1587992657", "caption": "Portrait of a male athlete in the starting blocks", "id": "1587992657"}, {"image_id": "1766928495", "caption": "Woman in white bikini on beach in Seychelles", "id": "1766928495"}, {"image_id": "1859347193", "caption": "A medium shot of a happy woman holding a basket of vegetables and standing next to a large solar panels with her family in background", "id": "1859347193"}, {"image_id": "1873350878", "caption": "Close up of ornate male Venetian mask with feathers for Venice Carnival Italy", "id": "1873350878"}, {"image_id": "1844769095", "caption": "Young man with a backpack holding a coffee cup and using a digital tablet", "id": "1844769095"}, {"image_id": "1839585584", "caption": "Family of four with two children smiling for a picture while sledding on snow with the boy holding a snowball in his hands", "id": "1839585584"}, {"image_id": "1578216596", "caption": "A young woman wearing a winter hat smiling", "id": "1578216596"}, {"image_id": "1590160370", "caption": "Cliffs over water with lighthouse in background Biarritz France", "id": "1590160370"}, {"image_id": "1585874798", "caption": "View from Gornercrest Zermatt Valais Switzerland Europe", "id": "1585874798"}, {"image_id": "1864651028", "caption": "Little boy and girl sitting on bean bags and watching television in the living room", "id": "1864651028"}, {"image_id": "1570160942", "caption": "Young woman in white standing in wind farm", "id": "1570160942"}, {"image_id": "1844731748", "caption": "Medium shot of an elderly man putting bait on a fishing rod while sitting in a rowboat over a lake", "id": "1844731748"}, {"image_id": "1665809786", "caption": "Woman wearing high heels next to toast with jam on rug", "id": "1665809786"}, {"image_id": "1840647806", "caption": "American flag waving in front with skyscraper in background", "id": "1840647806"}, {"image_id": "1586693087", "caption": "High school student testing electronics of drone in shop class", "id": "1586693087"}, {"image_id": "1570522946", "caption": "Palm trees and windows in Miami Beach Florida USA", "id": "1570522946"}, {"image_id": "1588027652", "caption": "A letting agent showing businesswoman around an empty office", "id": "1588027652"}, {"image_id": "1840647827", "caption": "Mid shot of confident businessman holding Eyeglasses in network server room on a background of blue lan cabinets", "id": "1840647827"}, {"image_id": "1766903679", "caption": "Sleeping girl and dog on sofa near Christmas tree", "id": "1766903679"}, {"image_id": "1277235689", "caption": "Cute smiling baby boy wearing diaper on white background at camera", "id": "1277235689"}, {"image_id": "1586663900", "caption": "Drought Stricken Landscape In Area Of Western Cape In South Africa", "id": "1586663900"}, {"image_id": "1587051902", "caption": "Woman in red shirt and sun hat", "id": "1587051902"}, {"image_id": "1587997706", "caption": "Close up of wine bottles and glasses on table outdoors", "id": "1587997706"}, {"image_id": "1572527732", "caption": "Sunrise near Los Islotes Baja California Sur Mexico", "id": "1572527732"}, {"image_id": "1846406135", "caption": "Close up of a stealthy snail slowly crawling through the green grass", "id": "1846406135"}, {"image_id": "1586736392", "caption": "Portrait Of Farmer Standing In Wheat Field At Harvest", "id": "1586736392"}, {"image_id": "1839580721", "caption": "Headshot of a confident businesswoman in a suit among her colleagues", "id": "1839580721"}, {"image_id": "1277232758", "caption": "Businessmen shaking hands by partially built house", "id": "1277232758"}, {"image_id": "1585873994", "caption": "Alphorns Zermatt Valais Switzerland Europe", "id": "1585873994"}, {"image_id": "1590071285", "caption": "Portrait of a senior woman in a swimsuit at the beach wearing a straw hat", "id": "1590071285"}, {"image_id": "1572528845", "caption": "Sun shade on the beach of La Paz Baja California Sur Mexico", "id": "1572528845"}, {"image_id": "1874803700", "caption": "Surgeon with patient in operating room low angle view", "id": "1874803700"}, {"image_id": "1578932864", "caption": "A group of four business colleagues holding an informal meeting", "id": "1578932864"}, {"image_id": "1572469544", "caption": "Happy mother with young daughter carrying the washing", "id": "1572469544"}, {"image_id": "1572522950", "caption": "Father and mother carrying children children at beach", "id": "1572522950"}, {"image_id": "1839580670", "caption": "A couple of businesspeople laughing and talking with each other before heading for an important presentation", "id": "1839580670"}, {"image_id": "1590353585", "caption": "Portrait of girl with pimple applications making a face", "id": "1590353585"}, {"image_id": "217368554", "caption": "French Flag on Top of Soccer Ball", "id": "217368554"}, {"image_id": "1839581768", "caption": "Horizontal shot of a happy young woman moving mattress with copy space", "id": "1839581768"}, {"image_id": "1277240144", "caption": "Girl in sunglasses making snowballs on winter vacation at camera", "id": "1277240144"}, {"image_id": "1766903859", "caption": "Doctor talking to mother and ill son in hospital", "id": "1766903859"}, {"image_id": "1839585527", "caption": "A senior businessman is consulting with his junior colleague about his scheduled appointments for the day", "id": "1839585527"}, {"image_id": "1578909968", "caption": "Businessman sitting down without jacket", "id": "1578909968"}, {"image_id": "1586727209", "caption": "Smiling Baby Boy In Wooden Playpen At Home", "id": "1586727209"}, {"image_id": "1669108721", "caption": "Woman leaning chin on hand", "id": "1669108721"}, {"image_id": "1587835922", "caption": "Sunlight on fountain Saint Peter's Basilica Vatican Rome Italy", "id": "1587835922"}, {"image_id": "1872076997", "caption": "Two American football players leaving pitch at sunset side by side front view backlit", "id": "1872076997"}, {"image_id": "1844769239", "caption": "Business employees ascending stairs in an office", "id": "1844769239"}, {"image_id": "1590051068", "caption": "Little girl in a party dress holding a pink feather duster", "id": "1590051068"}, {"image_id": "1859331836", "caption": "A portrait Close up shot of a young female trainee garnishing food in a kitchen", "id": "1859331836"}, {"image_id": "1590337247", "caption": "Spring Mountain Ranch State Park in autumn Las Vegas Nevada USA", "id": "1590337247"}, {"image_id": "1578924698", "caption": "A portrait of a young mother with her baby and dog", "id": "1578924698"}, {"image_id": "1851475637", "caption": "Smiling waitress holding a cup of coffee", "id": "1851475637"}, {"image_id": "1578932834", "caption": "Woman holding a pot of moisturising cream a blob on her nose", "id": "1578932834"}, {"image_id": "1590163859", "caption": "Group of people whitewater rafting", "id": "1590163859"}, {"image_id": "1716618329", "caption": "Man swinging in park on sunny day", "id": "1716618329"}, {"image_id": "1587121178", "caption": "Overview parents and daughter lying on the floor talking", "id": "1587121178"}, {"image_id": "1578939155", "caption": "Woman crying wiping tears away with a tissue", "id": "1578939155"}, {"image_id": "1844728907", "caption": "Vertical shot of a senior engineer speaking on the telephone at the control desk in the control room of a nuclear power station", "id": "1844728907"}, {"image_id": "1813172048", "caption": "Selective focus on laughing man wearing hands free holding coffee and croissant while a woman smiles in background", "id": "1813172048"}, {"image_id": "1587982505", "caption": "A middle aged woman listening to mp3 music player", "id": "1587982505"}, {"image_id": "1587137246", "caption": "Underview of family hiking in Colorado USA", "id": "1587137246"}, {"image_id": "1859352206", "caption": "A medium shot of a teacher holding paperwork and smiling at camera with school lockers in background", "id": "1859352206"}, {"image_id": "1572482039", "caption": "A young girl using a mobile phone", "id": "1572482039"}, {"image_id": "1590200753", "caption": "Shovel Excavator digging nickelous soil near Koumac New Caledonia Overseas Territory of France", "id": "1590200753"}, {"image_id": "1572661466", "caption": "A young man blowing his nose", "id": "1572661466"}, {"image_id": "1859144666", "caption": "Woman with a backpack sitting on a rock and drinking water in the woods", "id": "1859144666"}, {"image_id": "1711231061", "caption": "Senior couple embracing against a white curtain", "id": "1711231061"}, {"image_id": "1590355031", "caption": "View to Ponte Vecchio Florence Italy", "id": "1590355031"}, {"image_id": "1878695789", "caption": "Portrait shot of a middle school student sitting in a classroom and looking at the camera with her classmates in the background", "id": "1878695789"}, {"image_id": "1297800488", "caption": "Portrait of smiling senior couple sitting on rocks by sea", "id": "1297800488"}, {"image_id": "1866123266", "caption": "Medium shot of an elderly couple sitting on mountain bikes along a country track", "id": "1866123266"}, {"image_id": "1864613879", "caption": "Horizontal shot a young woman biting into a pillow and looking at the camera with her husband in the background", "id": "1864613879"}, {"image_id": "1862121671", "caption": "A horizontal portrait of a smiling businessman in formal suit sitting at the table with a laptop in an office lobby", "id": "1862121671"}, {"image_id": "1578212909", "caption": "Portrait of a young woman looking over her shoulder", "id": "1578212909"}, {"image_id": "1851488738", "caption": "Vertical portrait of happy parents looking at their son lying in the kayak floating over the water in the lake", "id": "1851488738"}, {"image_id": "1590361820", "caption": "Close up of assorted US paper currency", "id": "1590361820"}, {"image_id": "1590347672", "caption": "Grandfather father and son portrait", "id": "1590347672"}, {"image_id": "1590060053", "caption": "Young man listening to music", "id": "1590060053"}, {"image_id": "1586672525", "caption": "Mature woman with headache on winter day", "id": "1586672525"}, {"image_id": "1878692813", "caption": "High school students smiling at the camera while completing their homework in the classroom with a teacher helping them", "id": "1878692813"}, {"image_id": "1576779989", "caption": "A woman in a bikini", "id": "1576779989"}, {"image_id": "1839589223", "caption": "Overhead shot of pre school children at nursery making craft with scissors and colored paper at table", "id": "1839589223"}, {"image_id": "1851483983", "caption": "Close up of crossed feet hanging in the air of a family sitting on the wooden pier over the lake", "id": "1851483983"}, {"image_id": "1846008092", "caption": "A portrait shot of a young woman listening music while stretching in a park", "id": "1846008092"}, {"image_id": "1851481568", "caption": "Wide shot of a woman washing soap suds off her car by spraying water on it using a hose", "id": "1851481568"}, {"image_id": "1857286265", "caption": "Vertical shot of a senior couple walking arm in arm on the beach on a bright sunny day and smiling at each other", "id": "1857286265"}, {"image_id": "1859331869", "caption": "A portrait shot of a chef watching two young trainees cleaning table surface in a kitchen", "id": "1859331869"}, {"image_id": "1585868900", "caption": "Seaside with Cruiser in the background Hornsund Spitsbergen Svalbard Norway Europe", "id": "1585868900"}, {"image_id": "217367066", "caption": "Mountains and forest in Canada", "id": "217367066"}, {"image_id": "1865999534", "caption": "Vertical shot of a multi generational family unloading from car for camping with a senior woman holding a container as the adult daughter looks on", "id": "1865999534"}, {"image_id": "1585850123", "caption": "Cookies Santa Claus against white background", "id": "1585850123"}, {"image_id": "1866109511", "caption": "A Doctor and the patient reviewing results on the monitor at the CT scanner in the hospital", "id": "1866109511"}, {"image_id": "1851488744", "caption": "Wide shot of a loving couple sitting on a wooden pier over the lake wearing swimming suit", "id": "1851488744"}, {"image_id": "1852922861", "caption": "Middle aged man sitting on a rock at the beach with his dog and embracing him on a bright sunny day", "id": "1852922861"}, {"image_id": "1578225863", "caption": "A mid adult woman holding a pumpkin", "id": "1578225863"}, {"image_id": "1576772060", "caption": "A woman choosing a dress to wear", "id": "1576772060"}, {"image_id": "1590200738", "caption": "Close up of pile of coconuts", "id": "1590200738"}, {"image_id": "1839588572", "caption": "Vertical shot of a woman making fruit salad with her daughter in the kitchen", "id": "1839588572"}, {"image_id": "1590323588", "caption": "Truck on remote dirt road near Khasab Musandam peninsula exclave of Oman", "id": "1590323588"}, {"image_id": "1571664314", "caption": "A woman having fun in a waterpark", "id": "1571664314"}, {"image_id": "1864651037", "caption": "Little girl standing and holding up a painting of a house with her family standing in the background", "id": "1864651037"}, {"image_id": "1572549887", "caption": "A young couple laying on grass", "id": "1572549887"}, {"image_id": "1869318053", "caption": "Combine harvester running across a dusty field on a sunny day", "id": "1869318053"}, {"image_id": "1587031196", "caption": "Detail view of pink magnolias", "id": "1587031196"}, {"image_id": "1868714480", "caption": "Scientists in clean suits cautiously working in a silicon wafer manufacturing laboratory", "id": "1868714480"}, {"image_id": "1590347594", "caption": "Businessman outdoors throwing paper airplane", "id": "1590347594"}, {"image_id": "1570552784", "caption": "Undermined Tree at Riverside near Balaio Amazon River Brazil", "id": "1570552784"}, {"image_id": "1813180646", "caption": "A female teenager trying out new clothes in the trial room", "id": "1813180646"}, {"image_id": "1297797335", "caption": "cutout of businessman with briefcase listening to music on earphones", "id": "1297797335"}, {"image_id": "1844196050", "caption": "Tight shot of woman holding fresh strawberry in a kitchen", "id": "1844196050"}, {"image_id": "1859334866", "caption": "A portrait shot of a young teacher watching a senior man using a computer from behind in a lab", "id": "1859334866"}, {"image_id": "1851488735", "caption": "Happy family enjoying outing on a lake in the forest", "id": "1851488735"}, {"image_id": "1670340674", "caption": "Loving bride and groom walking outdoors through arch together", "id": "1670340674"}, {"image_id": "1590056606", "caption": "Portrait of a man using a call centre headset", "id": "1590056606"}, {"image_id": "1586683922", "caption": "Senior Man Asleep In Vegetable Garden Whilst Wife Works", "id": "1586683922"}, {"image_id": "1859181131", "caption": "Vertical portrait of a male rock climber smiling at the camera", "id": "1859181131"}, {"image_id": "216582731", "caption": "Countryside Bourdeilles Dordogne France", "id": "216582731"}, {"image_id": "1570405586", "caption": "The Bridge over the River Kwai Singapore Republic of Singapore", "id": "1570405586"}, {"image_id": "1862086757", "caption": "Vertical shot of scattered white clouds rolling over the blue sky", "id": "1862086757"}, {"image_id": "1869908249", "caption": "Overhead shot of a group of businesspeople with large jigsaw pieces in two rows standing face to face with a businessman moving forward", "id": "1869908249"}, {"image_id": "1873340927", "caption": "Wide shot of a Tree surgeon wearing safety harness while pruning the copper beech tree branch with a saw", "id": "1873340927"}, {"image_id": "1587031376", "caption": "View of a statue with buildings in the background Mirabell garden Fort Hohensalzburg Salzburg Austria", "id": "1587031376"}, {"image_id": "1590200669", "caption": "Peninsula near Hienghene New Caledonia Overseas Territory of France", "id": "1590200669"}, {"image_id": "1297792571", "caption": "Bull with ring through nose grazing in field on livestock farm", "id": "1297792571"}, {"image_id": "1840562306", "caption": "Businessman and businesswoman with a digital tablet having a discussion in the aisle of a manufacturing plant", "id": "1840562306"}, {"image_id": "1878688538", "caption": "Vertical shot of a Surfer With a prosthetic Leg Standing On Beach holding a surfboard", "id": "1878688538"}, {"image_id": "1845922283", "caption": "Close up of measurements on ship to measure the depth of the anchor", "id": "1845922283"}, {"image_id": "1590179438", "caption": "Roots of Norfolk Pine tree", "id": "1590179438"}, {"image_id": "1590315725", "caption": "Man talking to woman on motor scooter", "id": "1590315725"}, {"image_id": "1570363868", "caption": "Snow covered mountain in the Austrian Alps at Flachauwinkl", "id": "1570363868"}, {"image_id": "1587145145", "caption": "Detail view of floating burning candles in a bowl of water", "id": "1587145145"}, {"image_id": "1572538721", "caption": "A young man holding an engagement ring", "id": "1572538721"}, {"image_id": "1297800530", "caption": "Loving father hugging smiling daughter outdoors at camera", "id": "1297800530"}, {"image_id": "1590056609", "caption": "Portrait of young woman holding her hand to her face", "id": "1590056609"}, {"image_id": "1766918226", "caption": "Young family in boat on lake", "id": "1766918226"}, {"image_id": "1851483926", "caption": "Smiling couple on a cycling adventure in the forest", "id": "1851483926"}, {"image_id": "217368578", "caption": "German Flag on Top of Soccer Ball", "id": "217368578"}, {"image_id": "1297798526", "caption": "Portrait of active senior couple riding bike on road in countryside", "id": "1297798526"}, {"image_id": "1840648490", "caption": "A worker holding a controller in the foreground observing the high speed robotic arm on an assembly line in a factory with some motion blur", "id": "1840648490"}, {"image_id": "1665808490", "caption": "Reflection of woman smiling in mirror with dentist", "id": "1665808490"}, {"image_id": "1855886150", "caption": "Vertical shot of a flower girl on grass smiling at the camera with the wedding party in the background on a sunny day", "id": "1855886150"}, {"image_id": "1590044066", "caption": "A portrait of an attractive senior woman smiling", "id": "1590044066"}, {"image_id": "1844194343", "caption": "Back view of the couple sitting next to each other and chatting in a red and pink foldable chair in the garden", "id": "1844194343"}, {"image_id": "1578946151", "caption": "A woman relaxing in a deck chair", "id": "1578946151"}, {"image_id": "1670246459", "caption": "A young woman wearing silk pyjamas", "id": "1670246459"}, {"image_id": "1868723351", "caption": "A medium shot of a happy man on bicycle looking at wildflowers in a field with his wife in background", "id": "1868723351"}, {"image_id": "1844727506", "caption": "Vertical shot of a young design student of African ethnicity measuring a garment worn by a mannequin", "id": "1844727506"}, {"image_id": "1586687297", "caption": "Greece Kefalonia Fiskardo Boats in sunny Harbour", "id": "1586687297"}, {"image_id": "1590164798", "caption": "Group of teenage students smiling outdoors", "id": "1590164798"}, {"image_id": "1586724662", "caption": "Businesswoman Throwing Paper Into Waste Basket", "id": "1586724662"}, {"image_id": "1671848261", "caption": "Woman applying deodorant anti perspirant in mirror", "id": "1671848261"}, {"image_id": "1567863530", "caption": "Boy with blueprints on construction site", "id": "1567863530"}, {"image_id": "1586675249", "caption": "Christmas Ornaments in a Row", "id": "1586675249"}, {"image_id": "1873296698", "caption": "Portrait shot of a frustrated home economics teacher cooking in a kitchen classroom", "id": "1873296698"}, {"image_id": "1855878032", "caption": "Close up of a woman's hands pushing gambling chips onto the table during a game of poker at the casino", "id": "1855878032"}, {"image_id": "1840552718", "caption": "Businesswoman wearing headset talking on call and using digital tablet near window", "id": "1840552718"}, {"image_id": "1590070310", "caption": "A redundant businessman taking his belongings home in a box", "id": "1590070310"}, {"image_id": "1587835871", "caption": "The Hall of Justice Rome Italy", "id": "1587835871"}, {"image_id": "1576774367", "caption": "Portrait of a young man looking pensive", "id": "1576774367"}, {"image_id": "216576980", "caption": "Turtle crawling along deserted dirt road", "id": "216576980"}, {"image_id": "1572535982", "caption": "A woman holding a shell to her ear", "id": "1572535982"}, {"image_id": "1586730098", "caption": "Portrait Of Mature Couple Enjoying Beach Vacation Together", "id": "1586730098"}, {"image_id": "1860726761", "caption": "Side view of a businessman working with a laptop on a filing cabinet in the desert", "id": "1860726761"}, {"image_id": "1590347909", "caption": "Woman holding fringe over head in wind", "id": "1590347909"}, {"image_id": "1859143124", "caption": "Vertical shot of a father lifting his daughter off the dock with wife watching them", "id": "1859143124"}, {"image_id": "1878692708", "caption": "Medium shot of high school girls conducting scientific experiment on a plant during a biology class", "id": "1878692708"}, {"image_id": "1866001040", "caption": "Horizontal surface level shot of a mature woman with her daughter on a road trip in a red convertible smiles at the camera", "id": "1866001040"}, {"image_id": "1590149657", "caption": "Figurine of couple in sleigh in snow globe", "id": "1590149657"}, {"image_id": "1297794659", "caption": "Businesswoman at camera with office colleagues meeting in background", "id": "1297794659"}, {"image_id": "216389351", "caption": "Deck chair cutout", "id": "216389351"}, {"image_id": "1571518010", "caption": "Portrait of a young woman", "id": "1571518010"}, {"image_id": "1878869618", "caption": "A wide angle shot of Gym teacher watching a school girl jumping high above the ground with other students in the background trying to climb up the equipment", "id": "1878869618"}, {"image_id": "1852963967", "caption": "Worker on a ladder stocking bottles in a wine shop", "id": "1852963967"}, {"image_id": "1297775291", "caption": "Family walking through snow in winter landscape", "id": "1297775291"}, {"image_id": "1868723399", "caption": "A side profile medium shot of a blonde woman smiling while riding bicycle in a wildflower field", "id": "1868723399"}, {"image_id": "217374353", "caption": "Green hills with single tree Switzerland", "id": "217374353"}, {"image_id": "1578942875", "caption": "A woman eating a bowl of strawberries", "id": "1578942875"}, {"image_id": "1862083478", "caption": "Combine harvester harvesting wheat into the trailer in a rural field", "id": "1862083478"}, {"image_id": "1297788770", "caption": "Two excited girls on winter vacation pulling sled up snowy hill", "id": "1297788770"}, {"image_id": "1587997637", "caption": "Close up of wine glasses in man's hand outdoors", "id": "1587997637"}, {"image_id": "216570866", "caption": "Young wheat field", "id": "216570866"}, {"image_id": "1576739426", "caption": "A businessman pulling suitcase along", "id": "1576739426"}, {"image_id": "1586685776", "caption": "Picnic basket and blanket in a tree lined meadow", "id": "1586685776"}, {"image_id": "1588012880", "caption": "Portrait of a smiling senior man", "id": "1588012880"}, {"image_id": "1587984248", "caption": "woman doing a handstand by the waterside", "id": "1587984248"}, {"image_id": "1840553963", "caption": "Salesman showing brochure to couple at a table in a car dealership showroom", "id": "1840553963"}, {"image_id": "1859176955", "caption": "Vertical shot of an engineer inspecting the turbine engine of a passenger jet at a hangar", "id": "1859176955"}, {"image_id": "1587656774", "caption": "Low angle view of a barbed wire fence", "id": "1587656774"}, {"image_id": "1663815677", "caption": "Baby hand in adult hand", "id": "1663815677"}, {"image_id": "1869278816", "caption": "Portrait of a smiling worker taping box in a food processing plant", "id": "1869278816"}, {"image_id": "1710418406", "caption": "Two young people exercising on stationary bikes", "id": "1710418406"}, {"image_id": "1839581495", "caption": "Tilted shot of a doctor smiling at a patient with reports in hand", "id": "1839581495"}, {"image_id": "1852965587", "caption": "Close up shot a woman showing off manicured fingernails in nail salon with a nail technician in the background", "id": "1852965587"}, {"image_id": "1578947198", "caption": "Portrait of a male athlete with UK flag", "id": "1578947198"}, {"image_id": "1864634696", "caption": "Vertical shot of multi generation family celebrating a birthday in the summer garden in front of the house", "id": "1864634696"}, {"image_id": "1859201045", "caption": "Sheep suckling milk to its lamb", "id": "1859201045"}, {"image_id": "1586721830", "caption": "Sun Shining Through Over Autumn Trees Surrounding Lake", "id": "1586721830"}, {"image_id": "1590149381", "caption": "Facade of house decorated for Christmas", "id": "1590149381"}, {"image_id": "1587058253", "caption": "Group of young people at reception desk in gym", "id": "1587058253"}, {"image_id": "1844764277", "caption": "Vertical portrait of a cute little girl sitting in a convertible car and her family watching the horizon in the background", "id": "1844764277"}, {"image_id": "1297780814", "caption": "Smiling doctor in white coat with patient adjusting stethoscope", "id": "1297780814"}, {"image_id": "1587987839", "caption": "Portrait of young woman smiling looking away from camera", "id": "1587987839"}, {"image_id": "1711067282", "caption": "Three graduates celebrating in their graduation gowns", "id": "1711067282"}, {"image_id": "1576745264", "caption": "A confident and attractive middle aged woman in white smiling", "id": "1576745264"}, {"image_id": "1864634702", "caption": "Horizontal shot of multi generation family celebrating a birthday in the summer garden in front of the house", "id": "1864634702"}, {"image_id": "1570350299", "caption": "A line of urinals in a public toilet", "id": "1570350299"}, {"image_id": "1864622714", "caption": "Little boy and girl making snowballs in a snow field while smiling at the camera with their parents in the background", "id": "1864622714"}, {"image_id": "1576746143", "caption": "Father and daughter in a bowling alley celebrating victory", "id": "1576746143"}, {"image_id": "1650274619", "caption": "Footsteps in sand on a sunny day", "id": "1650274619"}, {"image_id": "1859342204", "caption": "A medium shot of a young couple looking at a blueprint while standing near a large solar panel", "id": "1859342204"}, {"image_id": "1571687294", "caption": "Blaeserturm and town hall Ravensburg Baden Wurttemberg Germany", "id": "1571687294"}, {"image_id": "217367375", "caption": "Tree in Field Switzerland", "id": "217367375"}, {"image_id": "1840647824", "caption": "Wide shot of an IT Technician holding pen and paper examining Lan network in selective focus inside the server room with cabinets in the foreground", "id": "1840647824"}, {"image_id": "1590224696", "caption": "Close up of young girl with freckles", "id": "1590224696"}, {"image_id": "1590359906", "caption": "Los Roques rock formation in front of Mount Teide Teide National Park Tenerife Canary Islands Spain", "id": "1590359906"}, {"image_id": "1587995873", "caption": "Young woman sitting in a boat talking on a mobile phone", "id": "1587995873"}, {"image_id": "1297797461", "caption": "Detail of pregnant woman touching belly relaxing in park", "id": "1297797461"}, {"image_id": "1587131942", "caption": "Teenage paddle boarding on lake under sun Lake Starnberg Bavaria Germany Europe", "id": "1587131942"}, {"image_id": "1852965713", "caption": "Hairdresser placing rollers in a woman's hair at a salon", "id": "1852965713"}, {"image_id": "216120050", "caption": "Green fields and tree", "id": "216120050"}, {"image_id": "1873351118", "caption": "Vertical shot of an Ornate jester Venetian mask for Venice Carnival hanging in market stall Italy", "id": "1873351118"}, {"image_id": "1590354869", "caption": "Woman laughing and holding apple", "id": "1590354869"}, {"image_id": "1590319592", "caption": "Aerial view of Munich Bavaria Germany", "id": "1590319592"}, {"image_id": "1590046034", "caption": "Young girl in a party dress holding a Christmas decoration", "id": "1590046034"}, {"image_id": "1586693084", "caption": "Focused high school student conducting scientific experiment in biology class", "id": "1586693084"}, {"image_id": "1576771991", "caption": "Profile of young woman's face", "id": "1576771991"}, {"image_id": "1587857282", "caption": "High angle view of a man playing the steel drums Georgetown Guyana", "id": "1587857282"}, {"image_id": "1844767151", "caption": "Doctor running down the hospital corridor with a medical chart", "id": "1844767151"}, {"image_id": "1572388604", "caption": "Mixed Forest in Northern Black Forest Bad Herrenalb Baden Wuerttemberg Germany", "id": "1572388604"}, {"image_id": "1587850184", "caption": "Statue of St Nepomuk in Prague", "id": "1587850184"}, {"image_id": "1586684546", "caption": "Staff At Fresh Fish Counter In Supermarket", "id": "1586684546"}, {"image_id": "1868709506", "caption": "A vertical Close up of a confident engineer measuring a part of the machine with calipers", "id": "1868709506"}, {"image_id": "1570280723", "caption": "Father and son looking at mobile phones in a mobile phone shop", "id": "1570280723"}, {"image_id": "217368494", "caption": "High angle view of a green plain Kruger National Park Mpumalanga South Africa", "id": "217368494"}, {"image_id": "1846708202", "caption": "A wide view of a truly modern operating room a with a team of surgeons performing an operation", "id": "1846708202"}, {"image_id": "1590160817", "caption": "Snow covered mountain under blue sky Sertig Valley Davos Graubuenden Grisons Switzerland", "id": "1590160817"}, {"image_id": "1869046598", "caption": "Joyous father and son playing on a double bed at home with the son hugging father lying on his back", "id": "1869046598"}, {"image_id": "1588020797", "caption": "Surface shot of people sitting in chairs in waiting room", "id": "1588020797"}, {"image_id": "1852967129", "caption": "Children playing on a carousel in a park", "id": "1852967129"}, {"image_id": "1872076856", "caption": "Woman decorating at home painting wall with paint roller smiling side view", "id": "1872076856"}, {"image_id": "1572510440", "caption": "Family lying on rug together smiling", "id": "1572510440"}, {"image_id": "1665809783", "caption": "Woman walking past toast with jam on rug", "id": "1665809783"}, {"image_id": "1857288746", "caption": "Side view of female students studying with a laptop in the library", "id": "1857288746"}, {"image_id": "1570537094", "caption": "Exterior of Basilica Nossa Senhora de Nazare Belem Brazil", "id": "1570537094"}, {"image_id": "1587145154", "caption": "Detail toned view of Christmas ornaments with a burning candle in foreground", "id": "1587145154"}, {"image_id": "1571610899", "caption": "A man sitting by a pool", "id": "1571610899"}, {"image_id": "1570365641", "caption": "Rural landscape and church Milborne Port Sherborne Dorset UK", "id": "1570365641"}, {"image_id": "1590045962", "caption": "Business people traveling waiting in airport or station", "id": "1590045962"}, {"image_id": "1665810146", "caption": "Close up of woman's feet in high heels", "id": "1665810146"}, {"image_id": "1587139088", "caption": "View of an athlete doing the long jump", "id": "1587139088"}, {"image_id": "1590209231", "caption": "View from autumn Luesener Alm to Peitlerkofel Dolomite Alps South Tyrol Italy", "id": "1590209231"}, {"image_id": "1588025585", "caption": "A senior woman in profile detail of nose mouth and chin", "id": "1588025585"}, {"image_id": "1587992681", "caption": "Two young lovers sitting beneath a tree", "id": "1587992681"}, {"image_id": "1844194397", "caption": "Beautiful sunset sky above clouds with dramatic light", "id": "1844194397"}, {"image_id": "1297793786", "caption": "Portrait of smiling plumber holding toolbox standing next to van", "id": "1297793786"}, {"image_id": "1587990152", "caption": "Woman holding seed in tweezers", "id": "1587990152"}, {"image_id": "1840648595", "caption": "Side view of an IT technician working in the aisle of a data center room in selective focus with a row of network server cabinets softly blurred in the foreground", "id": "1840648595"}, {"image_id": "1590216773", "caption": "Close up of cattle with calf in field", "id": "1590216773"}, {"image_id": "1586722985", "caption": "Portrait Of Young Boy Riding Bicycle In Urban Setting", "id": "1586722985"}, {"image_id": "1665810860", "caption": "Two men looking away from each other", "id": "1665810860"}, {"image_id": "217367000", "caption": "Sunset Los Cabos Mexico", "id": "217367000"}, {"image_id": "1570300802", "caption": "View to snow covered mountains in the Alps Flachauwinkl Salzburg Austria", "id": "1570300802"}, {"image_id": "1587133091", "caption": "Empty railway station platform Munich Bavaria Germany Europe", "id": "1587133091"}, {"image_id": "1576777214", "caption": "portrait woman standing in tropical garden", "id": "1576777214"}, {"image_id": "1590319625", "caption": "Senior woman walking in park", "id": "1590319625"}, {"image_id": "1865993573", "caption": "Group of young adults undressing at the side of lake with two women on the jetty another one on the shore and a man sitting in boot of car undoing his shoes", "id": "1865993573"}, {"image_id": "1590349859", "caption": "View to Sa Cova Foradada Mallorca Spain", "id": "1590349859"}, {"image_id": "1588008413", "caption": "Couple lying in snow hugging and smiling", "id": "1588008413"}, {"image_id": "1587982595", "caption": "A young man with a ball on the beach", "id": "1587982595"}, {"image_id": "1587038534", "caption": "Circle of blue rabbit cutouts", "id": "1587038534"}, {"image_id": "1590220502", "caption": "Girl and boy putting money in piggy bank on sofa", "id": "1590220502"}, {"image_id": "1263320693", "caption": "Farm field of wheat crop against blue summer sky", "id": "1263320693"}, {"image_id": "1590215495", "caption": "Children buying ticket at box office", "id": "1590215495"}, {"image_id": "1297792502", "caption": "Farmer with laptop in barley field with combine harvesters at work", "id": "1297792502"}, {"image_id": "1846752431", "caption": "A wide view of professional surgeons performing a serious operation in the properly lit operation theatre", "id": "1846752431"}, {"image_id": "1587141272", "caption": "View of water being poured into a glass", "id": "1587141272"}, {"image_id": "1590102587", "caption": "Businessman preparing a meal in the kitchen", "id": "1590102587"}, {"image_id": "1297793753", "caption": "Builder on construction site checking outdoor wall with spirit level", "id": "1297793753"}, {"image_id": "1570350278", "caption": "Cows grazing on a mountain in summer Rinerhorn Grisons Switzerland", "id": "1570350278"}, {"image_id": "1571579096", "caption": "A young man playing air guitar", "id": "1571579096"}, {"image_id": "1840553861", "caption": "Salesman wiping automobile hood with tie in a car dealership showroom", "id": "1840553861"}, {"image_id": "1710280346", "caption": "Portrait of a businessman city backdrop", "id": "1710280346"}, {"image_id": "1277234618", "caption": "Active woman doing yoga in exercise studio at camera", "id": "1277234618"}, {"image_id": "1571333324", "caption": "Pylon powerline on stormy day in Pfullendorf Bavaria Germany", "id": "1571333324"}, {"image_id": "1590047366", "caption": "A young woman walking up stairs", "id": "1590047366"}, {"image_id": "1587148190", "caption": "Close up of a glass of lemonade", "id": "1587148190"}, {"image_id": "1873296923", "caption": "High school boy assembling a helix DNA model in a science class with her classmates and professor in the background", "id": "1873296923"}, {"image_id": "1572661535", "caption": "A woman washing with a sponge", "id": "1572661535"}, {"image_id": "1570357124", "caption": "Temple Inner Courtyard of Banteay Kdei Angkor Siem Reap Cambodia", "id": "1570357124"}, {"image_id": "1846708256", "caption": "Vertical view of experienced surgeons looking down at the patient while carefully performing the operation in the operating suite", "id": "1846708256"}, {"image_id": "1277367191", "caption": "Looking up at group of teenagers in swimwear on summer vacation holiday smiling at camera", "id": "1277367191"}, {"image_id": "1716617834", "caption": "Couple kissing under sweater on beach", "id": "1716617834"}, {"image_id": "1846771121", "caption": "Aerial view of a commercial dock at the edge of the city", "id": "1846771121"}, {"image_id": "1590179375", "caption": "Close up of flower and cactus", "id": "1590179375"}, {"image_id": "1297732865", "caption": "Active woman jogging along urban street", "id": "1297732865"}, {"image_id": "1588010177", "caption": "Woman using eye drops outdoors", "id": "1588010177"}, {"image_id": "1859331779", "caption": "A Close up shot of doctor smiling at a patient while listening to her heartbeat with a stethoscope", "id": "1859331779"}, {"image_id": "1857297398", "caption": "Close up shot of a young woman studying with a laptop in the library", "id": "1857297398"}, {"image_id": "1873346294", "caption": "Vertical shot of Teacher and middle school students learning gardening in plant greenhouse", "id": "1873346294"}, {"image_id": "1843586648", "caption": "Close up of a woman in a red bathing suit on a bright sunny day with the ocean and a sailboat in background", "id": "1843586648"}, {"image_id": "1840562420", "caption": "Technician with safety glasses reading a list at a steel roller bearing inventory in a warehouse", "id": "1840562420"}, {"image_id": "1864640831", "caption": "Vertical shot of smiling male teacher standing by a whiteboard in a classroom looking at the children with raised hands", "id": "1864640831"}, {"image_id": "1747441218", "caption": "Figurines of bride and groom standing in flower petals", "id": "1747441218"}, {"image_id": "1578906830", "caption": "Portrait of young woman with eyes closed and mouth open wide screaming", "id": "1578906830"}, {"image_id": "1571337401", "caption": "Little village at Traenfjorden Helgeland Nordland Norway", "id": "1571337401"}, {"image_id": "1590359903", "caption": "Summit of Mount Teide Teide National Park Tenerife Canary Islands Spain", "id": "1590359903"}, {"image_id": "1852961618", "caption": "Smiling girl swinging on a swing", "id": "1852961618"}, {"image_id": "1816749642", "caption": "Close up overhead view of a boy and girl riding toy tricycle and push scooter in playground", "id": "1816749642"}, {"image_id": "1587811604", "caption": "Misty meadow with trees at dusk", "id": "1587811604"}, {"image_id": "1587139100", "caption": "Low angle view of man holding head in hands", "id": "1587139100"}, {"image_id": "1570559144", "caption": "Harbor of Manaus Amazonas Amazon River Brazil", "id": "1570559144"}, {"image_id": "1866088826", "caption": "Quality control workers inspecting tomatoes on production line in a food processing plant", "id": "1866088826"}, {"image_id": "1576741859", "caption": "A couple eating at a restaurant", "id": "1576741859"}, {"image_id": "1844723924", "caption": "Low angle vertical shot of a supervisor inspecting boxes at the production line of a distribution warehouse", "id": "1844723924"}, {"image_id": "1578212861", "caption": "Portrait of a young woman with blue eyes touching her face", "id": "1578212861"}, {"image_id": "1873425437", "caption": "A portrait shot of a happy businessman handing passport to a woman at airport counter", "id": "1873425437"}, {"image_id": "1277238344", "caption": "Mature woman on sofa wearing white bathrobe reading book at camera", "id": "1277238344"}, {"image_id": "1277252066", "caption": "Two young women on beach using mobile phone on summer vacation", "id": "1277252066"}, {"image_id": "1587121256", "caption": "Young man looking at a reflection of himself as a businessman", "id": "1587121256"}, {"image_id": "216348866", "caption": "Piggy bank with wings flying in blue sky with sun and clouds", "id": "216348866"}, {"image_id": "1857297566", "caption": "Vertical shot of a businessman with hands on hips on a yellow line in the middle of a road in the desert", "id": "1857297566"}, {"image_id": "1844194112", "caption": "Portrait of blond woman holding basket full of harvested vegetables in her garden", "id": "1844194112"}, {"image_id": "1839589334", "caption": "Portrait of a female surfer sitting on a rock with dog on beach on a sunny day", "id": "1839589334"}, {"image_id": "1852925660", "caption": "Elevated vertical view of a senior couple decorating the Christmas tree", "id": "1852925660"}, {"image_id": "1570537100", "caption": "Lumber industry at Breves Channels Brazil", "id": "1570537100"}, {"image_id": "1860743072", "caption": "Horizontal shot of a businessman sitting on a filing cabinet using a mobile phone in the middle of an open road in the desert", "id": "1860743072"}, {"image_id": "1590338246", "caption": "Young happy woman with glass of water portrait", "id": "1590338246"}, {"image_id": "1876650680", "caption": "Male rock climber leaping between rocks", "id": "1876650680"}, {"image_id": "1590202223", "caption": "Red pencil writing No Way", "id": "1590202223"}, {"image_id": "1578942641", "caption": "Portrait of a young woman in the street carrying a shoulder bag", "id": "1578942641"}, {"image_id": "1869316976", "caption": "Vertical silhouette shot of a technician using a laptop in the aisle of a server room", "id": "1869316976"}, {"image_id": "1277231417", "caption": "Paramedic helping male patient on stretcher with head in brace", "id": "1277231417"}, {"image_id": "1855882715", "caption": "Portrait of a senior tourist couple sitting by fountain with pen and paper", "id": "1855882715"}, {"image_id": "1587142646", "caption": "View of the South african Fur Seal Arctocephalus pusillus on Duiker Island near Haut Bay Cape Peninsula Western Cape South Africa", "id": "1587142646"}, {"image_id": "1586672624", "caption": "Senior couple walking together on frozen lake", "id": "1586672624"}, {"image_id": "1868703248", "caption": "A businessman and a businesswoman working together in an office while looking down at the digital tablet", "id": "1868703248"}, {"image_id": "1587825011", "caption": "City on mountainside Flavella Brazil", "id": "1587825011"}, {"image_id": "1839588467", "caption": "Portrait of a young woman talking on cell phone seated in front of a laptop computer in a cafe", "id": "1839588467"}, {"image_id": "1570348097", "caption": "View to Sertigtal Davos Grisons Switzerland", "id": "1570348097"}, {"image_id": "1590316565", "caption": "African man selling jewelry inside jacket", "id": "1590316565"}, {"image_id": "1840559672", "caption": "Wide shot of smiling businessman with clipboard in front of steel tubes in warehouse", "id": "1840559672"}, {"image_id": "1571520335", "caption": "A senior couple in a sports car", "id": "1571520335"}, {"image_id": "1864651109", "caption": "Young woman crouching over a bathtub outdoors and smiling at the camera with a rubber duck kept on the edge of the bathtub", "id": "1864651109"}, {"image_id": "1586664161", "caption": "Man pushing shopping trolley mother holding daughter's hand smiling front view cutout", "id": "1586664161"}, {"image_id": "1852959794", "caption": "Children squirting water at each other at a park using plastic bottles", "id": "1852959794"}, {"image_id": "1586666192", "caption": "woman eating bowl of fruit cutout", "id": "1586666192"}, {"image_id": "1840563596", "caption": "Vertical shot of a technician holding a tray of aluminum products in a hi tech manufacturing plant", "id": "1840563596"}, {"image_id": "1844733080", "caption": "High angle shot of a group of girls with backpacks looking at a map and compass over a wooden fence in a green field", "id": "1844733080"}, {"image_id": "216390656", "caption": "Tree on hillside against blue sky", "id": "216390656"}, {"image_id": "1571658911", "caption": "A businesswoman holding a pencil", "id": "1571658911"}, {"image_id": "1587999317", "caption": "Female fortune teller with illuminated globe", "id": "1587999317"}, {"image_id": "1578225827", "caption": "A mid adult woman eating ice cream Close up", "id": "1578225827"}, {"image_id": "1852964030", "caption": "Smiling waiters preparing and serving coffee in a coffee shop", "id": "1852964030"}, {"image_id": "1844727695", "caption": "Medium shot of a happy elderly couple in a rowboat on a lake surrounded by tall grass", "id": "1844727695"}, {"image_id": "1588020971", "caption": "Man lying on sofa with gift on Christmas", "id": "1588020971"}, {"image_id": "1590181229", "caption": "Businesswoman accepting gift from co workers", "id": "1590181229"}, {"image_id": "1570537085", "caption": "Exterior of Basilica Nossa Senhora de Nazare Belem Brazil", "id": "1570537085"}, {"image_id": "1572537401", "caption": "Portrait of a girl leaning against railing bars", "id": "1572537401"}, {"image_id": "1813179956", "caption": "Reflection of businessman sitting on floor with laptop looking out of the window as seen from rear", "id": "1813179956"}, {"image_id": "1869314819", "caption": "Child leaping in an autumn forest with her parents holding hands and walking in the background", "id": "1869314819"}, {"image_id": "1766906310", "caption": "Two couples playing at beach", "id": "1766906310"}, {"image_id": "1587850103", "caption": "Staircase leading down in Prague", "id": "1587850103"}, {"image_id": "1297789934", "caption": "Portrait of smiling young man outdoors wearing fleece in autumn", "id": "1297789934"}, {"image_id": "1855882694", "caption": "Close up portrait of a young couple posing for the camera on leafy camera", "id": "1855882694"}, {"image_id": "1588020458", "caption": "Farm in snowy landscape Upper Bavaria Germany", "id": "1588020458"}, {"image_id": "1571664065", "caption": "A man drinking a glass of water", "id": "1571664065"}, {"image_id": "1297792517", "caption": "Farmer inspecting barley crop in summer farm field", "id": "1297792517"}, {"image_id": "1840561322", "caption": "Top shot of workers scanning and packing boxes on conveyor belts at a distribution warehouse", "id": "1840561322"}, {"image_id": "1277238272", "caption": "Portrait of loving young couple relaxing on outdoor sofa at home", "id": "1277238272"}, {"image_id": "1590061580", "caption": "A businessman in an office with two colleagues in background looking at laptop", "id": "1590061580"}, {"image_id": "1578907001", "caption": "A businessman walking down stairs", "id": "1578907001"}, {"image_id": "1572550082", "caption": "Young man splashing water on face", "id": "1572550082"}, {"image_id": "1725713087", "caption": "Twin brothers playing in sandpit with toy cars", "id": "1725713087"}, {"image_id": "1590225530", "caption": "Three girls jumping with arms raised outdoors", "id": "1590225530"}, {"image_id": "1578916778", "caption": "A Young Woman Carrying A Bunch Of Yellow Roses In A Shopping Bag", "id": "1578916778"}, {"image_id": "1866104786", "caption": "Wide shot of worker at production in a cheese processing plant", "id": "1866104786"}, {"image_id": "1852938092", "caption": "Vertical waist up shot of a joyous son helping his father in washing up the dishes by the counter in kitchen", "id": "1852938092"}, {"image_id": "1588003487", "caption": "Scenic view of meadow and mountains Carinthia Austria", "id": "1588003487"}, {"image_id": "1862121458", "caption": "A vertical view of Business people in selective focus drinking coffee and talking while a woman is working on a laptop in the foreground", "id": "1862121458"}, {"image_id": "1588015202", "caption": "Overhead view of informal business meeting at table of staff canteen", "id": "1588015202"}, {"image_id": "1572537374", "caption": "A man about to go scuba diver", "id": "1572537374"}, {"image_id": "1866123557", "caption": "Low angle shot of a couple hiking along a mountainous path with their children", "id": "1866123557"}, {"image_id": "1567889354", "caption": "Village at Nosy Komba Madagascar", "id": "1567889354"}, {"image_id": "1586735456", "caption": "Female Engineer With Clipboard In Electronics Clean Room", "id": "1586735456"}, {"image_id": "1587825008", "caption": "Aerial view of snow covered mountains Swiss Alps Switzerland", "id": "1587825008"}, {"image_id": "1297750442", "caption": "Smiling mother and daughter lying on picnic rug in park", "id": "1297750442"}, {"image_id": "1590201311", "caption": "Dock and radar station at sunset Key West Florida United States", "id": "1590201311"}, {"image_id": "1843586543", "caption": "A blue tractor ploughing the field with a cloudy blue sky in the background on a bright sunny day", "id": "1843586543"}, {"image_id": "1590209306", "caption": "High angle view of autumn forest Dolomite Alps South Tyrol Italy", "id": "1590209306"}, {"image_id": "1590056537", "caption": "Portrait of a young man with cigarettes", "id": "1590056537"}, {"image_id": "1587142634", "caption": "Low angle view of the Freedom column against blue sky Apartheid Museum Johannesburg Gauteng South Africa", "id": "1587142634"}, {"image_id": "1587149084", "caption": "Close up of a gold Christmas ornament", "id": "1587149084"}, {"image_id": "1578942626", "caption": "A young girl feeding her dog", "id": "1578942626"}, {"image_id": "1578904175", "caption": "A middle aged woman with a vitamin supplement capsule on her tongue", "id": "1578904175"}, {"image_id": "1570160999", "caption": "Aldabra giant tortoise Aldabra Atoll Seychelles", "id": "1570160999"}, {"image_id": "1587128762", "caption": "Young snowboarder taking a break in ski resort", "id": "1587128762"}, {"image_id": "1864637525", "caption": "Horizontal shot of a girl talking over a mobile phone sitting on steps on a sunny day", "id": "1864637525"}, {"image_id": "1868718413", "caption": "A portrait shot of a young blonde woman in pajamas smiling at camera while doing homework and drinking coffee in bed", "id": "1868718413"}, {"image_id": "1862063159", "caption": "Elderly man jumping on a wooden jetty by the lake with his wife smiling at him", "id": "1862063159"}, {"image_id": "1587139037", "caption": "Blurred view of a woman skating with white ice skates", "id": "1587139037"}, {"image_id": "1851485501", "caption": "Low angle shot of a couple with their children walking along the shore of a sunny beach", "id": "1851485501"}, {"image_id": "1297793720", "caption": "Plasterer on construction site working on inside wall of new house", "id": "1297793720"}, {"image_id": "1590219863", "caption": "Historical landmark Abbey of St Gallen St Gallen Canton of St Gallen Switzerland", "id": "1590219863"}, {"image_id": "1587983462", "caption": "A woman walking in the desert holding a bunch of balloons", "id": "1587983462"}, {"image_id": "1846059797", "caption": "A woman looking up and relaxing on log of tree in a sunny forest", "id": "1846059797"}, {"image_id": "1587842939", "caption": "Courtyard of Monastery in Germany", "id": "1587842939"}, {"image_id": "1587855617", "caption": "Close up of woman's lips and teeth", "id": "1587855617"}, {"image_id": "1586672588", "caption": "Senior woman wearing fur hat in winter day", "id": "1586672588"}, {"image_id": "1587046703", "caption": "Blueberries and blackberries in a bowl", "id": "1587046703"}, {"image_id": "1869043580", "caption": "Young woman carries a rucksack and sleeping bag to a camp in the woodland clearing smiles at the camera with friends in the background assembling tent", "id": "1869043580"}, {"image_id": "1578207215", "caption": "A young woman applying lip balm in wintertime", "id": "1578207215"}, {"image_id": "1572475913", "caption": "Teenage boy in a bowling alley rolling the ball", "id": "1572475913"}, {"image_id": "1586666927", "caption": "Close up of grapes on vines in French vineyard", "id": "1586666927"}, {"image_id": "1571579078", "caption": "Boy in baseball gear sitting on a bench", "id": "1571579078"}, {"image_id": "1766908257", "caption": "Pregnant couple with young daughter hugging on sofa", "id": "1766908257"}, {"image_id": "1716610052", "caption": "Colleagues in meeting in conference room", "id": "1716610052"}, {"image_id": "1590352163", "caption": "Bay at Cabo de Formentor Mallorca Spain", "id": "1590352163"}, {"image_id": "1587126701", "caption": "Two businesswomen descending a staircase", "id": "1587126701"}, {"image_id": "1844196368", "caption": "Tight shot of woman eating a fresh strawberry in a kitchen", "id": "1844196368"}, {"image_id": "1855880564", "caption": "Wide shot of a pilot and flight attendant walking towards the private jet on the runway", "id": "1855880564"}, {"image_id": "216584132", "caption": "Trees and field in snow covered winter landscape", "id": "216584132"}, {"image_id": "1844766959", "caption": "Smiling doctor's talking to a nurse in a hospital", "id": "1844766959"}, {"image_id": "1878692816", "caption": "High school student copying a classmate s homework in the classroom", "id": "1878692816"}, {"image_id": "1578905444", "caption": "A young couple on a beach", "id": "1578905444"}, {"image_id": "1664821145", "caption": "Overview of man in a strongroom with headset", "id": "1664821145"}, {"image_id": "1570308335", "caption": "Ancient stone cave dwellings at Syracuse Sicily Italy", "id": "1570308335"}, {"image_id": "1587030212", "caption": "Low angle view of construction worker standing on edge of brick building", "id": "1587030212"}, {"image_id": "1571677088", "caption": "Electric car recharging batteries Close up", "id": "1571677088"}, {"image_id": "1277229362", "caption": "Active young couple walking in sand dunes by beach in autumn at camera", "id": "1277229362"}, {"image_id": "1578952919", "caption": "A woman in a bikini eating an apple", "id": "1578952919"}, {"image_id": "1590355028", "caption": "View to Ponte Vecchio Florence Italy", "id": "1590355028"}, {"image_id": "1588000682", "caption": "Close up of middle aged man indoors", "id": "1588000682"}, {"image_id": "1587811568", "caption": "City on mountainside Flavella Brazil", "id": "1587811568"}, {"image_id": "1851405509", "caption": "Horizontal low angle shot of two playful children running by the father packing a suitcase into a car with mother striding in the background", "id": "1851405509"}, {"image_id": "1570574048", "caption": "Heavy rain at Amazon River near Pevas Peru", "id": "1570574048"}, {"image_id": "1807085645", "caption": "USA California San Diego woman wearing pink sports vest and shorts jogging listening to MP3 player strapped to arm smiling marina in background tilt", "id": "1807085645"}, {"image_id": "1868723465", "caption": "A medium shot of a happy couple riding bicycles while looking at wildflowers in a field", "id": "1868723465"}, {"image_id": "1533007542", "caption": "Sushi and bamboo background with room to put your text in a Japan travel blog background collage stories", "id": "1533007542"}, {"image_id": "1868722220", "caption": "A medium shot of a happy senior couple smiling at camera while eating strawberries in a field of wildflowers", "id": "1868722220"}, {"image_id": "1586693717", "caption": "Close up focused high school student conducting chemistry scientific experiment", "id": "1586693717"}, {"image_id": "1670264750", "caption": "Man playing golf Close up of feet on the tee", "id": "1670264750"}, {"image_id": "1811160812", "caption": "A close up shot a palm leaf spreading across the frame", "id": "1811160812"}, {"image_id": "1590339857", "caption": "Young girl relaxing in hammock", "id": "1590339857"}, {"image_id": "1590034403", "caption": "Businessman standing in modern office building looking out of the window", "id": "1590034403"}, {"image_id": "1590341195", "caption": "Young woman holding Dutch flag", "id": "1590341195"}, {"image_id": "1586684441", "caption": "Farmer In Field As Oat Crop Is Harvested", "id": "1586684441"}, {"image_id": "1572538895", "caption": "portrait girl resting arm on knee", "id": "1572538895"}, {"image_id": "1860761729", "caption": "A vertical portrait of a smiling farmer holding a lamb in a pasture while sitting in front of a fence", "id": "1860761729"}, {"image_id": "1297786268", "caption": "Climbing equipment and exercise mats in school gymnasium", "id": "1297786268"}, {"image_id": "1859201150", "caption": "Farmer guiding tractor driver while baling hay in the field", "id": "1859201150"}, {"image_id": "1817410446", "caption": "Close up portrait of a smiling boy and girl lying on grass cheek to cheek shot from top and looking at the camera", "id": "1817410446"}, {"image_id": "1663817606", "caption": "Teenage friends in bathrobes looking away", "id": "1663817606"}, {"image_id": "1844731841", "caption": "Medium shot of a happy elderly couple riding a bicycle together in a meadow full of wildflowers", "id": "1844731841"}, {"image_id": "1665809846", "caption": "Man looking down at woman from balcony indoors", "id": "1665809846"}, {"image_id": "1590361874", "caption": "Close up of assorted Euro banknotes", "id": "1590361874"}, {"image_id": "1587103877", "caption": "Male archer aiming at target rear view", "id": "1587103877"}, {"image_id": "1586723585", "caption": "Romantic Young Couple Kissing In Countryside Together", "id": "1586723585"}, {"image_id": "1572535172", "caption": "A businesswoman using a laptop", "id": "1572535172"}, {"image_id": "1587651290", "caption": "View of Christmas decorations in a bowl beside some floating burning candles in a bowl of water", "id": "1587651290"}, {"image_id": "1590338237", "caption": "Young woman looking at apples", "id": "1590338237"}, {"image_id": "1570327478", "caption": "A senior couple walking hand in hand through the snow", "id": "1570327478"}, {"image_id": "1852936727", "caption": "Wide shot of a young woman sitting with legs crossed on floor with color swatches smiles at the camera", "id": "1852936727"}, {"image_id": "1869317066", "caption": "Technician checking cables in the server room of a data center", "id": "1869317066"}, {"image_id": "1860726674", "caption": "Elevated view of a glamorous woman alighting from a car at night", "id": "1860726674"}, {"image_id": "1586652266", "caption": "Senior women sitting back to back in living room", "id": "1586652266"}, {"image_id": "1862121422", "caption": "A vertical portrait of a confident businessman in formal suit holding a folder in the office lobby with other people working in the foreground out of focus", "id": "1862121422"}, {"image_id": "1578212924", "caption": "Portrait of a young woman touching her face", "id": "1578212924"}, {"image_id": "1586693114", "caption": "High school student assembling bicycle in shop class", "id": "1586693114"}, {"image_id": "1586684012", "caption": "Senior Couple Standing In Garden Of Beautiful Cottage", "id": "1586684012"}, {"image_id": "1590352211", "caption": "Craggy landscape and bay at Cabo de Formentor Mallorca Spain", "id": "1590352211"}, {"image_id": "1590209321", "caption": "Mother and children walking on dirt road close to Luesen South Tyrol Italy", "id": "1590209321"}, {"image_id": "1859237018", "caption": "A low angle view of smart and professional architects in safety vests at a construction site while reviewing blueprints on a bright sunny day", "id": "1859237018"}, {"image_id": "1572527696", "caption": "California sea lion Zalophus californianus Sea of Cortez Los Islotes Baja California Sur Mexico", "id": "1572527696"}, {"image_id": "1277235599", "caption": "Businesswoman at head of boardroom conference table", "id": "1277235599"}, {"image_id": "1766925072", "caption": "Happy woman relaxing in hammock on beach", "id": "1766925072"}, {"image_id": "1844196305", "caption": "Horizontal shot of a young woman holding spicy peppers", "id": "1844196305"}, {"image_id": "1570572305", "caption": "Passenger ferry near Manaus Amazonas Amazon River Brazil", "id": "1570572305"}, {"image_id": "1571342708", "caption": "Scenic view of river in Ehrwald Tyrol Austria", "id": "1571342708"}, {"image_id": "1571658905", "caption": "A young woman sitting on a suitcase in the desert", "id": "1571658905"}, {"image_id": "1852925762", "caption": "Senior man standing with a small ornamental tree in hand with a Christmas tree in the background", "id": "1852925762"}, {"image_id": "1855882751", "caption": "Low angle full shot view of a senior traveler couple on the railings of an ornate bridge with a map in hands", "id": "1855882751"}, {"image_id": "1851485396", "caption": "Medium Close up of woman smiling with her hands behind head on a sunny beach", "id": "1851485396"}, {"image_id": "1862083244", "caption": "Supervisor and technician smiling while working in a solar panel factory", "id": "1862083244"}, {"image_id": "1590315329", "caption": "Businessman figurine on stack of Euro coins", "id": "1590315329"}, {"image_id": "1590360536", "caption": "Woman performing handstand on beach", "id": "1590360536"}, {"image_id": "1859349095", "caption": "A portrait shot of a teacher helping a young girl in constructing an electric vehicle prototype through a wheel", "id": "1859349095"}, {"image_id": "1817411535", "caption": "Portrait of happy mother and daughter listening to MP3 player on sofa at home on shared headphones with copy space", "id": "1817411535"}, {"image_id": "1571685638", "caption": "Footpath in forest near Binz Ruegen Mecklenburg Western Pommerania Germany", "id": "1571685638"}, {"image_id": "1586691593", "caption": "Exuberant brother and sister jumping for joy over grass", "id": "1586691593"}, {"image_id": "1586683766", "caption": "Senior Woman Working In Beautiful Cottage Flower Garden", "id": "1586683766"}, {"image_id": "1862086844", "caption": "Close up of flowers in a field and a Beekeeper checking honey on the beehive frame in the background", "id": "1862086844"}, {"image_id": "1586673278", "caption": "Portrait of young woman holding colorful tulips on winter day in mountains", "id": "1586673278"}, {"image_id": "1572381359", "caption": "At the End of Yoho National Park Valley British Columbia Canada", "id": "1572381359"}, {"image_id": "1868714531", "caption": "Close up of a scientist in clean suit with selective focus examining the silicon wafer under a microscope in a special laboratory", "id": "1868714531"}, {"image_id": "1657965941", "caption": "Couple carrying Christmas tree in remote countryside", "id": "1657965941"}, {"image_id": "1878869768", "caption": "A proud Chemistry teacher in a school lab smiling at the camera while standing with a group of smart students wearing lab coats and safety goggles", "id": "1878869768"}, {"image_id": "1587128837", "caption": "Young friends pulling girls friends on sleds through snow", "id": "1587128837"}, {"image_id": "1862086772", "caption": "Blue sky with multiple white clouds rolling over it", "id": "1862086772"}, {"image_id": "1588024022", "caption": "A family sitting down for Christmas dinner", "id": "1588024022"}, {"image_id": "1571664776", "caption": "A businesswoman sitting in a gym using a laptop", "id": "1571664776"}, {"image_id": "1851483929", "caption": "Happy parents and their daughter sitting on a wooden pier over the lake", "id": "1851483929"}, {"image_id": "1571342681", "caption": "Father and son leaping over stream in mountains", "id": "1571342681"}, {"image_id": "1670342045", "caption": "Family wearing pajamas brushing teeth in bathroom in morning", "id": "1670342045"}, {"image_id": "1865943779", "caption": "Mid length shot of a mature woman in red jacket standing on the deck of the sailing boat below sail", "id": "1865943779"}, {"image_id": "1586685212", "caption": "Farmer Driving Lorry Loaded With Straw Bales In Field", "id": "1586685212"}, {"image_id": "1297776791", "caption": "Bunches of ripe purple grapes growing on vines in vineyard", "id": "1297776791"}, {"image_id": "1578205928", "caption": "A young woman standing in a rape seed field Close up", "id": "1578205928"}, {"image_id": "1590316793", "caption": "Low angle view of girl jumping", "id": "1590316793"}, {"image_id": "1590319490", "caption": "Aerial view of Autobahn near Munich Germany", "id": "1590319490"}, {"image_id": "1590183785", "caption": "Domestic pig on chain Lifou Island Loyalty Islands New Caledonia Overseas Territory of France", "id": "1590183785"}, {"image_id": "1839586298", "caption": "Close up of shimmering water in a clear swimming pool on a bright sunny day", "id": "1839586298"}, {"image_id": "1869318026", "caption": "High angle shot of a technician working on a computer in a server room at a data center", "id": "1869318026"}, {"image_id": "1766918283", "caption": "African woman holding world championship soccer ball", "id": "1766918283"}, {"image_id": "1851471998", "caption": "Portrait shot of a boy using a cell phone in an electronics store", "id": "1851471998"}, {"image_id": "1586690789", "caption": "Quality control workers inspecting ripe red vine tomatoes in boxes in food processing plant", "id": "1586690789"}, {"image_id": "1590350069", "caption": "Winding road to Torrent de Pareis Mallorca Spain", "id": "1590350069"}, {"image_id": "1852961633", "caption": "Vertical shot of boys sitting on climbing rods and blowing soap bubbles using a bubble wand", "id": "1852961633"}, {"image_id": "1590214487", "caption": "Close up of autumn leaves on ground", "id": "1590214487"}, {"image_id": "1588000232", "caption": "High angle view of man using laptop in armchair", "id": "1588000232"}, {"image_id": "1846038875", "caption": "A wide shot of a blue tractor and straw baler in a wheat field", "id": "1846038875"}, {"image_id": "1644223652", "caption": "A group of wind turbines part of a wind farm or wind park create electricity and power contributing to a new form of green energy clean background 3D 3D artwork 3D rendering 3D illustration", "id": "1644223652"}, {"image_id": "1866128471", "caption": "Pharmacist in an apron reading the labels on a pack of medicine from the pharmacy shelf", "id": "1866128471"}, {"image_id": "1862131415", "caption": "A portrait Close up shot of a surveyor from backside looking at a co worker through a theodolite at a construction site", "id": "1862131415"}, {"image_id": "1297775246", "caption": "Businessman and businesswoman shaking hands in conference room", "id": "1297775246"}, {"image_id": "1844764340", "caption": "Senior couple leaning on a car and watching the splendid horizon from a hill", "id": "1844764340"}, {"image_id": "1852938059", "caption": "Tilted low angle shot of a joyous father embracing son in driveway with mother and daughter walking hand in hand in the background", "id": "1852938059"}, {"image_id": "1590217379", "caption": "Biker riding in rural area", "id": "1590217379"}, {"image_id": "1297757039", "caption": "Female high school student wearing earphones leaving home for school", "id": "1297757039"}, {"image_id": "1868709494", "caption": "A smiling businesswoman enjoying the breakfast of coffee and croissants with her co worker while holding a glass of juice in the cafeteria", "id": "1868709494"}, {"image_id": "1670341712", "caption": "Woman on vacation with luggage trolley in airport building at camera", "id": "1670341712"}, {"image_id": "1586659874", "caption": "Portrait of businessman and businesswoman standing outside office building", "id": "1586659874"}, {"image_id": "1846400813", "caption": "A sporty couple carrying their skis while walking down on a snowy slope with deep blue sky in the background", "id": "1846400813"}, {"image_id": "1578920087", "caption": "A young woman holding a basket of fruit Close up", "id": "1578920087"}, {"image_id": "1766932269", "caption": "Businessman holding co worker with boxing gloves at arms length", "id": "1766932269"}, {"image_id": "1590178118", "caption": "Couple reading contract at financial office", "id": "1590178118"}, {"image_id": "1859178500", "caption": "Vertical wide shot of an engineer working on a passenger jet in a hangar", "id": "1859178500"}, {"image_id": "1852925582", "caption": "Horizontal shot of a family exchanging gifts in front of a Christmas tree", "id": "1852925582"}, {"image_id": "1586693093", "caption": "High school student measuring cog with calipers in shop class", "id": "1586693093"}, {"image_id": "1578208655", "caption": "A young woman wearing a winter coat holding a mug of hot chocolate", "id": "1578208655"}, {"image_id": "1572535151", "caption": "Portrait young woman lying on the beach", "id": "1572535151"}, {"image_id": "1297775255", "caption": "Businessman and businesswoman with digital tablet meeting in office smiling at camera", "id": "1297775255"}, {"image_id": "1851472028", "caption": "Vertical shot of a salesman showing a cellphone to a young man with headphones in an electronics store", "id": "1851472028"}, {"image_id": "1851485570", "caption": "Medium shot of a happy couple standing at the shore of a sunny beach", "id": "1851485570"}, {"image_id": "1586723021", "caption": "Wooden Jetty Leading Out On Lake Starnberg In Germany", "id": "1586723021"}, {"image_id": "1297793813", "caption": "Detail of poppies growing in garden with lens flare", "id": "1297793813"}, {"image_id": "1586727146", "caption": "Messy Baby Boy Sits In High Chair Covered In Spaghetti And Sauce", "id": "1586727146"}, {"image_id": "1588014248", "caption": "Businessman with hand in mouth at desk", "id": "1588014248"}, {"image_id": "216586112", "caption": "Trees and field in snow covered winter landscape", "id": "216586112"}, {"image_id": "1852938101", "caption": "Horizontal waist up shot of a happy husband and wife making fruit salad in the kitchen", "id": "1852938101"}, {"image_id": "1587810977", "caption": "Street with Christmas decorations at night Getreidegasse Salzburg Austria", "id": "1587810977"}, {"image_id": "1710331553", "caption": "A climber and harness Close up", "id": "1710331553"}, {"image_id": "1588004099", "caption": "Close up of apple blossoms outdoors", "id": "1588004099"}, {"image_id": "1586724698", "caption": "Businessman Walks Past Glass Wall Covered In Sticky Notes", "id": "1586724698"}, {"image_id": "1590025229", "caption": "Two teenage girls applying makeup", "id": "1590025229"}, {"image_id": "1570162076", "caption": "Panoramic of Desroches Island Seychelles", "id": "1570162076"}, {"image_id": "1852925822", "caption": "Horizontal shot of grandfather taking pictures of family decorating the Christmas tree and setting up presents", "id": "1852925822"}, {"image_id": "1297774343", "caption": "Mature businessman with laptop in conference room meeting smiling at camera", "id": "1297774343"}, {"image_id": "1571368178", "caption": "English fans at soccer game in Cape Town South Africa", "id": "1571368178"}, {"image_id": "1578906950", "caption": "Two young lovers holding hands", "id": "1578906950"}, {"image_id": "1581296936", "caption": "Multi generation family on walk in autumn woods together", "id": "1581296936"}, {"image_id": "1873296875", "caption": "High school girls assembling a robotic structure in a science class with a classmate watching them", "id": "1873296875"}, {"image_id": "1813181363", "caption": "Vertical shot of a woman dressed in a formal attire holding a leather case and a diary looking at the camera", "id": "1813181363"}, {"image_id": "1664815649", "caption": "View of two brothers using a laptop", "id": "1664815649"}, {"image_id": "1570311791", "caption": "View over the city of Cefalu with Cathedral Sicily Italy", "id": "1570311791"}, {"image_id": "1859347427", "caption": "A Close up portrait shot of a young girl's hands planning wood in a vocational school", "id": "1859347427"}, {"image_id": "1747435521", "caption": "Rolls of assorted Euro coins with businessmen figurines", "id": "1747435521"}, {"image_id": "1570311782", "caption": "Carpet of purple and yellow flowers", "id": "1570311782"}, {"image_id": "1590352226", "caption": "Lookout at Cabo de Formentor Mallorca Spain", "id": "1590352226"}, {"image_id": "1846771283", "caption": "Vertical portrait of a sports scientist monitoring exercising data of a cyclist working out on an exercise bike", "id": "1846771283"}, {"image_id": "1576738319", "caption": "A young couple at home", "id": "1576738319"}, {"image_id": "1571608289", "caption": "A woman drinking a glass of water by a waterfall", "id": "1571608289"}, {"image_id": "1587991013", "caption": "Low angle view of scientists testing liquid in Petri dish", "id": "1587991013"}, {"image_id": "1670343479", "caption": "Student couple listening to MP3 player sharing earphones outdoors", "id": "1670343479"}, {"image_id": "1862067380", "caption": "Close up shot of poppy flowers in a meadow full of wildflowers", "id": "1862067380"}, {"image_id": "1570300736", "caption": "View to the Doric temple of Segesta Province of Trapani Sicily Italy", "id": "1570300736"}, {"image_id": "1710028550", "caption": "A teenage couple on a date", "id": "1710028550"}, {"image_id": "1873343684", "caption": "Smiling Brewery Workers Checking Fermentation Process In Steel Vat", "id": "1873343684"}, {"image_id": "1571686643", "caption": "Part of a sailing vessel Ruegen Mecklenburg Western Pommerania Germany", "id": "1571686643"}, {"image_id": "1665810209", "caption": "Couple with baby carrier outdoors", "id": "1665810209"}, {"image_id": "1572527765", "caption": "Beach of Isla San Jose Baja California Sur Mexico", "id": "1572527765"}, {"image_id": "1590076970", "caption": "A young man playing a guitar", "id": "1590076970"}, {"image_id": "1570298723", "caption": "Snow covered mountains in the Austrian Alps at Flachauwinkl", "id": "1570298723"}, {"image_id": "1571691458", "caption": "Kjeungskjaer Fyr a lighthouse in Kjeungskjar Orland Sor Trondelag Trondelag Norway", "id": "1571691458"}, {"image_id": "1869314801", "caption": "Vertical shot of a family having a leaf fight in an autumn park", "id": "1869314801"}, {"image_id": "1590353639", "caption": "Close up of woman with eyelash curler", "id": "1590353639"}, {"image_id": "1590222452", "caption": "Close up of senior man smiling with towel around neck", "id": "1590222452"}, {"image_id": "1570552793", "caption": "Sunset near Alter do Chao Rio Tapajos Amazon River Brazil", "id": "1570552793"}, {"image_id": "1846771256", "caption": "Young man working out on a exercise bicycle and a sports scientist is observing his movements standing behind the two monitors", "id": "1846771256"}, {"image_id": "1570219553", "caption": "Benedictine monastery Benedictine Convent of Saint John Val Mustair Swiss Alps Grisons Switzerland", "id": "1570219553"}, {"image_id": "1590359192", "caption": "Young girl jumping in air", "id": "1590359192"}, {"image_id": "1857286268", "caption": "Senior couple standing in the shallows on the beach on a bright sunny day and hugging each other", "id": "1857286268"}, {"image_id": "1586681435", "caption": "cutout Of Middle Aged Male Executive Holding Documents And Checking Watch", "id": "1586681435"}, {"image_id": "1859202755", "caption": "Senior woman and her family having a picnic in the countryside", "id": "1859202755"}, {"image_id": "1297788749", "caption": "Loving couple on winter vacation hugging on snowy hill smiling at camera", "id": "1297788749"}, {"image_id": "1766919177", "caption": "Young couple on lake with man pointing", "id": "1766919177"}, {"image_id": "1670343398", "caption": "Graphic with figure running from circle formed by people symbols", "id": "1670343398"}, {"image_id": "1663817609", "caption": "portrait of teenage girlfriends with cucumber slices over eyes", "id": "1663817609"}, {"image_id": "1571691161", "caption": "View of Sildpollnes famous church Sildpollnes Lofoten Austvagoy Island Nordland County Norway", "id": "1571691161"}, {"image_id": "1868723288", "caption": "A low angle medium shot of a happy mother and daughter smiling at camera while looking at wildflowers in a field", "id": "1868723288"}, {"image_id": "1571337416", "caption": "View to Henningsvaer Lofoten Nordland Norway", "id": "1571337416"}, {"image_id": "1570368221", "caption": "An alpine meadow and mountains Reiter Alpe Bavaria Germany", "id": "1570368221"}, {"image_id": "1590164849", "caption": "Male teenage student sleeping on stack of books in library", "id": "1590164849"}, {"image_id": "1874804642", "caption": "Engineer drilling into wing of passenger jet in hangar", "id": "1874804642"}, {"image_id": "1578902414", "caption": "A young woman on the beach", "id": "1578902414"}, {"image_id": "1297796600", "caption": "Cut out of pet gold trophy against white background", "id": "1297796600"}, {"image_id": "1766932272", "caption": "Businessman bringing coffee to businesswoman", "id": "1766932272"}, {"image_id": "1588001735", "caption": "Male pharmacist smiling and holding medication", "id": "1588001735"}, {"image_id": "1297725863", "caption": "Young man in summer park smiling at camera", "id": "1297725863"}, {"image_id": "1578927176", "caption": "A young man playing with his dog amongst the autumn leaves", "id": "1578927176"}, {"image_id": "1590102626", "caption": "Young man with a laptop sitting on a bed", "id": "1590102626"}, {"image_id": "1862086811", "caption": "Vertical shot of a beekeeper checking honey on the beehive frame in the field full of flowers", "id": "1862086811"}, {"image_id": "1572523001", "caption": "Children digging sand on beach", "id": "1572523001"}, {"image_id": "1874804258", "caption": "Engineer at computers in control room of nuclear power station", "id": "1874804258"}, {"image_id": "1587982907", "caption": "Church in rural area Riezlern Kleinwalsertal Vorarlberg Austria", "id": "1587982907"}, {"image_id": "1571544890", "caption": "Bare chested young man practicing yoga", "id": "1571544890"}, {"image_id": "1571690498", "caption": "Vineyards at Jenins Grisons Switzerland", "id": "1571690498"}, {"image_id": "1847335562", "caption": "A beautiful high angle view of a snowy mountain peaks with a blue sky", "id": "1847335562"}, {"image_id": "1588006730", "caption": "Young man sitting on a bed", "id": "1588006730"}, {"image_id": "216580106", "caption": "Tranquil field of blooming buttercups", "id": "216580106"}, {"image_id": "1588021769", "caption": "Close up of a woman's face showing nose mouth and chin", "id": "1588021769"}, {"image_id": "1852961783", "caption": "Wide portrait of a beautiful woman holding a cup of coffee", "id": "1852961783"}, {"image_id": "1590360548", "caption": "Couple in athletic gear at beach", "id": "1590360548"}, {"image_id": "1864660052", "caption": "Vertical tilted shot of a businesswoman with a luggage bag smiles at the camera at an airport", "id": "1864660052"}, {"image_id": "1590160391", "caption": "Scenic view of Dischma Brook and cattle herd Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160391"}, {"image_id": "1844767067", "caption": "Portrait of a confident businesswoman standing among co workers", "id": "1844767067"}, {"image_id": "1571690534", "caption": "View from Livigno Pass to Switzerland", "id": "1571690534"}, {"image_id": "1587137291", "caption": "Close up view of an IV drip being inserted into a patient's hand", "id": "1587137291"}, {"image_id": "1859331851", "caption": "A wide shot of a boy and a girl running in opposite directions in a hallway with school locker in background", "id": "1859331851"}, {"image_id": "1587990338", "caption": "A couple relaxing by a pool", "id": "1587990338"}, {"image_id": "1576741970", "caption": "A young woman in a swimming pool", "id": "1576741970"}, {"image_id": "1578905387", "caption": "Portrait of a senior woman in a swimsuit at the beach", "id": "1578905387"}, {"image_id": "1571660432", "caption": "A young woman tourist in a city", "id": "1571660432"}, {"image_id": "1572527747", "caption": "Beach of Isla San Jose Baja California Sur Mexico", "id": "1572527747"}, {"image_id": "1570161014", "caption": "Full moon over Aldabra Atoll Seychelles", "id": "1570161014"}, {"image_id": "1578927227", "caption": "A young mother holding her baby smiling", "id": "1578927227"}, {"image_id": "1588003499", "caption": "Religious structure in garden Carinthia Austria", "id": "1588003499"}, {"image_id": "1578924989", "caption": "A senior couple walking and lovingly embracing in autumn time", "id": "1578924989"}, {"image_id": "1297732829", "caption": "Loving senior couple in countryside hugging smiling at camera", "id": "1297732829"}, {"image_id": "1859334686", "caption": "A Close up shot of a pink beanie cap with a winter landscape background", "id": "1859334686"}, {"image_id": "1868703374", "caption": "A portrait of a confident businessman standing in the doorway of a conference room with his hands in pockets", "id": "1868703374"}, {"image_id": "1846038974", "caption": "A smiling couple holding a golden frame and looking at the camera through it", "id": "1846038974"}, {"image_id": "1878694181", "caption": "High school girls with a netball standing in a huddle before a game", "id": "1878694181"}, {"image_id": "1590323594", "caption": "Landscape near Strait of Hormuz Musandam peninsula exclave of Oman", "id": "1590323594"}, {"image_id": "1860761597", "caption": "A horizontal side view of a surveyor looking through a theodolite at a construction site", "id": "1860761597"}, {"image_id": "1874803628", "caption": "Hospital Radiographer Giving Mammogram To Female Patient", "id": "1874803628"}, {"image_id": "1590337367", "caption": "Campanile of Graun submerged in water Lake Resia Province of Bolzano Trentino Alto Adige Italy", "id": "1590337367"}, {"image_id": "1847350067", "caption": "A portrait shot of a manager inspecting aluminum light fitting held by a young blonde female worker", "id": "1847350067"}, {"image_id": "1590164219", "caption": "Close up of pile of meatballs", "id": "1590164219"}, {"image_id": "1586683304", "caption": "Exhibitor In Flower Tent At Agricultural Show", "id": "1586683304"}, {"image_id": "1766922819", "caption": "Young princess in mid air with colorful balloons", "id": "1766922819"}, {"image_id": "1304266763", "caption": "Senior man with laptop on sofa at working on home finances", "id": "1304266763"}, {"image_id": "1816749639", "caption": "Angled overhead view of a boy and girl riding toy tricycle and push scooter in playground", "id": "1816749639"}, {"image_id": "1587826367", "caption": "Close up of woman smiling indoors", "id": "1587826367"}, {"image_id": "1846060076", "caption": "A smiling woman leaning against a hay bale with her eyes closed", "id": "1846060076"}, {"image_id": "1571686586", "caption": "Ruins of Hammerhus Bornholm Island Denmark", "id": "1571686586"}, {"image_id": "1571342630", "caption": "Young men standing near campsite", "id": "1571342630"}, {"image_id": "1588026293", "caption": "Left side of a young woman's face", "id": "1588026293"}, {"image_id": "1859339567", "caption": "A Close up shot of a teacher watching a young boy and a girl conducting experiment in a beaker in a school chemistry laboratory", "id": "1859339567"}, {"image_id": "1578942683", "caption": "Three teenage friends having fun by a swimming pool", "id": "1578942683"}, {"image_id": "1868714525", "caption": "A distant vertical view of a Scientist in clean suit examining the silicon wafer under a microscope in a special laboratory", "id": "1868714525"}, {"image_id": "1587988841", "caption": "Businessman reaching for water glasses in conference room", "id": "1587988841"}, {"image_id": "1570154741", "caption": "Mountain Zebras at Tsavo East National Park Kenya Africa", "id": "1570154741"}, {"image_id": "1875313676", "caption": "Cheese maker checking farmhouse cheddar cheese wheels on shelf in cellar", "id": "1875313676"}, {"image_id": "1669107884", "caption": "Businessman playing golf in office", "id": "1669107884"}, {"image_id": "1570572266", "caption": "Aerial view of the confluence of the Rio Negro s water and the Solimoes River's water", "id": "1570572266"}, {"image_id": "1869279014", "caption": "Wide shot of clouds in the stormy sky", "id": "1869279014"}, {"image_id": "1587128831", "caption": "Young friends standing with sleds in mountain resort", "id": "1587128831"}, {"image_id": "1665810212", "caption": "Couple with man in foreground and woman in background", "id": "1665810212"}, {"image_id": "1578942902", "caption": "Portrait of young woman yawning", "id": "1578942902"}, {"image_id": "1263322061", "caption": "Dramatic beautiful sunset with sun setting in blue sky with clouds", "id": "1263322061"}, {"image_id": "1869908156", "caption": "Overhead shot a businessman and a businesswoman with hands crossed in center surrounded by their colleagues in a ring formation", "id": "1869908156"}, {"image_id": "1590079901", "caption": "Baton passing between relay runners", "id": "1590079901"}, {"image_id": "1570522838", "caption": "Autumn forest and Wuerm River Starnberg Bavaria Germany", "id": "1570522838"}, {"image_id": "1576739411", "caption": "Young man styling his hair", "id": "1576739411"}, {"image_id": "1590053546", "caption": "Portrait of young woman with dark hair in a bun", "id": "1590053546"}, {"image_id": "1572509105", "caption": "A woman holding a shell to her ear", "id": "1572509105"}, {"image_id": "1572512639", "caption": "Woman and daughter playing in snow", "id": "1572512639"}, {"image_id": "1590047606", "caption": "Business people traveling waiting in airport or station", "id": "1590047606"}, {"image_id": "1586652242", "caption": "Senior woman resting at home with ice pack on stomach", "id": "1586652242"}, {"image_id": "1297788785", "caption": "Woman on winter vacation riding down hill on sled smiling at camera", "id": "1297788785"}, {"image_id": "1590200759", "caption": "Overflowing garbage can in woods", "id": "1590200759"}, {"image_id": "1852935113", "caption": "Vertical ground level shot of a young woman carrying out plumbing work under the kitchen sink smiles at the camera with copy space", "id": "1852935113"}, {"image_id": "1578942800", "caption": "couple having fun in the park", "id": "1578942800"}, {"image_id": "1878688712", "caption": "Silhouette Of a Technician Walking Between the Servers In the Data Centre", "id": "1878688712"}, {"image_id": "1895443046", "caption": "Worker guiding crane lifting cargo container at commercial dock", "id": "1895443046"}, {"image_id": "1587825056", "caption": "Church with horse drawn carriages Paqueta Island Rio de Janeiro Brazil", "id": "1587825056"}, {"image_id": "1860726872", "caption": "Rear View of middle age couple sitting on the grass by bicycle with the sea in the background", "id": "1860726872"}, {"image_id": "1587836972", "caption": "Father and young children cooking in kitchen", "id": "1587836972"}, {"image_id": "1571590034", "caption": "Portrait of a young woman", "id": "1571590034"}, {"image_id": "1578927440", "caption": "A businesswoman sitting at a table looking at a mobile phone", "id": "1578927440"}, {"image_id": "1868703395", "caption": "A vertical portrait of smiling businessmen and businesswomen standing together near the office desk", "id": "1868703395"}, {"image_id": "1860742193", "caption": "Rear View of two male surfers in wetsuits with surfboards on the beach", "id": "1860742193"}, {"image_id": "1571676431", "caption": "Wind Turbines Repperndorf Bavaria Germany", "id": "1571676431"}, {"image_id": "1868709308", "caption": "An Engineer examining a circuit board under the magnification lamp with selective focus next to a telescope in laboratory", "id": "1868709308"}, {"image_id": "1587831152", "caption": "Boy smiling and lying on sofa", "id": "1587831152"}, {"image_id": "1578946178", "caption": "Young man using a cashpoint or ATM", "id": "1578946178"}, {"image_id": "1878695690", "caption": "High school students conducting a scientific experiment using a burner with their chemistry teacher guiding them", "id": "1878695690"}, {"image_id": "1844729147", "caption": "Vertical shot of an elderly woman serving vegetable salad to elderly men having lunch on a patio table with a woman carrying grapes in the background", "id": "1844729147"}, {"image_id": "1277246411", "caption": "Teenage girls in cafe at table with bags after shopping trip", "id": "1277246411"}, {"image_id": "1860726905", "caption": "Vertical shot of a businessman at a desk using a telephone in the middle of a road in the desert", "id": "1860726905"}, {"image_id": "1586722916", "caption": "Man Lifting Woman On His Back During Countryside Walk", "id": "1586722916"}, {"image_id": "1590202217", "caption": "Red pencil writing a check mark", "id": "1590202217"}, {"image_id": "1709253596", "caption": "A teenage girl blowing bubble gum", "id": "1709253596"}, {"image_id": "1840559801", "caption": "Worker controlling robotic machinery lifting steel fencing in manufacturing plant", "id": "1840559801"}, {"image_id": "1586703938", "caption": "Rear View Of Farmer Walking Through Harvested Field", "id": "1586703938"}, {"image_id": "1572549863", "caption": "A young woman sitting by a pool", "id": "1572549863"}, {"image_id": "1576782434", "caption": "A young couple sitting in a park", "id": "1576782434"}, {"image_id": "1587982622", "caption": "A barman flirting with a customer", "id": "1587982622"}, {"image_id": "1859176808", "caption": "Wide shot of engineers working below the tail of a passenger jet at a hangar", "id": "1859176808"}, {"image_id": "1576738316", "caption": "A senior couple carrying a picnic basket", "id": "1576738316"}, {"image_id": "1859201021", "caption": "Close up of highland Cattles grazing on the moor", "id": "1859201021"}, {"image_id": "1868709320", "caption": "A vertical view of a circuit board under the magnification lamp being examined by an Engineer in a laboratory", "id": "1868709320"}, {"image_id": "1840555082", "caption": "Man looking into hatchback of car in a car dealership showroom", "id": "1840555082"}, {"image_id": "1586722523", "caption": "Kite Surfer With Dog On Beach Shoreline", "id": "1586722523"}, {"image_id": "1878871256", "caption": "A Side View of a school girl with blonde hair in a laboratory looking into a microscope with her classmates and Teacher sitting in background", "id": "1878871256"}, {"image_id": "1297780787", "caption": "Detail of nurse giving male patient medication in hospital", "id": "1297780787"}, {"image_id": "1297781444", "caption": "Active senior man drinking water after exercise in gym looking at camera", "id": "1297781444"}, {"image_id": "1567889321", "caption": "Landscape south of Moroni Grand Comore Island Ngazidja Comores Africa", "id": "1567889321"}, {"image_id": "1586681417", "caption": "cutout Of Senior Woman On Ski Holiday", "id": "1586681417"}, {"image_id": "1297800368", "caption": "Female athlete preparing to throw javelin in athletics competition", "id": "1297800368"}, {"image_id": "1586690921", "caption": "Worker behind machinery at production line in cheese processing plant", "id": "1586690921"}, {"image_id": "1586668637", "caption": "Typical countryside La Gomera Canary Islands", "id": "1586668637"}, {"image_id": "1587842135", "caption": "Best wishes gift in Munich", "id": "1587842135"}, {"image_id": "1590355022", "caption": "View from Uffizi to Palazzo Vecchio Florence Italy", "id": "1590355022"}, {"image_id": "1590164885", "caption": "Group of teenage students cheering outdoors", "id": "1590164885"}, {"image_id": "1590352256", "caption": "Marina and Cathedral of Palma de Mallorca at sunrise Mallorca Spain", "id": "1590352256"}, {"image_id": "1860742115", "caption": "Full length shot of a businesswoman with luggage on a road in the desert", "id": "1860742115"}, {"image_id": "1650257255", "caption": "Foreman and boy on construction site", "id": "1650257255"}, {"image_id": "1578915785", "caption": "Two young men sitting on a bench smiling", "id": "1578915785"}, {"image_id": "1844733107", "caption": "Two girls with backpacks smiling at the camera while looking at a map and compass in a green field", "id": "1844733107"}, {"image_id": "1587139070", "caption": "Blurred view of a woman skating with white ice skates", "id": "1587139070"}, {"image_id": "1587144365", "caption": "Portrait of two young women embracing in a park", "id": "1587144365"}, {"image_id": "1588001747", "caption": "Male pharmacist smiling and holding medication", "id": "1588001747"}, {"image_id": "1864650950", "caption": "Little girl lying down on the rug in front of a television with an open magazine lying on the floor next to her", "id": "1864650950"}, {"image_id": "1570300739", "caption": "Beach bar overlooking the sea at Marinella near Agrigento Sicily Italy", "id": "1570300739"}, {"image_id": "1588016273", "caption": "Cherry blossoms on tree in snow", "id": "1588016273"}, {"image_id": "1578935441", "caption": "Female asthma sufferer using an inhaler", "id": "1578935441"}, {"image_id": "1670340653", "caption": "Senior man moving in holding keys to new home", "id": "1670340653"}, {"image_id": "1843586507", "caption": "Smiling couple in ski wear hugging each other on a cold sunny day at a ski resort in the mountains with copy space", "id": "1843586507"}, {"image_id": "1581271643", "caption": "Smiling senior man in garden collecting autumn leaves at camera", "id": "1581271643"}, {"image_id": "1571330852", "caption": "Mature woman walking on beach", "id": "1571330852"}, {"image_id": "1873346264", "caption": "Close up of Middle school students watering seedlings using a watering can in a greenhouse", "id": "1873346264"}, {"image_id": "1873296911", "caption": "Professor watching his students assemble a helix DNA model in a science class", "id": "1873296911"}, {"image_id": "1578226802", "caption": "A businesswoman using a mobile phone", "id": "1578226802"}, {"image_id": "1840555148", "caption": "Wide angle shot of Businessman and workers talking among large bags of recycled plastic pellets in warehouse", "id": "1840555148"}, {"image_id": "217371155", "caption": "High angle view of two young crocodiles", "id": "217371155"}, {"image_id": "1578920069", "caption": "A young woman holding a basket of fruit close up", "id": "1578920069"}, {"image_id": "1588027574", "caption": "A young woman brushing her teeth", "id": "1588027574"}, {"image_id": "1869314642", "caption": "Wide shot of the silhouette of an elderly couple enjoying wine while sitting on a bench against the sunset over the ocean", "id": "1869314642"}, {"image_id": "1590353372", "caption": "Small harbor for fisher boats at Cala Figuera Mallorca Spain", "id": "1590353372"}, {"image_id": "1572524384", "caption": "Portrait of young girl smiling", "id": "1572524384"}, {"image_id": "1846400555", "caption": "A red tractor cutting the grass for silage in selective focus on a bright sunny day", "id": "1846400555"}, {"image_id": "1576745252", "caption": "Portrait of a young couple man giving woman piggyback", "id": "1576745252"}, {"image_id": "1590178448", "caption": "Artistic public restroom Kawakawa North Island New Zealand", "id": "1590178448"}, {"image_id": "216567248", "caption": "Sun shining over poppies", "id": "216567248"}, {"image_id": "1277235701", "caption": "Cute baby boy watching baby girl crawling on white background", "id": "1277235701"}, {"image_id": "1587857279", "caption": "View of Botanic Gardens and Presidential Residence Port of Spain Trinidad", "id": "1587857279"}, {"image_id": "1844728922", "caption": "Medium shot of a senior engineer speaking on the telephone at the control desk in the control room of a nuclear power station", "id": "1844728922"}, {"image_id": "1297790081", "caption": "Outdoor portrait young friends holding hands on walk in countryside", "id": "1297790081"}, {"image_id": "1277255717", "caption": "Group of friends relaxing on summer vacation beach with umbrella cooler and surfboards enjoying holiday", "id": "1277255717"}, {"image_id": "1572463103", "caption": "A young man standing in the desert with a spade", "id": "1572463103"}, {"image_id": "1859323601", "caption": "A shot of a young boy using wrench to tighten valve on a copper pipe", "id": "1859323601"}, {"image_id": "1588026551", "caption": "A woman blow drying her hair", "id": "1588026551"}, {"image_id": "1590359156", "caption": "Rocky outcroppings in front of Mount Teide Teide National Park Tenerife Canary Islands Spain", "id": "1590359156"}, {"image_id": "1587033716", "caption": "Close up of skydiving man in colourful costume in mid air", "id": "1587033716"}, {"image_id": "1587982634", "caption": "Close up of ashtray with cigarette butts", "id": "1587982634"}, {"image_id": "1869314669", "caption": "Silhouette of a couple sitting on a bench in opposite directions against the sunset over the ocean", "id": "1869314669"}, {"image_id": "1567864778", "caption": "Boy playing with backhoe on construction site", "id": "1567864778"}, {"image_id": "1587984380", "caption": "Hopscotch game chalked on the playground", "id": "1587984380"}, {"image_id": "1587141221", "caption": "Still life of a bunch of basil", "id": "1587141221"}, {"image_id": "216347807", "caption": "Clouds in sunset sky", "id": "216347807"}, {"image_id": "1578927179", "caption": "A senior man relaxing beneath a tree reading a book", "id": "1578927179"}, {"image_id": "1846039067", "caption": "A shot of an old couple standing on a narrow boat in a canal", "id": "1846039067"}, {"image_id": "1650267005", "caption": "Foreman shaking hands with worker on construction site", "id": "1650267005"}, {"image_id": "1578932831", "caption": "A group of four business colleagues holding an informal meeting", "id": "1578932831"}, {"image_id": "1844191958", "caption": "Wide shot of a small group of businessmen and woman standing in hall posing for the camera", "id": "1844191958"}, {"image_id": "1571615264", "caption": "Woman standing on a golf course", "id": "1571615264"}, {"image_id": "1590350873", "caption": "Lookout Miramar at the coast near Deia Mallorca Spain", "id": "1590350873"}, {"image_id": "1570161023", "caption": "Sunset at Aldabra Atoll Seychelles", "id": "1570161023"}, {"image_id": "1859328347", "caption": "A shot of a happy teacher helping her young student with the drawing in a classroom", "id": "1859328347"}, {"image_id": "1297787513", "caption": "Active men and women exercising on running machines in gym", "id": "1297787513"}, {"image_id": "1576782482", "caption": "A chauffer standing by a stretch limousine", "id": "1576782482"}, {"image_id": "1878688532", "caption": "Surfer With a prosthetic Leg Standing On the Beach holding a surfboard", "id": "1878688532"}, {"image_id": "1571356379", "caption": "Flowers in Herbert Park Dublin Ireland", "id": "1571356379"}, {"image_id": "1590044102", "caption": "Elegant young woman in a white silk dress or wrap rear view", "id": "1590044102"}, {"image_id": "1571615219", "caption": "Close up of a woman's face", "id": "1571615219"}, {"image_id": "1816211364", "caption": "Horizontal shot of a woman decorating home sitting on floor beside window holding wallpaper and looking at camera", "id": "1816211364"}, {"image_id": "1586663702", "caption": "Portrait of young adult woman practicing yoga on a shrub studio shot", "id": "1586663702"}, {"image_id": "1590314939", "caption": "Close up of wine bottles", "id": "1590314939"}, {"image_id": "1585935884", "caption": "Man shopping in organic grocery store close up", "id": "1585935884"}, {"image_id": "1570365638", "caption": "A ploughed field in Autumn Milborne Port Sherborne Dorset UK", "id": "1570365638"}, {"image_id": "1590073406", "caption": "Mother hugging son in baseball gear", "id": "1590073406"}, {"image_id": "1581288719", "caption": "Businesswoman looking out of office window by whiteboard", "id": "1581288719"}, {"image_id": "1851407603", "caption": "Horizontal waist up profile shot of two jubilant mature couples drinking champagne outdoors on a balcony during sunset with copy space", "id": "1851407603"}, {"image_id": "1590347654", "caption": "Young happy boy using laptop", "id": "1590347654"}, {"image_id": "1844196365", "caption": "Horizontal shot of woman holding celery and chocolates a nutrition concept", "id": "1844196365"}, {"image_id": "1844190368", "caption": "Wide shot of a man and dog walking in the forest among bluebell flowers", "id": "1844190368"}, {"image_id": "1843610555", "caption": "Horizontal head and shoulder profile shot of a businessman speaking and gesturing in microphone at a conference with female colleague in the background and copy space", "id": "1843610555"}, {"image_id": "1587024569", "caption": "Blurred view of a young boy jumping on a beach", "id": "1587024569"}, {"image_id": "1586688353", "caption": "Technician worker cleaning new solar panel on production line on factory floor", "id": "1586688353"}, {"image_id": "1578939119", "caption": "Overhead view of informal business meeting at table of staff canteen", "id": "1578939119"}, {"image_id": "1868703371", "caption": "A serious businessman in formal suit text messaging on a cell phone while standing at the conference room doorway", "id": "1868703371"}, {"image_id": "1587843926", "caption": "Studio shot of businessman holding fanned out Euros", "id": "1587843926"}, {"image_id": "1587138296", "caption": "Portrait of a young man using headphones", "id": "1587138296"}, {"image_id": "1587997616", "caption": "High angle view of wine in wine glass", "id": "1587997616"}, {"image_id": "1297787534", "caption": "Active man with towel after exercising on equipment in health club", "id": "1297787534"}, {"image_id": "1839589349", "caption": "Portrait of a joyous family of four having picnic with boy taking a photograph of parents", "id": "1839589349"}, {"image_id": "1859353355", "caption": "Citrus tree and ornate cathedral Seville Spain", "id": "1859353355"}, {"image_id": "1844194103", "caption": "Horizontal shot of summer bright blue sky with fluffy clouds", "id": "1844194103"}, {"image_id": "1587855620", "caption": "Close up of woman smiling", "id": "1587855620"}, {"image_id": "1570348067", "caption": "Pin wheel at Portoferraio Elba Italy", "id": "1570348067"}, {"image_id": "1578921359", "caption": "A portrait of a young blonde woman", "id": "1578921359"}, {"image_id": "1297797329", "caption": "Cut out of smiling colleagues in sales team with name badge at camera", "id": "1297797329"}, {"image_id": "1587649259", "caption": "View of a snowy field against blue sky", "id": "1587649259"}, {"image_id": "1587145118", "caption": "Low angle view of a colorful hot air balloon against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145118"}, {"image_id": "1725717722", "caption": "Computer technician and businessman talking in server room in Cape Town South Africa", "id": "1725717722"}, {"image_id": "1297800548", "caption": "Girl lying on circular bale of hay in harvested farm field", "id": "1297800548"}, {"image_id": "1840559834", "caption": "Worker controlling robotic machinery lifting steel fencing in manufacturing plant", "id": "1840559834"}, {"image_id": "1663817603", "caption": "Three teenage friends looking at digital tablet", "id": "1663817603"}, {"image_id": "1571668373", "caption": "Portrait of a young woman", "id": "1571668373"}, {"image_id": "1578930578", "caption": "Woman eating a bar of chocolate", "id": "1578930578"}, {"image_id": "1277235632", "caption": "Portrait of smiling business team in office at camera", "id": "1277235632"}, {"image_id": "1670077610", "caption": "Portrait Of Three Casually Dressed Businessmen In Office", "id": "1670077610"}, {"image_id": "1869318125", "caption": "Extreme wide shot of a tractor with a trailer moving alongside a combine harvester in a rural field", "id": "1869318125"}, {"image_id": "1570573415", "caption": "Lecythis Cuxiu Muni Amazon River Brazil", "id": "1570573415"}, {"image_id": "1277229104", "caption": "Woman in wetsuit exercising pet dog on beach", "id": "1277229104"}, {"image_id": "1570552811", "caption": "Riverside near Alter do Chao Rio Tapajos Amazon River Brazil", "id": "1570552811"}, {"image_id": "1852961663", "caption": "Close up of a smiling girl playing with pinwheel outdoors", "id": "1852961663"}, {"image_id": "1844764172", "caption": "A little boy sitting on the lap of his father beside his grandfather on a wooden bridge", "id": "1844764172"}, {"image_id": "1590209096", "caption": "autumn forest and Wuerm River Wuerm Valley Bavaria Germany", "id": "1590209096"}, {"image_id": "1859144561", "caption": "Medium shot of an elderly couple with a backpack and fishing rod walking along the shore of a sunny beach", "id": "1859144561"}, {"image_id": "1590314951", "caption": "Senior man holding up glass of milk", "id": "1590314951"}, {"image_id": "1864630883", "caption": "Horizontal overhead shot of two girls studying on a desk in a classroom", "id": "1864630883"}, {"image_id": "1590361277", "caption": "Close up of assorted Euro banknotes and coins", "id": "1590361277"}, {"image_id": "1587031187", "caption": "High angle view of miniature bull and bear and shadows", "id": "1587031187"}, {"image_id": "1578939224", "caption": "A young woman driving a car", "id": "1578939224"}, {"image_id": "1590337232", "caption": "Spring Mountain Ranch State Park in autumn Las Vegas Nevada USA", "id": "1590337232"}, {"image_id": "1590034352", "caption": "Elegant young woman in a white silk dress or wrap", "id": "1590034352"}, {"image_id": "1578212900", "caption": "Portrait of a young woman with blue eyes and blonde hair", "id": "1578212900"}, {"image_id": "1297780868", "caption": "Studio shot of burning candle and christmas ornaments on white background", "id": "1297780868"}, {"image_id": "1586676668", "caption": "cutout Of Male Cheesemaker", "id": "1586676668"}, {"image_id": "217369628", "caption": "Pastures and fence Vermont USA", "id": "217369628"}, {"image_id": "1868709551", "caption": "A vertical view of business people in formal suits with briefcases and suitcase talking to each other in a modern lobby", "id": "1868709551"}, {"image_id": "1586689157", "caption": "Portrait of Technician worker smiling at camera in solar panel factory", "id": "1586689157"}, {"image_id": "1859201000", "caption": "Wide shot of a beautiful sunset over the sea", "id": "1859201000"}, {"image_id": "1590329714", "caption": "Roofs of Havana view to Catedral San Cristobal de la Habana Havana Cuba", "id": "1590329714"}, {"image_id": "1571665529", "caption": "Portrait boy with catcher's mitt", "id": "1571665529"}, {"image_id": "1588002734", "caption": "A young woman using a mobile phone", "id": "1588002734"}, {"image_id": "1866123515", "caption": "Two generations of a family with backpacks resting by the fence of a field at the countryside", "id": "1866123515"}, {"image_id": "1846059953", "caption": "A low angle portrait shot of a businessman standing in a wheat field and using his laptop", "id": "1846059953"}, {"image_id": "1578904409", "caption": "Portrait of a young girl", "id": "1578904409"}, {"image_id": "1852963985", "caption": "Smiling baker removing a tray of baked bread from an oven in the bakery", "id": "1852963985"}, {"image_id": "1851401351", "caption": "Young man carrying a tea cup in his hand smiling at his friend while walking in a garden in front of a huge manor on sunny day", "id": "1851401351"}, {"image_id": "1670342024", "caption": "Two teenage girls lying on bed with laptop listening to mp3 player", "id": "1670342024"}, {"image_id": "1817411595", "caption": "Father son and daughter sitting on a grassy ground with the daughter wearing a baseball glove and the father holding a baseball", "id": "1817411595"}, {"image_id": "1874804231", "caption": "Aerial view of container ship moored at commercial dock", "id": "1874804231"}, {"image_id": "1869911510", "caption": "A medium shot of a businessman and businesswoman talking with each other while drinking coffee in office", "id": "1869911510"}, {"image_id": "1710417455", "caption": "Close up of a bright blue eye on the face of a male", "id": "1710417455"}, {"image_id": "1873351055", "caption": "Wide shot of Gondolier paddling gondola in sunny Grand Canal in front of Santa Maria Della Salute and architectural buildings in Venice Italy", "id": "1873351055"}, {"image_id": "1586735732", "caption": "Portrait Of Medical Team In Modern Hospital", "id": "1586735732"}, {"image_id": "1872077420", "caption": "Girl sitting on beach in sunglasses and red swimsuit smiling Close up portrait tilt", "id": "1872077420"}, {"image_id": "1670339996", "caption": "Teacher helping male elementary school pupil to tie shoelaces in classroom", "id": "1670339996"}, {"image_id": "1725717716", "caption": "Computer technician and businessman in server room in Cape Town South Africa", "id": "1725717716"}, {"image_id": "1671831644", "caption": "Portrait of two teenagers posing in the park", "id": "1671831644"}, {"image_id": "1725719261", "caption": "Young boy with friends eating potato chips", "id": "1725719261"}, {"image_id": "1857288779", "caption": "Horizontal shot of a group of men and women gambling at the roulette table in the casino", "id": "1857288779"}, {"image_id": "1578909998", "caption": "Portrait of a senior man wrapped in a towel at the beach", "id": "1578909998"}, {"image_id": "1590160763", "caption": "Panoramic view of sunset over Ammersee with boats Herrsching Bavaria Germany", "id": "1590160763"}, {"image_id": "1864613981", "caption": "Horizontal shot of a young woman lying in the bath outdoors with her eyes closed and relaxing", "id": "1864613981"}, {"image_id": "1587048509", "caption": "Two young executives fighting each other", "id": "1587048509"}, {"image_id": "1587030803", "caption": "View of a man leaning on a car talking on his cell phone with a construction site in the background", "id": "1587030803"}, {"image_id": "1862121440", "caption": "A portrait of a young adult Businesswoman in formal suit sitting at a table in the office lobby while using a laptop", "id": "1862121440"}, {"image_id": "1586684510", "caption": "Scientist Examining Oat Crop In Field With Digital Tablet", "id": "1586684510"}, {"image_id": "1587651248", "caption": "Low angle view of clouds against blue sky", "id": "1587651248"}, {"image_id": "1869911477", "caption": "A medium shot of an engineer in lab coat talking about a circular part with a businesswoman in office", "id": "1869911477"}, {"image_id": "1586693060", "caption": "Exuberant brother and sister jumping for joy over sand dune beach grass", "id": "1586693060"}, {"image_id": "1840560434", "caption": "Backside of an elderly couple holding each other while standing in a meadow full of sunflowers", "id": "1840560434"}, {"image_id": "1277235605", "caption": "Mature businessman at head of boardroom conference table on phone", "id": "1277235605"}, {"image_id": "1587856019", "caption": "Low angle view of palm trees against blue sky", "id": "1587856019"}, {"image_id": "1843609379", "caption": "Vertical tilted shot of a frustrated senior woman calling for help on a cell phone next to a broken down red convertible on a sunny day", "id": "1843609379"}, {"image_id": "1587145064", "caption": "Low angle view of hot air balloons against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145064"}, {"image_id": "1855915937", "caption": "portrait of a couple relaxing on a sofa and making a purchase on a digital tablet", "id": "1855915937"}, {"image_id": "1590210545", "caption": "Couple sitting back to back", "id": "1590210545"}, {"image_id": "1866104699", "caption": "Close up shot of a farmer examining crop in barley crop field in summer", "id": "1866104699"}, {"image_id": "1844764238", "caption": "Close up of a cargo ship being unloaded while it is anchored in the dockyard", "id": "1844764238"}, {"image_id": "1857288830", "caption": "Young male sprinters starting a sprint race from their starting blocks on a bright sunny day at the track", "id": "1857288830"}, {"image_id": "1878688475", "caption": "Close up of a Carpenter making markings on the wooden planks at a construction site in a building wearing a high visibility safety vest", "id": "1878688475"}, {"image_id": "1567876838", "caption": "Aerial view of Saint Paul s Bay Malta", "id": "1567876838"}, {"image_id": "1878784283", "caption": "Selective focus in foreground on hand of a businessman signing at reception with smiling receptionist on telephone in the background", "id": "1878784283"}, {"image_id": "1587986996", "caption": "Two businessmen eating at restaurant", "id": "1587986996"}, {"image_id": "1590067814", "caption": "Businesswoman shaking hands with male client or colleague", "id": "1590067814"}, {"image_id": "1590336032", "caption": "Cracked desert earth with crust of salt Death Valley National Park Nevada U", "id": "1590336032"}, {"image_id": "1572522962", "caption": "Father carrying boy on shoulders at beach", "id": "1572522962"}, {"image_id": "216573533", "caption": "View of rooftops in country town", "id": "216573533"}, {"image_id": "1862067158", "caption": "Elderly couple with a digital tablet sitting on a wooden jetty by the lake and smiling at each other", "id": "1862067158"}, {"image_id": "1725719318", "caption": "Young girl doing homework at kitchen table mother and sister in background", "id": "1725719318"}, {"image_id": "1866123422", "caption": "Pharmacist showing medication to a customer at the pharmacy counter", "id": "1866123422"}, {"image_id": "1587983372", "caption": "A portrait of a businessman sitting at a desk", "id": "1587983372"}, {"image_id": "1297786244", "caption": "Couple on winter vacation with man giving woman piggyback in snow smiling at camera", "id": "1297786244"}, {"image_id": "1572536693", "caption": "A senior woman sitting in a sports car", "id": "1572536693"}, {"image_id": "1590025187", "caption": "A businessman in an office three colleagues in background having a discussion", "id": "1590025187"}, {"image_id": "1578927392", "caption": "A businesswoman sitting at a table typing on a laptop", "id": "1578927392"}, {"image_id": "1855886135", "caption": "Horizontal shot of wedding guests waving at bride and groom being escorted towards a helicopter by the pilot outdoors", "id": "1855886135"}, {"image_id": "1852936730", "caption": "Vertical ground level shot of lower section of a young woman sitting with legs crossed on floor with color swatches with selective focus", "id": "1852936730"}, {"image_id": "1588016180", "caption": "Close up of roast pig head", "id": "1588016180"}, {"image_id": "1587838199", "caption": "Couple on sofa with man using laptop and woman filing her nails", "id": "1587838199"}, {"image_id": "1859142974", "caption": "Wide shot of a couple running with their children along the shore of a beach", "id": "1859142974"}, {"image_id": "1590353426", "caption": "Young woman with swiss flag mug", "id": "1590353426"}, {"image_id": "1816751121", "caption": "Profile shot of a businesswoman wearing mobile phone hands free device sitting in car and reading a document", "id": "1816751121"}, {"image_id": "1567881278", "caption": "Flame tree or Flamboyant at Port Louis Mauritius Africa", "id": "1567881278"}, {"image_id": "1588002611", "caption": "A man wearing a headset", "id": "1588002611"}, {"image_id": "1865943716", "caption": "Close up shot of a mature woman in red jacket sitting on the deck of the sailing boat below sail", "id": "1865943716"}, {"image_id": "216350816", "caption": "Sun setting in sky with clouds", "id": "216350816"}, {"image_id": "1665809504", "caption": "Couple sitting on floor next to lit candles", "id": "1665809504"}, {"image_id": "1862067185", "caption": "Elderly couple with digital tablets on a jetty by the lake", "id": "1862067185"}, {"image_id": "1297774193", "caption": "Senior man riding touring motorbike outdoors smiling at camera", "id": "1297774193"}, {"image_id": "1873346150", "caption": "Silhouetted reflection of a woman standing on the wet portion of the beach during low tide", "id": "1873346150"}, {"image_id": "1297800287", "caption": "Portrait of smiling family standing in field of spring daffodils", "id": "1297800287"}, {"image_id": "1852924217", "caption": "Young woman in pajamas standing in the kitchen and spreading butter on toast", "id": "1852924217"}, {"image_id": "1277231360", "caption": "Underwater view of woman wearing goggles in swimming pool at camera", "id": "1277231360"}, {"image_id": "1866123377", "caption": "Female pharmacist discussing about prescription and medicine with a colleague at a pharmacy", "id": "1866123377"}, {"image_id": "1567864799", "caption": "Foreman shaking hands with boy on construction site", "id": "1567864799"}, {"image_id": "1669206395", "caption": "Close up of circus clown s shoes", "id": "1669206395"}, {"image_id": "1587842165", "caption": "Inscription of text in Germany", "id": "1587842165"}, {"image_id": "1572528557", "caption": "El Chepe near El Fuente State of Chihuahua Mexico", "id": "1572528557"}, {"image_id": "1866109442", "caption": "Farmer walking in the sunny rural barley crop field in summer", "id": "1866109442"}, {"image_id": "1878688703", "caption": "Technician working on a Laptop in the secured data center", "id": "1878688703"}, {"image_id": "1587048542", "caption": "Grandfather and grandson talking together outdoors", "id": "1587048542"}, {"image_id": "1587142637", "caption": "Close up of an airplane nose", "id": "1587142637"}, {"image_id": "1864634402", "caption": "Low angle view of a young boy and girl playing with a ball on a garden lawn", "id": "1864634402"}, {"image_id": "1664813651", "caption": "Portrait of a young girl in goggles making a silly face", "id": "1664813651"}, {"image_id": "1567878596", "caption": "View to Prattigau near Davos Grisons Switzerland", "id": "1567878596"}, {"image_id": "1570348118", "caption": "Demolishing a building in Worthsee Bavaria Germany", "id": "1570348118"}, {"image_id": "1844190584", "caption": "Low angle view of a female gymnast performing on the bar against the white background", "id": "1844190584"}, {"image_id": "1859331989", "caption": "A top angle portrait shot of a senior woman smiling at camera while lying on the grass with red autumn leaves in foreground", "id": "1859331989"}, {"image_id": "1588000238", "caption": "Female fortune teller holding illuminated globe", "id": "1588000238"}, {"image_id": "1297750301", "caption": "Woman in underwear in bathroom applying moisturizing cream to legs", "id": "1297750301"}, {"image_id": "1590047402", "caption": "Portrait of young woman touching her cheek", "id": "1590047402"}, {"image_id": "1663818029", "caption": "Teenage friends kicking legs in gym", "id": "1663818029"}, {"image_id": "1297789991", "caption": "Detail of cute baby boy lying on towel", "id": "1297789991"}, {"image_id": "1873351046", "caption": "Architectural buildings on Grand Canal in Venice Italy under the sunny blue sky", "id": "1873351046"}, {"image_id": "1297750286", "caption": "Studio detail of male nude torso against white", "id": "1297750286"}, {"image_id": "1586693159", "caption": "High school student wearing school uniform in school gymnasium", "id": "1586693159"}, {"image_id": "1587982904", "caption": "Houses with church in background Kleinwalsertal Vorarlberg Austria", "id": "1587982904"}, {"image_id": "1586691764", "caption": "Businessman and grower with crate of ripe tomatoes handshaking in greenhouse", "id": "1586691764"}, {"image_id": "1590179387", "caption": "Norfolk Pine trees along coastline Norfolk Island External Territory of Australia", "id": "1590179387"}, {"image_id": "1570518995", "caption": "Businessman looking at sexy businesswoman on the phone and walking", "id": "1570518995"}, {"image_id": "1878688592", "caption": "Close up of the prosthetic Leg of a man Running Along the Beach", "id": "1878688592"}, {"image_id": "1847201867", "caption": "A cute girl standing on the top of a snow mountain wearing winter clothes while smiling and adjusting her sunglasses with her family sitting in the background", "id": "1847201867"}, {"image_id": "1576746026", "caption": "A female nude midsection", "id": "1576746026"}, {"image_id": "1878692714", "caption": "Vertical shot of a high school student taking notes during a scientific experiment in a biology class", "id": "1878692714"}, {"image_id": "217368851", "caption": "Image of Penguin on Yalour islands Antarctica", "id": "217368851"}, {"image_id": "1873296770", "caption": "High school students in uniforms eating lunch at the cafeteria", "id": "1873296770"}, {"image_id": "1570354589", "caption": "Raindrops on a pink phlox flower", "id": "1570354589"}, {"image_id": "1843609292", "caption": "Top view of a person in rubber boots standing in puddles of muddy water with copy space", "id": "1843609292"}, {"image_id": "1277240018", "caption": "Young woman running water for bath at home", "id": "1277240018"}, {"image_id": "1572383423", "caption": "Bridge Spanning the Columbia River Revelstoke British Columbia Canada", "id": "1572383423"}, {"image_id": "1878688745", "caption": "Low angle vertical shot of a technician checking wires in the data center", "id": "1878688745"}, {"image_id": "1587851081", "caption": "Person touching statue in Prague", "id": "1587851081"}, {"image_id": "1590210563", "caption": "Boy and girl with angel halos over head", "id": "1590210563"}, {"image_id": "1570235120", "caption": "Garden spider Araneus diadematus on web", "id": "1570235120"}, {"image_id": "1590345056", "caption": "Man opening champagne with girlfriend", "id": "1590345056"}, {"image_id": "1844764379", "caption": "Happy family playing on a hill near the sea beside a parked car", "id": "1844764379"}, {"image_id": "1844766956", "caption": "Smiling doctor's talking in a hospital", "id": "1844766956"}, {"image_id": "1570160792", "caption": "Satellite dishes in Stone Town part of Zanzibar City Zanzibar Tanzania Africa", "id": "1570160792"}, {"image_id": "1868709590", "caption": "A wide eyed engineer in lab coat holding and looking at the computer data cable in laboratory", "id": "1868709590"}, {"image_id": "216582401", "caption": "View of town and bridge spanning river on sunny day Jarnac and the Charente river West Central France", "id": "216582401"}, {"image_id": "1587028808", "caption": "View of elderly person sitting on park bench", "id": "1587028808"}, {"image_id": "1586687480", "caption": "Greece Kefalonia Fiskardo view of yachts and sunny coastal harbour", "id": "1586687480"}, {"image_id": "1586696012", "caption": "Aerial View Of Green English Farm Fields In Dorset", "id": "1586696012"}, {"image_id": "1570572842", "caption": "Mouth of Rio Badajos Amazon River Brazil", "id": "1570572842"}, {"image_id": "1587990092", "caption": "Businesswoman talking to scientist in laboratory", "id": "1587990092"}, {"image_id": "1711190648", "caption": "Two businessmen shaking hands as seen from above", "id": "1711190648"}, {"image_id": "1725713012", "caption": "Boy blowing bubbles in park", "id": "1725713012"}, {"image_id": "1590209396", "caption": "Row of vines in autumn vineyard", "id": "1590209396"}, {"image_id": "217372085", "caption": "Single tree with view of Seville Andalusia Spain", "id": "217372085"}, {"image_id": "1585850393", "caption": "Cookie Businessman against white background", "id": "1585850393"}, {"image_id": "1587833729", "caption": "Ancient roman stone street Ancient Ostia Rome Italy", "id": "1587833729"}, {"image_id": "1878778766", "caption": "Couple looking at closed sign stuck on a garage while standing next to their broken down car", "id": "1878778766"}, {"image_id": "1570219565", "caption": "Stream and buildings in Susch Lower Engadin Engadin Grisons Switzerland", "id": "1570219565"}, {"image_id": "1816210998", "caption": "Business colleagues working at desk in office with focus on man using electronic organizer in foreground", "id": "1816210998"}, {"image_id": "1590215609", "caption": "Senior couple looking at laptop", "id": "1590215609"}, {"image_id": "1572661520", "caption": "A bride sitting in a chair", "id": "1572661520"}, {"image_id": "1586682350", "caption": "cutout Of Male Executive With Hands In Pockets", "id": "1586682350"}, {"image_id": "1865895095", "caption": "Two women having lunch and drinking wine at the table and smiling", "id": "1865895095"}, {"image_id": "1576738535", "caption": "A young woman running in the desert", "id": "1576738535"}, {"image_id": "1865999789", "caption": "Vertical shot of a man crouching besides a golden retriever smiles at the camera in an autumn park", "id": "1865999789"}, {"image_id": "1277233118", "caption": "Loving young couple taking selfie in desert", "id": "1277233118"}, {"image_id": "1862083241", "caption": "Portrait of a Businessman with the forklift truck driver working in the background in the solar panel factory warehouse", "id": "1862083241"}, {"image_id": "1304264975", "caption": "Family wearing pajamas brushing teeth in bathroom in morning", "id": "1304264975"}, {"image_id": "1570316318", "caption": "A reindeer pulling a senior couple on a sleigh", "id": "1570316318"}, {"image_id": "1588020749", "caption": "Close up of woman smiling indoors", "id": "1588020749"}, {"image_id": "1865993660", "caption": "Horizontal shot of a multi generational family with boy on the father's shoulder hiking on a mountain trail smile at the camera", "id": "1865993660"}, {"image_id": "1571664818", "caption": "A businessman talking on a mobile phone", "id": "1571664818"}, {"image_id": "1840649711", "caption": "A portrait shot of a farmer kneeled down in a wheat field with a tractor in background", "id": "1840649711"}, {"image_id": "1576745363", "caption": "A young girl on a bike", "id": "1576745363"}, {"image_id": "1297781504", "caption": "Female high school students performing experiment in chemistry lab", "id": "1297781504"}, {"image_id": "1585850102", "caption": "Cookies in form of pieces of a puzzle against white background", "id": "1585850102"}, {"image_id": "1572527849", "caption": "Copper Canyon near Posada Barracas Chihuahua Mexico", "id": "1572527849"}, {"image_id": "1586682917", "caption": "Exhibitor With Prize Winning Jam At Agricultural Show", "id": "1586682917"}, {"image_id": "1846008038", "caption": "A Close up portrait shot of loaves of bread with an inspector shaking hands with baker in background", "id": "1846008038"}, {"image_id": "1564940417", "caption": "Two men doing hand stands", "id": "1564940417"}, {"image_id": "1860742049", "caption": "Low angle view of male swimmers lined up at the starting blocks", "id": "1860742049"}, {"image_id": "1811163215", "caption": "A vertical shot of sun piercing through a tree in center during autumn", "id": "1811163215"}, {"image_id": "1588015148", "caption": "Detail of young woman's face showing mouth", "id": "1588015148"}, {"image_id": "1571686592", "caption": "Ruins of Hammerhus Bornholm Island Denmark", "id": "1571686592"}, {"image_id": "1813171970", "caption": "Vertical shot with selective focus on sand castle with a family of four standing behind on the beach", "id": "1813171970"}, {"image_id": "1590061676", "caption": "A teenage girl having fun by a swimming pool", "id": "1590061676"}, {"image_id": "1587032324", "caption": "Young businessman pointing at the camera and smiling", "id": "1587032324"}, {"image_id": "1843605590", "caption": "Doctor in pink scrubs with a stethoscope around her neck preparing to give her patient an injection with a syringe in one hand", "id": "1843605590"}, {"image_id": "217361570", "caption": "Hot air balloon festival", "id": "217361570"}, {"image_id": "1570365626", "caption": "River Thames and Golden Jubilee Bridge London UK", "id": "1570365626"}, {"image_id": "1576777148", "caption": "Young man swimming in a natural pool", "id": "1576777148"}, {"image_id": "1571347718", "caption": "Dome of Reichstag building Berlin Germany", "id": "1571347718"}, {"image_id": "1586683958", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586683958"}, {"image_id": "1587144251", "caption": "Silhouette of glazed frost on a tree Upper Bavaria Germany", "id": "1587144251"}, {"image_id": "1587850202", "caption": "View of castle in Prague", "id": "1587850202"}, {"image_id": "1862081297", "caption": "Smiling businessman sitting in a private jet and working on a digital tablet while talking to a colleague", "id": "1862081297"}, {"image_id": "1590067799", "caption": "A portrait of a young woman smiling", "id": "1590067799"}, {"image_id": "1571353223", "caption": "Houses and church in Ftan Lower Engadine Grisons Switzerland", "id": "1571353223"}, {"image_id": "1844727620", "caption": "A group of multi ethnic design students working together in a classroom with mannequins in the background", "id": "1844727620"}, {"image_id": "1588004078", "caption": "Close up of pear blossoms outdoors", "id": "1588004078"}, {"image_id": "1264213472", "caption": "Close up of whipped cream on cup of hot chocolate on cafe table with focus on foreground", "id": "1264213472"}, {"image_id": "1869314678", "caption": "Wide shot of the silhouette of a couple enjoying drinks while sitting on a bench against the sunset over the ocean", "id": "1869314678"}, {"image_id": "1866109235", "caption": "Portrait of a smiling farmer sitting in the barley crop field in summer", "id": "1866109235"}, {"image_id": "1581272372", "caption": "Portrait of senior businessman working in back seat of car at camera", "id": "1581272372"}, {"image_id": "1578922295", "caption": "A young girl holding an Easter egg", "id": "1578922295"}, {"image_id": "1581281519", "caption": "Girl at camera in sunglasses by camper van on family beach vacation", "id": "1581281519"}, {"image_id": "1570568192", "caption": "Aerial View of the region around Manaus Amazonas Amazon River Brazil", "id": "1570568192"}, {"image_id": "1571615213", "caption": "A teenage girl playing pool", "id": "1571615213"}, {"image_id": "1844765840", "caption": "Smiling young couple watching something on a digital tablet together", "id": "1844765840"}, {"image_id": "1766919147", "caption": "Friends hanging out at lake together", "id": "1766919147"}, {"image_id": "1860742052", "caption": "Vertical portrait of a builder in hard hat with a drill in a partially built house", "id": "1860742052"}, {"image_id": "1816751115", "caption": "Overhead shot of a businesswoman walking with luggage in car park following arrow sign seen from rear", "id": "1816751115"}, {"image_id": "1840561370", "caption": "Vertical wide shot of a worker packing steel roller bearings in a wooden crate at a warehouse", "id": "1840561370"}, {"image_id": "1572512645", "caption": "Woman in woolly hat and scarf in snow", "id": "1572512645"}, {"image_id": "1766923878", "caption": "Happy boy jumping in air", "id": "1766923878"}, {"image_id": "1571337422", "caption": "Landscape near Bostad Vestvagoy Lofoten Nordland Norway", "id": "1571337422"}, {"image_id": "1576739477", "caption": "Happy little girl sitting on the front step of her new home", "id": "1576739477"}, {"image_id": "1297794872", "caption": "Cut out of senior couple holding small fencing panel home concept", "id": "1297794872"}, {"image_id": "1587842132", "caption": "Harbor with boats in Germany", "id": "1587842132"}, {"image_id": "1304265980", "caption": "Mature couples playing golf on course with man driving ball from tee", "id": "1304265980"}, {"image_id": "1572538757", "caption": "A surfer sitting on a beach", "id": "1572538757"}, {"image_id": "1578253478", "caption": "Two young men setting up a tent in a field", "id": "1578253478"}, {"image_id": "1846771271", "caption": "Portrait of a sports scientist monitoring exercising data of a cyclist working out on an exercise bike in a laboratory", "id": "1846771271"}, {"image_id": "1586669624", "caption": "A selection of colorful beads close up", "id": "1586669624"}, {"image_id": "1570552826", "caption": "Water Buffalos at Rio Guajara Pururui Amazon River Brazil", "id": "1570552826"}, {"image_id": "1587830120", "caption": "Young woman smiling on beach", "id": "1587830120"}, {"image_id": "1586693321", "caption": "High school student removing baked cookies from oven in home economics class", "id": "1586693321"}, {"image_id": "1840560482", "caption": "Low angle wide shot of a happy family with the father carrying his daughter on the shoulders while she holds onto a sunflower in a meadow full of wildflowers", "id": "1840560482"}, {"image_id": "1844765879", "caption": "Close up of a man standing with his friends and using a cell phone", "id": "1844765879"}, {"image_id": "1878871265", "caption": "A close vertical Side View of a school girl with blonde hair in a laboratory smiling and looking into a microscope under selective focus", "id": "1878871265"}, {"image_id": "1588010693", "caption": "Low angle view of senior woman walking in park", "id": "1588010693"}, {"image_id": "1586673437", "caption": "Young woman with skis standing in mountains on winter day", "id": "1586673437"}, {"image_id": "1576746128", "caption": "Portrait of a senior man looking out to sea", "id": "1576746128"}, {"image_id": "1813180643", "caption": "A female teenager smiling at the mirror as she tries out new clothes in the trial room", "id": "1813180643"}, {"image_id": "1583609624", "caption": "Young woman standing behind young man studying in library smiling portrait elevated view", "id": "1583609624"}, {"image_id": "1570573520", "caption": "Ochna serrulata Igapo forest Rio Jutai Brazil", "id": "1570573520"}, {"image_id": "1844765930", "caption": "Portrait of a young man smiling while texting on a cell phone", "id": "1844765930"}, {"image_id": "1578939293", "caption": "A portrait of a mature businessman in the foyer of an office building", "id": "1578939293"}, {"image_id": "1846708118", "caption": "An experienced team of surgeons performing an operation for a serious patient under the bright set of electric surgical lamps", "id": "1846708118"}, {"image_id": "1878688754", "caption": "Wide shot of a technician checking the wires in the data center holding a laptop", "id": "1878688754"}, {"image_id": "1865993417", "caption": "Vertical head and shoulder portrait of a senior woman wearing a sun hat in a rural setting smiles at the camera", "id": "1865993417"}, {"image_id": "1869314822", "caption": "Full shot of a couple running through an autumn forest with their children", "id": "1869314822"}, {"image_id": "1874804843", "caption": "Male Brewery Worker Checking Fermentation Process In Steel Vat", "id": "1874804843"}, {"image_id": "1587830096", "caption": "Woman wearing cowboy hat at beach", "id": "1587830096"}, {"image_id": "1844196782", "caption": "Vertical shot of shining sun at cloudy blue sky with copy space", "id": "1844196782"}, {"image_id": "1844731592", "caption": "Portrait shot an elderly man with a hat smiling at the camera while standing in a meadow full of wildflowers", "id": "1844731592"}, {"image_id": "1851481589", "caption": "Vertical shot of a woman washing soap suds off her car by spraying water on it using a hose", "id": "1851481589"}, {"image_id": "1864631039", "caption": "Little boy handing over passports at the check in counter with his family in the background while checking in at the airport", "id": "1864631039"}, {"image_id": "1590160463", "caption": "Parent and child on bench overlooking town", "id": "1590160463"}, {"image_id": "1590350951", "caption": "Yachts at the marina of Palma de Mallorca Mallorca Spain", "id": "1590350951"}, {"image_id": "1869043463", "caption": "Horizontal low angle rear view of a boy playing with a toy aeroplane in garden on a sunny day", "id": "1869043463"}, {"image_id": "1868709602", "caption": "A vertical view of a magnification lamp illuminating the circuit board held by an engineer for examining in a laboratory", "id": "1868709602"}, {"image_id": "1264115609", "caption": "Overhead view looking down on cricket stumps with bat and ball out of focus on grass", "id": "1264115609"}, {"image_id": "1297786319", "caption": "Female teacher helping students working in computer science class", "id": "1297786319"}, {"image_id": "1590164264", "caption": "Female teenage student smiling at desk in classroom", "id": "1590164264"}, {"image_id": "1572495872", "caption": "Young girl crouching by cattle in barn", "id": "1572495872"}, {"image_id": "1716620870", "caption": "View of a swimming pool with man at the other side Cape Town South Africa", "id": "1716620870"}, {"image_id": "1571666165", "caption": "A man walking alone on a beach", "id": "1571666165"}, {"image_id": "1578236732", "caption": "A mid adult woman eating popcorn", "id": "1578236732"}, {"image_id": "1865943743", "caption": "Side view of a senior couple loading suitcases into parked car boot on the driveway in front of the house", "id": "1865943743"}, {"image_id": "1878694289", "caption": "High school student cooking during a home economics class with their teacher", "id": "1878694289"}, {"image_id": "1590223049", "caption": "Surface shot of businessmen paddling in kayaks", "id": "1590223049"}, {"image_id": "1588002851", "caption": "A young woman using a mobile phone", "id": "1588002851"}, {"image_id": "1846749416", "caption": "A senior surgeon in selective focus performing an operation with his teammates in the foreground in an operation suite", "id": "1846749416"}, {"image_id": "1578916826", "caption": "A Young Woman Carrying Heavy Shopping Bags", "id": "1578916826"}, {"image_id": "1859237132", "caption": "A horizontal side view of an architect and an engineer reviewing blueprints at the construction site", "id": "1859237132"}, {"image_id": "1277256896", "caption": "Home diy Close up of power tool drill screwing screw into timber", "id": "1277256896"}, {"image_id": "1590317663", "caption": "Stack of One Euro coins next to bull figurine", "id": "1590317663"}, {"image_id": "1857286271", "caption": "Close up of a senior couple standing in the shallows on the beach on a bright sunny day and hugging each other", "id": "1857286271"}, {"image_id": "1840648079", "caption": "A low angle vertical shot of a middle aged worker in uniform using forklift to move the merchandise on a sunny day while smiling at the camera", "id": "1840648079"}, {"image_id": "1578935330", "caption": "Woman holding a bunch of dried lavender", "id": "1578935330"}, {"image_id": "1859339816", "caption": "A low angle shot of happy senior man pulling his wife's hand in an autumn park with a big tree in background", "id": "1859339816"}, {"image_id": "1588010675", "caption": "Senior man running on nature trail", "id": "1588010675"}, {"image_id": "1847201708", "caption": "A low angle view of a happy family holding solar panels while standing out the house on green grass field", "id": "1847201708"}, {"image_id": "1586672612", "caption": "Senior couple walking on frozen lake near boats", "id": "1586672612"}, {"image_id": "1571331545", "caption": "Part of of Trollstigen The Troll Path Rauma Norway", "id": "1571331545"}, {"image_id": "1570292714", "caption": "A portrait of a young woman wearing a red woolen hat and gloves", "id": "1570292714"}, {"image_id": "1840648583", "caption": "A horizontal portrait of a confident businessman looking up with arms crossed standing in the middle of a large data center room with blue glowing network server cabinets on both sides", "id": "1840648583"}, {"image_id": "1586723816", "caption": "Young Couples Making A Toast With Beer Standing By River", "id": "1586723816"}, {"image_id": "1590350093", "caption": "Close up of branch with oranges", "id": "1590350093"}, {"image_id": "1586673368", "caption": "Sun rising over snow capped mountains in Fimbatal the border between Switzerland and Austria near Eschol", "id": "1586673368"}, {"image_id": "1586693990", "caption": "High school student assembling robot in science class", "id": "1586693990"}, {"image_id": "1586735534", "caption": "Team Of Aero Engineers Working On Aircraft In Hangar", "id": "1586735534"}, {"image_id": "1873340894", "caption": "Wide shot of a hiker standing near a lake under the autumn trees", "id": "1873340894"}, {"image_id": "1590213665", "caption": "Doctor talking to patient in office", "id": "1590213665"}, {"image_id": "1277231648", "caption": "Builder on construction site with blueprints in partially built house", "id": "1277231648"}, {"image_id": "1585878071", "caption": "Calving glacier Isfjorden Spitsbergen Svalbard Norway Europe", "id": "1585878071"}, {"image_id": "1670342075", "caption": "Man giving woman piggyback in snow on winter vacation at camera", "id": "1670342075"}, {"image_id": "1297786340", "caption": "Female high school student taking test at desk in classroom", "id": "1297786340"}, {"image_id": "1851401390", "caption": "Teenage boy with blond hair holding a skateboard in hands and posing in the park with his friends in the background", "id": "1851401390"}, {"image_id": "1873350941", "caption": "Wide shot of the Architectural footbridge and buildings along sunny canal in Venice Italy", "id": "1873350941"}, {"image_id": "1572528755", "caption": "Gray Whale Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528755"}, {"image_id": "1878695834", "caption": "Vertical shot of a high school student holding a volleyball during a gym class", "id": "1878695834"}, {"image_id": "1851481580", "caption": "Wide shot of a beautiful woman washing her car", "id": "1851481580"}, {"image_id": "1572524330", "caption": "Mother applying sunscreen to daughter smiling", "id": "1572524330"}, {"image_id": "1587028136", "caption": "View of gas cylinders on a truck", "id": "1587028136"}, {"image_id": "1578212969", "caption": "A young woman wearing a Santa hat holding a Christmas present", "id": "1578212969"}, {"image_id": "1297774187", "caption": "Smiling male graduate posing for family photograph on graduation day", "id": "1297774187"}, {"image_id": "1865986955", "caption": "Senior woman and her adult daughter hiking on a woodland trail with the young woman looking through binoculars holding a map", "id": "1865986955"}, {"image_id": "1578924755", "caption": "A senior couple in autumn time", "id": "1578924755"}, {"image_id": "1590336047", "caption": "View from Twenty Mule Team Canyon Death Valley National Park Nevada USA", "id": "1590336047"}, {"image_id": "1567876817", "caption": "Fishing net and boat in harbor of Marsalforn Gozo Malta", "id": "1567876817"}, {"image_id": "1766918214", "caption": "Happy young couple at lake", "id": "1766918214"}, {"image_id": "1588012814", "caption": "Close up of a woman yawning", "id": "1588012814"}, {"image_id": "1586685119", "caption": "Frustrated Woman Broken Down With Flat Tyre On car", "id": "1586685119"}, {"image_id": "1570164665", "caption": "College students on campus in autumn", "id": "1570164665"}, {"image_id": "1590352142", "caption": "Yellow lilies in front of a lemon tree", "id": "1590352142"}, {"image_id": "1839580727", "caption": "A senior businessman standing with his hands in his pockets and looking confidently at the camera", "id": "1839580727"}, {"image_id": "1588011629", "caption": "Businessmen walking out of elevator", "id": "1588011629"}, {"image_id": "1857288767", "caption": "Young woman celebrating her victory at the poker table with her arm raised while her husband and friends are cheering for her", "id": "1857288767"}, {"image_id": "216356894", "caption": "Clouds in blue sky", "id": "216356894"}, {"image_id": "1844190422", "caption": "Horizontal shot of two young girls lying in a field of daffodils on a sunny day", "id": "1844190422"}, {"image_id": "1277238107", "caption": "Woman exercising at home stretching on floor mat in living room", "id": "1277238107"}, {"image_id": "1813180760", "caption": "A vertical shot of sunset on a beach", "id": "1813180760"}, {"image_id": "1571337437", "caption": "View to Svolvaer Lofoten Nordland Norway", "id": "1571337437"}, {"image_id": "1297732676", "caption": "A mature woman pegging out washing on a washing line", "id": "1297732676"}, {"image_id": "1578942659", "caption": "Businesswoman reading her valentines card in an office", "id": "1578942659"}, {"image_id": "1587996212", "caption": "Friends toasting with wine over dinner table", "id": "1587996212"}, {"image_id": "1862126105", "caption": "A Close up shot of a student's hand writing exam at his desk", "id": "1862126105"}, {"image_id": "1840648592", "caption": "Side view of an IT technician working in the aisle of a data center room in selective focus facing direct warm light with a row of glowing network server cabinets softly blurred in the foreground", "id": "1840648592"}, {"image_id": "1590178595", "caption": "Tracks and crossovers for closed mine Karangahake Gorge Coromandel Peninsula North Island New Zealand", "id": "1590178595"}, {"image_id": "1571691158", "caption": "Fishing boats at Reine Lofoten Nordland County Norway", "id": "1571691158"}, {"image_id": "217368557", "caption": "Iceberg Antarctica", "id": "217368557"}, {"image_id": "1844731859", "caption": "Portrait shot of a happy elderly man riding a bicycle in a meadow full of wildflowers", "id": "1844731859"}, {"image_id": "1657996766", "caption": "Father and son fishing in lake", "id": "1657996766"}, {"image_id": "1590214643", "caption": "Close up of man holding apple outdoors", "id": "1590214643"}, {"image_id": "1851401237", "caption": "Horizontal shot of a businessman and businesswoman in armchairs in office with the man glaring over shoulder at woman", "id": "1851401237"}, {"image_id": "1571337443", "caption": "White tailed eagle taking off from water with fish", "id": "1571337443"}, {"image_id": "1571686631", "caption": "Coast of Ruegen Mecklenburg Western Pommerania Germany", "id": "1571686631"}, {"image_id": "1277228987", "caption": "Female athlete with gold medal and relay race baton at camera", "id": "1277228987"}, {"image_id": "1586683499", "caption": "Senior Couple Working In Vegetable Garden", "id": "1586683499"}, {"image_id": "1590178592", "caption": "Tracks and crossovers for closed mine Karangahake Gorge Coromandel Peninsula North Island New Zealand", "id": "1590178592"}, {"image_id": "1590073445", "caption": "A woman holding a peach", "id": "1590073445"}, {"image_id": "1576780151", "caption": "A young woman filing her nails", "id": "1576780151"}, {"image_id": "1572661505", "caption": "A woman drinking a glass of wine with a meal", "id": "1572661505"}, {"image_id": "1587048551", "caption": "Boy lying on the grass with a laptop", "id": "1587048551"}, {"image_id": "1304263382", "caption": "Mature couple on road trip in classic convertible car on country road", "id": "1304263382"}, {"image_id": "1587838010", "caption": "Woman in fancy clothing holding fanned out Euros", "id": "1587838010"}, {"image_id": "1578930443", "caption": "Woman applying blusher or bronzer on her face", "id": "1578930443"}, {"image_id": "1570388279", "caption": "Banteay Srei Temple Angkor Siem Reap Cambodia", "id": "1570388279"}, {"image_id": "1571518034", "caption": "A family moving into a new home", "id": "1571518034"}, {"image_id": "1578922415", "caption": "A young girl walking through daffodils with a basket full of Easter eggs", "id": "1578922415"}, {"image_id": "1590023090", "caption": "A young woman wearing silk pyjamas", "id": "1590023090"}, {"image_id": "1846708181", "caption": "A vertical Close up of an IV bag hanging in the foreground in selective focus with a team surgeons and nurses working in the background", "id": "1846708181"}, {"image_id": "1570327397", "caption": "A young man pushing a sled through a snowy street", "id": "1570327397"}, {"image_id": "1570160798", "caption": "Facades in Stone Town made of coral rag part of Zanzibar City Zanzibar Tanzania Africa", "id": "1570160798"}, {"image_id": "1587138344", "caption": "A businessman and a businesswoman shaking hands on a deal", "id": "1587138344"}, {"image_id": "1587849455", "caption": "View of city on isle of Elba", "id": "1587849455"}, {"image_id": "1297796729", "caption": "cutout of smiling children bouncing on inflatable balls", "id": "1297796729"}, {"image_id": "1576772057", "caption": "A businesswoman standing on a balcony", "id": "1576772057"}, {"image_id": "1590070412", "caption": "A mature woman performing yoga on a beach", "id": "1590070412"}, {"image_id": "1859202752", "caption": "Mother and daughter hugging while on a picnic with their family", "id": "1859202752"}, {"image_id": "1663683863", "caption": "Teenage boys drinking from bottle in gym", "id": "1663683863"}, {"image_id": "1872077114", "caption": "Mother and daughter in white clothing standing on beach smiling front view portrait", "id": "1872077114"}, {"image_id": "1572527672", "caption": "Los Islotes Sea Cave in the Sea of Cortez Los Islotes Baja California Sur Mexico", "id": "1572527672"}, {"image_id": "1590339008", "caption": "Young girl looking at snails", "id": "1590339008"}, {"image_id": "1297783715", "caption": "Couple on winter vacation holding snowball smiling at camera", "id": "1297783715"}, {"image_id": "1590027413", "caption": "A businessman in an office three colleagues in background having a discussion", "id": "1590027413"}, {"image_id": "1843607216", "caption": "Horizontal front shot of a joyous father and son driving a red convertible on a sunny day with copy space", "id": "1843607216"}, {"image_id": "1859182868", "caption": "Close up shot of a female s feet in rock climbing shoes", "id": "1859182868"}, {"image_id": "1588023989", "caption": "Businessman in a light suit using a mobile telephone", "id": "1588023989"}, {"image_id": "1851471929", "caption": "Multi generations of a happy family sitting on a wooden pier of a lake", "id": "1851471929"}, {"image_id": "1859323628", "caption": "A medium shot of a chef watching two young trainees grating and cutting slices of cheese in a kitchen", "id": "1859323628"}, {"image_id": "1860742184", "caption": "Portrait of a mature woman in swimming cap by a friend in sunhat on the beach", "id": "1860742184"}, {"image_id": "1304266796", "caption": "Girl dressed in bridesmaid dress holding wedding flowers at camera", "id": "1304266796"}, {"image_id": "1571686661", "caption": "Ropes on a ship Ruegen Mecklenburg Western Pommerania Germany", "id": "1571686661"}, {"image_id": "1590343523", "caption": "Cliffs of Moulin Huet Bay South Coast Guernsey Channel Islands UK", "id": "1590343523"}, {"image_id": "1725907856", "caption": "A young couple standing with bicycles holding a map", "id": "1725907856"}, {"image_id": "1587991697", "caption": "Female scientist examining test tubes in rack", "id": "1587991697"}, {"image_id": "1588025561", "caption": "Portrait of young woman smiling", "id": "1588025561"}, {"image_id": "1583609294", "caption": "Cows grazing together in rural field", "id": "1583609294"}, {"image_id": "1869314750", "caption": "Medium shot of a boy standing in an autumn park with his father", "id": "1869314750"}, {"image_id": "1590215531", "caption": "man's hand on woman's thigh in movie theater", "id": "1590215531"}, {"image_id": "1587987647", "caption": "Woman jumping in puddle with umbrella", "id": "1587987647"}, {"image_id": "1590220547", "caption": "Boy peeking out from curtains", "id": "1590220547"}, {"image_id": "1572509003", "caption": "Portrait of a girl leaning against railing bars", "id": "1572509003"}, {"image_id": "1587997634", "caption": "Close up of wine bottle and glasses outdoors", "id": "1587997634"}, {"image_id": "1844729084", "caption": "Close up shot of an elderly couple having lunch and toasting wine glasses at the patio table with friends", "id": "1844729084"}, {"image_id": "1587060989", "caption": "Men in protective wear walking down stairs", "id": "1587060989"}, {"image_id": "1572388631", "caption": "Maypole Etterschlag Woerthsee Bavaria Germany", "id": "1572388631"}, {"image_id": "1588000322", "caption": "Female fortune teller with crystal ball talking to client", "id": "1588000322"}, {"image_id": "1839578870", "caption": "Horizontal three quarter profile shot of a young businesswoman working on a laptop at a caf with copy space", "id": "1839578870"}, {"image_id": "1587119147", "caption": "Couple walking arm in arm carrying shopping bags", "id": "1587119147"}, {"image_id": "1710247385", "caption": "A businessman having lunch by the water", "id": "1710247385"}, {"image_id": "1590179291", "caption": "Kiwi fruits hanging on tree North Island New Zealand", "id": "1590179291"}, {"image_id": "1862086763", "caption": "Scattered white clouds rolling over the blue sky", "id": "1862086763"}, {"image_id": "1865993597", "caption": "Vertical shot of a father resting against a tree tickled by his mischievous son in ear by a stick as his sister watches on in a woodland clearing", "id": "1865993597"}, {"image_id": "1571664752", "caption": "A bride and groom in a car", "id": "1571664752"}, {"image_id": "1587857291", "caption": "View of a couple dancing", "id": "1587857291"}, {"image_id": "1590323633", "caption": "Minaret and cupola of Mosque of Sohar Al Batinah Region Oman", "id": "1590323633"}, {"image_id": "1277256734", "caption": "Boy learning from teacher teaching in elementary school class by playing with weighing scales in classroom", "id": "1277256734"}, {"image_id": "1878694187", "caption": "Low angle shot of high school students playing basketball during a gym class", "id": "1878694187"}, {"image_id": "1590319388", "caption": "Woman walking in surf at beach", "id": "1590319388"}, {"image_id": "1571351795", "caption": "Female soccer player carrying german flag", "id": "1571351795"}, {"image_id": "1851407780", "caption": "Horizontal low angle shot of three joyous teenage girls leaping in air by the drum kit and amplifiers in a garage", "id": "1851407780"}, {"image_id": "1571601965", "caption": "A young woman with her hair in curlers", "id": "1571601965"}, {"image_id": "1578935336", "caption": "A young businessman sitting in a waiting room eyes closed", "id": "1578935336"}, {"image_id": "1868723420", "caption": "A portrait shot of a happy children looking at the father laying on their mother's lap in a sunny wildflower field", "id": "1868723420"}, {"image_id": "1843605506", "caption": "Side view of senior couple catching fish in the lake using a fishing rod against the background of clear blue sky and mountains", "id": "1843605506"}, {"image_id": "1840555037", "caption": "Salesman showing customer hatchback of car in a car dealership showroom", "id": "1840555037"}, {"image_id": "1869311939", "caption": "Woman resting on a towel with a hat and a book on a sunny beach", "id": "1869311939"}, {"image_id": "1873296704", "caption": "Medium shot of a gym teacher standing in front of a whiteboard and teaching high school students badminton at the gymnasium", "id": "1873296704"}, {"image_id": "1587028127", "caption": "View of a road in the countryside Cape Town South Africa", "id": "1587028127"}, {"image_id": "1586690990", "caption": "Workers stacking cheese on production line in processing plant", "id": "1586690990"}, {"image_id": "1587982256", "caption": "Close up of cherries on tree", "id": "1587982256"}, {"image_id": "1586682932", "caption": "Judge Awarding Trophy At Flower Show", "id": "1586682932"}, {"image_id": "1852927187", "caption": "Horizontal shot of a seated baby boy in diaper pants sucking fingers looks at the camera with copy space", "id": "1852927187"}, {"image_id": "1297799915", "caption": "Low angle view of yellow autumn leaves in forest tree canopy", "id": "1297799915"}, {"image_id": "1855916165", "caption": "Store owner standing with crossed arms and holding a file in a bicycle shop", "id": "1855916165"}, {"image_id": "1578226847", "caption": "A businesswoman listening to music on a flight", "id": "1578226847"}, {"image_id": "1843605515", "caption": "Wide shot of a family on pier getting into a boat for boating in a bright sunny day", "id": "1843605515"}, {"image_id": "1587119117", "caption": "Woman acting like a ringmaster dominating a man", "id": "1587119117"}, {"image_id": "1865999621", "caption": "Horizontal shot of midsection of a mature man adjusting his rucksack waist strap during hiking on a mountain trail standing by his wife", "id": "1865999621"}, {"image_id": "1297788671", "caption": "Group of active young friends jumping from dune on winter beach", "id": "1297788671"}, {"image_id": "1864634420", "caption": "Young man lying on a chaise longue and using his laptop while wearing headphones in his ears", "id": "1864634420"}, {"image_id": "1857286046", "caption": "Middle aged businessman standing in his office and taking notes while his colleagues are standing outside and having tea on a break from work", "id": "1857286046"}, {"image_id": "1587649232", "caption": "View of an empty beer garden in autumn Stegen at Ammersee Upper Bavaria Germany", "id": "1587649232"}, {"image_id": "1588016240", "caption": "Cars driving on highway in snow with lights on", "id": "1588016240"}, {"image_id": "1572527714", "caption": "California sea lion Zalophus californianus Sea of Cortez Los Islotes Baja California Sur Mexico", "id": "1572527714"}, {"image_id": "1297750358", "caption": "Man takes photo with camera as senior couple enjoy meal with friends", "id": "1297750358"}, {"image_id": "1859339615", "caption": "A top angle portrait shot of a senior man relaxing on the grass and smiling at camera with red autumn leaves in foreground", "id": "1859339615"}, {"image_id": "1587838220", "caption": "Middle aged couple holding hands in park", "id": "1587838220"}, {"image_id": "1873296878", "caption": "Wide shot of an art teacher watching middle school students paint during an art class", "id": "1873296878"}, {"image_id": "1839579071", "caption": "Front midsection view of a woman holding a basket of vegetables in the garden", "id": "1839579071"}, {"image_id": "1586676692", "caption": "cutout Of Man Hidden Behind Armful Of Vegetables", "id": "1586676692"}, {"image_id": "1590338879", "caption": "Young woman drinking glass of milk", "id": "1590338879"}, {"image_id": "1570311800", "caption": "Cathedral of Cefalu Cefalu Sicily Italy", "id": "1570311800"}, {"image_id": "1590164792", "caption": "Female teenage student smiling outdoors", "id": "1590164792"}, {"image_id": "1845993722", "caption": "A high angle medium shot of a worker operating machines through computer in a aluminum light fittings factory", "id": "1845993722"}, {"image_id": "1587149030", "caption": "Detail view of a Christmas ornament lying on a platter", "id": "1587149030"}, {"image_id": "216387149", "caption": "Cloudy skyscape", "id": "216387149"}, {"image_id": "1571517989", "caption": "A teenage girl using a computer", "id": "1571517989"}, {"image_id": "1839580598", "caption": "Senior businessman riding a wave board while looking at his watch inside the airport to get to his departure gate quicker", "id": "1839580598"}, {"image_id": "1570235681", "caption": "Jumbo jet and airplanes flying across blue sky", "id": "1570235681"}, {"image_id": "1873415564", "caption": "Horizontal full length profile shot of a smiling senior woman in a wheelchair holding hand of her husband and smiling", "id": "1873415564"}, {"image_id": "1590102605", "caption": "Male and female business colleagues looking at notes in a folder", "id": "1590102605"}, {"image_id": "1868703239", "caption": "A portrait of a confident businesswoman sitting at the edge of a desk in an office while reviewing a report", "id": "1868703239"}, {"image_id": "1587865385", "caption": "Studio shot of businesspeople exchanging money", "id": "1587865385"}, {"image_id": "1572395051", "caption": "Moldy old pretzel in lunchbox Woerthsee Bavaria Germany", "id": "1572395051"}, {"image_id": "1578904442", "caption": "A woman relaxing by a waterfall", "id": "1578904442"}, {"image_id": "1590338870", "caption": "Young woman in underwear cropped view of legs", "id": "1590338870"}, {"image_id": "1578920285", "caption": "A young woman holding a bunch of pink tulips", "id": "1578920285"}, {"image_id": "1813180016", "caption": "Two girls playing with inflatable toy at beach with copy space", "id": "1813180016"}, {"image_id": "1851483722", "caption": "Vertical tilted shot of a boy holding a multicolored kite on a sunny beach", "id": "1851483722"}, {"image_id": "1844196266", "caption": "Butcher in uniform behind the counter selling meat to customer", "id": "1844196266"}, {"image_id": "1587982898", "caption": "Rooftops and chimneys London United Kingdom", "id": "1587982898"}, {"image_id": "1878871142", "caption": "Group of students attentively listening to their teacher explaining solar panel and wind turbines in classroom", "id": "1878871142"}, {"image_id": "1576780049", "caption": "Two schoolgirls running for the school bus", "id": "1576780049"}, {"image_id": "1570162181", "caption": "View to Praslin Island from ship Seychelles", "id": "1570162181"}, {"image_id": "1807085666", "caption": "USA California San Diego man cycling smiling Coronado Bay Bridge in background", "id": "1807085666"}, {"image_id": "1297750196", "caption": "Young man outdoors smiling at camera", "id": "1297750196"}, {"image_id": "1586681303", "caption": "cutout Of Male Executive With Folded Arms", "id": "1586681303"}, {"image_id": "1578935492", "caption": "Young woman spraying perfume on her neck", "id": "1578935492"}, {"image_id": "1586738528", "caption": "Family Taking Photos On Beach Vacation Together", "id": "1586738528"}, {"image_id": "1587855632", "caption": "Close up of woman smiling", "id": "1587855632"}, {"image_id": "1587031433", "caption": "View of miniature bull on Euro", "id": "1587031433"}, {"image_id": "1587110939", "caption": "Young woman lying in bed", "id": "1587110939"}, {"image_id": "1840650458", "caption": "Two young farmers holding and shearing sheep for wool in a barn", "id": "1840650458"}, {"image_id": "1859178383", "caption": "Vertical shot of an engineer in a reflector vest repairing the flap on the wing of a passenger jet at a hangar", "id": "1859178383"}, {"image_id": "1655856812", "caption": "Young boy frowning well lit", "id": "1655856812"}, {"image_id": "1585868759", "caption": "Monacobreen glacier Liefdefjorden Haakon VII Land Spitsbergen Svalbard Norway Europe", "id": "1585868759"}, {"image_id": "1572508304", "caption": "A senior woman standing by a sports car", "id": "1572508304"}, {"image_id": "1865856617", "caption": "Middle aged businesswoman walking and pulling a suitcase in the airport lobby", "id": "1865856617"}, {"image_id": "1571686685", "caption": "Baltic Sea Spa Binz Ruegen Mecklenburg Vorpommern Germany", "id": "1571686685"}, {"image_id": "1859143181", "caption": "Medium shot of an elderly couple with a fishing rod on a sunny beach with a cloudy sky in the background", "id": "1859143181"}, {"image_id": "1840648532", "caption": "A vertical view of snowy mountain range with a bright shining sun diffused by moving clouds", "id": "1840648532"}, {"image_id": "1277260760", "caption": "Couple shopping for homeware carrying stack of towels in shop obscuring their faces standing in front of colorful goods arranged on shelves", "id": "1277260760"}, {"image_id": "1852646690", "caption": "A Close up of surgeon's hands in rubber gloves while making an incision with a scalpel", "id": "1852646690"}, {"image_id": "1864656119", "caption": "Selective focus on red car s boot with two men looking under the hood at the engine in the background", "id": "1864656119"}, {"image_id": "1839588641", "caption": "Three quarter length portrait of a joyous senior couple outdoors with the man on push scooter smiling at each other", "id": "1839588641"}, {"image_id": "216779531", "caption": "Sun beach and ocean at Gerrans Bay Cornwall United Kingdom", "id": "216779531"}, {"image_id": "1844724098", "caption": "Low angle shot of a worker smiling at the camera while taping cardboard boxes at the production line of a distribution warehouse", "id": "1844724098"}, {"image_id": "1868723291", "caption": "A vertical shot of a happy family smiling at camera while riding bicycles in a wildflower field", "id": "1868723291"}, {"image_id": "1586668580", "caption": "Two young boys standing beside a lake", "id": "1586668580"}, {"image_id": "1844733188", "caption": "Girl with a backpack sitting on a fence in a green field and smiling at the camera while reading a map with a group on friends in the background", "id": "1844733188"}, {"image_id": "1869317168", "caption": "Wide shot of a manager standing under a blue light in the aisle of a data center aligned with storage cabinets", "id": "1869317168"}, {"image_id": "1590347663", "caption": "Grandfather and grandson using laptop", "id": "1590347663"}, {"image_id": "1570235027", "caption": "Close up of baby eating pureed food", "id": "1570235027"}, {"image_id": "1846060046", "caption": "A low angle shot of vibrant sunflowers with sun shining behind them", "id": "1846060046"}, {"image_id": "1859339783", "caption": "A Close up portrait shot of a young blonde boy performing experiment in a beaker in a school chemistry laboratory", "id": "1859339783"}, {"image_id": "1817411649", "caption": "Vertical shot of a man in an apron cutting leaves off a plant at a greenhouse", "id": "1817411649"}, {"image_id": "1766918361", "caption": "Young woman aiming slingshot at apple on man's head", "id": "1766918361"}, {"image_id": "1570355504", "caption": "Great Mormon Papilio memnon Blurred Motion Out of Focus Phnom Penh Cambodia", "id": "1570355504"}, {"image_id": "1586736533", "caption": "Portrait Of Business Team Working In Modern Office", "id": "1586736533"}, {"image_id": "1670339441", "caption": "Young boy being lifted up by baseball coach", "id": "1670339441"}, {"image_id": "1869317177", "caption": "Wide shot of a manager standing in the aisle of a data center aligned with storage cabinets", "id": "1869317177"}, {"image_id": "1590220055", "caption": "Angel statues on roof of Zurich Opera House Zurich Canton of Zurich Switzerland", "id": "1590220055"}, {"image_id": "1590161897", "caption": "High angle view of Lake Constance and Lindau from Pf\ufffd\ufffdnder Mountain Austria Germany", "id": "1590161897"}, {"image_id": "1588017254", "caption": "Tree in grassy meadow Bavaria Germany", "id": "1588017254"}, {"image_id": "1587992639", "caption": "Portrait of a young African American man in front of a graffiti covered wall", "id": "1587992639"}, {"image_id": "1852935128", "caption": "Vertical portrait of a family of four with fishing rod on a jetty smiles at the camera", "id": "1852935128"}, {"image_id": "1852967069", "caption": "A young man drinking red wine in a wine shop", "id": "1852967069"}, {"image_id": "1297741907", "caption": "Female florist in shop making bouquet from pink and white roses smiling at camera", "id": "1297741907"}, {"image_id": "1862067146", "caption": "Elderly couple with a digital tablet sitting on a wooden jetty by the lake", "id": "1862067146"}, {"image_id": "1590202229", "caption": "A check mark written in red pencil", "id": "1590202229"}, {"image_id": "1590179900", "caption": "Coastline Norfolk Island External Territory of Australia", "id": "1590179900"}, {"image_id": "1587826376", "caption": "Businessman with laptop talking to woman in her home", "id": "1587826376"}, {"image_id": "1586672558", "caption": "Senior couple in coats on winter day in the mountains", "id": "1586672558"}, {"image_id": "1844196362", "caption": "Horizontal shot of woman holding celery and chocolates a nutrition concept", "id": "1844196362"}, {"image_id": "1571576858", "caption": "A man meditating by a pool", "id": "1571576858"}, {"image_id": "1570316207", "caption": "Snowy landscape with trees near Wessling Bavaria Germany", "id": "1570316207"}, {"image_id": "1576779914", "caption": "For sale sign outside a family house", "id": "1576779914"}, {"image_id": "1859339570", "caption": "A portrait shot of a teacher watching a young boy and a girl conducting experiment in a beaker in a school chemistry laboratory", "id": "1859339570"}, {"image_id": "1817410167", "caption": "Two boys making cards at desk in classroom with focus on smiling teacher in foreground", "id": "1817410167"}, {"image_id": "1864640705", "caption": "Vertical portrait of a woman on a sofa working on a laptop smiles at the camera", "id": "1864640705"}, {"image_id": "1572390227", "caption": "Highland cattle on farm Seefeld Bavaria Germany", "id": "1572390227"}, {"image_id": "1588004090", "caption": "Close up of pear blossoms outdoors", "id": "1588004090"}, {"image_id": "1852646705", "caption": "A low angle shot of an experienced Paramedic checking the intravenous saline drip on an emergency airlift helicopter", "id": "1852646705"}, {"image_id": "1304266799", "caption": "Portrait of Bride and groom at wedding hugging bridesmaid", "id": "1304266799"}, {"image_id": "1588024832", "caption": "Close up of a young man holding a knife", "id": "1588024832"}, {"image_id": "1865895089", "caption": "Horizontal shot of a young woman wearing a white bathrobe and sitting next to a swimming pool with legs dipped in water and her friends relaxing on deckchairs", "id": "1865895089"}, {"image_id": "1297789835", "caption": "Smiling couple taking selfie with cell phone on walk in autumn park", "id": "1297789835"}, {"image_id": "1859237114", "caption": "A professional architect with helmet and safety vest while holding blueprints at the construction site on a bright sunny day", "id": "1859237114"}, {"image_id": "1590341132", "caption": "Hispanic woman cheering with Brazilian flag", "id": "1590341132"}, {"image_id": "1868723498", "caption": "A side profile shot of a happy family holding hands and walking in a row in wildflower field", "id": "1868723498"}, {"image_id": "1277255708", "caption": "Friends walking in surf on summer beach vacation with surfboards", "id": "1277255708"}, {"image_id": "1859323736", "caption": "A medium shot of a young couple looking at a blueprint while standing in front of a large solar panel", "id": "1859323736"}, {"image_id": "1587128399", "caption": "Young woman and teenage friend throwing snow on mountain", "id": "1587128399"}, {"image_id": "1578212948", "caption": "Portrait of a young woman touching her face", "id": "1578212948"}, {"image_id": "1586727047", "caption": "Portrait Of Male Radiographer In Hospital X Ray Department", "id": "1586727047"}, {"image_id": "1851471974", "caption": "Wide shot of a couple flying a kite with their children and elderly parents running in the background at a sunny beach", "id": "1851471974"}, {"image_id": "1590031952", "caption": "Two teenage girls using a laptop", "id": "1590031952"}, {"image_id": "1570355486", "caption": "Temple Banteay Srei Angkor Siem Reap Angkor Cambodia", "id": "1570355486"}, {"image_id": "1578252518", "caption": "Two young women camping one drinking a bottle of beer", "id": "1578252518"}, {"image_id": "1840648535", "caption": "A wide angle view of snowy mountain range with green trees and a bright blue sky", "id": "1840648535"}, {"image_id": "1572383462", "caption": "Aerial view of Tunnel Mountain to Bow River Banff National Park Alberta Canada", "id": "1572383462"}, {"image_id": "1840563155", "caption": "Vertical wide shot of a formally dressed businessman and an engineer in apron holding a machine part in a manufacturing plant", "id": "1840563155"}, {"image_id": "1586663939", "caption": "Straight Country Road Leading Through Arid South African Landscape", "id": "1586663939"}, {"image_id": "216583037", "caption": "Hungry sheep grazing on grass", "id": "216583037"}, {"image_id": "1844766023", "caption": "Young woman wearing sunglasses sitting on a park bench cross legged and holding a digital tablet", "id": "1844766023"}, {"image_id": "1862121449", "caption": "A vertical view of Architects and engineers in formal business suits reviewing blueprints in an office", "id": "1862121449"}, {"image_id": "1297794593", "caption": "cutout of woman applying face powder makeup with compact", "id": "1297794593"}, {"image_id": "1846059965", "caption": "A portrait shot of a barley field with blue sky over it", "id": "1846059965"}, {"image_id": "1852935203", "caption": "Vertical portrait of a girl with a plastic hoop on the beach smiles at the camera", "id": "1852935203"}, {"image_id": "1576782605", "caption": "Detail of young woman's face hand held under her chin", "id": "1576782605"}, {"image_id": "1868716364", "caption": "A side profile medium shot of an engineer using tablet while inspecting a large solar panel", "id": "1868716364"}, {"image_id": "1817410197", "caption": "Overhead shot of teacher standing in huddle with children on grass", "id": "1817410197"}, {"image_id": "1840561325", "caption": "Wide top shot of workers having a discussion among boxes laid on conveyor belts at a distribution warehouse", "id": "1840561325"}, {"image_id": "1843610573", "caption": "Low angle rear view of lower section of a businessman wheeling luggage in an airport lobby reflected on floor with copy space", "id": "1843610573"}, {"image_id": "1866091286", "caption": "Aerial view of a farmer walking in a sunny golden barley field", "id": "1866091286"}, {"image_id": "1572515954", "caption": "Styrofoam heads kissing against white background", "id": "1572515954"}, {"image_id": "1844723984", "caption": "Portrait shot of a confident businessman leaning on a bundle of stacked steel in a warehouse", "id": "1844723984"}, {"image_id": "1297776785", "caption": "Bunches of ripe purple grapes growing on vines in vineyard", "id": "1297776785"}, {"image_id": "1878695831", "caption": "Vertical shot of a gym teacher and high school students with netballs", "id": "1878695831"}, {"image_id": "1869314699", "caption": "Vertical shot of a couple playfully throwing leaves in an autumn park", "id": "1869314699"}, {"image_id": "1578910088", "caption": "A young woman looking at old photographs", "id": "1578910088"}, {"image_id": "1663685435", "caption": "Teenage boys training on exercise bikes in gym", "id": "1663685435"}, {"image_id": "1570219580", "caption": "View from Fluela Pass to Susch Lower Engadin Engadin Grisons Switzerland", "id": "1570219580"}, {"image_id": "1297774409", "caption": "Loving couple sitting and hugging on winter beach from behind", "id": "1297774409"}, {"image_id": "1588004072", "caption": "Rows of pear trees in meadow", "id": "1588004072"}, {"image_id": "1868716628", "caption": "A close up shot of an engineer using a tablet while standing in front of big solar panels", "id": "1868716628"}, {"image_id": "1585874846", "caption": "Ruinaulta Canyon of the river Rhine Grisons Switzerland Europe", "id": "1585874846"}, {"image_id": "1851487436", "caption": "Vertical shot of the aerial view of waves hitting the shore of a sunny beach", "id": "1851487436"}, {"image_id": "1571356433", "caption": "Exterior of Chiesa di Sant Anastasia Verona Veneto Italy", "id": "1571356433"}, {"image_id": "1581271664", "caption": "Family standing in garden with wheelbarrow full of autumn leaves", "id": "1581271664"}, {"image_id": "1817410293", "caption": "Angled view of an active senior woman leaning against tree and stretching her leg", "id": "1817410293"}, {"image_id": "1840555169", "caption": "Businessman and workers among large bags of recycled plastic pellets in warehouse and a worker driving a forklift through the bags", "id": "1840555169"}, {"image_id": "1865986901", "caption": "Vertical shot of a seated couple with their daughter having grilled food on a camping trip smile at the camera on a sunny day", "id": "1865986901"}, {"image_id": "1860726929", "caption": "Low angle view of a businessman standing on a desk in the desert and using a telephone", "id": "1860726929"}, {"image_id": "1859182799", "caption": "Wide shot of a female rock climber swinging on a rope while abseiling down a rock face", "id": "1859182799"}, {"image_id": "1658726105", "caption": "Young couple getting on bicycles blurred motion", "id": "1658726105"}, {"image_id": "1844196209", "caption": "Cropped shot of farmers hands examining wheat standing in a wheat field with a golf hat on his head", "id": "1844196209"}, {"image_id": "1585871138", "caption": "Young woman taking banknotes from handbag Close up", "id": "1585871138"}, {"image_id": "1297794782", "caption": "Smiling doctor in office checking female patients blood pressure", "id": "1297794782"}, {"image_id": "1571353160", "caption": "White Stork Ciconia ciconia in nest", "id": "1571353160"}, {"image_id": "1669207298", "caption": "Young woman with happy face drawn on thumbs", "id": "1669207298"}, {"image_id": "1297781591", "caption": "Engineer looking at plans standing next to large solar panels", "id": "1297781591"}, {"image_id": "1869317189", "caption": "Full shot of a technician standing in the aisle of a server room and replacing servers in a cabinet", "id": "1869317189"}, {"image_id": "1590341258", "caption": "African woman with paint on fingertips", "id": "1590341258"}, {"image_id": "1840560557", "caption": "Medium shot of a happy elderly couple picking flowers in a meadow full of wildflowers", "id": "1840560557"}, {"image_id": "1590316556", "caption": "Low angle view of boy jumping", "id": "1590316556"}, {"image_id": "1878692939", "caption": "Low angle shot of high school students standing in a huddle before a netball game and smiling at the camera", "id": "1878692939"}, {"image_id": "1586723018", "caption": "Vaduz Castle In Liechtenstein With Vineyards In Foreground", "id": "1586723018"}, {"image_id": "1570568150", "caption": "Aerial View of Amazon River near Manaus Amazonas Brazil", "id": "1570568150"}, {"image_id": "1590047414", "caption": "A businesswoman in an office three colleagues in background having a discussion", "id": "1590047414"}, {"image_id": "1813178627", "caption": "Angled shot of father and daughter embracing and smiling on the beach", "id": "1813178627"}, {"image_id": "1869908177", "caption": "Vertical overhead shot of businesspeople standing in formation of a question mark with hands on each other's shoulder smile at the camera", "id": "1869908177"}, {"image_id": "1590361211", "caption": "Close up of assorted US paper currency", "id": "1590361211"}, {"image_id": "1878695645", "caption": "Backside of a high school boy playing badminton during a gym class", "id": "1878695645"}, {"image_id": "1590209345", "caption": "View from Luesener Alm Dolomite Alps Alps South Tyrol Italy", "id": "1590209345"}, {"image_id": "1846752701", "caption": "A wide angle view of young wheat growing on a vast green farm field under the bright sky filled with white fluffy clouds", "id": "1846752701"}, {"image_id": "1865993426", "caption": "Profile shot of a businessman and a businesswoman in back seat of a car with focus on the woman wearing a hands free in the foreground", "id": "1865993426"}, {"image_id": "1852959830", "caption": "Children sitting on monkey bars and blowing bubbles at a playground", "id": "1852959830"}, {"image_id": "1865986748", "caption": "Horizontal shot of a male patient lying in a hospital bed using a laptop with an obscured face as a doctor looks on", "id": "1865986748"}, {"image_id": "1587048476", "caption": "Man and woman shaking hands in the boardroom", "id": "1587048476"}, {"image_id": "1590338972", "caption": "Young happy girls looking at each other outdoors", "id": "1590338972"}, {"image_id": "1297787657", "caption": "Studio cut out of female doctor with stethoscope smiling at camera", "id": "1297787657"}, {"image_id": "1868718245", "caption": "A vertical Close up shot of a large solar panel with an engineer standing in background", "id": "1868718245"}, {"image_id": "1840552994", "caption": "Portrait of confident businessman and businesswoman wearing headsets and using digital tablet", "id": "1840552994"}, {"image_id": "1590358655", "caption": "Woman talking on cell phone", "id": "1590358655"}, {"image_id": "1590059975", "caption": "Portrait of a young man", "id": "1590059975"}, {"image_id": "1571577515", "caption": "Young woman standing on a balcony", "id": "1571577515"}, {"image_id": "1586673398", "caption": "Young woman probing for avalanche victims in winter", "id": "1586673398"}, {"image_id": "1586681456", "caption": "cutout Of Senior Male Doctor Wearing White Coat", "id": "1586681456"}, {"image_id": "1297750208", "caption": "Detail of businesswoman sitting at boardroom table holding glasses", "id": "1297750208"}, {"image_id": "1590355007", "caption": "Meadow with dandelion and cross", "id": "1590355007"}, {"image_id": "1859328602", "caption": "A side profile close up shot of a young female technician looking into a microscope in a laboratory", "id": "1859328602"}, {"image_id": "1846749374", "caption": "A vertical shot of a young adult radiologist holding a clipboard while smiling at the camera in a radiology department with her colleague working in the background", "id": "1846749374"}, {"image_id": "1571602994", "caption": "A couple preparing to scuba dive", "id": "1571602994"}, {"image_id": "1846749428", "caption": "A shot of surgical scissor being handed over to a surgeon while performing an operation in the operating room", "id": "1846749428"}, {"image_id": "1846749287", "caption": "A vertical portrait of a Radiographer smiling at the camera in a clinic with his patient lying in the background while getting scanned under the X ray machine", "id": "1846749287"}, {"image_id": "1859353397", "caption": "A wide shot of happy teacher and students studying biology and planting vegetables in a classroom", "id": "1859353397"}, {"image_id": "1868703176", "caption": "A businesswoman with short hair sitting at the table while using a laptop in a conference room", "id": "1868703176"}, {"image_id": "1839582782", "caption": "Senior couple holding hands and walking through a field of flowers on a beautiful spring day in the park", "id": "1839582782"}, {"image_id": "1590061574", "caption": "A young woman lying on a bed", "id": "1590061574"}, {"image_id": "1864656173", "caption": "Low angle waist up shot of a senior couple standing in the garden during autumn with arms wrapped around each other", "id": "1864656173"}, {"image_id": "1857285962", "caption": "Senior couple standing in the middle of an electronics appliances store and shopping for a new television", "id": "1857285962"}, {"image_id": "1590206171", "caption": "Santa Claus figurine in snow globe", "id": "1590206171"}, {"image_id": "1587132776", "caption": "Teenage girl in yoga pose on paddle board Lake Starnberg Bavaria Germany Europe", "id": "1587132776"}, {"image_id": "1277231591", "caption": "Active mature woman with bike doing up shoelaces in park at camera", "id": "1277231591"}, {"image_id": "1587990275", "caption": "Little girls sharing birthday presents", "id": "1587990275"}, {"image_id": "1855882772", "caption": "Horizontal shot of a mature couple leaning on a wall by the Christmas tree holding pink gift boxes smile at the camera", "id": "1855882772"}, {"image_id": "1866091574", "caption": "Portrait smiling quality control worker inspecting tomatoes on production line in a food processing plant", "id": "1866091574"}, {"image_id": "1586691629", "caption": "Exuberant brother and sister jumping for joy over grass", "id": "1586691629"}, {"image_id": "1865986865", "caption": "Vertical shot of a family cooking food by the lakeside on a camping trip with the man grilling corn cobs on campfire besides wife and daughter smiles at the camera", "id": "1865986865"}, {"image_id": "216580229", "caption": "Close up of tranquil field of blooming buttercups", "id": "216580229"}, {"image_id": "1588008527", "caption": "Close up of woman in winter clothing on snowy mountain", "id": "1588008527"}, {"image_id": "1859144609", "caption": "Full shot of a woman with a backpack hiking in the woods", "id": "1859144609"}, {"image_id": "1869314507", "caption": "Close up shot of a lock on the cabinet of a server room with a technician working on a laptop in the background", "id": "1869314507"}, {"image_id": "1840650428", "caption": "A young farmer carefully shearing his white sheep for wool in a barn", "id": "1840650428"}, {"image_id": "1570154864", "caption": "Pollution on the beach of Zanzibar Tanzania Africa", "id": "1570154864"}, {"image_id": "1590179306", "caption": "Kiwi Fruit Plantation North Island New Zealand", "id": "1590179306"}, {"image_id": "1590359936", "caption": "Close up of Bald Eagle", "id": "1590359936"}, {"image_id": "1297793582", "caption": "Detail of potato crop growing in soil in farm field", "id": "1297793582"}, {"image_id": "1846749431", "caption": "A low angle view of two surgeons performing an operation in a modern operating room", "id": "1846749431"}, {"image_id": "1578946118", "caption": "Portrait of young woman yawning", "id": "1578946118"}, {"image_id": "1585869983", "caption": "Elderly couple sitting in garden slicing apples", "id": "1585869983"}, {"image_id": "1878692822", "caption": "High school students completing their homework in the classroom with a teacher helping them", "id": "1878692822"}, {"image_id": "1851483791", "caption": "Smiling father looking at his son sitting in the kayak", "id": "1851483791"}, {"image_id": "1297792520", "caption": "Combine harvester emptying harvested wheat grain into tractor trailer", "id": "1297792520"}, {"image_id": "1586735516", "caption": "Male Worker With Clipboard Checking Waste Recycling Bales", "id": "1586735516"}, {"image_id": "1859144495", "caption": "Couple watching their children run on the beach with the sea in the background", "id": "1859144495"}, {"image_id": "1581280190", "caption": "Smiling mother sitting on exercise ball with baby daughter at camera", "id": "1581280190"}, {"image_id": "1855882733", "caption": "Vertical mid shot of a senior couple holding hands with a fountain in the background", "id": "1855882733"}, {"image_id": "1585935404", "caption": "Man buying organic eggplant and pepper in organic store", "id": "1585935404"}, {"image_id": "1843609178", "caption": "Horizontal shot of a joyous family of four collecting dried leaves in a wheelbarrow from the yard during a sunny day in autumn", "id": "1843609178"}, {"image_id": "1843586669", "caption": "Vertical shot of a Close up of a woman's face by the poolside wearing polka dot pattern sunglasses", "id": "1843586669"}, {"image_id": "1570340063", "caption": "St Sebastian's Church Ramsau near Berchtesgaden Bavaria Germany", "id": "1570340063"}, {"image_id": "1855880507", "caption": "Close up shot of a small group of business executive team alighting from a private jet on the runway", "id": "1855880507"}, {"image_id": "1297789943", "caption": "Group of young friends camping at festival inside tent at camera", "id": "1297789943"}, {"image_id": "1839578909", "caption": "Horizontal upper section close up shot of a young woman with headset at desk in office looking at the camera with copy space", "id": "1839578909"}, {"image_id": "1852961708", "caption": "Children jumping off a merry go round at a playground", "id": "1852961708"}, {"image_id": "1578224369", "caption": "A mid adult woman drinking a glass of fruit carrot juice", "id": "1578224369"}, {"image_id": "1586675237", "caption": "Clouds floating in blue sky", "id": "1586675237"}, {"image_id": "1570327403", "caption": "A senior woman walking through a snowy street", "id": "1570327403"}, {"image_id": "1571686673", "caption": "Baltic Sea Spa Binz Ruegen Mecklenburg Vorpommern Germany", "id": "1571686673"}, {"image_id": "1855878071", "caption": "Little girl running into her father's open arms at the beach as her mother is watching from the background on an overcast windy day", "id": "1855878071"}, {"image_id": "1844764214", "caption": "Close of cargo from the ship being transported by trucks and haulers", "id": "1844764214"}, {"image_id": "1587811031", "caption": "Street light on foggy night", "id": "1587811031"}, {"image_id": "1587859688", "caption": "View of a bowl of steaming ginger tea beside ginger root", "id": "1587859688"}, {"image_id": "1570354577", "caption": "A plane coming into land", "id": "1570354577"}, {"image_id": "1570355459", "caption": "The Smile of Angkor Bayon Temple Angkor Thom Siem Reap Cambodia", "id": "1570355459"}, {"image_id": "1277282483", "caption": "Family on deck chair on summer beach vacation looking into camera with ocean and blue sky in background", "id": "1277282483"}, {"image_id": "1588000670", "caption": "Female pharmacist with medication talking to customer", "id": "1588000670"}, {"image_id": "1710331535", "caption": "Young woman practicing yoga indoors", "id": "1710331535"}, {"image_id": "1578207191", "caption": "A young woman wearing a grey woollen hat keeping warm", "id": "1578207191"}, {"image_id": "1297776680", "caption": "Young woman at home lying on bed reading magazine smiling at camera", "id": "1297776680"}, {"image_id": "1590224888", "caption": "High angle view of young boy smiling outdoors", "id": "1590224888"}, {"image_id": "1859323616", "caption": "A side profile shot of a young female technician looking into a microscope in a laboratory", "id": "1859323616"}, {"image_id": "1865999612", "caption": "Selective focus on a smiling woman in foreground holding a map during hike on a mountain trail as her husband looks on in the background", "id": "1865999612"}, {"image_id": "1844727527", "caption": "Medium shot of a young design student smiling at the camera while measuring a garment worn by a mannequin with a classmate in the background", "id": "1844727527"}, {"image_id": "1866091556", "caption": "Businessman and gardeners with crate of tomatoes shaking hands in a greenhouse", "id": "1866091556"}, {"image_id": "1877642744", "caption": "View Of Fans Enjoying Rock Concert With Light Show", "id": "1877642744"}, {"image_id": "1588015208", "caption": "A businessman in a smart suit talking on a mobile phone", "id": "1588015208"}, {"image_id": "1866109220", "caption": "Portrait of a smiling farmer examining the yield in the sunny rural barley crop field in summer", "id": "1866109220"}, {"image_id": "1587845762", "caption": "Close up of coffee beans", "id": "1587845762"}, {"image_id": "1578927167", "caption": "A businesswoman sitting at a table looking at a mobile phone", "id": "1578927167"}, {"image_id": "1578904343", "caption": "An elderly man wearing shorts", "id": "1578904343"}, {"image_id": "1725718001", "caption": "Businessman standing in server room in Cape Town South Africa", "id": "1725718001"}, {"image_id": "1908116561", "caption": "Sea fishing from beach Offset", "id": "1908116561"}, {"image_id": "1587030203", "caption": "View of two wheelbarrows at a construction site", "id": "1587030203"}, {"image_id": "1865993663", "caption": "Vertical shot of a joyous senior couple with arms around each other walk during hiking on a mountain trail", "id": "1865993663"}, {"image_id": "1845993893", "caption": "A medium shot of a young baker smiling and looking the camera while kneading bread dough in a bakery", "id": "1845993893"}, {"image_id": "1578932960", "caption": "A man on his lunch break using a laptop computer", "id": "1578932960"}, {"image_id": "1578902531", "caption": "Businessman in office drinking a cup of coffee", "id": "1578902531"}, {"image_id": "1571353142", "caption": "White Stork Ciconia ciconia in nest", "id": "1571353142"}, {"image_id": "1869278966", "caption": "Scenic view of Ionian islands Kefalonia Greece", "id": "1869278966"}, {"image_id": "1587032360", "caption": "High angle view of a globe with chains around it", "id": "1587032360"}, {"image_id": "1578921110", "caption": "A young woman holding a plate of hot cross buns", "id": "1578921110"}, {"image_id": "1571330879", "caption": "Young boy and girl running on beach holding hands", "id": "1571330879"}, {"image_id": "1297775297", "caption": "Father carrying daughter on shoulders in snowy winter landscape smiling at camera", "id": "1297775297"}, {"image_id": "1587034640", "caption": "Pregnant woman in hospital bed", "id": "1587034640"}, {"image_id": "1869279005", "caption": "Scenic view of sun shining in the blue sky from northern Kefalonia to Ithaca Greece", "id": "1869279005"}, {"image_id": "1865999801", "caption": "Vertical full length shot of a man walking with his golden retriever in an autumn park on a sunny day smiles at the camera", "id": "1865999801"}, {"image_id": "1587983366", "caption": "Young man drinking a hangover remedy", "id": "1587983366"}, {"image_id": "1876092134", "caption": "Man working out on rowing machine in health club", "id": "1876092134"}, {"image_id": "1874256509", "caption": "Mature man reading newspaper newspaper obscuring face elevated view", "id": "1874256509"}, {"image_id": "1851401333", "caption": "Businessman holding up a laptop computer and pointing angrily at the screen showing a downloading message", "id": "1851401333"}, {"image_id": "1590183773", "caption": "Superchef Areal wooden fence Mouly Ouvea Island Loyalty Islands New Caledonia Overseas Territory of France", "id": "1590183773"}, {"image_id": "1570572863", "caption": "Part of Cuxiu Muni Amazon River Brazil", "id": "1570572863"}, {"image_id": "1583872835", "caption": "A teenage boy using a computer", "id": "1583872835"}, {"image_id": "1590070469", "caption": "A businessman standing on a block", "id": "1590070469"}, {"image_id": "1859347388", "caption": "A low angle shot of clouds floating in a blue sky", "id": "1859347388"}, {"image_id": "1869314594", "caption": "Vertical wide shot of a woman in swimwear standing in water at the shore of a sunny beach", "id": "1869314594"}, {"image_id": "1839589307", "caption": "Portrait of a young couple asleep in bed with woman embracing the man as seen from a side", "id": "1839589307"}, {"image_id": "1590359498", "caption": "High angle view of Garachico Tenerife Canary Islands Spain", "id": "1590359498"}, {"image_id": "1277239424", "caption": "Female designers by drawing board in home office at camera", "id": "1277239424"}, {"image_id": "1711228529", "caption": "A young boy jumping in the air with excitement", "id": "1711228529"}, {"image_id": "1587144485", "caption": "View of moai statues against blue sky Chile Easter Island Rapa Nui", "id": "1587144485"}, {"image_id": "1859328524", "caption": "A medium shot of two young trainee chefs enjoying while working together in a kitchen", "id": "1859328524"}, {"image_id": "1766902725", "caption": "Guests sitting at a table at an outdoor reception", "id": "1766902725"}, {"image_id": "1590327452", "caption": "View from fortress over Nizwa Ad Dakhiliyah Oman", "id": "1590327452"}, {"image_id": "1859178506", "caption": "Low angle wide shot of an engineer working on a passenger jet in a hangar", "id": "1859178506"}, {"image_id": "1588026224", "caption": "Portrait of young woman holding her hands beside her face", "id": "1588026224"}, {"image_id": "1844766206", "caption": "Vertical portrait of a happy family sitting on a small wooden bridge over the stream", "id": "1844766206"}, {"image_id": "1578947159", "caption": "A woman relaxing in a deck chair", "id": "1578947159"}, {"image_id": "1571356355", "caption": "Rhine Falls near Schaffhausen Switzerland", "id": "1571356355"}, {"image_id": "1873346435", "caption": "Wide shot of Middle schoolgirls having fun with wheelbarrow in vegetable garden", "id": "1873346435"}, {"image_id": "1840561154", "caption": "Close up vertical shot of an engineer s hand in gloves tightening bolts using a wrench on a gearbox casing at a factory", "id": "1840561154"}, {"image_id": "1866105002", "caption": "Back shot of a farmer walking through barley crop field in summer", "id": "1866105002"}, {"image_id": "1578942890", "caption": "Two friends in party dresses", "id": "1578942890"}, {"image_id": "1583613611", "caption": "Businesswoman standing beside car carrying shoulder bag smiling portrait", "id": "1583613611"}, {"image_id": "1590337379", "caption": "autumn forest road near Pfullendorf Baden Wurttemberg Germany", "id": "1590337379"}, {"image_id": "1590220535", "caption": "High angle view of boy and girl smiling on sofa", "id": "1590220535"}, {"image_id": "1587139085", "caption": "Detail view of a man shooting off a pistol", "id": "1587139085"}, {"image_id": "1586685818", "caption": "Pharmacist smiling at pharmacy counter", "id": "1586685818"}, {"image_id": "1859337137", "caption": "A young blonde woman looking at a computer in a computer lab with other students in background", "id": "1859337137"}, {"image_id": "1587842954", "caption": "Studio shot of money falling on businesswoman", "id": "1587842954"}, {"image_id": "1578236738", "caption": "A mid adult woman sitting in an armchair thinking", "id": "1578236738"}, {"image_id": "1590209300", "caption": "Close up of pine cones on tree", "id": "1590209300"}, {"image_id": "1277235614", "caption": "Mature businessman at head of boardroom conference table at camera", "id": "1277235614"}, {"image_id": "1572528641", "caption": "Tourists watching a Gray Whale Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528641"}, {"image_id": "1587987800", "caption": "A student disappointed by his exam results", "id": "1587987800"}, {"image_id": "1846008230", "caption": "A Close up shot of hand throwing a green plastic disc", "id": "1846008230"}, {"image_id": "1859352107", "caption": "A side profile Close up shot of a young boy using a drill machine in a class", "id": "1859352107"}, {"image_id": "1586683916", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586683916"}, {"image_id": "1587833114", "caption": "Statue of winged goddess Victoria and quadriga Rome Italy", "id": "1587833114"}, {"image_id": "1570327496", "caption": "Santa Claus on a sled with a sack full of presents", "id": "1570327496"}, {"image_id": "1868703281", "caption": "A serious businessman working on a laptop at a table next to a whiteboard in the conference room", "id": "1868703281"}, {"image_id": "1873346210", "caption": "Silhouette of a windsurfer carrying windsurfing board in the sunny ocean from the beach", "id": "1873346210"}, {"image_id": "1576774214", "caption": "Portrait of a smiling man in a tropical garden", "id": "1576774214"}, {"image_id": "1590352166", "caption": "Anchored sailboat in bay at Cabo de Formentor Mallorca Spain", "id": "1590352166"}, {"image_id": "1572538937", "caption": "Couple flirting at a party", "id": "1572538937"}, {"image_id": "1586672522", "caption": "Portrait of happy mature woman in mountains on winter day", "id": "1586672522"}, {"image_id": "1878869621", "caption": "Gym teacher helping a school girl in sports uniform to climb a rope while other students sitting down and watching in the foreground", "id": "1878869621"}, {"image_id": "1277367176", "caption": "Boy and girl teenager friends and piggyback on summer vacation beach smiling at camera", "id": "1277367176"}, {"image_id": "1866104783", "caption": "Vertical shot of a smiling worker inspecting cheese in the factory", "id": "1866104783"}, {"image_id": "1859233553", "caption": "A horizontal portrait of a smiling handyman knelt down to fix a radiator with a wrench while smiling at the camera", "id": "1859233553"}, {"image_id": "1571666207", "caption": "A male midsection", "id": "1571666207"}, {"image_id": "1297732898", "caption": "Studio shot of smiling young woman applying sunscreen to skin", "id": "1297732898"}, {"image_id": "1840562300", "caption": "Portrait shot of a formally dressed businessman writing on a clipboard with the aisle of a manufacturing plant in the background", "id": "1840562300"}, {"image_id": "1868703350", "caption": "A businesswoman and her colleague working together on a digital tablet at the desk in an office", "id": "1868703350"}, {"image_id": "1663817693", "caption": "Teenage friends cooking in kitchen", "id": "1663817693"}, {"image_id": "1297781492", "caption": "Female high school students performing experiment in chemistry lab", "id": "1297781492"}, {"image_id": "1766907600", "caption": "Senior couple holding hands in park", "id": "1766907600"}, {"image_id": "1263322085", "caption": "Farm field of wheat crop against blue summer sky", "id": "1263322085"}, {"image_id": "1807085759", "caption": "Man lying on grass hands behind head eyes closed Close up", "id": "1807085759"}, {"image_id": "1857286262", "caption": "Vertical shot of a senior couple standing in the shallows on the beach on a bright sunny day and hugging each other", "id": "1857286262"}, {"image_id": "1578906977", "caption": "A group of teenage friends at a barbeque", "id": "1578906977"}, {"image_id": "1590067721", "caption": "A businessman talking on a mobile phone in a modern office building", "id": "1590067721"}, {"image_id": "1578922436", "caption": "A young girl collecting Easter eggs", "id": "1578922436"}, {"image_id": "1578921158", "caption": "A young woman holding a basket full of vegetables Close up", "id": "1578921158"}, {"image_id": "1816212813", "caption": "A female teacher speaking to her student while he cuts a paper using a pair of scissors", "id": "1816212813"}, {"image_id": "1588014347", "caption": "Senior man's hand holding sweets or candy", "id": "1588014347"}, {"image_id": "1869314672", "caption": "Silhouette of a couple sitting separately on a bench against the sunset over the ocean", "id": "1869314672"}, {"image_id": "1277238416", "caption": "Mature woman wearing bathrobes relaxing in chairs at hotel spa", "id": "1277238416"}, {"image_id": "1859143217", "caption": "Full shot of a multi generation family walking along the shore of a sunny beach", "id": "1859143217"}, {"image_id": "1578212888", "caption": "Portrait of a young woman showing the right half of her face", "id": "1578212888"}, {"image_id": "1876092107", "caption": "Front view of horse with hunter on horseback", "id": "1876092107"}, {"image_id": "1586690948", "caption": "Portrait smiling worker with cheese at production line in processing plant", "id": "1586690948"}, {"image_id": "1587845309", "caption": "Coffee filter next to cup", "id": "1587845309"}, {"image_id": "1855878131", "caption": "Young boy pushing a bale of hay out in the field on a bright sunny day", "id": "1855878131"}, {"image_id": "1576774271", "caption": "A young woman holding a box", "id": "1576774271"}, {"image_id": "1572537386", "caption": "A man practicing Tai Chi by the sea", "id": "1572537386"}, {"image_id": "1852959839", "caption": "Wide shot of children sitting on monkey bars and blowing bubbles at a playground", "id": "1852959839"}, {"image_id": "1657956410", "caption": "Picnic table on the waterfront of River Wurm Wuerm Gauting Bavaria Germany", "id": "1657956410"}, {"image_id": "1586684951", "caption": "Livestock Farmer Using Digital Tablet in Cattle Pen", "id": "1586684951"}, {"image_id": "1590217349", "caption": "Senior couple riding motorcycle in rural area", "id": "1590217349"}, {"image_id": "1873346048", "caption": "Portrait of a focused schoolgirl in a private school uniform studying at a computer in a computer lab", "id": "1873346048"}, {"image_id": "1297789916", "caption": "Family gathering autumn pumpkins from garden smiling at camera", "id": "1297789916"}, {"image_id": "1571353211", "caption": "Geranium in a window Ardez Lower Engadine Grisons Switzerland", "id": "1571353211"}, {"image_id": "1878778784", "caption": "Woman sitting at the wheel of a car at the dealership with her husband looking on in the background", "id": "1878778784"}, {"image_id": "1572463127", "caption": "A middle aged woman wearing a headset", "id": "1572463127"}, {"image_id": "1586729936", "caption": "Portrait Of Female Clothing Designer Working In Studio", "id": "1586729936"}, {"image_id": "1840560518", "caption": "Low angle vertical shot of a happy family with the father carrying his daughter on the shoulders in a meadow full of wildflowers", "id": "1840560518"}, {"image_id": "217368203", "caption": "Iceberg South Shetland Islands Antarctica", "id": "217368203"}, {"image_id": "1590361829", "caption": "Close up of assorted Euro banknotes", "id": "1590361829"}, {"image_id": "1570363877", "caption": "Santa Claus leading his reindeer through the snow", "id": "1570363877"}, {"image_id": "1277238182", "caption": "Businesswoman writing on whiteboard in office at camera", "id": "1277238182"}, {"image_id": "1843607021", "caption": "Businessman holding a briefcase walking towards the stairs in the routine of working with determination and confidence", "id": "1843607021"}, {"image_id": "1840563197", "caption": "Medium Close up shot of business people with files and a digital tablet and engineers with a printed circuit board and machine part in a manufacturing plant", "id": "1840563197"}, {"image_id": "1578207899", "caption": "A young woman holding a handful of snow smiling", "id": "1578207899"}, {"image_id": "1572463157", "caption": "A young man looking at a new car", "id": "1572463157"}, {"image_id": "1669206413", "caption": "Aerial view of Nymphenburg Palace Munich Germany", "id": "1669206413"}, {"image_id": "1868705297", "caption": "A Businessman and businesswoman casually sitting at the desk having coffee in the office while talking to each other", "id": "1868705297"}, {"image_id": "1297794791", "caption": "Smiling female doctor with stethoscope in office at camera", "id": "1297794791"}, {"image_id": "1878688568", "caption": "Close up Of a Surfer With a prosthetic Leg Walking On the Beach carrying a surfboard", "id": "1878688568"}, {"image_id": "1586682908", "caption": "Judge Awarding Prize In Jam Making Category At Agricultural Show", "id": "1586682908"}, {"image_id": "216384743", "caption": "Close up of a rabbit in grass with daffodils", "id": "216384743"}, {"image_id": "1571690459", "caption": "High angle view of Klosters in winter Davos Grisons Switzerland", "id": "1571690459"}, {"image_id": "1862121551", "caption": "A horizontal view of Business people in selective focus drinking coffee and talking while a woman is working on a laptop in the foreground", "id": "1862121551"}, {"image_id": "1571353250", "caption": "White Stork Ciconia ciconia in nest", "id": "1571353250"}, {"image_id": "1868720807", "caption": "A medium shot of beautiful snow covered cow parsley stalks in winter", "id": "1868720807"}, {"image_id": "1817410332", "caption": "Portrait of an active senior man cycling through the woods looking at the camera", "id": "1817410332"}, {"image_id": "1571519105", "caption": "A teenager with a skateboard", "id": "1571519105"}, {"image_id": "1586666978", "caption": "Surfer with surfboard standing on rocks wearing wetsuit watching ocean", "id": "1586666978"}, {"image_id": "1590047585", "caption": "A businessman talking on a mobile phone in a modern office building", "id": "1590047585"}, {"image_id": "216111272", "caption": "Clouds in blue sky over barley field", "id": "216111272"}, {"image_id": "1590316601", "caption": "African man selling jewelry inside jacket", "id": "1590316601"}, {"image_id": "1571358248", "caption": "View over Adige River with The Ponte Pietra to Verona Cathedral Verona Veneto Italy", "id": "1571358248"}, {"image_id": "1570574054", "caption": "Huts at the edge of Iquitos Peru", "id": "1570574054"}, {"image_id": "1844194277", "caption": "The sun shines bright in the daytime in summer with fluffy clouds in the blue sky", "id": "1844194277"}, {"image_id": "1587106484", "caption": "Businessman using stylus on PDA Close up", "id": "1587106484"}, {"image_id": "1572538784", "caption": "A pair of feet in the desert", "id": "1572538784"}, {"image_id": "1859144516", "caption": "Low angle shot of a girl jumping against the blue sky", "id": "1859144516"}, {"image_id": "1869046586", "caption": "Vertical shot of a young couple on a hiking trip with the woman in foreground with a map and man with luggage in the background smile at the camera", "id": "1869046586"}, {"image_id": "1277246378", "caption": "Nurse giving ultrasound scan to pregnant woman in hospital", "id": "1277246378"}, {"image_id": "1578953099", "caption": "Male and female business colleagues chatting in office building", "id": "1578953099"}, {"image_id": "1277231384", "caption": "Portrait of male surgeon wearing scrubs in hospital operating room", "id": "1277231384"}, {"image_id": "1869317195", "caption": "Technician kneeling in the aisle of a server room and replacing servers in a cabinet", "id": "1869317195"}, {"image_id": "1572538877", "caption": "A woman holding a shell", "id": "1572538877"}, {"image_id": "1571590010", "caption": "Pregnant woman relaxing whilst listening to music on headphones", "id": "1571590010"}, {"image_id": "1588016036", "caption": "Office workers in modern office building", "id": "1588016036"}, {"image_id": "1586684501", "caption": "Farmer With Tablet Computer Inspecting Oat Crop In Field", "id": "1586684501"}, {"image_id": "1859200907", "caption": "Annoyed wife sitting on a fence while her husband is watching birds using binoculars", "id": "1859200907"}, {"image_id": "1873340819", "caption": "Two smiling girlfriends taking selfies in the Bar", "id": "1873340819"}, {"image_id": "1586696027", "caption": "Aerial View Of Combine Harvester Harvesting Wheat Crop", "id": "1586696027"}, {"image_id": "1590160808", "caption": "Scenic view of Dischma Brook view into Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160808"}, {"image_id": "1587982958", "caption": "man's hand feeding woman raw oyster", "id": "1587982958"}, {"image_id": "1570316282", "caption": "Santa Claus hanging out his clothes to dry", "id": "1570316282"}, {"image_id": "217365923", "caption": "Austrian Flag on Top of Soccer Ball", "id": "217365923"}, {"image_id": "216583802", "caption": "Close up of rooster", "id": "216583802"}, {"image_id": "1586684489", "caption": "Farmer With Tablet Computer Inspecting Oat Crop In Field", "id": "1586684489"}, {"image_id": "1860727001", "caption": "Side view of two male surfers in wetsuits walking with a surfboard on the beach", "id": "1860727001"}, {"image_id": "1567877750", "caption": "Powerboat and helicopter Mediterranean Sea Malta", "id": "1567877750"}, {"image_id": "1587833111", "caption": "Scenic view of Trajan s Markets Rome Italy", "id": "1587833111"}, {"image_id": "1663598228", "caption": "Futuristic power plant created in 3D 3D artwork 3D rendering 3D illustration showcasing a factory that is forward thinking and looking to the future demonstrating how fossil fuels are evolving", "id": "1663598228"}, {"image_id": "1869041678", "caption": "Profile of a male shop assistant in white gloves standing in front of a shelf display in glamorous boutique and cleaning designer handbag with a feather duster", "id": "1869041678"}, {"image_id": "1590053363", "caption": "A young man standing on scales", "id": "1590053363"}, {"image_id": "1590338147", "caption": "Young girl feeding stuffed animal horse in meadow", "id": "1590338147"}, {"image_id": "1576777160", "caption": "A businessman waiting in an office lobby or airport concourse with a suitcase", "id": "1576777160"}, {"image_id": "1590316706", "caption": "African man wearing hat at beach", "id": "1590316706"}, {"image_id": "1576741820", "caption": "A confident and attractive middle aged woman in white smiling", "id": "1576741820"}, {"image_id": "1587056546", "caption": "Group of young business people looking at laptop", "id": "1587056546"}, {"image_id": "1852959764", "caption": "Senior man riding a mountain bike in the forest", "id": "1852959764"}, {"image_id": "1576738349", "caption": "A young woman brushing her teeth", "id": "1576738349"}, {"image_id": "1578954401", "caption": "A mature couple on a beach", "id": "1578954401"}, {"image_id": "1860742235", "caption": "Horizontal shot of a male and female surfers in wetsuits by the beach on a cloudy day", "id": "1860742235"}, {"image_id": "1843610576", "caption": "Low angle shot of lower section of a businessman wheeling luggage in an airport lobby reflected on floor with copy space", "id": "1843610576"}, {"image_id": "1590347615", "caption": "Couple sitting inside picture frame", "id": "1590347615"}, {"image_id": "1873296815", "caption": "An art teacher guiding a middle school student during an art class", "id": "1873296815"}, {"image_id": "1590327398", "caption": "Incense Trees at Wahibi Sands Oman", "id": "1590327398"}, {"image_id": "1852936895", "caption": "Horizontal overhead shot of a bare chested man having beauty treatment holding cucumber slices smiles at the camera", "id": "1852936895"}, {"image_id": "1862086931", "caption": "Vertical shot of white clouds over the blue sky with the sun shining", "id": "1862086931"}, {"image_id": "1895443025", "caption": "Farmer examining wheat", "id": "1895443025"}, {"image_id": "1588010672", "caption": "Businessman with suitcase talking to businessman in cubicle", "id": "1588010672"}, {"image_id": "1570388225", "caption": "Bayon temple Angkor Thom Siem Reap Cambodia", "id": "1570388225"}, {"image_id": "1590053426", "caption": "A young woman sitting on stairs", "id": "1590053426"}, {"image_id": "216583907", "caption": "Blue sky and hill peeking out from fog in distance", "id": "216583907"}, {"image_id": "1587996005", "caption": "Estate agent outside property for sale", "id": "1587996005"}, {"image_id": "1878778787", "caption": "Salesman helping out a smiling couple with the purchase of a new car by showing them all the features of the car", "id": "1878778787"}, {"image_id": "1586738522", "caption": "Family Having Fun On Beach Vacation Together", "id": "1586738522"}, {"image_id": "1875313754", "caption": "Concentrating surgeons performing operation in operating room", "id": "1875313754"}, {"image_id": "1277234552", "caption": "Businesswoman with laptop computer working in cafe", "id": "1277234552"}, {"image_id": "1576780076", "caption": "A woman looking in a wardrobe", "id": "1576780076"}, {"image_id": "1570574009", "caption": "Amazon River at Iquitos Peru", "id": "1570574009"}, {"image_id": "1567877720", "caption": "Red poppy field", "id": "1567877720"}, {"image_id": "1846406201", "caption": "A vast farmland covered by grass in selective focus being cut by a red mowing tractor in the background", "id": "1846406201"}, {"image_id": "1590224939", "caption": "Rear view of young children running in grass", "id": "1590224939"}, {"image_id": "1581271697", "caption": "Couple moving in carrying rug through door into new home", "id": "1581271697"}, {"image_id": "1766931735", "caption": "Couple at mountain chalet on Christmas Luesener Alm Dolomite Alps South Tyrol Italy", "id": "1766931735"}, {"image_id": "1878869777", "caption": "A vertical front view of a chemistry teacher smiling at the camera in a school lab wearing a Lab coat with a DNA model in the foreground", "id": "1878869777"}, {"image_id": "1578207293", "caption": "A young woman wiping her tears away with a tissue", "id": "1578207293"}, {"image_id": "1839586325", "caption": "Close up of glistening water in a clear swimming pool on a bright sunny day", "id": "1839586325"}, {"image_id": "1587811022", "caption": "Close up of shattered window", "id": "1587811022"}, {"image_id": "1587833696", "caption": "Ruins of the Temple of Antoninus and Faustina Roman Forum Rome Italy", "id": "1587833696"}, {"image_id": "1571579087", "caption": "Teenage boy and girl in a bowling alley girl on her mobile phone", "id": "1571579087"}, {"image_id": "1868722022", "caption": "A Close up shot of a small frost covered cow parsley stalk in winter", "id": "1868722022"}, {"image_id": "1588014260", "caption": "Businessman sleeping with head on computer keyboard", "id": "1588014260"}, {"image_id": "1590341156", "caption": "Hispanic woman holding Argentine flag", "id": "1590341156"}, {"image_id": "1590317492", "caption": "Trees along marshland in the west of Mahe Seychelles", "id": "1590317492"}, {"image_id": "1868716649", "caption": "A low angle side profile shot of an engineer inspecting a big solar panel while holding a tablet", "id": "1868716649"}, {"image_id": "1572524303", "caption": "Young boy pulling body board on beach", "id": "1572524303"}, {"image_id": "1590315326", "caption": "Businessman figurine on stack of Euro coins", "id": "1590315326"}, {"image_id": "216578399", "caption": "View of snowy mountain", "id": "216578399"}, {"image_id": "1665809519", "caption": "Male magician with Euros coming out of top hat", "id": "1665809519"}, {"image_id": "1570573502", "caption": "Vespiary in Igapo forest Riverside of Rio Jutai Brazil", "id": "1570573502"}, {"image_id": "1578946274", "caption": "Male and female business colleagues chatting in office building", "id": "1578946274"}, {"image_id": "1852965686", "caption": "Side view of a woman getting her hair sprayed by a hairdresser in a salon", "id": "1852965686"}, {"image_id": "1590222764", "caption": "Low angle view of couple drinking white wine and smiling at each other", "id": "1590222764"}, {"image_id": "1590056501", "caption": "A young woman sitting on stairs", "id": "1590056501"}, {"image_id": "1277230667", "caption": "Daughter touching stomach of pregnant mother having ultrasound scan", "id": "1277230667"}, {"image_id": "1590222707", "caption": "Couple looking in shop window", "id": "1590222707"}, {"image_id": "1839585545", "caption": "Businessman wearing a suit and holding up a red card", "id": "1839585545"}, {"image_id": "1277257658", "caption": "Office businessman and businesswoman meeting in start up small business discussing document at desk", "id": "1277257658"}, {"image_id": "1297796597", "caption": "Cut out of loving young couple hugging at camera", "id": "1297796597"}, {"image_id": "1297793861", "caption": "Young wheat crop growing in farm field with blue sky", "id": "1297793861"}, {"image_id": "1570363850", "caption": "Turbine of an airplane in flight", "id": "1570363850"}, {"image_id": "1590213575", "caption": "Woman throwing autumn leaves in air", "id": "1590213575"}, {"image_id": "1844727530", "caption": "Vertical wide shot of a young design student smiling at the camera while measuring a garment worn by a mannequin with a classmate in the background", "id": "1844727530"}, {"image_id": "1663683767", "caption": "Teenage boys training in gym", "id": "1663683767"}, {"image_id": "1570343000", "caption": "Chairs in front of Lake Davos Davos Grisons Switzerland", "id": "1570343000"}, {"image_id": "1587145121", "caption": "Low angle view of a colorful hot air balloon against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145121"}, {"image_id": "1586666129", "caption": "An abstract image of yellow green and red lights", "id": "1586666129"}, {"image_id": "1572530288", "caption": "Young couple embracing on sofa", "id": "1572530288"}, {"image_id": "1865993699", "caption": "Horizontal profile shot of a couple with their two children walking in a line on a hike on a mountain trail with copy space", "id": "1865993699"}, {"image_id": "1297725689", "caption": "Beautiful young on walk in autumn countryside woman smiling at camera", "id": "1297725689"}, {"image_id": "1839581621", "caption": "Beach umbrella on a tropical beach Umbrella and a beautiful sand beach with clear water and blue sky on a sunny day", "id": "1839581621"}, {"image_id": "1581271601", "caption": "Mature couple with classic red convertible car on road trip using map", "id": "1581271601"}, {"image_id": "1590024167", "caption": "Little girl sitting in an ornate chair wearing a party dress", "id": "1590024167"}, {"image_id": "1576774178", "caption": "Two residents in a retirement home playing cards", "id": "1576774178"}, {"image_id": "1817411604", "caption": "Young female throwing a disc at her dad on an open green field", "id": "1817411604"}, {"image_id": "1570537091", "caption": "Lumber industry at Breves Channels Brazil", "id": "1570537091"}, {"image_id": "1578916895", "caption": "A young couple walking hand in hand through long grass", "id": "1578916895"}, {"image_id": "1570572878", "caption": "Branch of the Amazon River at Cuxiu Muni Brazil", "id": "1570572878"}, {"image_id": "1873398569", "caption": "Horizontal shot of an engineer holding a clipboard with other hand in the pocket on a factory floor smiles at the camera with copy space", "id": "1873398569"}, {"image_id": "1590160358", "caption": "Scenic view of sandbank in water Biarritz France", "id": "1590160358"}, {"image_id": "1868703242", "caption": "A pensive businessman in formal wear standing next to a desk in an office while holding important paperwork", "id": "1868703242"}, {"image_id": "1571664305", "caption": "Portrait of a senior man standing on a beach smiling", "id": "1571664305"}, {"image_id": "1263325031", "caption": "Clear sparkling water in empty outdoor swimming pool", "id": "1263325031"}, {"image_id": "1844196173", "caption": "Natural blue sky with fluffy clouds hiding the sun behind", "id": "1844196173"}, {"image_id": "1587051863", "caption": "Casually dressed businesswoman in her office", "id": "1587051863"}, {"image_id": "1588015331", "caption": "Emergency exit in tunnel Klosters Grisons Switzerland", "id": "1588015331"}, {"image_id": "1572383468", "caption": "Water Lilies in river Wuerm Starnberg Bavaria Germany", "id": "1572383468"}, {"image_id": "1846708268", "caption": "A vertical view of watchful surgeons performing a difficult operation in the operating room", "id": "1846708268"}, {"image_id": "1670341259", "caption": "Mature man surprising woman with gift at outdoor restaurant table", "id": "1670341259"}, {"image_id": "1578212828", "caption": "A young woman with blue eyes finger touching her face", "id": "1578212828"}, {"image_id": "1578212957", "caption": "Portrait of a young woman looking over her shoulder smiling", "id": "1578212957"}, {"image_id": "1866091550", "caption": "Wide shot of a Businessman and gardener discussing ripe vine tomatoes in a greenhouse", "id": "1866091550"}, {"image_id": "1297774244", "caption": "Frightened man watching scary film at cinema spilling popcorn", "id": "1297774244"}, {"image_id": "1570164641", "caption": "College student on campus in autumn", "id": "1570164641"}, {"image_id": "1868714303", "caption": "An Engineer examining the circuit board on an electrical test bench through a magnifying lamp", "id": "1868714303"}, {"image_id": "1578217439", "caption": "A mid adult woman eating a blackberry", "id": "1578217439"}, {"image_id": "1586682302", "caption": "cutout Of Team Of Male And Female Executives", "id": "1586682302"}, {"image_id": "1851401387", "caption": "Headshot of a teenage girl standing against a wall in the park and looking at the camera and smiling", "id": "1851401387"}, {"image_id": "217371089", "caption": "Three African Wild Dogs fighting Kruger National Park", "id": "217371089"}, {"image_id": "1852926920", "caption": "Horizontal portrait of midsection of a pregnant woman holding hands of her toddler son smiling at the camera with copy space", "id": "1852926920"}, {"image_id": "1572508163", "caption": "Young man brushing his teeth Close up", "id": "1572508163"}, {"image_id": "1578930542", "caption": "A senior woman with a heart rate monitor on her wrist", "id": "1578930542"}, {"image_id": "1570572272", "caption": "Aerial View of Manaus Amazonas Amazon River Brazil", "id": "1570572272"}, {"image_id": "1587854102", "caption": "View of waterfall Fallbach Kaernten Maltatal Austria", "id": "1587854102"}, {"image_id": "1868723300", "caption": "A medium shot of a happy family riding bicycles in a line in a wildflower field", "id": "1868723300"}, {"image_id": "1864634705", "caption": "Close up shot of a senior couple embracing each other in summer garden near the house", "id": "1864634705"}, {"image_id": "1567881233", "caption": "Businessman staring out from crowd of co workers", "id": "1567881233"}, {"image_id": "1874803328", "caption": "Woman relaxing in sea looking at view into land rear view", "id": "1874803328"}, {"image_id": "1851475895", "caption": "A young boy holding a remote control and pointing it at the televisions in an electronics store with his grandfather behind him", "id": "1851475895"}, {"image_id": "1590315302", "caption": "Stacks of assorted Euro coins", "id": "1590315302"}, {"image_id": "1865895197", "caption": "Couple wearing white robes and holding hands next to an iron gate while smiling at each other", "id": "1865895197"}, {"image_id": "1570340051", "caption": "View over Ramsau to Reiter Alpe and Hochkalter near Berchtesgaden Bavaria Germany", "id": "1570340051"}, {"image_id": "1590056351", "caption": "A portrait of a young woman smiling to camera", "id": "1590056351"}, {"image_id": "1572542207", "caption": "Young man splashing water on face close up", "id": "1572542207"}, {"image_id": "1766904372", "caption": "Young family eating cherries on picnic", "id": "1766904372"}, {"image_id": "1567878614", "caption": "Hiking trail between Davos and Dischmatal", "id": "1567878614"}, {"image_id": "1570308371", "caption": "Facade of Villa Goethe Agrigento Sicily Italy", "id": "1570308371"}, {"image_id": "1297789868", "caption": "Loving couple on walk in autumn countryside smiling at camera", "id": "1297789868"}, {"image_id": "1576779953", "caption": "A female scuba diver sitting on a boat", "id": "1576779953"}, {"image_id": "1590314108", "caption": "Children walking towards chalet Luesener Alm Dolomite Alps South Tyrol Italy", "id": "1590314108"}, {"image_id": "1572535178", "caption": "Portrait of a senior woman", "id": "1572535178"}, {"image_id": "1844767142", "caption": "Doctor and a patient reviewing medical chart in a hospital corridor", "id": "1844767142"}, {"image_id": "1590061685", "caption": "A middle aged woman holding a pot of moisturising cream", "id": "1590061685"}, {"image_id": "1586664152", "caption": "Female teacher posing with children smiling front view portrait cutout", "id": "1586664152"}, {"image_id": "1590160778", "caption": "Dirt road next to Dischma Brook view out of Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160778"}, {"image_id": "1590076853", "caption": "Portrait of a young black woman with long straight hair", "id": "1590076853"}, {"image_id": "1869318110", "caption": "Wide shot of a combine harvester and a tractor harvesting wheat at a rural field", "id": "1869318110"}, {"image_id": "1864650971", "caption": "Family of four sitting on a sofa in the living room looking at the camera and cheering with their arms raised", "id": "1864650971"}, {"image_id": "1852959845", "caption": "Vertical shot of a girl spinning plastic hoop at a playground", "id": "1852959845"}, {"image_id": "1297797308", "caption": "Cut out of male doctor in glasses wearing white coat and stethoscope", "id": "1297797308"}, {"image_id": "1868703188", "caption": "A portrait of an engineer and business people discussing a machine part during a meeting in the conference room", "id": "1868703188"}, {"image_id": "1859178539", "caption": "Young engineer in uniform assembling the wing of a passenger jet at a hangar", "id": "1859178539"}, {"image_id": "1572477389", "caption": "Cropped portrait of young woman looking upwards", "id": "1572477389"}, {"image_id": "1277239163", "caption": "Portrait of mature woman in pajamas eating fruit salad outdoors", "id": "1277239163"}, {"image_id": "1868714507", "caption": "A high angle view with Reflection of the scientist on a silicon wafer next to the microscope in a special laboratory", "id": "1868714507"}, {"image_id": "1847350106", "caption": "A medium shot of a young female worker working on an aluminum light fittings on the production line with other workers", "id": "1847350106"}, {"image_id": "1869318134", "caption": "Wide shot of a straw field with a combine harvester reaping wheat into a trailer attached to a tractor", "id": "1869318134"}, {"image_id": "1588016057", "caption": "Senior couple smiling and embracing", "id": "1588016057"}, {"image_id": "1277226113", "caption": "Multi generation family walk with grandson on grandfather's shoulders", "id": "1277226113"}, {"image_id": "1587136481", "caption": "View of a man hitting a golf ball out of a bunker on a golf course", "id": "1587136481"}, {"image_id": "1868716532", "caption": "A wide shot of an engineer watching hot molten forged steel rods in a furnace in a factory", "id": "1868716532"}, {"image_id": "1709253587", "caption": "Couple playing golf on a sunny day", "id": "1709253587"}, {"image_id": "1852924244", "caption": "Middle aged woman standing outdoors and looking at the camera and smiling on a bright sunny day", "id": "1852924244"}, {"image_id": "1855880474", "caption": "Smiling portrait of a mature man leaning out of the window of the motorhome", "id": "1855880474"}, {"image_id": "1859328392", "caption": "A portrait Close up shot of a young boy attaching roof to a birdhouse in a class", "id": "1859328392"}, {"image_id": "1578207938", "caption": "A young woman wearing a winter coat holding a mug of hot chocolate", "id": "1578207938"}, {"image_id": "217361843", "caption": "Close up of plants in soil", "id": "217361843"}, {"image_id": "1586690963", "caption": "Portrait smiling worker on production line in cheese processing plant", "id": "1586690963"}, {"image_id": "1587137216", "caption": "Close up of young woman using an automated banking machine", "id": "1587137216"}, {"image_id": "1590337241", "caption": "Red Rock Canyon Las Vegas Nevada USA", "id": "1590337241"}, {"image_id": "1852924364", "caption": "Young woman with blond hair standing in her house in white underwear and peeking outside through the blinds", "id": "1852924364"}, {"image_id": "1878694163", "caption": "High school student holding a netball during a match in a gym class", "id": "1878694163"}, {"image_id": "1852961777", "caption": "Woman looking at the phone with surprised expression", "id": "1852961777"}, {"image_id": "1844764331", "caption": "Senior man holding his wife and trying to watch the horizon shading his eyes with a hand", "id": "1844764331"}, {"image_id": "1864637798", "caption": "Horizontal shot of a joyous father walking out of the water at the beach carrying his daughter around his back", "id": "1864637798"}, {"image_id": "1570552823", "caption": "Riverside near Alter do Chao Rio Tapajos Amazon River Brazil", "id": "1570552823"}, {"image_id": "1859204651", "caption": "Vertical shot of a family having a picnic in the countryside", "id": "1859204651"}, {"image_id": "1586695922", "caption": "Aerial View Of Tractor Baling Hay In Field", "id": "1586695922"}, {"image_id": "1869318047", "caption": "Over the shoulder shot of a technician working on a computer in a server room at a data center", "id": "1869318047"}, {"image_id": "1588025612", "caption": "Two architects wearing hard hats looking at plans", "id": "1588025612"}, {"image_id": "1864622669", "caption": "Young woman standing in a snowy field wearing a woolen hat and scarf with her arms outstretched on a bright sunny day", "id": "1864622669"}, {"image_id": "1578225866", "caption": "A mid adult woman lifting a pumpkin", "id": "1578225866"}, {"image_id": "1570552781", "caption": "Montrichardia arborescens at Riverside near Balaio Amazon River Brazil", "id": "1570552781"}, {"image_id": "1843609040", "caption": "Horizontal three quarter profile shot of a joyous businesswoman talking on cell phone seated near the luggage carousel while waiting for luggage in baggage claim area with copy space", "id": "1843609040"}, {"image_id": "1590102614", "caption": "A young woman sitting on the floor", "id": "1590102614"}, {"image_id": "1571580044", "caption": "A young girl eating an ice cream", "id": "1571580044"}, {"image_id": "1587145067", "caption": "Low angle view of hot air balloons against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145067"}, {"image_id": "1859339849", "caption": "A Close up shot of a young blonde boy smiling at camera while performing experiment in a beaker in a chemistry laboratory", "id": "1859339849"}, {"image_id": "1587119126", "caption": "male animal tamer dominating a judge", "id": "1587119126"}, {"image_id": "1297784891", "caption": "Smiling radiologists in hospital department with patient smiling at camera", "id": "1297784891"}, {"image_id": "1586666987", "caption": "Family on holiday driving convertible on road with beach and ocean in background", "id": "1586666987"}, {"image_id": "1590065486", "caption": "Portrait of a young girl leaning against a metallic wall", "id": "1590065486"}, {"image_id": "1855880516", "caption": "Wide shot of businessman and woman shaking hands on runway with a private jet in the background", "id": "1855880516"}, {"image_id": "1576741955", "caption": "A senior couple on a beach", "id": "1576741955"}, {"image_id": "1570348043", "caption": "A calf Dischma Valley Grisons Switzerland", "id": "1570348043"}, {"image_id": "1843606994", "caption": "Horizontal portrait of a joyous girl standing on a hay bale in a field with outstretched arms with a rainbow in sky", "id": "1843606994"}, {"image_id": "1572516008", "caption": "Brightly colored flowers against white background", "id": "1572516008"}, {"image_id": "1588015154", "caption": "A businesswoman holding a mobile phone and briefcase", "id": "1588015154"}, {"image_id": "1587105251", "caption": "Four male athletes compete with one woman in tug of war", "id": "1587105251"}, {"image_id": "1868721908", "caption": "A portrait shot of a happy senior couple laughing together while carrying a picnic basket through a field of wildflowers", "id": "1868721908"}, {"image_id": "1586682428", "caption": "cutout Of Teenage Girl Taking Driving Lessons", "id": "1586682428"}, {"image_id": "1590220031", "caption": "Woman leaning against palm tree", "id": "1590220031"}, {"image_id": "1852936706", "caption": "Vertical full length shot of a businessman holding an orange folder with hand in pocket looks at the camera with copy space", "id": "1852936706"}, {"image_id": "1581281513", "caption": "Mother putting sunscreen on daughter on beach vacation at camera", "id": "1581281513"}, {"image_id": "1859328461", "caption": "A side profile Close up shot of a young boy working on robotic device in a class with students in background", "id": "1859328461"}, {"image_id": "1766922825", "caption": "Young angel girl sitting on shooting star", "id": "1766922825"}, {"image_id": "1908116594", "caption": "Man holding bodyboard looking out to sea", "id": "1908116594"}, {"image_id": "1855878299", "caption": "Rear View of a bare chested man working out using exercise equipment", "id": "1855878299"}, {"image_id": "1865943701", "caption": "Vertical shot of father and son standing at the helm of sailing boat out at sea on a sunny day", "id": "1865943701"}, {"image_id": "1570573508", "caption": "Igapo forest Riverside of Rio Jutai Brazil", "id": "1570573508"}, {"image_id": "1590025169", "caption": "Little girl sitting in an ornate chair wearing a party dress", "id": "1590025169"}, {"image_id": "1843610615", "caption": "Three quarter length portrait of a joyous businesswoman talking on cell phone in office lobby with her colleagues in the background and copy space", "id": "1843610615"}, {"image_id": "1840559729", "caption": "Wide angle shot of Workers controlling robotic machinery lifting steel fencing in manufacturing plant", "id": "1840559729"}, {"image_id": "1587995966", "caption": "A businessman greeting a client in the foyer of an office building", "id": "1587995966"}, {"image_id": "1587983438", "caption": "A barman pouring a cocktail for a customer", "id": "1587983438"}, {"image_id": "1277229206", "caption": "Women at roulette table in casino holding gambling chip at camera", "id": "1277229206"}, {"image_id": "1587984170", "caption": "Young man drinking a hangover remedy", "id": "1587984170"}, {"image_id": "1840563086", "caption": "Medium Close up shot of formally dressed businessman and engineer examining a machine part that is in focus", "id": "1840563086"}, {"image_id": "216584810", "caption": "Close up of growing green grapes on vine", "id": "216584810"}, {"image_id": "1670340023", "caption": "Group of young adults on road trip unloading carin countryside", "id": "1670340023"}, {"image_id": "1817411658", "caption": "Vertical shot of a woman speaking to a man holding a booklet", "id": "1817411658"}, {"image_id": "1586666930", "caption": "View of long open road at sunrise in arid landscape", "id": "1586666930"}, {"image_id": "1587997730", "caption": "Close up of wine bottle and glasses outdoors", "id": "1587997730"}, {"image_id": "1570574069", "caption": "Part of the Bora Village Rio Ampyacu Amazon River Peru", "id": "1570574069"}, {"image_id": "1572486326", "caption": "A young woman practicing yoga", "id": "1572486326"}, {"image_id": "1578924776", "caption": "A young woman standing with her bicycle", "id": "1578924776"}, {"image_id": "1297744721", "caption": "Detail of young boy exploring on beach discovering starfish", "id": "1297744721"}, {"image_id": "1587138233", "caption": "Low angle view of a young male surgeon thinking in office setting", "id": "1587138233"}, {"image_id": "1587108923", "caption": "Overview of carrots and parsnips on white background", "id": "1587108923"}, {"image_id": "1869317978", "caption": "Backside of a technician with a laptop walking through a server room at a data center", "id": "1869317978"}, {"image_id": "1844191970", "caption": "Back view of a girl with fairy wings looking through the curtains before the performance", "id": "1844191970"}, {"image_id": "1851487394", "caption": "Portrait of happy family standing next to a white car with ocean in the background", "id": "1851487394"}, {"image_id": "1859331995", "caption": "A young blonde boy using a mouse on a computer in a school computer lab with selective focus on boy s hand", "id": "1859331995"}, {"image_id": "1664813738", "caption": "View of teacher smiling for the camera", "id": "1664813738"}, {"image_id": "1868705261", "caption": "A portrait of a happy businesswoman and her co worker using a digital tablet while sitting at the table near a glass window", "id": "1868705261"}, {"image_id": "1852925702", "caption": "Horizontal shot of a family standing and posing for the camera near Christmas tree", "id": "1852925702"}, {"image_id": "1586683970", "caption": "Senior Man Working In Beautiful Cottage Flower Garden", "id": "1586683970"}, {"image_id": "1277260994", "caption": "Portrait of smiling young girl playing in garden climbing tree in countryside hanging from branch wearing purple top", "id": "1277260994"}, {"image_id": "1868718338", "caption": "A Close up portrait shot of a happy young blonde woman adjusting thread on the needle of a sewing machine", "id": "1868718338"}, {"image_id": "1844194457", "caption": "A Close up of split pea pod in the hands of a woman", "id": "1844194457"}, {"image_id": "1590341201", "caption": "Young woman holding Italian flag", "id": "1590341201"}, {"image_id": "1571353238", "caption": "Robinson Club Schweizerhof Vulpera Lower Engadine Grisons Switzerland", "id": "1571353238"}, {"image_id": "1843607282", "caption": "Overhead shot of teacher with five young students in a circle forming a human chain look at the camera playing on a green lawn outdoors with overlay of recycle symbol", "id": "1843607282"}, {"image_id": "1576745306", "caption": "A man standing by a pool", "id": "1576745306"}, {"image_id": "1864631120", "caption": "Vertical shot of a senior woman and adult daughter preparing for a birthday party at home with balloons and cake lying on the table in the foreground", "id": "1864631120"}, {"image_id": "1816749606", "caption": "Portrait of a happy couple with their son posing beside car boot with basket of vegetables", "id": "1816749606"}, {"image_id": "1578208625", "caption": "Portrait of a young woman with blue eyes looking away", "id": "1578208625"}, {"image_id": "1807085660", "caption": "USA California San Diego man cycling side view portrait Coronado Bay Bridge in background", "id": "1807085660"}, {"image_id": "1572542327", "caption": "A student using a laptop computer", "id": "1572542327"}, {"image_id": "1572388703", "caption": "Amazon parrot Hellabrunn Zoo Munich Bavaria Germany", "id": "1572388703"}, {"image_id": "1570154684", "caption": "Street trader at market in Zanzibar City Zanzibar Tanzania Africa", "id": "1570154684"}, {"image_id": "1586703428", "caption": "Dumper Truck Unloading Wheat Into Grain Store", "id": "1586703428"}, {"image_id": "1590222923", "caption": "Businessman paddling kayak in whitewater", "id": "1590222923"}, {"image_id": "1821544410", "caption": "Happy multi generation family with kite walking on sunny beach", "id": "1821544410"}, {"image_id": "1586693282", "caption": "High school student using drill in woodworking class", "id": "1586693282"}, {"image_id": "1571338151", "caption": "Landscape near Oppdal Sor Trondelag Trondelag Norway", "id": "1571338151"}, {"image_id": "1590223010", "caption": "Businessmen paddling kayaks in whitewater", "id": "1590223010"}, {"image_id": "1587048473", "caption": "Executives leaning over a balcony", "id": "1587048473"}, {"image_id": "216581339", "caption": "Young wheat growing in green farm field under blue sky", "id": "216581339"}, {"image_id": "1572527852", "caption": "Copper Canyon near Posada Barracas Chihuahua Mexico", "id": "1572527852"}, {"image_id": "1578922253", "caption": "A young woman lying in the grass holding a daffodil", "id": "1578922253"}, {"image_id": "1586690792", "caption": "Portrait confident businesswoman with laptop at production line in cheese processing plant", "id": "1586690792"}, {"image_id": "1590160802", "caption": "Scenic view of Dischma Brook view into Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160802"}, {"image_id": "1844727692", "caption": "High angle wide shot of a happy elderly couple in a rowboat on a lake", "id": "1844727692"}, {"image_id": "1576739231", "caption": "A man relaxing by a waterfall", "id": "1576739231"}, {"image_id": "1587141179", "caption": "Portrait of a young woman posing for the camera at the beach", "id": "1587141179"}, {"image_id": "1277235521", "caption": "Loving mature bride and groom outdoors waving at guests on wedding day", "id": "1277235521"}, {"image_id": "1588023242", "caption": "A middle aged woman listening to mp3 music player", "id": "1588023242"}, {"image_id": "1304264969", "caption": "Children bringing breakfast to parents lying in bed at camera", "id": "1304264969"}, {"image_id": "1587810959", "caption": "Scenic view of Salzburg and Hohensalzburg Fortress in winter Salzburg Austria", "id": "1587810959"}, {"image_id": "1852925561", "caption": "Portrait shot of a boy with a pink gift box with a girl decorating Christmas tree in the background", "id": "1852925561"}, {"image_id": "1572528683", "caption": "Fluke of a Gray Whale Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528683"}, {"image_id": "1843609370", "caption": "Tilted low angle profile shot of a woman driving a red convertible on a sunny day smiling at the camera with copy space", "id": "1843609370"}, {"image_id": "1844731754", "caption": "Vertical shot of an elderly man smiling at the camera while fishing over a lake in a rowboat with grass blades in the foreground", "id": "1844731754"}, {"image_id": "1587031385", "caption": "View of a wind engine and a power poll against blue sky St Poelten Austria", "id": "1587031385"}, {"image_id": "1586649095", "caption": "Flock Of Ostriches In South African Countryside", "id": "1586649095"}, {"image_id": "1590070526", "caption": "A middle aged woman looking at her face in the mirror", "id": "1590070526"}, {"image_id": "1572527873", "caption": "Copper Canyon near Posada Barracas Chihuahua Mexico", "id": "1572527873"}, {"image_id": "1865999588", "caption": "Vertical shot of a mother and daughter fishing above a stream on a small wooden footbridge with a fish net in hand smile at the camera", "id": "1865999588"}, {"image_id": "1840648520", "caption": "A full length vertical shot of a Truck driver holding a delivery package on a sunny day with a semi truck standing in the background", "id": "1840648520"}, {"image_id": "1578904400", "caption": "A young couple sitting in a car", "id": "1578904400"}, {"image_id": "1587126692", "caption": "Overview of a mature man using a laptop outdoors", "id": "1587126692"}, {"image_id": "1578212987", "caption": "A young woman using a laptop talking on the phone", "id": "1578212987"}, {"image_id": "1588020461", "caption": "Sun shining over snowy landscape Upper Bavaria Germany", "id": "1588020461"}, {"image_id": "1587997661", "caption": "High angle view of wine cheese grapes tomatoes and bread", "id": "1587997661"}, {"image_id": "1587827696", "caption": "Close up of businesspeople high fiving", "id": "1587827696"}, {"image_id": "1869907961", "caption": "Overhead shot of motion blur of businesspeople in a ring walking towards the center", "id": "1869907961"}, {"image_id": "1876239746", "caption": "Girl in pajamas reading in bed low angle view", "id": "1876239746"}, {"image_id": "1817411073", "caption": "Medium shot of a shirtless male and female in swim wear with a cloudy sky in the background", "id": "1817411073"}, {"image_id": "1587145898", "caption": "View of a hot air balloon against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145898"}, {"image_id": "1571687243", "caption": "Tower of New Town Hall and Glockenspiel Munich Bavaria Germany", "id": "1571687243"}, {"image_id": "1277242526", "caption": "Car mechanic in auto repair garage with customer beside car", "id": "1277242526"}, {"image_id": "1869278822", "caption": "Portrait of a smiling worker taping box in a food processing plant", "id": "1869278822"}, {"image_id": "1587999008", "caption": "A young couple on a beach", "id": "1587999008"}, {"image_id": "1578236798", "caption": "A young woman stretching at the gym", "id": "1578236798"}, {"image_id": "1570154900", "caption": "Close up of cloves Zanzibar Tanzania Africa", "id": "1570154900"}, {"image_id": "1866118388", "caption": "Young boy and girl jumping in air on a jetty by the lake with a splash of water", "id": "1866118388"}, {"image_id": "1585871555", "caption": "Barefoot young woman holding high heels low section", "id": "1585871555"}, {"image_id": "1576782479", "caption": "Portrait of a young woman", "id": "1576782479"}, {"image_id": "216584888", "caption": "Jet plane flying over historic bell tower", "id": "216584888"}, {"image_id": "1578207902", "caption": "A young woman wearing a grey woollen hat looking over her shoulder", "id": "1578207902"}, {"image_id": "1578927161", "caption": "An architect or developer standing on walkway of office building looking at plans", "id": "1578927161"}, {"image_id": "1587991070", "caption": "Laboratory mice in glass tank", "id": "1587991070"}, {"image_id": "1846752470", "caption": "A vertical view of an experienced team of surgeons wearing surgical binoculars while performing an operation with complete precision and concentration in the operating room", "id": "1846752470"}, {"image_id": "1277234585", "caption": "Active young woman exercising on cycling machine in gym", "id": "1277234585"}, {"image_id": "1586676686", "caption": "cutout Of Male Executive Using Walkie Talkie", "id": "1586676686"}, {"image_id": "1587031193", "caption": "High angle view of icy road", "id": "1587031193"}, {"image_id": "1588004081", "caption": "Close up of pear trees", "id": "1588004081"}, {"image_id": "1497687810", "caption": "Stock market trading graphs and candlestick charts representing the financial market Economic trends in business and finance background", "id": "1497687810"}, {"image_id": "1868709419", "caption": "A full length vertical view of a happy businessman and businesswoman walking arm in arm outside the building", "id": "1868709419"}, {"image_id": "1572529490", "caption": "Cable car at Copper Canyon Sierra Tarahumara Chihuahua Mexico", "id": "1572529490"}, {"image_id": "1588015442", "caption": "Snow covered town with mountains in background Davos Grisons Switzerland", "id": "1588015442"}, {"image_id": "1590353636", "caption": "Close up of woman with foundation sponge", "id": "1590353636"}, {"image_id": "1590067796", "caption": "A man holding a grapefruit", "id": "1590067796"}, {"image_id": "216582896", "caption": "Close up of red blooming flowers St Jean de Cole Dordogne France", "id": "216582896"}, {"image_id": "1813180529", "caption": "Vertical shot of a couple choosing from a set of bicycle helmets at a store", "id": "1813180529"}, {"image_id": "1878869609", "caption": "A front view of smiling School children sitting on a bench in sports uniform with a proud Gym teacher standing behind with his arms crossed", "id": "1878869609"}, {"image_id": "1297776674", "caption": "Smiling family on summer vacation playing with ball together at beach", "id": "1297776674"}, {"image_id": "1860742307", "caption": "Vertical shot of a young woman with champagne flute talking on a telephone in the car", "id": "1860742307"}, {"image_id": "1844731889", "caption": "Portrait shot an elderly woman smelling the scent of a flower while standing in a meadow full of wildflowers", "id": "1844731889"}, {"image_id": "1590317717", "caption": "Two women shopping in boutique", "id": "1590317717"}, {"image_id": "217378889", "caption": "Neumayer Channel Antarctica", "id": "217378889"}, {"image_id": "1571519093", "caption": "Mother talking on mobile phone small daughter in background", "id": "1571519093"}, {"image_id": "1576774370", "caption": "Male florist or gardener pruning shrubs", "id": "1576774370"}, {"image_id": "1297780760", "caption": "Technician wearing white coat handing vials to colleague in laboratory", "id": "1297780760"}, {"image_id": "1578922439", "caption": "A young girl collecting Easter eggs", "id": "1578922439"}, {"image_id": "1297775225", "caption": "Mature businessman working at computer in office", "id": "1297775225"}, {"image_id": "1576774154", "caption": "A senior man riding a motorbike", "id": "1576774154"}, {"image_id": "1297789940", "caption": "Teenage girl with fairy wings camping at festival at camera", "id": "1297789940"}, {"image_id": "1587145865", "caption": "View of hot air balloons against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145865"}, {"image_id": "1587148172", "caption": "View of a candle in an oil burner", "id": "1587148172"}, {"image_id": "1590061625", "caption": "A middle aged woman holding a pot of moisturising cream", "id": "1590061625"}, {"image_id": "1869908126", "caption": "Overhead shot of a group of businesspeople standing in a pyramid formation behind their boss smiling at the camera", "id": "1869908126"}, {"image_id": "1585874819", "caption": "Detail of Matterhorn in the evening Zermatt Valais Switzerland Europe", "id": "1585874819"}, {"image_id": "1590201374", "caption": "American and British flags illuminated at night", "id": "1590201374"}, {"image_id": "1587994214", "caption": "An elderly man using a TV remote control", "id": "1587994214"}, {"image_id": "1578904172", "caption": "Portrait of a young girl", "id": "1578904172"}, {"image_id": "1297774292", "caption": "Engineer checks circuit board under magnifier with microscope", "id": "1297774292"}, {"image_id": "1839578930", "caption": "Horizontal waist up portrait of a smiling young businesswoman with crossed arms by the fish tank in office looks at the camera", "id": "1839578930"}, {"image_id": "1864613831", "caption": "Man carrying a tray containing breakfast for his wife who is resting on the bed in the bedroom", "id": "1864613831"}, {"image_id": "1862121692", "caption": "A portrait of a man and woman in formal business suits holding a digital tablet in an office with selective focus", "id": "1862121692"}, {"image_id": "1859201084", "caption": "Wide shot of a lighthouse and a ship at sunset", "id": "1859201084"}, {"image_id": "1297781585", "caption": "Engineers discussing plans standing next to large solar panels", "id": "1297781585"}, {"image_id": "1844731661", "caption": "Vertical shot of a senior engineer in a reflector vest standing with a young engineer in the control room of a nuclear power station", "id": "1844731661"}, {"image_id": "1590361274", "caption": "Close up of US paper currency in envelope", "id": "1590361274"}, {"image_id": "1862081231", "caption": "Happy Engineer and Technician working on coating glass for use in production of solar panels", "id": "1862081231"}, {"image_id": "1811160824", "caption": "A wide shot of two paddleboarders riding a wave in an ocean", "id": "1811160824"}, {"image_id": "1859181455", "caption": "Wide shot of a male rock climber hanging from a rock while ascending a cliff", "id": "1859181455"}, {"image_id": "1846038914", "caption": "A portrait shot of a smiling salesman standing at a counter and wrapping a wedge of cheese in cheese shop", "id": "1846038914"}, {"image_id": "1587990869", "caption": "Laboratory mice in glass tank", "id": "1587990869"}, {"image_id": "1852925792", "caption": "Portrait of a young man smiling with the family having Christmas dinner in the background", "id": "1852925792"}, {"image_id": "1590213653", "caption": "Doctor talking to couple in office", "id": "1590213653"}, {"image_id": "1766918367", "caption": "Young boy eating hot dog at campfire", "id": "1766918367"}, {"image_id": "1590345065", "caption": "Couple in kitchen with champagne", "id": "1590345065"}, {"image_id": "1817411646", "caption": "Man in an apron holding a plant and speaking to a woman at a greenhouse", "id": "1817411646"}, {"image_id": "1590351212", "caption": "Farmhouse and terraced garden at Banyalbufar Mallorca Spain", "id": "1590351212"}, {"image_id": "1864613921", "caption": "Vertical shot of a little girl brushing her teeth and holding a toothpaste in her hand in the bathroom while smiling at the camera", "id": "1864613921"}, {"image_id": "1587843905", "caption": "Studio shot of businesswoman holding fanned out Euros", "id": "1587843905"}, {"image_id": "1578921287", "caption": "A portrait of a young blonde woman Close up", "id": "1578921287"}, {"image_id": "1840647995", "caption": "A happy production manager holding and showing the final packed and labelled box picked up from the assembly line in a large factory", "id": "1840647995"}, {"image_id": "1670342087", "caption": "Children on sled in snow on mountain winter vacation at camera", "id": "1670342087"}, {"image_id": "1868718242", "caption": "A vertical shot of an engineer standing near large solar panels", "id": "1868718242"}, {"image_id": "1571343290", "caption": "Mature man hiking in mountains with pet dog", "id": "1571343290"}, {"image_id": "1588002836", "caption": "A woman drinking from a bowl sitting on a bed", "id": "1588002836"}, {"image_id": "1586659112", "caption": "Woman looking at a pregnancy test", "id": "1586659112"}, {"image_id": "1587980747", "caption": "A barman flirting with a customer", "id": "1587980747"}, {"image_id": "1590161891", "caption": "Waves from storm in the Bay of Biodola with view to Scalieri Elba Tuscany Italy", "id": "1590161891"}, {"image_id": "1277229026", "caption": "Young man standing car in garage at camera", "id": "1277229026"}, {"image_id": "1587990005", "caption": "A couple relaxing on holiday", "id": "1587990005"}, {"image_id": "1297798604", "caption": "Two smiling women drinking milkshakes at outdoor cafe", "id": "1297798604"}, {"image_id": "1851483968", "caption": "Vertical shot of boy s hand flying a multicolored kite under the blue sky", "id": "1851483968"}, {"image_id": "1588011563", "caption": "Rear view of businesspeople walking down aisle in office", "id": "1588011563"}, {"image_id": "1277226029", "caption": "Man decorating room with paint chart and wallpaper samples at camera", "id": "1277226029"}, {"image_id": "1844196218", "caption": "Close up shot of farmer examining wheat standing in a wheat field with a golf hat on his head", "id": "1844196218"}, {"image_id": "1578946313", "caption": "Two young girls with hoops", "id": "1578946313"}, {"image_id": "1570161008", "caption": "Aldabra giant tortoise Aldabra Atoll Seychelles", "id": "1570161008"}, {"image_id": "1570357049", "caption": "The Smile of Angkor Bayon Temple Angkor Thom Siem Reap Cambodia", "id": "1570357049"}, {"image_id": "1864640585", "caption": "Horizontal portrait of a girl painting a yellow flower on an easel in a classroom", "id": "1864640585"}, {"image_id": "1571691083", "caption": "Between Ornes and Bodo north of the Arctic Circle Norway", "id": "1571691083"}, {"image_id": "1587842129", "caption": "Person playing drums in Munich", "id": "1587842129"}, {"image_id": "1859342006", "caption": "A wide shot of a happy senior man giving his wife a piggyback ride in a park in autumn", "id": "1859342006"}, {"image_id": "1297794623", "caption": "Smiling female friends in cafe reading text message on mobile phone", "id": "1297794623"}, {"image_id": "1572524267", "caption": "Close up of young boy looking away", "id": "1572524267"}, {"image_id": "1852924382", "caption": "Senior mechanic and his colleague standing next to the open hood of a broken down car and posing with a diagnostic computer", "id": "1852924382"}, {"image_id": "1590214580", "caption": "Maple leaves on ground in autumn", "id": "1590214580"}, {"image_id": "1839579032", "caption": "Horizontal low angle shot of motion blur of a businesswoman pulling luggage trolley bag in front of a large glass window at the airport lounge", "id": "1839579032"}, {"image_id": "1657956374", "caption": "Couple hugging outdoors with heart shape balloon", "id": "1657956374"}, {"image_id": "1576782452", "caption": "A woman looking in a wardrobe", "id": "1576782452"}, {"image_id": "1581270692", "caption": "Outdoor portrait of smiling businesswoman with mobile phone", "id": "1581270692"}, {"image_id": "1722076460", "caption": "teenage boy using camcorder on skiing holiday Tirol Austria Europe", "id": "1722076460"}, {"image_id": "1586666096", "caption": "Reflections of light in rainbow colours", "id": "1586666096"}, {"image_id": "1277233181", "caption": "Mature woman with map in back of motorhome in countryside at camera", "id": "1277233181"}, {"image_id": "1587851105", "caption": "Flowers in meadow The Alps Graubunden Switzerland", "id": "1587851105"}, {"image_id": "1587112889", "caption": "Close up bottle of water", "id": "1587112889"}, {"image_id": "1587850196", "caption": "View of castle in Prague", "id": "1587850196"}, {"image_id": "1873340771", "caption": "bartender Serving young women Pints Of Beer In the Bar", "id": "1873340771"}, {"image_id": "1297741847", "caption": "Young woman relaxing on sofa at home using laptop", "id": "1297741847"}, {"image_id": "1570298747", "caption": "A horse pulling a sleigh full of people through the snow", "id": "1570298747"}, {"image_id": "1572527594", "caption": "Cactus at Punta Colorado Isla San Jose Gulf of California Sea of Cortez Mexico", "id": "1572527594"}, {"image_id": "1484419698", "caption": "Beautiful space view of the Earth with cloud formation", "id": "1484419698"}, {"image_id": "1578922400", "caption": "A young girl holding Easter eggs", "id": "1578922400"}, {"image_id": "1590363338", "caption": "Sunset over rock formations at beach Anse Takamaka Mahe Seychelles", "id": "1590363338"}, {"image_id": "1872077147", "caption": "Couple sitting at bottom of staircase at home stack of packed boxes beside wall low section", "id": "1872077147"}, {"image_id": "1567881296", "caption": "Coconut Palm Tree at Botanical Garden near Port Louis Mauritius Africa", "id": "1567881296"}, {"image_id": "1852924349", "caption": "Young man standing bare chested and drying his hair with a towel while looking at a small mirror and smiling", "id": "1852924349"}, {"image_id": "1587846149", "caption": "Close up of woman with contact lens on finger", "id": "1587846149"}, {"image_id": "1571603033", "caption": "A businessman adjusting his tie", "id": "1571603033"}, {"image_id": "1578935381", "caption": "A young businessman sitting in a waiting room eyes closed", "id": "1578935381"}, {"image_id": "1590222719", "caption": "Couple smiling on motor scooter outdoors", "id": "1590222719"}, {"image_id": "1571353172", "caption": "Stream near Davos Grisons Switzerland", "id": "1571353172"}, {"image_id": "1586693309", "caption": "High school student removing baked cookies from oven in home economics class", "id": "1586693309"}, {"image_id": "1587652148", "caption": "Detail view of a \ufffd\ufffd \ufffd\ufffdDo Not Enter\ufffd\ufffd \ufffd\ufffd sign", "id": "1587652148"}, {"image_id": "1578208673", "caption": "A young woman wearing a winter coat with a fur hood looking up", "id": "1578208673"}, {"image_id": "1587058163", "caption": "Young woman with shopping bags embraces man", "id": "1587058163"}, {"image_id": "1587988457", "caption": "Woman talking to businessman at table", "id": "1587988457"}, {"image_id": "1670341253", "caption": "Mature couple playing golf driving buggy on course at camera", "id": "1670341253"}, {"image_id": "1297750334", "caption": "Mature businessman standing by plane straightening tie", "id": "1297750334"}, {"image_id": "1843609076", "caption": "Horizontal shot of a tractor working in a green field with a cloudy sky overhead on a sunny day with copy space", "id": "1843609076"}, {"image_id": "1587849449", "caption": "Fish being cooked on barbecue", "id": "1587849449"}, {"image_id": "1585936934", "caption": "View over Davos with Fluela valley and mount Jakobshorn in the background Davos Grisons Switzerland Europe", "id": "1585936934"}, {"image_id": "1587034607", "caption": "Close up of woman talking on headset", "id": "1587034607"}, {"image_id": "1670343470", "caption": "Woman smelling flowers in florists store", "id": "1670343470"}, {"image_id": "1590065444", "caption": "A middle aged woman applying eye cream", "id": "1590065444"}, {"image_id": "1865999624", "caption": "Horizontal shot of a seated mature couple on a hike on a mountain trail with the woman embracing the man smile at the camera with copy space", "id": "1865999624"}, {"image_id": "1590315683", "caption": "Close up of mother and daughter hugging", "id": "1590315683"}, {"image_id": "1277231426", "caption": "Doctor in white coat with stethoscope working on computer in hospital", "id": "1277231426"}, {"image_id": "217368263", "caption": "King Penguin Aptenodytes Patagonicus Volunteer Point Falkland Islands", "id": "217368263"}, {"image_id": "1571691170", "caption": "View of Sildpollnes famous church Sildpollnes Lofoten Austvagoy Island Nordland County Norway", "id": "1571691170"}, {"image_id": "1586666123", "caption": "An abstract image of purple pink and yellow lights", "id": "1586666123"}, {"image_id": "1590349982", "caption": "Detail of father and daughters legs", "id": "1590349982"}, {"image_id": "1578915794", "caption": "A Young Woman Spraying Suntan Lotion On Her Arm", "id": "1578915794"}, {"image_id": "1570343042", "caption": "Heart shape carved in wood", "id": "1570343042"}, {"image_id": "1859201003", "caption": "Vertical shot of a beautiful sunset over the sea", "id": "1859201003"}, {"image_id": "1840560638", "caption": "Low angle shot of a happy woman with a man walking in the background in a meadow full of wildflowers", "id": "1840560638"}, {"image_id": "1846400540", "caption": "A front view of a red tractor in selective leaving behind the tracks of grass in stripe pattern in the background while mowing the field for silage", "id": "1846400540"}, {"image_id": "1588000277", "caption": "Female palm reader with client", "id": "1588000277"}, {"image_id": "1865986958", "caption": "Horizontal shot of a couple with their two children mountain biking on a woodland trail smile at the camera", "id": "1865986958"}, {"image_id": "1588014437", "caption": "A family sitting down for Christmas dinner", "id": "1588014437"}, {"image_id": "1576738499", "caption": "A young man with a ball on the beach", "id": "1576738499"}, {"image_id": "1766923893", "caption": "Teenage girl with red heart lipstick and heart lollipop", "id": "1766923893"}, {"image_id": "1586683979", "caption": "Senior Man Working In Beautiful Cottage Flower Garden", "id": "1586683979"}, {"image_id": "1570559081", "caption": "Termite nest near Parintins Amazon River Brazil", "id": "1570559081"}, {"image_id": "1578953114", "caption": "A couple paddling in a lake", "id": "1578953114"}, {"image_id": "1590216821", "caption": "Senior couple talking next to log building with motorcycle in foreground", "id": "1590216821"}, {"image_id": "1862081243", "caption": "Close up of a woman holding a champagne glass while sitting with a handsome man on the private jet", "id": "1862081243"}, {"image_id": "1587982265", "caption": "Close up of cherries on tree", "id": "1587982265"}, {"image_id": "1844194469", "caption": "Close up shot of a woman holding pea from split pea pod", "id": "1844194469"}, {"image_id": "1586684471", "caption": "Farmer Watching As Field Is Harrowed By Tractor", "id": "1586684471"}, {"image_id": "1590316760", "caption": "Close up of woman smiling", "id": "1590316760"}, {"image_id": "1852936796", "caption": "Vertical portrait of a senior couple resting in an embrace reclining against a tree in a field on a sunny day", "id": "1852936796"}, {"image_id": "1571579141", "caption": "A couple on a boat preparing to go scuba diving", "id": "1571579141"}, {"image_id": "1587040553", "caption": "Man biking on rural road", "id": "1587040553"}, {"image_id": "1590360683", "caption": "Couple holding hands at beach", "id": "1590360683"}, {"image_id": "1878688433", "caption": "low angle shot of a technician holding a cable connected to the laptop in the secure data center", "id": "1878688433"}, {"image_id": "1297797464", "caption": "Detail of pregnant woman touching bare belly relaxing in park", "id": "1297797464"}, {"image_id": "1578215051", "caption": "Portrait of a young woman smiling", "id": "1578215051"}, {"image_id": "1864650977", "caption": "Little girl applying paint to her father's face using her fingers while they are painting", "id": "1864650977"}, {"image_id": "1590222224", "caption": "Close up of red wine being poured from carafe into glass", "id": "1590222224"}, {"image_id": "1587031229", "caption": "Low angle view of ferris wheel against blue sky Prater Vienna Austria", "id": "1587031229"}, {"image_id": "1588021781", "caption": "A family sitting down for Christmas dinner", "id": "1588021781"}, {"image_id": "1844727578", "caption": "Vertical shot of a young fashion design student working on a garment worn by a mannequin", "id": "1844727578"}, {"image_id": "1862081408", "caption": "Wide shot of business employees talking while sitting in a private jet", "id": "1862081408"}, {"image_id": "1839580643", "caption": "Handsome young businessman in a grey suit is looking at the camera and smiling in the office corridor", "id": "1839580643"}, {"image_id": "1852959470", "caption": "Wide shot of a boy playing with pinwheel sitting on monkey bars at a playground", "id": "1852959470"}, {"image_id": "1590051185", "caption": "A businessman looking at his mobile phone in a modern office building", "id": "1590051185"}, {"image_id": "1851485399", "caption": "Vertical shot of woman standing with her hands behind head on a sunny beach", "id": "1851485399"}, {"image_id": "1590317666", "caption": "Stack of One Euro coins next to bull figurine", "id": "1590317666"}, {"image_id": "216111254", "caption": "Clouds in blue sky over wheat field", "id": "216111254"}, {"image_id": "1859177141", "caption": "Vertical wide shot of an engineer in safety glasses repairing the engine on a passenger jet at a hangar", "id": "1859177141"}, {"image_id": "1586668664", "caption": "Brass lamps for sale in the Souk Marrakech Morocco", "id": "1586668664"}, {"image_id": "1590313958", "caption": "Unwrapped bar of chocolate and glass of wine", "id": "1590313958"}, {"image_id": "1570162247", "caption": "Granite formations at Anse Source D Argent La Digue Island Seychelles", "id": "1570162247"}, {"image_id": "1590360017", "caption": "Close up of crocodile with mouth open", "id": "1590360017"}, {"image_id": "1587990881", "caption": "Female scientist using tester in laboratory", "id": "1587990881"}, {"image_id": "1590351167", "caption": "View to Port de Valldemossa Mallorca Spain", "id": "1590351167"}, {"image_id": "1844766083", "caption": "Smiling young couple using digital devices in an urban park", "id": "1844766083"}, {"image_id": "1865993438", "caption": "Horizontal profile shot of a couple with their two children and grandparents walking in line on a hike on a mountain trail with copy space", "id": "1865993438"}, {"image_id": "1722076895", "caption": "Teenagers cross country skiing Tirol Austria Europe", "id": "1722076895"}, {"image_id": "1587839462", "caption": "Close up of man's pants around ankles in bathroom", "id": "1587839462"}, {"image_id": "1844766962", "caption": "Close up of a nurse listening to a doctor", "id": "1844766962"}, {"image_id": "1590024107", "caption": "A woman straightening her hair", "id": "1590024107"}, {"image_id": "1710319400", "caption": "A senior woman gardening outdoors", "id": "1710319400"}, {"image_id": "1578916802", "caption": "A Young Woman Carrying A Shopping Bag Over Her Shoulder", "id": "1578916802"}, {"image_id": "1654572791", "caption": "Young couple mountain biking along dirt track front view", "id": "1654572791"}, {"image_id": "1586664194", "caption": "Businessman by briefcase using mobile phone cut out", "id": "1586664194"}, {"image_id": "1766903664", "caption": "Fans screaming and cheering behind fence", "id": "1766903664"}, {"image_id": "1590060056", "caption": "Close up of a golf club and ball on a tee", "id": "1590060056"}, {"image_id": "1869041666", "caption": "Front view portrait of a male shop assistant in white gloves holding red designer handbag and standing in front of shelf display in the glamorous boutique", "id": "1869041666"}, {"image_id": "1587138239", "caption": "View of a female patient being examined by a female doctor", "id": "1587138239"}, {"image_id": "1851405326", "caption": "Horizontal head and shoulder profile shot of a young woman in a bubble bath smiling at the camera with copy space", "id": "1851405326"}, {"image_id": "1868720804", "caption": "A Close up shot of big snow covered cow parsley stalks in winter", "id": "1868720804"}, {"image_id": "1277247470", "caption": "Family unload camping equipment and tent from car boot in countryside", "id": "1277247470"}, {"image_id": "1816211379", "caption": "Couple moving house as woman packs clothing in bedroom and man carries a sealed box", "id": "1816211379"}, {"image_id": "1277237102", "caption": "Businessman in wearing hard hat with briefcase on stairs at camera", "id": "1277237102"}, {"image_id": "1586687441", "caption": "Greece Kefalonia Brightly coloured boats moored in harbour", "id": "1586687441"}, {"image_id": "1263322046", "caption": "Farm field of young green barley growing with single tree", "id": "1263322046"}, {"image_id": "1859331812", "caption": "A side profile medium up shot of a chef garnishing gourmet food with parley in a kitchen", "id": "1859331812"}, {"image_id": "1766906286", "caption": "Man and two women walking in the snow", "id": "1766906286"}, {"image_id": "1851471923", "caption": "Multi generations of a family sitting in the hatchback of a car and chatting", "id": "1851471923"}, {"image_id": "1844728997", "caption": "Medium shot of an elderly man in hat holding a flower and painting in a meadow full of wildflowers", "id": "1844728997"}, {"image_id": "1588026629", "caption": "A female business project leader at a team meeting", "id": "1588026629"}, {"image_id": "1571679275", "caption": "Interior view of French Cathedral Berlin Germany", "id": "1571679275"}, {"image_id": "217378091", "caption": "Cloud", "id": "217378091"}, {"image_id": "1816749465", "caption": "Wide rear view of a young happy couple carrying yellow surfboards on beach", "id": "1816749465"}, {"image_id": "1860742043", "caption": "Elevated view of professional swimmers training in the swimming pool", "id": "1860742043"}, {"image_id": "1586684165", "caption": "Senior Man Wearing Chef's Apron Picking Vegetables In Garden", "id": "1586684165"}, {"image_id": "1843607267", "caption": "Horizontal over the shoulder shot of a businessman using hands free device while working on a laptop at an airport with copy space", "id": "1843607267"}, {"image_id": "1572509054", "caption": "Portrait of a young boy", "id": "1572509054"}, {"image_id": "1766904432", "caption": "Sad female sports fan sitting on ground", "id": "1766904432"}, {"image_id": "1851471938", "caption": "Multi generations of a family smiling while walking in the forest", "id": "1851471938"}, {"image_id": "1865999657", "caption": "Vertical shot of two boys playing with an inflatable ball at the beach on a sunny day with copy space", "id": "1865999657"}, {"image_id": "1868714276", "caption": "A Businessman and businesswoman discussing a report at the table counter in a telemarketing office", "id": "1868714276"}, {"image_id": "1297797359", "caption": "Detail of teenage girls eye wearing knitted hat in winter outdoors", "id": "1297797359"}, {"image_id": "1657955663", "caption": "Couple hugging near Christmas tree", "id": "1657955663"}, {"image_id": "1571590127", "caption": "Portrait of a teenage couple", "id": "1571590127"}, {"image_id": "1587842159", "caption": "Harbor with boats in Germany", "id": "1587842159"}, {"image_id": "1576738346", "caption": "Portrait of a young woman", "id": "1576738346"}, {"image_id": "1864656440", "caption": "Vertical profile shot of a businesswoman wearing a mobile s hands free working over a laptop smiles at the camera with copy space", "id": "1864656440"}, {"image_id": "1576741751", "caption": "A young woman running in the desert", "id": "1576741751"}, {"image_id": "1571668358", "caption": "Woman trying on sun tops in the changing room of a shop", "id": "1571668358"}, {"image_id": "1585871510", "caption": "Low section of high heels on young woman", "id": "1585871510"}, {"image_id": "1587148232", "caption": "View of a middle aged man rock climbing", "id": "1587148232"}, {"image_id": "1578932870", "caption": "A woman on her lunch break using a laptop computer", "id": "1578932870"}, {"image_id": "1840649684", "caption": "A portrait shot of a smiling electrician in a light blue t shirt holding a drill and cable spool", "id": "1840649684"}, {"image_id": "1840562480", "caption": "Medium shot of a technician in safety glasses using measurement probe on the assembly line of a steel bearing manufacturing plant", "id": "1840562480"}, {"image_id": "1590161879", "caption": "Harbor with boats and buildings in background Marciana Marina Elba Tuscany Italy", "id": "1590161879"}, {"image_id": "1590161783", "caption": "Harbor with boats Marciana Marina Elba Tuscany Italy", "id": "1590161783"}, {"image_id": "1590343499", "caption": "Embrasure in the ancient walls of Mont Orgueil Castle Gorey Jersey Channel Islands UK", "id": "1590343499"}, {"image_id": "1716620837", "caption": "View of teenage girl posing by blue wall", "id": "1716620837"}, {"image_id": "1588017308", "caption": "Low angle view of colonnade Saint Peters Basilica Rome Italy", "id": "1588017308"}, {"image_id": "1304265968", "caption": "Mature man surprising woman with gift at outdoor restaurant table", "id": "1304265968"}, {"image_id": "1297794803", "caption": "cutout of active mature woman exercising with weights at camera", "id": "1297794803"}, {"image_id": "1843586504", "caption": "View of a clear blue sky with clouds below taken from an airplane window", "id": "1843586504"}, {"image_id": "1859143223", "caption": "Low angle wide shot of a multi generation family walking along the shore of a sunny beach", "id": "1859143223"}, {"image_id": "1725713093", "caption": "Kindergarten child in a wood kindergarten portrait", "id": "1725713093"}, {"image_id": "1846752464", "caption": "A low angle view of surgeons wearing glasses and surgical binoculars while performing an intricate operation in the operating theatre", "id": "1846752464"}, {"image_id": "1572510428", "caption": "Mother lying on floor holding up daughter", "id": "1572510428"}, {"image_id": "1585939355", "caption": "Palm trees at the beach of Mauna Lani Big Island Hawaii USA", "id": "1585939355"}, {"image_id": "1590317534", "caption": "Rock formations in surf Baie Lazare Mahe Seychelles", "id": "1590317534"}, {"image_id": "1297799918", "caption": "Senior woman carrying firewood in autumn landscape", "id": "1297799918"}, {"image_id": "1590183647", "caption": "Island with Monkey Puzzle trees at sunset near Ile Des Pins New Caledonia Overseas Territory of France", "id": "1590183647"}, {"image_id": "1590053402", "caption": "A businesswoman in an office holding a red folder smiling", "id": "1590053402"}, {"image_id": "1844724173", "caption": "Medium shot of a worker and supervisor examining paperwork and scanning a box at the production line of a distribution warehouse", "id": "1844724173"}, {"image_id": "1671842753", "caption": "A young woman stretching in the morning", "id": "1671842753"}, {"image_id": "1846038959", "caption": "A woman talking to a craftsman working on frame in frame shop", "id": "1846038959"}, {"image_id": "1590065450", "caption": "A portrait of a young woman smiling", "id": "1590065450"}, {"image_id": "1807085741", "caption": "Spain Barcelona woman reading text message on mobile phone side view close up", "id": "1807085741"}, {"image_id": "1869312014", "caption": "High angle vertical shot of a woman in swimwear standing at the shore of a sunny beach", "id": "1869312014"}, {"image_id": "1846771328", "caption": "Sports scientist recording the performance of a runner jogging on a treadmill with a mask on that is connected to two monitors that are reflecting some data", "id": "1846771328"}, {"image_id": "1572550373", "caption": "A young woman standing on a beach", "id": "1572550373"}, {"image_id": "1590061697", "caption": "A young woman sitting on a bed with a laptop", "id": "1590061697"}, {"image_id": "1878682910", "caption": "Mid length shot of a mature woman holding aloft set of keys to a new house with a senior man carrying rolled up carpet and lamp in the background", "id": "1878682910"}, {"image_id": "1590053516", "caption": "A young man cleaning his ear", "id": "1590053516"}, {"image_id": "1839578933", "caption": "Horizontal waist up portrait of a young businesswoman with crossed arms by the fish tank in office looks at the camera", "id": "1839578933"}, {"image_id": "1587031694", "caption": "View of rural road in countryside Starnberg District Upper Bavaria Germany", "id": "1587031694"}, {"image_id": "1587831044", "caption": "Father and son talking on sofa", "id": "1587831044"}, {"image_id": "1571686589", "caption": "Coast at Hammershus Bornholm Island Denmark", "id": "1571686589"}, {"image_id": "1277235542", "caption": "Business colleagues with laptop collaborating in office meeting", "id": "1277235542"}, {"image_id": "1297786262", "caption": "Female student making notes on plant seedlings in science class smiling at camera", "id": "1297786262"}, {"image_id": "1571664764", "caption": "Portrait of a young woman", "id": "1571664764"}, {"image_id": "1571615177", "caption": "A woman standing in a gym", "id": "1571615177"}, {"image_id": "1851488750", "caption": "Vertical portrait of a loving couple sitting on a wooden pier over the lake wearing swimming suit", "id": "1851488750"}, {"image_id": "1587141251", "caption": "Still life of a bunch of chilies", "id": "1587141251"}, {"image_id": "1587995813", "caption": "Mother and young daughter preparing healthy salad lunch together", "id": "1587995813"}, {"image_id": "1710285014", "caption": "Two young lovers relaxing on a beautiful day", "id": "1710285014"}, {"image_id": "1277242601", "caption": "Young smiling couple in tent on camping trip in countryside at camera", "id": "1277242601"}, {"image_id": "1864631111", "caption": "Vertical shot of a young boy jumping on trampoline in garden with the focus on plimsolls in foreground", "id": "1864631111"}, {"image_id": "1590317675", "caption": "Stack of One Euro coins next to bull figurine", "id": "1590317675"}, {"image_id": "1590347903", "caption": "Woman holding fringe over head in wind", "id": "1590347903"}, {"image_id": "1576774388", "caption": "A woman in a bikini", "id": "1576774388"}, {"image_id": "217361663", "caption": "Detail of water rapids", "id": "217361663"}, {"image_id": "1590183839", "caption": "Residential House at Lifou Island Loyalty Islands New Caledonia Overseas Territory of France", "id": "1590183839"}, {"image_id": "1862125916", "caption": "A vertical close up shot of a young girl's hand in school uniform writing a test at her desk in a classroom", "id": "1862125916"}, {"image_id": "1277368427", "caption": "Woman on sandy summer beach vacation in wetsuit with surfboard watching waves breaking", "id": "1277368427"}, {"image_id": "1586726966", "caption": "Portrait Of Nurse Talking To Patient On Gurney In Corridor", "id": "1586726966"}, {"image_id": "1571544896", "caption": "A young girl with a water pistol", "id": "1571544896"}, {"image_id": "1844766935", "caption": "Portrait of a smiling young man using a digital tablet sitting against a tree in the park", "id": "1844766935"}, {"image_id": "1590314120", "caption": "Friends cooking on outdoor fire in winter", "id": "1590314120"}, {"image_id": "1844196827", "caption": "Horizontal shot of a woman holding fresh tomatoes and cucumber in kitchen", "id": "1844196827"}, {"image_id": "1859144624", "caption": "Low angle shot of a woman with a backpack jumping over a log in the woods", "id": "1859144624"}, {"image_id": "1766928984", "caption": "Group of people practicing yoga Kleinwalsertal Allgau Germany", "id": "1766928984"}, {"image_id": "1590350798", "caption": "Inner courtyard of the Monastery Santuari de Lluc near Escorca Mallorca Spain", "id": "1590350798"}, {"image_id": "1297794713", "caption": "cutout of smiling female doctor with stethoscope at camera", "id": "1297794713"}, {"image_id": "1587642905", "caption": "Seawater at the rear of a rry", "id": "1587642905"}, {"image_id": "1571366066", "caption": "Bug with pearl Merzouga Morocco Africa", "id": "1571366066"}, {"image_id": "1587030965", "caption": "View of a woman letting heart shaped balloons go on the beach", "id": "1587030965"}, {"image_id": "1587835895", "caption": "Fountain and dome Saint Peter's Basilica Vatican Rome Italy", "id": "1587835895"}, {"image_id": "1590353621", "caption": "Portrait of woman with leaf", "id": "1590353621"}, {"image_id": "1868703278", "caption": "A serious businessman working on a laptop at a table in front of a presentation monitor in the conference room", "id": "1868703278"}, {"image_id": "1581270530", "caption": "Overhead view of female elementary school pupil painting picture on easel in classroom art class", "id": "1581270530"}, {"image_id": "1570355477", "caption": "South Gate of Angkor Thom Siem Reap Cambodia", "id": "1570355477"}, {"image_id": "1586735663", "caption": "Portrait Of Businessman In Aircraft Maintenance Hangar", "id": "1586735663"}, {"image_id": "1817410899", "caption": "Close up shot of a woman in a hoodie with the sea in the background", "id": "1817410899"}, {"image_id": "1855878269", "caption": "Young male sprinter stepping over the finishing line while his competitors are behind him on a bright sunny day at an athletics event", "id": "1855878269"}, {"image_id": "1590316826", "caption": "Woman using exercise ball at beach", "id": "1590316826"}, {"image_id": "1840560419", "caption": "Vertical shot of an elderly couple standing in a meadow full of sunflowers with the man pointing up", "id": "1840560419"}, {"image_id": "1840559585", "caption": "Vertical shot of stacked steel tubes and safety barriers", "id": "1840559585"}, {"image_id": "1852963970", "caption": "Worker stocking bottles in a wine shop", "id": "1852963970"}, {"image_id": "1587031670", "caption": "View of a paraglider over the ocean", "id": "1587031670"}, {"image_id": "1817411052", "caption": "Wide shot of a female in swimwear walking on the beach with an equipment bucket", "id": "1817411052"}, {"image_id": "1590317759", "caption": "Woman making purchase at store", "id": "1590317759"}, {"image_id": "1588003538", "caption": "Close up of rabbit in cage", "id": "1588003538"}, {"image_id": "1585876529", "caption": "Church tower of a Byzantine church and Greek flag Lindos Rhodes Dodecanese Greece Europe", "id": "1585876529"}, {"image_id": "1852938173", "caption": "Vertical shot of a man embracing his wife by the barbeque smile at the camera", "id": "1852938173"}, {"image_id": "1277255792", "caption": "Portrait of young woman carrying kite surfboard on beach vacation", "id": "1277255792"}, {"image_id": "1844764391", "caption": "Happy family out on a vacation clicking pictures on a hill near the sea", "id": "1844764391"}, {"image_id": "1585850426", "caption": "Gingerbread cookies family against white background", "id": "1585850426"}, {"image_id": "1866091370", "caption": "Scenic aerial landscape view of green fields in a sunny rural countryside under the blue sky", "id": "1866091370"}, {"image_id": "217370885", "caption": "Swedish Flag on Top of Soccer Ball", "id": "217370885"}, {"image_id": "1570363742", "caption": "View from Prudential Skywalk Observatory over Boston Common in the snow Boston Massachusetts USA", "id": "1570363742"}, {"image_id": "1567883183", "caption": "Ylang ylang outspread to be dried Nosy Be Madagascar", "id": "1567883183"}, {"image_id": "1868724128", "caption": "A wide shot of a happy family sitting on a fence with bicycles on the side in a wildflower field", "id": "1868724128"}, {"image_id": "1297800443", "caption": "Portrait of grandmother with grandchildren in autumn countryside", "id": "1297800443"}, {"image_id": "1650247151", "caption": "A woman smiling in a bikini snorkeling", "id": "1650247151"}, {"image_id": "1864634678", "caption": "Front view portrait of a young blonde boy standing with the bicycle in a driveway on a sunny day", "id": "1864634678"}, {"image_id": "1572538775", "caption": "A young woman sitting in the corner of an empty room", "id": "1572538775"}, {"image_id": "1664820536", "caption": "Portrait of a young businessman taking a coffee break", "id": "1664820536"}, {"image_id": "1590317510", "caption": "Rock formations on beach Baie Lazare Mahe Seychelles", "id": "1590317510"}, {"image_id": "1844769077", "caption": "Portrait of a young man using a digital tablet in the street with a stop sign in the background", "id": "1844769077"}, {"image_id": "1839579038", "caption": "Portrait of waiting businesswoman in chair being called upon by a businessman standing at his cabin door in office", "id": "1839579038"}, {"image_id": "1586690837", "caption": "Portrait smiling worker at control panel in food processing plant production line", "id": "1586690837"}, {"image_id": "1851485519", "caption": "Full shot of a happy couple hugging at the shore of a sunny beach", "id": "1851485519"}, {"image_id": "1585876439", "caption": "View to the acropolis Lindos Rhodes Dodecanese Greece Europe", "id": "1585876439"}, {"image_id": "1277235620", "caption": "Business colleagues around table collaborating in office meeting room", "id": "1277235620"}, {"image_id": "1869314726", "caption": "Portrait shot of a mother holding her daughter in an autumn park", "id": "1869314726"}, {"image_id": "1859143274", "caption": "Portrait shot of couple piggybacking their children on a sunny beach", "id": "1859143274"}, {"image_id": "1859331728", "caption": "A side profile medium shot of a young boy carefully using a drill machine in a class", "id": "1859331728"}, {"image_id": "1587149099", "caption": "Tilted view of a dried orange slice and cloves", "id": "1587149099"}, {"image_id": "1590215360", "caption": "Senior woman sitting in armchair", "id": "1590215360"}, {"image_id": "1711247564", "caption": "Woman applying mascara in a dimly lit room", "id": "1711247564"}, {"image_id": "1590200795", "caption": "View off front of ship to the Nature Reserve of East Diamond Island Australia", "id": "1590200795"}, {"image_id": "1578217502", "caption": "A son helping his father recycle cardboard boxes", "id": "1578217502"}, {"image_id": "1586722919", "caption": "Thoughtful Young Woman Sitting On Outdoor Swing", "id": "1586722919"}, {"image_id": "1844733110", "caption": "Low angle shot of two girls with backpacks looking at a map and compass in a green field with the sky in the background", "id": "1844733110"}, {"image_id": "1590201440", "caption": "Close up of fire in fireplace", "id": "1590201440"}, {"image_id": "1590053486", "caption": "A young woman sitting on stairs", "id": "1590053486"}, {"image_id": "1587825038", "caption": "Historical airplanes flying in formation", "id": "1587825038"}, {"image_id": "1868709527", "caption": "A vertical Close up of a handshake between a businessman and a businesswoman in formal suits", "id": "1868709527"}, {"image_id": "1844731634", "caption": "Senior engineer speaking on the telephone while operating a computer at the control desk in the control room of a nuclear power station", "id": "1844731634"}, {"image_id": "1670374844", "caption": "Mature businessman standing in lobby of modern office building", "id": "1670374844"}, {"image_id": "1839588617", "caption": "Tilted portrait of a seated young woman outdoors smiling at the camera with sun flare", "id": "1839588617"}, {"image_id": "1878681158", "caption": "Mid length shot of an active senior woman in pink sports vest jogging along the suburban street", "id": "1878681158"}, {"image_id": "1747434315", "caption": "Stack of One Euro coins next to bull figurine", "id": "1747434315"}, {"image_id": "1586693267", "caption": "Portrait confident chemistry teacher in laboratory classroom", "id": "1586693267"}, {"image_id": "1571330825", "caption": "Romantic couple feeding fresh fruit to each other in park", "id": "1571330825"}, {"image_id": "1277240942", "caption": "Mature woman on coastal road trip holding map by car", "id": "1277240942"}, {"image_id": "1576745900", "caption": "An elderly woman playing cards", "id": "1576745900"}, {"image_id": "1852925666", "caption": "Horizontal Close up shot of a young girl with illuminated Christmas lights on her head", "id": "1852925666"}, {"image_id": "1571608364", "caption": "A businesswoman carrying a laptop", "id": "1571608364"}, {"image_id": "1578942848", "caption": "A businessman at the top of stairs", "id": "1578942848"}, {"image_id": "1859342081", "caption": "A portrait shot of a young blonde girl cutting different clothing materials in a classroom with teachers and students standing in background", "id": "1859342081"}, {"image_id": "1766908170", "caption": "Low angle view of hikers on snowy mountain", "id": "1766908170"}, {"image_id": "1590178103", "caption": "Close up of couple hugging", "id": "1590178103"}, {"image_id": "1277239211", "caption": "Senior man wearing bathrobe drinking glass of water at camera", "id": "1277239211"}, {"image_id": "1587037112", "caption": "Woman stretching in yoga pose touching her toes", "id": "1587037112"}, {"image_id": "1578924866", "caption": "A young couple standing with a bicycle in autumn time", "id": "1578924866"}, {"image_id": "1578921266", "caption": "A portrait of a young blonde woman touching her face", "id": "1578921266"}, {"image_id": "1878681026", "caption": "Vertical Close up of a businesswoman sitting in the backseat of the car using a hands free mobile phone device and looking out of an open window", "id": "1878681026"}, {"image_id": "1670343416", "caption": "Mature man selecting timber for DIY project in hardware store", "id": "1670343416"}, {"image_id": "1873346105", "caption": "Silhouetted of a windsurfer windsurfing on sunny windy waves", "id": "1873346105"}, {"image_id": "1587149060", "caption": "Close up of two oranges stuck with cloves", "id": "1587149060"}, {"image_id": "1586693330", "caption": "Focused male high school student plating dessert in home economics class", "id": "1586693330"}, {"image_id": "1571353139", "caption": "Barbary Macaque Macaca sylvanus Gibraltar British Overseas Territory", "id": "1571353139"}, {"image_id": "1851483860", "caption": "Vertical shot of a multicolored kite flying over a blue sky", "id": "1851483860"}, {"image_id": "1297787678", "caption": "Studio cutout of yellow sunflower on white background", "id": "1297787678"}, {"image_id": "1578922271", "caption": "A young woman holding a bunch of daffodils", "id": "1578922271"}, {"image_id": "1578236783", "caption": "little boy standing by picket fence", "id": "1578236783"}, {"image_id": "1590219848", "caption": "Half timbered house and fountain Gallusplatz St Gallen Canton of St Gallen Switzerland", "id": "1590219848"}, {"image_id": "1839589169", "caption": "Head and shoulder portrait of a father and son smiling at the camera", "id": "1839589169"}, {"image_id": "1816749447", "caption": "Full length of a young happy couple carrying yellow surfboards on beach over sand dunes", "id": "1816749447"}, {"image_id": "1725717704", "caption": "Businessman walking in server room in Cape Town South Africa", "id": "1725717704"}, {"image_id": "1725714212", "caption": "Young man looking up and smiling outdoors", "id": "1725714212"}, {"image_id": "1855882769", "caption": "Horizontal waist up portrait of a mature man holding a pink gift box by the Christmas tree smiles at the camera", "id": "1855882769"}, {"image_id": "1869311858", "caption": "Vertical wide shot of a woman in swimwear standing at the shore of a sunny beach with an exotic tree in the foreground", "id": "1869311858"}, {"image_id": "1570574210", "caption": "Anaconda Eunectes murinus in Libertad Peru", "id": "1570574210"}, {"image_id": "1878695783", "caption": "Portrait shot of a middle school student looking at the camera while writing an assignment with her classmates in the background", "id": "1878695783"}, {"image_id": "1851481466", "caption": "Wide shot of a man holding a cute calf and round bales stacked in the background", "id": "1851481466"}, {"image_id": "1578922451", "caption": "A young girl holding a basket walking through a field of daffodils", "id": "1578922451"}, {"image_id": "1571338163", "caption": "Old storehouses at Nidelva in Trondheim Sor Trondelag Trondelag Norway", "id": "1571338163"}, {"image_id": "1766902656", "caption": "A woman celebrating her birthday guests with glasses raised", "id": "1766902656"}, {"image_id": "1868723252", "caption": "A portrait shot of a mother and son smiling at camera while father is lying in a sunny wildflower field", "id": "1868723252"}, {"image_id": "1873346033", "caption": "Schoolgirl in private school uniform using computer in the computer lab", "id": "1873346033"}, {"image_id": "1586690978", "caption": "Portrait quality control worker with digital tablet at production line in cheese processing plant", "id": "1586690978"}, {"image_id": "1844766215", "caption": "Vertical portrait of a smiling boy with a fishing net lying on a small wooden bridge over the stream", "id": "1844766215"}, {"image_id": "1669109912", "caption": "Close up of woman wearing patterned pantyhose", "id": "1669109912"}, {"image_id": "216352226", "caption": "Clouds in blue sky over oat fields", "id": "216352226"}, {"image_id": "1576777208", "caption": "low angle shot couple holding hands having fun", "id": "1576777208"}, {"image_id": "1576774253", "caption": "Two school girls on a school bus", "id": "1576774253"}, {"image_id": "1570160810", "caption": "Houses in Stone Town part of Zanzibar City Zanzibar Tanzania Africa", "id": "1570160810"}, {"image_id": "1586648888", "caption": "Drought Stricken Landscape In Area Of Western Cape In South Africa", "id": "1586648888"}, {"image_id": "1813180688", "caption": "A portrait of smiling little girl hugging a tree trunk", "id": "1813180688"}, {"image_id": "1578952916", "caption": "A woman with her hand resting on her stomach", "id": "1578952916"}, {"image_id": "1590056591", "caption": "A mother applying sunscreen on her daughter", "id": "1590056591"}, {"image_id": "1572524300", "caption": "Father and boys running on beach", "id": "1572524300"}, {"image_id": "1586725784", "caption": "Road Through Snowy Landscape In Woerthsee Region Of Germany", "id": "1586725784"}, {"image_id": "1868714261", "caption": "A low angle Close up of a tray with stacked silicon wafers being held by an engineer wearing a clean suit", "id": "1868714261"}, {"image_id": "1590339788", "caption": "The Corbiere Lighthouse during high tide at dusk in the south west of Jersey Channel Islands UK", "id": "1590339788"}, {"image_id": "1587987830", "caption": "Little girls sharing birthday presents", "id": "1587987830"}, {"image_id": "1570568201", "caption": "Ships in the harbor of Manaus at dusk Amazonas Amazon River Brazil", "id": "1570568201"}, {"image_id": "1839588416", "caption": "Vertical shot of a businesswoman showing the paperwork to her colleagues in lobby with reflection on the floor", "id": "1839588416"}, {"image_id": "1586730125", "caption": "Portrait Of Senior Couple Flying Kite In Countryside", "id": "1586730125"}, {"image_id": "1576745309", "caption": "A woman wearing a bikini relaxing in a pool", "id": "1576745309"}, {"image_id": "1862067299", "caption": "Side view of a cargo ship travelling at the sea", "id": "1862067299"}, {"image_id": "1587984143", "caption": "Senior couple relaxing at the beach", "id": "1587984143"}, {"image_id": "1860742292", "caption": "Low angle view of a young glamorous couple surrounded by paparazzi", "id": "1860742292"}, {"image_id": "1587998453", "caption": "Man kissing pregnant wife's belly", "id": "1587998453"}, {"image_id": "1578906959", "caption": "A young woman sitting on an exercise ball", "id": "1578906959"}, {"image_id": "1816212861", "caption": "A boy doing an activity using a weighing balance at his school", "id": "1816212861"}, {"image_id": "1297787504", "caption": "Active woman with towel after exercising on equipment in health club smiling at camera", "id": "1297787504"}, {"image_id": "1571660525", "caption": "A businesswoman in a gym using a mobile phone", "id": "1571660525"}, {"image_id": "1840649402", "caption": "A portrait shot of two farmers in sunny field watching a tractor fertilizing wheat field", "id": "1840649402"}, {"image_id": "1277239415", "caption": "Woman exercising at home sitting on floor in living room at camera", "id": "1277239415"}, {"image_id": "1297780790", "caption": "Doctor in white coat taking female patients blood pressure", "id": "1297780790"}, {"image_id": "1852646720", "caption": "A low angle Close up of paramedics tending to a patient on an emergency airlift helicopter", "id": "1852646720"}, {"image_id": "1766918223", "caption": "Teenage boys balancing on pier", "id": "1766918223"}, {"image_id": "1571338169", "caption": "Old town of Trondheim Sor Trondelag Trondelag Norway", "id": "1571338169"}, {"image_id": "1588016024", "caption": "A young woman posing with a tropical leaf", "id": "1588016024"}, {"image_id": "1588005185", "caption": "Senior couple smiling at each other in chalet", "id": "1588005185"}, {"image_id": "1587833708", "caption": "Bronze door on the Temple of Romulus Roman Forum Rome Italy", "id": "1587833708"}, {"image_id": "1852922900", "caption": "Senior woman playing with her Labrador retriever dog on the beach on a bright sunny day", "id": "1852922900"}, {"image_id": "1590200648", "caption": "Rock formations at sunset New Caledonia Overseas Territory of France", "id": "1590200648"}, {"image_id": "1590216629", "caption": "Senior biker couple smiling with motorcycle and mountains in background", "id": "1590216629"}, {"image_id": "1851407534", "caption": "Horizontal head and shoulder portrait of two jubilant young women outdoors with one smiling and holding a mobile phone", "id": "1851407534"}, {"image_id": "1587845750", "caption": "Spilled coffee beans on table", "id": "1587845750"}, {"image_id": "1590317561", "caption": "Close up of water lily Seychelles", "id": "1590317561"}, {"image_id": "1590338030", "caption": "Close up of young woman's lips", "id": "1590338030"}, {"image_id": "1586684936", "caption": "Farmer Supervising Straw Bales Being Loaded Onto Lorry", "id": "1586684936"}, {"image_id": "1590178133", "caption": "Santa Claus decoration on building facade Auckland New Zealand", "id": "1590178133"}, {"image_id": "1570348088", "caption": "View to Sertigtal Davos Grisons Switzerland", "id": "1570348088"}, {"image_id": "1570357133", "caption": "Bracing of a tree at Ta Prohm temple at Angkor Siem Reap Cambodia", "id": "1570357133"}, {"image_id": "1297776620", "caption": "Man with outstretched arms greeting woman running along beach", "id": "1297776620"}, {"image_id": "1859178650", "caption": "Engineers discussing paperwork at the wing of a passenger jet at a hangar", "id": "1859178650"}, {"image_id": "1670342012", "caption": "Children pulling parents on sled on family winter vacation at camera", "id": "1670342012"}, {"image_id": "1862083520", "caption": "Wide top angle shot of a Combine harvester harvesting wheat into the trailer in a rural field", "id": "1862083520"}, {"image_id": "1864650875", "caption": "Horizontal low angle waist up shot of a joyous senior couple embracing in the garden during autumn", "id": "1864650875"}, {"image_id": "1586668472", "caption": "A female car assistant talking to a senior woman with a dog", "id": "1586668472"}, {"image_id": "1590214433", "caption": "Close up of wine leaves in autumn", "id": "1590214433"}, {"image_id": "1590319631", "caption": "Senior couple riding bicycles in park", "id": "1590319631"}, {"image_id": "1590200624", "caption": "Colonial age residential houses Key West Florida United States", "id": "1590200624"}, {"image_id": "1583880827", "caption": "Young man in bed working on laptop", "id": "1583880827"}, {"image_id": "1846059956", "caption": "A wide shot of a combine harvesting wheat with clouds over a sunny rural field", "id": "1846059956"}, {"image_id": "1570230914", "caption": "View to Forte Falcone Portoferraio Island of Elba Province of Livorno Tuscany Italy", "id": "1570230914"}, {"image_id": "1878695732", "caption": "Vertical shot of a group of high school students using digital tablets in a classroom", "id": "1878695732"}, {"image_id": "1586729939", "caption": "Senior Couple Enjoying Fishing Trip By Lake Together", "id": "1586729939"}, {"image_id": "1590224903", "caption": "High angle view of three young girls smiling outdoors", "id": "1590224903"}, {"image_id": "1865993558", "caption": "Horizontal rear view of a young woman wrapped in a towel on the jetty by the lake looking at the horizon on a sunny day", "id": "1865993558"}, {"image_id": "1587060059", "caption": "People looking at computer screen architect model in foreground", "id": "1587060059"}, {"image_id": "1581272369", "caption": "Active family walking in autumn park through leaves", "id": "1581272369"}, {"image_id": "1664821070", "caption": "Portrait of two young women stretching", "id": "1664821070"}, {"image_id": "1297776536", "caption": "Tired businesswoman sitting at desk working late and rubbing neck", "id": "1297776536"}, {"image_id": "1586723048", "caption": "Green Energy Concept With Nature Landscape In light bulb", "id": "1586723048"}, {"image_id": "1869908195", "caption": "Overhead shot of a group of people holding black umbrellas with one pink umbrella in between", "id": "1869908195"}, {"image_id": "1576777103", "caption": "Portrait of a young couple Close up", "id": "1576777103"}, {"image_id": "1277234687", "caption": "Active senior man with gym bag over shoulder at camera", "id": "1277234687"}, {"image_id": "1578946946", "caption": "A businessman sitting on steps", "id": "1578946946"}, {"image_id": "1665809729", "caption": "Close up of couple with underwear around ankles", "id": "1665809729"}, {"image_id": "1578902441", "caption": "Portrait of a young woman", "id": "1578902441"}, {"image_id": "1587850121", "caption": "View of Tyn church in Prague", "id": "1587850121"}, {"image_id": "1843588691", "caption": "Smiling middle aged farmer standing on his ploughed field on a sunny day with his hands in his pockets and a tractor and a plough in the background", "id": "1843588691"}, {"image_id": "1843607054", "caption": "A row of trees ascending in size from left to right with the background of blue sky with clouds", "id": "1843607054"}, {"image_id": "1587107774", "caption": "Computer with a graph on the monitor and arrows pointing up", "id": "1587107774"}, {"image_id": "1670342558", "caption": "Two businessmen working at desk in office looking at computer", "id": "1670342558"}, {"image_id": "1571576867", "caption": "A couple sitting on a boat preparing to go scuba diving", "id": "1571576867"}, {"image_id": "1570308374", "caption": "Ruined columns against the sky Temple of Hera Valley of the Temples Agrigento Sicily Italy", "id": "1570308374"}, {"image_id": "1578906872", "caption": "A young man playing with a football", "id": "1578906872"}, {"image_id": "1277224523", "caption": "Boy and girl fishing with nets in lake", "id": "1277224523"}, {"image_id": "1847201897", "caption": "A small family of three sitting on a cold beach in front of tall grass while smiling at the camera", "id": "1847201897"}, {"image_id": "1588001771", "caption": "Close up of bouquet of dried roses outdoors", "id": "1588001771"}, {"image_id": "1864640678", "caption": "Young woman sitting on a chair next to her desk in her home office and smiling confidently at the camera", "id": "1864640678"}, {"image_id": "1590338186", "caption": "Mother and daughter with flower necklace", "id": "1590338186"}, {"image_id": "1590178571", "caption": "Original vegetation Rotopounamu Walk Tongariro National Park North Island New Zealand", "id": "1590178571"}, {"image_id": "1663683764", "caption": "Teenage boys training in gym portrait", "id": "1663683764"}, {"image_id": "1664820506", "caption": "Mature businessman deep in thought", "id": "1664820506"}, {"image_id": "1844764268", "caption": "Wide portrait of a cute little girl sitting in a convertible car and her family is in the background", "id": "1844764268"}, {"image_id": "1297800545", "caption": "Female gymnast jumping on balance beam with crowd watching", "id": "1297800545"}, {"image_id": "1588001801", "caption": "Horse walking in field of buttercup flowers", "id": "1588001801"}, {"image_id": "1588012928", "caption": "A portrait of an attractive black woman", "id": "1588012928"}, {"image_id": "1567876811", "caption": "House facade at Gozo Malta", "id": "1567876811"}, {"image_id": "1844194190", "caption": "Beautiful sky painted by the sun above horizon leaving bright golden shades", "id": "1844194190"}, {"image_id": "1586667011", "caption": "Surfer with surfboard standing on rocks wearing wetsuit with ocean in background and mood sky", "id": "1586667011"}, {"image_id": "1297781597", "caption": "Engineers discussing plans standing next to large solar panels", "id": "1297781597"}, {"image_id": "1576771910", "caption": "A young woman using a laptop", "id": "1576771910"}, {"image_id": "1590327362", "caption": "Bibi Miriam tombs Qalhat near Sur Oman", "id": "1590327362"}, {"image_id": "1590216665", "caption": "Couple sitting on bench hugging outdoors in autumn", "id": "1590216665"}, {"image_id": "216581375", "caption": "Young barley crop growing in green farm field under blue sky", "id": "216581375"}, {"image_id": "1297797536", "caption": "Action shot of smiling senior couple dancing in living room at home", "id": "1297797536"}, {"image_id": "1587128735", "caption": "Portrait of young woman in wooly hat and scarf", "id": "1587128735"}, {"image_id": "1869314612", "caption": "Silhouette of an elderly couple enjoying wine while sitting on a bench against the sunset over the ocean", "id": "1869314612"}, {"image_id": "1572529430", "caption": "Aerial view of Cerocahui Chihuahua Mexico", "id": "1572529430"}, {"image_id": "1665809777", "caption": "Female couple with cookbook chopping vegetables", "id": "1665809777"}, {"image_id": "1587851096", "caption": "Nuclear Power plant in Germany", "id": "1587851096"}, {"image_id": "1297788536", "caption": "Corn on cob and kebabs on table at backyard barbecue", "id": "1297788536"}, {"image_id": "1572528773", "caption": "Sand Dollar Beach Isla Magdalena Baja California Sur Mexico", "id": "1572528773"}, {"image_id": "1766907087", "caption": "Family opening gifts on Christmas", "id": "1766907087"}, {"image_id": "1586684444", "caption": "Farmer In Field As Oat Crop Is Harvested", "id": "1586684444"}, {"image_id": "1859331746", "caption": "A Close up shot of a nurse s hand checking a patient's blood pressure in a hospital room", "id": "1859331746"}, {"image_id": "1590177977", "caption": "Close up of bull figurine", "id": "1590177977"}, {"image_id": "1576746167", "caption": "Portrait of stylish and confident woman in a white wraparound top", "id": "1576746167"}, {"image_id": "1587839045", "caption": "Close up of wallet with Euros sticking out", "id": "1587839045"}, {"image_id": "1570154762", "caption": "View from Hotel Voi Safari Lodge Tsavo East National Park Kenya Africa", "id": "1570154762"}, {"image_id": "1868716490", "caption": "A portrait shot of a senior farmer smiling at camera while kneeling down with a sack of potatoes in a sunny rural field with tractors in background", "id": "1868716490"}, {"image_id": "1571358266", "caption": "ArcheoParc Schnals with Baroque pilgrimage church Unser Frau in background Schnalstal Trentino Alto Adige South Tyrol Italy", "id": "1571358266"}, {"image_id": "1846771259", "caption": "Young man working out on a exercise bicycle and a sports scientist recording his movements in the background on a computer", "id": "1846771259"}, {"image_id": "1576774391", "caption": "A man relaxing by a pool", "id": "1576774391"}, {"image_id": "1587033803", "caption": "Pregnant woman drinking glass of milk", "id": "1587033803"}, {"image_id": "1590338909", "caption": "Close up of young woman's eye", "id": "1590338909"}, {"image_id": "1864614092", "caption": "Close up of a baby girl being fed milk from the bottle by her mother", "id": "1864614092"}, {"image_id": "1590359519", "caption": "View to Garachico at sunset Tenerife Canary Islands Spain", "id": "1590359519"}, {"image_id": "1586727920", "caption": "Portrait Of Senior Couple In Airport Departure Lounge", "id": "1586727920"}, {"image_id": "1588000286", "caption": "Female palm reader with client", "id": "1588000286"}, {"image_id": "1874803349", "caption": "Hospital Radiographer Giving Mammogram To Female Patient", "id": "1874803349"}, {"image_id": "1588007699", "caption": "Close up of senior woman in winter clothing with head back outdoors", "id": "1588007699"}, {"image_id": "1588012247", "caption": "Close up of trophy in businessmen s hands", "id": "1588012247"}, {"image_id": "1590032003", "caption": "Two businessman playing football in an empty office", "id": "1590032003"}, {"image_id": "1844724050", "caption": "Portrait shot of a worker with arms crossed smiling at the camera while standing next to a robotic machinery in a factory", "id": "1844724050"}, {"image_id": "217365818", "caption": "Italian Flag on Top of Soccer Ball", "id": "217365818"}, {"image_id": "1567863545", "caption": "Businessman talking on cell phone on construction site", "id": "1567863545"}, {"image_id": "1766905812", "caption": "Couple enjoying food at festival in Munich", "id": "1766905812"}, {"image_id": "1572524327", "caption": "Family running together on beach", "id": "1572524327"}, {"image_id": "1725908234", "caption": "A man sitting on railings using a laptop and mobile phone", "id": "1725908234"}, {"image_id": "1670341265", "caption": "Mature man pouring wine for friends dining at outdoor restaurant table", "id": "1670341265"}, {"image_id": "1843588463", "caption": "Smiling businesswoman standing with arms crossed and co workers working in the background", "id": "1843588463"}, {"image_id": "1864640774", "caption": "Vertical shot of a girl text messaging on a mobile phone seated on steps on a sunny day", "id": "1864640774"}, {"image_id": "1585876502", "caption": "Stormy weather Lardos beach Lindos Rhodes Greece Europe", "id": "1585876502"}, {"image_id": "1588016168", "caption": "Boat on Lake Geneva with Jet d'Eau Fountain in background Geneva Switzerland", "id": "1588016168"}, {"image_id": "1859339714", "caption": "A happy senior couple throwing autumn leaves at each other in a park", "id": "1859339714"}, {"image_id": "1859339681", "caption": "A portrait shot of a teacher helping a young girl in using a computer in a computer lab", "id": "1859339681"}, {"image_id": "1839578951", "caption": "Horizontal waist up profile shot of a young woman by the photocopier smiling at the camera with a fish tank in the background in office with copy space", "id": "1839578951"}, {"image_id": "1570518947", "caption": "Rear view of young businesswoman walking and pulling luggage", "id": "1570518947"}, {"image_id": "1864613909", "caption": "Young man standing in the bathroom and brushing his teeth along with his son who is looking up at his father", "id": "1864613909"}, {"image_id": "1586672675", "caption": "Winter scene with wind turbines in Flevoland The Netherlands", "id": "1586672675"}, {"image_id": "1277367290", "caption": "Obedient Labrador dog in park waiting for ball to be thrown by female owner", "id": "1277367290"}, {"image_id": "1813180748", "caption": "Close up shot of a shared earphone between a senior couple listening to music", "id": "1813180748"}, {"image_id": "1578942668", "caption": "A woman decorating a Christmas tree", "id": "1578942668"}, {"image_id": "1297732700", "caption": "Family with baby son at home looking at laptop and smiling", "id": "1297732700"}, {"image_id": "1578924830", "caption": "A portrait of a senior man in autumn time", "id": "1578924830"}, {"image_id": "1663818554", "caption": "Teenage girls in sportswear standing in gym", "id": "1663818554"}, {"image_id": "1866109523", "caption": "Close up of a farmer using a digital tablet in the sunny rural barley crop field in summer", "id": "1866109523"}, {"image_id": "1874366513", "caption": "Young man in wetsuit standing on beach with surfboard Close up profile", "id": "1874366513"}, {"image_id": "1484411595", "caption": "Beautiful 3D red bar graph fall down following the arrow", "id": "1484411595"}, {"image_id": "1587987527", "caption": "Businessman using laptop at desk", "id": "1587987527"}, {"image_id": "1590314081", "caption": "Woman biting flower Kleinwalsertal Allgau Germany", "id": "1590314081"}, {"image_id": "1855882781", "caption": "Horizontal shot of a family of four relaxing outdoors at a picnic in the field smiles at the camera", "id": "1855882781"}, {"image_id": "1572527657", "caption": "Old woman walking on the tracks of El Chepe State of Chihuahua Mexico", "id": "1572527657"}, {"image_id": "1587849806", "caption": "View of marina on the isle of Elba", "id": "1587849806"}, {"image_id": "1572524276", "caption": "Man and woman swinging daughter on beach", "id": "1572524276"}, {"image_id": "1586724545", "caption": "Couple Playing On Seesaw In Park Together", "id": "1586724545"}, {"image_id": "1873425302", "caption": "A medium shot of a happy businessman looking up while sitting in front of a computer with paperwork in office", "id": "1873425302"}, {"image_id": "1277238185", "caption": "Young woman in underwear eating healthy apple snack", "id": "1277238185"}, {"image_id": "1590351206", "caption": "Farmhouse and terraced garden at Banyalbufar Mallorca Spain", "id": "1590351206"}, {"image_id": "1590337208", "caption": "View over Death Valley National Park Nevada USA", "id": "1590337208"}, {"image_id": "1578217529", "caption": "A young boy in a recycling centre holding newspapers", "id": "1578217529"}, {"image_id": "1588012919", "caption": "A senior woman touching her neck", "id": "1588012919"}, {"image_id": "1840562369", "caption": "Vertical shot of a businessman with a digital tablet smiling at the camera and a technician with paperwork in a steel roller bearing manufacturing plant", "id": "1840562369"}, {"image_id": "1670340044", "caption": "Father and adult son breakdown with classic car using mobile phone", "id": "1670340044"}, {"image_id": "1590350849", "caption": "View to Valdemossa Mallorca Spain", "id": "1590350849"}, {"image_id": "1587989972", "caption": "A woman in a swimming hat in a pool", "id": "1587989972"}, {"image_id": "1878690434", "caption": "High school student reading sheet music and playing a violin in a music class", "id": "1878690434"}, {"image_id": "1766920431", "caption": "Sisters playing with lip gloss", "id": "1766920431"}, {"image_id": "1859201009", "caption": "Portrait of a sheep and her lamb on moorland", "id": "1859201009"}, {"image_id": "1586693138", "caption": "Portrait smiling middle school student doing homework in study hall", "id": "1586693138"}, {"image_id": "1588025564", "caption": "Two architects wearing hard hats looking at plans", "id": "1588025564"}, {"image_id": "1571652938", "caption": "A woman meditating by a waterfall", "id": "1571652938"}, {"image_id": "1586663876", "caption": "Portrait Of Male Surgeon Wearing Scrubs In Empty Operating Theater", "id": "1586663876"}, {"image_id": "1587144470", "caption": "View of moai statues against blue sky Chile Easter Island Rapa Nui", "id": "1587144470"}, {"image_id": "1578212801", "caption": "A young woman relaxing on an armchair reading a book", "id": "1578212801"}, {"image_id": "1572537398", "caption": "Portrait of a senior man standing on a beach looking out to sea", "id": "1572537398"}, {"image_id": "1860742262", "caption": "Vertical shot of a woman in a wetsuit posing with a surfboard on the beautiful beach", "id": "1860742262"}, {"image_id": "1851481595", "caption": "Wide shot of a beautiful woman checking the broken down automobile by opening the bonnet", "id": "1851481595"}, {"image_id": "1586672609", "caption": "Senior couple with ice skates walking on frozen lake", "id": "1586672609"}, {"image_id": "1766919183", "caption": "Teenagers in boat on lake", "id": "1766919183"}, {"image_id": "1851487418", "caption": "Wide shot of a container ship travelling at the sea", "id": "1851487418"}, {"image_id": "1572524177", "caption": "Father and son playing with ball on beach", "id": "1572524177"}, {"image_id": "1586724572", "caption": "Outdoor Head And Shoulders Portrait Of Attractive Woman", "id": "1586724572"}, {"image_id": "1567864808", "caption": "Bricklayer working at construction site", "id": "1567864808"}, {"image_id": "1869314573", "caption": "Backside of a woman in swimwear sitting at the shore of a sunny beach and looking at the sea", "id": "1869314573"}, {"image_id": "1878688448", "caption": "low angle Close up of a technician holding a cable connected to the laptop in the secured data center", "id": "1878688448"}, {"image_id": "1864643861", "caption": "Low angle view of children sitting side by side inside a tent on a garden lawn", "id": "1864643861"}, {"image_id": "1571343287", "caption": "Couple relaxing on bench in mountains", "id": "1571343287"}, {"image_id": "1587145139", "caption": "Detail toned view of Christmas ornaments with a burning candle in foreground", "id": "1587145139"}, {"image_id": "1586687552", "caption": "Greece Kefalonia Fiskardo view of palm tree and boats in harbour", "id": "1586687552"}, {"image_id": "1576745993", "caption": "A teenage couple playing pool", "id": "1576745993"}, {"image_id": "1567888433", "caption": "Lychee trees with ripe fruit Nosy Be Madagascar", "id": "1567888433"}, {"image_id": "1572538769", "caption": "Two women running on the beach", "id": "1572538769"}, {"image_id": "1567879988", "caption": "Woman with leaf on forehead", "id": "1567879988"}, {"image_id": "1710359954", "caption": "Young boy catching a baseball", "id": "1710359954"}, {"image_id": "1857297539", "caption": "Horizontal shot of a businessman holding up a compass beneath an overpass on a sunny day", "id": "1857297539"}, {"image_id": "1585850141", "caption": "Cookies couple and two kids against white background", "id": "1585850141"}, {"image_id": "1570568159", "caption": "Aerial view of the confluence of the Rio Negro s water and the Solimoes River's water", "id": "1570568159"}, {"image_id": "1590355037", "caption": "View to Ponte Vecchio Florence Italy", "id": "1590355037"}, {"image_id": "1304264864", "caption": "Mature couple on coastal road trip with car breakdown on mobile phone", "id": "1304264864"}, {"image_id": "1297775135", "caption": "Young businesswoman sitting at boardroom table with flipchart looking at camera", "id": "1297775135"}, {"image_id": "1570552856", "caption": "Cocoa beans Gurupa Amazon River Brazil", "id": "1570552856"}, {"image_id": "1840563473", "caption": "Portrait of an businesswoman smiling at the camera holding a digital tablet with colleagues in the background in a manufacturing plant", "id": "1840563473"}, {"image_id": "1578927152", "caption": "Woman holding stones for hot stone therapy", "id": "1578927152"}, {"image_id": "1851471950", "caption": "Low angle shot of a multi generation family walking along the shore of a sunny beach with a kite and a yellow van in the background", "id": "1851471950"}, {"image_id": "1873398467", "caption": "Horizontal waist up shot of a male and a female technician in a factory smiling at the camera", "id": "1873398467"}, {"image_id": "1811161121", "caption": "Boss coordinates with his employees in an office", "id": "1811161121"}, {"image_id": "1710349247", "caption": "Portrait of two children playing together", "id": "1710349247"}, {"image_id": "1277238239", "caption": "Man in underwear reading newspaper on balcony with bowl of strawberries", "id": "1277238239"}, {"image_id": "1590363344", "caption": "Trees along beach Port Launay Marine National Park Mahe Seychelles", "id": "1590363344"}, {"image_id": "1570573556", "caption": "Sun reflecting in Amazon River near Uara Brazil", "id": "1570573556"}, {"image_id": "1855882679", "caption": "Portrait of a smiling young man listening to music on an mp3 player with his hands in the pocket against the wall", "id": "1855882679"}, {"image_id": "1590201389", "caption": "High rises lit up at night Chicago Illinois United States", "id": "1590201389"}, {"image_id": "1587028223", "caption": "View of African woman smiling for the camera", "id": "1587028223"}, {"image_id": "1766919096", "caption": "Teenage boys sunbathing at lake", "id": "1766919096"}, {"image_id": "1852961744", "caption": "Smiling woman sitting at a caf table under a red umbrella", "id": "1852961744"}, {"image_id": "1576771907", "caption": "A woman in a stretch limousine", "id": "1576771907"}, {"image_id": "1840553990", "caption": "Salesman showing customer hatchback of car in a car dealership showroom", "id": "1840553990"}, {"image_id": "1859331773", "caption": "A Close up vertical shot of doctor smiling at a patient while checking a her heartbeat with a stethoscope", "id": "1859331773"}, {"image_id": "1840553969", "caption": "Salesman presenting contract to couple at a table in a car dealership showroom", "id": "1840553969"}, {"image_id": "1590213842", "caption": "Woman driving convertible in countryside", "id": "1590213842"}, {"image_id": "1843610531", "caption": "Vertical waist up profile shot of a young woman gesturing and speaking into a microphone at a conference", "id": "1843610531"}, {"image_id": "1572508340", "caption": "A businesswoman using a laptop", "id": "1572508340"}, {"image_id": "1663479167", "caption": "Mature woman with cattle on farm smiling", "id": "1663479167"}, {"image_id": "1587031439", "caption": "High angle view of miniature bull and bear on dollars", "id": "1587031439"}, {"image_id": "1578939263", "caption": "A young man holding his back", "id": "1578939263"}, {"image_id": "216584000", "caption": "Trees and field in snow covered winter landscape", "id": "216584000"}, {"image_id": "1859336927", "caption": "A medium shot of a smiling teacher looking at a young girl while she peels a potato in a economics class", "id": "1859336927"}, {"image_id": "1766919162", "caption": "Teenagers in inner tubes on lake", "id": "1766919162"}, {"image_id": "1878688688", "caption": "Close up Of a Technician With a Laptop standing In the Data Centre", "id": "1878688688"}, {"image_id": "1590206096", "caption": "Close up of sweet chestnuts", "id": "1590206096"}, {"image_id": "1588026590", "caption": "Young businessman sitting on office floor typing on a laptop", "id": "1588026590"}, {"image_id": "1725713027", "caption": "Twin brothers looking at cell phone in park", "id": "1725713027"}, {"image_id": "1297725734", "caption": "Mature couple looking at map on hike in countryside", "id": "1297725734"}, {"image_id": "1813180544", "caption": "Vertical shot of a young woman looking at the prices of different bicycles at a store", "id": "1813180544"}, {"image_id": "1840562510", "caption": "Engineers discussing about machine parts with a machine in the foreground at a manufacturing plant", "id": "1840562510"}, {"image_id": "1570573439", "caption": "Sunset over Amazon River with the declining sun reflecting in water near Panelas Brazil", "id": "1570573439"}, {"image_id": "1277274902", "caption": "Senior man shopping for flat screen tv in electrical store", "id": "1277274902"}, {"image_id": "1590352244", "caption": "Olive Trees near Arta Mallorca Spain", "id": "1590352244"}, {"image_id": "1297794857", "caption": "cutout of smiling senior couple carrying box with new television inside", "id": "1297794857"}, {"image_id": "1587148982", "caption": "Detail view of one Christmas ornament rolling away from the rest", "id": "1587148982"}, {"image_id": "1857288785", "caption": "Group of men and women enjoying their time and gambling at the roulette table in a casino", "id": "1857288785"}, {"image_id": "1571347796", "caption": "French flag on monument in Berlin Germany", "id": "1571347796"}, {"image_id": "1578224366", "caption": "A mid adult woman holding a plate of clementines", "id": "1578224366"}, {"image_id": "1571662058", "caption": "A man stretching by a pool", "id": "1571662058"}, {"image_id": "1570568132", "caption": "Aerial view of the confluence of the Rio Negro s water and the Solimoes River's water", "id": "1570568132"}, {"image_id": "1587849833", "caption": "View of Scaglieri over Bay of Biodola Island of Elba Tuscany Italy", "id": "1587849833"}, {"image_id": "1859353370", "caption": "Tourist sitting on waterfront in Seville Spain", "id": "1859353370"}, {"image_id": "217365902", "caption": "Swiss Flag on Top of Soccer Ball", "id": "217365902"}, {"image_id": "1297744766", "caption": "Man with book on vacation relaxing on lounger by swimming pool", "id": "1297744766"}, {"image_id": "1586693708", "caption": "Close up high school student conducting scientific experiment at microscope in biology class", "id": "1586693708"}, {"image_id": "1859201162", "caption": "Multi generations of a family hiking on a mountain path", "id": "1859201162"}, {"image_id": "1844727464", "caption": "Vertical shot of a worker holding a bin and smiling at the camera while standing at the production line of a distribution warehouse", "id": "1844727464"}, {"image_id": "1578947252", "caption": "A young couple on a beach", "id": "1578947252"}, {"image_id": "1844764334", "caption": "Vertical portrait of a senior couple hugging each other on hill beside a car with a splendid view of the sea in the background", "id": "1844764334"}, {"image_id": "1844194508", "caption": "Natural sky with clouds scene of sunset or sunrise", "id": "1844194508"}, {"image_id": "1590351203", "caption": "Farmhouse and terraced garden at Banyalbufar Mallorca Spain", "id": "1590351203"}, {"image_id": "216385226", "caption": "Close up of watering can in desert", "id": "216385226"}, {"image_id": "1590352268", "caption": "Lighthouse at Cap de Ses Salines Mallorca Spain", "id": "1590352268"}, {"image_id": "1590200714", "caption": "Cultural Center Goa Ma Bwarat close to Hienghene New Caledonia Overseas Territory of France", "id": "1590200714"}, {"image_id": "1576741841", "caption": "Male florist creating a bouquet of flowers", "id": "1576741841"}, {"image_id": "1844191910", "caption": "Close up shot of athletes hands on gymnast bar with copy space", "id": "1844191910"}, {"image_id": "1859181377", "caption": "Backside of a male rock climber standing on top of a rock with his arms outstretched and looking at the ocean", "id": "1859181377"}, {"image_id": "1571658881", "caption": "A woman meditating by a waterfall", "id": "1571658881"}, {"image_id": "1766903751", "caption": "Father and son sharpening stick near remote lake", "id": "1766903751"}, {"image_id": "1816212954", "caption": "Two people looking at a file with a woman using her cellphone in the foreground", "id": "1816212954"}, {"image_id": "216362525", "caption": "Sun setting in sky with clouds", "id": "216362525"}, {"image_id": "1572527618", "caption": "Humpback Whales Gorda Banks Baja California Sur Mexico", "id": "1572527618"}, {"image_id": "1570161017", "caption": "Ocean at Aldabra Atoll Seychelles", "id": "1570161017"}, {"image_id": "1588027688", "caption": "Two young women shopping for clothes", "id": "1588027688"}, {"image_id": "1590314045", "caption": "Close up of wine bottles", "id": "1590314045"}, {"image_id": "1587110891", "caption": "Teenage girl with cell phone", "id": "1587110891"}, {"image_id": "1847201888", "caption": "A portrait of a smiling woman standing in a jacket with fur hood during a snowfall while her husband stands in the background holding a handful of snow", "id": "1847201888"}, {"image_id": "1578922265", "caption": "A young woman lying in the grass holding a daffodil", "id": "1578922265"}, {"image_id": "1587148145", "caption": "View of American flags hanging between buildings in Larimer Street Denver Colorado USA", "id": "1587148145"}, {"image_id": "1844190608", "caption": "Horizontal shot of a male gymnast sitting on pommel horse and looking into the camera", "id": "1844190608"}, {"image_id": "1868716655", "caption": "A wide vertical shot of an engineer looking at blueprint while standing next to solar panels", "id": "1868716655"}, {"image_id": "1840553852", "caption": "Portrait of smiling woman lying on automobile hood in a car dealership showroom", "id": "1840553852"}, {"image_id": "1663816028", "caption": "Young woman trying on rings at the jewellery store", "id": "1663816028"}, {"image_id": "1576745885", "caption": "A groom carrying his bride", "id": "1576745885"}, {"image_id": "1297787696", "caption": "Studio cutout of baker with loaf of bread smiling at camera on white", "id": "1297787696"}, {"image_id": "1868709305", "caption": "A vertical front view of a smart engineer holding and examining the circuit board under a magnifying lamp", "id": "1868709305"}, {"image_id": "1590362510", "caption": "Portrait of woman in gold bikini", "id": "1590362510"}, {"image_id": "1570573559", "caption": "Riverside of Amazon River near Uara Brazil", "id": "1570573559"}, {"image_id": "1590178526", "caption": "View from Sky Tower to Auckland Yacht Harbor Auckland New Zealand", "id": "1590178526"}, {"image_id": "1813180598", "caption": "Two female teenagers in the trial room with one of them trying out an outfit", "id": "1813180598"}, {"image_id": "1277234750", "caption": "Tourist couple on city break vacation riding in horse drawn carriage", "id": "1277234750"}, {"image_id": "1590164285", "caption": "Group of teenage students cheering in classroom", "id": "1590164285"}, {"image_id": "217368161", "caption": "Country road and single tree Belgium", "id": "217368161"}, {"image_id": "1846771193", "caption": "A sports scientist holding treadmill controller and a digital tablet while observing the running movements of a senior man on the treadmill in a laboratory", "id": "1846771193"}, {"image_id": "1578927197", "caption": "A young family sitting on the grass in autumn time", "id": "1578927197"}, {"image_id": "1586673386", "caption": "Young woman searching for avalanche victims in snow", "id": "1586673386"}, {"image_id": "1587998405", "caption": "High angle view of wine bottle and glasses", "id": "1587998405"}, {"image_id": "1839585641", "caption": "A happy senior couple is working in the yard on a sunny autumn day with the husband hugging his wife and the wife is holding a rake in her hand", "id": "1839585641"}, {"image_id": "1869279071", "caption": "Wide scenic view from northern Kefalonia to Ithaca Greece", "id": "1869279071"}, {"image_id": "1581297476", "caption": "cutout of businessman carrying briefcase and pulling suitcase", "id": "1581297476"}, {"image_id": "1846059977", "caption": "A wide shot of straw bales in a sunny wheat field and a clear blue sky in background", "id": "1846059977"}, {"image_id": "1860761594", "caption": "A horizontal side view of a surveyor looking through a theodolite at a construction site with his arm outstretched", "id": "1860761594"}, {"image_id": "1587140837", "caption": "Portrait of a young woman playing a violin violin built in 1770 by Paulus Castello Genova", "id": "1587140837"}, {"image_id": "1868714462", "caption": "A portrait of scientists in clean suits standing together in a silicon wafer manufacturing laboratory", "id": "1868714462"}, {"image_id": "1590224924", "caption": "Young girl standing on step ladder outdoors", "id": "1590224924"}, {"image_id": "1813181258", "caption": "A formally dressed man speaking to the colleagues at the office", "id": "1813181258"}, {"image_id": "1908116555", "caption": "Surfer falling off surfboard on wave", "id": "1908116555"}, {"image_id": "1571615156", "caption": "Male florist standing amongst the flowers in his shop", "id": "1571615156"}, {"image_id": "1862083217", "caption": "Manager holding a digital tablet and talking to a worker on the production line on the factory floor", "id": "1862083217"}, {"image_id": "1588000748", "caption": "Female pharmacist giving change to customer", "id": "1588000748"}, {"image_id": "216587951", "caption": "Old fashioned carriage near cathedral Seville Spain", "id": "216587951"}, {"image_id": "1710331592", "caption": "Portrait of a climber holding ropes", "id": "1710331592"}, {"image_id": "1590362681", "caption": "Woman in white bikini on beach", "id": "1590362681"}, {"image_id": "1578907034", "caption": "Male and female business colleagues chatting in office building", "id": "1578907034"}, {"image_id": "1571327411", "caption": "Affectionate mid adult couple in park", "id": "1571327411"}, {"image_id": "1587998450", "caption": "Studio shot of pregnant couple hugging", "id": "1587998450"}, {"image_id": "1587843944", "caption": "Studio shot of businesspeople exchanging Euros", "id": "1587843944"}, {"image_id": "1852961627", "caption": "Wide shot of a boy blowing bubbles with bubble wand", "id": "1852961627"}, {"image_id": "1588012868", "caption": "A senior woman applying moisturising cream to her chest", "id": "1588012868"}, {"image_id": "1873346063", "caption": "Focused schoolgirls in private school uniform sharing computer in the computer lab", "id": "1873346063"}, {"image_id": "1297796477", "caption": "cutout of smiling businessman reading newspaper at camera", "id": "1297796477"}, {"image_id": "1297777367", "caption": "Construction worker in hard hat with plant machinery smiling at camera", "id": "1297777367"}, {"image_id": "1572542147", "caption": "A climber celebrating reaching the top", "id": "1572542147"}, {"image_id": "1862115743", "caption": "A portrait of a confident businesswoman having a meeting with her co workers in the conference room", "id": "1862115743"}, {"image_id": "1590344402", "caption": "Young couple looking in gift box at party", "id": "1590344402"}, {"image_id": "1846749125", "caption": "Three cheerful senior couples greeting each other with smiles while having lunch outdoors under a tree beside a beautiful lake", "id": "1846749125"}, {"image_id": "1570219556", "caption": "Benedictine monastery Val Mustair Swiss Alps Grisons Switzerland", "id": "1570219556"}, {"image_id": "1846771241", "caption": "A sports scientist and a senior man are discussing some data on a digital tablet in a laboratory", "id": "1846771241"}, {"image_id": "1869318029", "caption": "Technician looking at the camera while working on a laptop in a server room at a data center", "id": "1869318029"}, {"image_id": "1588006577", "caption": "Young woman talking on a mobile phone", "id": "1588006577"}, {"image_id": "1878694238", "caption": "Full shot of a gym teacher and high school students standing next to a badminton net in a gym", "id": "1878694238"}, {"image_id": "1869318182", "caption": "Extreme wide of a rural farm with a tractor and a combine harvester reaping straw over the field", "id": "1869318182"}, {"image_id": "1868705207", "caption": "A group of business people having a serious discussion during a meeting in the conference room", "id": "1868705207"}, {"image_id": "1578942653", "caption": "Young man using a cashpoint or ATM looking over his shoulder", "id": "1578942653"}, {"image_id": "1586682257", "caption": "cutout Of Male Executive", "id": "1586682257"}, {"image_id": "1766920428", "caption": "Portrait of sisters with sunscreen", "id": "1766920428"}, {"image_id": "1297787516", "caption": "Active men and women exercising on running machines in gym", "id": "1297787516"}, {"image_id": "1576741697", "caption": "A student checking his exam results", "id": "1576741697"}, {"image_id": "1710331607", "caption": "Back view of a climber Close up", "id": "1710331607"}, {"image_id": "1578215039", "caption": "A young woman applying lip balm", "id": "1578215039"}, {"image_id": "216348806", "caption": "Close up of woman xE4 xF3 xBBs hands cupping crystal ball globe with children", "id": "216348806"}, {"image_id": "216111293", "caption": "Clouds in blue sky over barley field", "id": "216111293"}, {"image_id": "1576738436", "caption": "Little girl with birthday presents", "id": "1576738436"}, {"image_id": "1590178505", "caption": "Senior couple hugging on boat", "id": "1590178505"}, {"image_id": "1857297395", "caption": "Vertical Close up shot of a young woman studying with a laptop in the library", "id": "1857297395"}, {"image_id": "1587128840", "caption": "Young friends pulling girls friends on sleds through snow", "id": "1587128840"}, {"image_id": "1587034619", "caption": "Nurse helping doctor into gloves", "id": "1587034619"}, {"image_id": "1587997733", "caption": "Wine bottle and glasses with corkscrew outdoors", "id": "1587997733"}, {"image_id": "1277367173", "caption": "Businessman in bathrobe working on laptop on hotel bed with coffee in room", "id": "1277367173"}, {"image_id": "1840648523", "caption": "A low angle vertical shot of a Truck driver holding a delivery package with both hands on a sunny day while smiling at the camera with a semi truck standing in the background", "id": "1840648523"}, {"image_id": "1590224753", "caption": "Low angle view of couple with flowers and shopping bag outdoors", "id": "1590224753"}, {"image_id": "1277282846", "caption": "Couple choosing decorations and furnishings for new home room sitting on floor beside window with wallpaper and paint samples", "id": "1277282846"}, {"image_id": "1860726947", "caption": "Vertical shot of a businessman standing on a desk in the desert and using binoculars", "id": "1860726947"}, {"image_id": "1766904456", "caption": "Multi ethnic sports fans taking cell phone photographs", "id": "1766904456"}, {"image_id": "1483531473", "caption": "Futuristic self driving smart car", "id": "1483531473"}, {"image_id": "1590046007", "caption": "Portrait of a young woman", "id": "1590046007"}, {"image_id": "1571676473", "caption": "Solar Power Photovoltaic Panels Repperndorf Bavaria Germany", "id": "1571676473"}, {"image_id": "1725722108", "caption": "Elderly man in wheelchair in hallway", "id": "1725722108"}, {"image_id": "1658731673", "caption": "Love letters at a wall of Juliet's house Casa di Giulietta Verona Veneto Italy", "id": "1658731673"}, {"image_id": "1654637597", "caption": "Toy sailboat behind sandcastle on beach near water's edge", "id": "1654637597"}, {"image_id": "1846771217", "caption": "A sports scientist monitoring a runner exercising with a mask on a treadmill in laboratory", "id": "1846771217"}, {"image_id": "1839580550", "caption": "Woman holding a paint roller is looking at samples of blue paint on the living room wall to decide the right shade", "id": "1839580550"}, {"image_id": "1578922268", "caption": "A young woman showing her torso", "id": "1578922268"}, {"image_id": "1846749089", "caption": "A young adult woman taking the mammogram test on a machine in a clinic while being examined by a professional radiologist in a lab coat through a high definition x ray monitor", "id": "1846749089"}, {"image_id": "1571691164", "caption": "Hut at Reine Lofoten Nordland County Norway", "id": "1571691164"}, {"image_id": "1297757156", "caption": "Young affectionate couple hugging indoors", "id": "1297757156"}, {"image_id": "1590315341", "caption": "Businessman figurine on pile of Euro coins", "id": "1590315341"}, {"image_id": "1297732862", "caption": "Young woman at home putting out recycling box on street", "id": "1297732862"}, {"image_id": "1588016957", "caption": "Close up of hotel key on table", "id": "1588016957"}, {"image_id": "1590201947", "caption": "Figurines of family in front of house", "id": "1590201947"}, {"image_id": "1587038573", "caption": "Children in front of fireplace", "id": "1587038573"}, {"image_id": "1586681531", "caption": "cutout Of Builder Looking At Plans", "id": "1586681531"}, {"image_id": "1868703194", "caption": "An engineer showing a machine part to a businessman in the conference room", "id": "1868703194"}, {"image_id": "1852926878", "caption": "Vertical portrait of a seated baby boy in sleeping suit smiles at the camera", "id": "1852926878"}, {"image_id": "1581271661", "caption": "Family in autumn countryside girl with maple leaves at camera", "id": "1581271661"}, {"image_id": "1587646385", "caption": "Combine in a wheat field", "id": "1587646385"}, {"image_id": "1857301826", "caption": "Side view of a businessman talking on a mobile phone in a car on a runway at the airport", "id": "1857301826"}, {"image_id": "1277238212", "caption": "Portrait of loving young couple hugging by sea at dusk", "id": "1277238212"}, {"image_id": "1878688451", "caption": "low angle vertical shot of a technician holding a cable connected to the laptop in the secured data center", "id": "1878688451"}, {"image_id": "1570316255", "caption": "A young man jumping in the snow", "id": "1570316255"}, {"image_id": "1277271518", "caption": "Woman holding mug on motorhome vacation by lake at camera", "id": "1277271518"}, {"image_id": "1586695751", "caption": "Aerial View Of Green English Farm Fields In Dorset", "id": "1586695751"}, {"image_id": "1844196206", "caption": "Mature farmer looking with satisfaction at his cultivated field and having car of wheat after a working day", "id": "1844196206"}, {"image_id": "1576782389", "caption": "Two young friends taking pictures with their mobile phone", "id": "1576782389"}, {"image_id": "1572528848", "caption": "Palm Tree on the beach of La Paz Baja California Sur Mexico", "id": "1572528848"}, {"image_id": "1578933041", "caption": "Portrait of a young woman", "id": "1578933041"}, {"image_id": "1851471914", "caption": "Wide shot of a happy joint family walking in the forest", "id": "1851471914"}, {"image_id": "1866000989", "caption": "Horizontal profile shot of a late businessman catching a passenger training leaning near the door smiles at the camera", "id": "1866000989"}, {"image_id": "1864643801", "caption": "Horizontal shot of a young man sitting on a chair at his desk and looking at a map on his computer screen while typing on a keyboard", "id": "1864643801"}, {"image_id": "1588003523", "caption": "Close up of rabbit in cage", "id": "1588003523"}, {"image_id": "216390821", "caption": "Low angle view of autumn leaves", "id": "216390821"}, {"image_id": "1587998984", "caption": "Two young girls at a lemonade stand", "id": "1587998984"}, {"image_id": "1263325007", "caption": "Clear sparkling water in empty outdoor swimming pool", "id": "1263325007"}, {"image_id": "1587046658", "caption": "Mature couple embracing and having breakfast with selective focus", "id": "1587046658"}, {"image_id": "1578225275", "caption": "A mid adult woman holding a dried leaf", "id": "1578225275"}, {"image_id": "1590070313", "caption": "A young woman looking at a pregnancy test", "id": "1590070313"}, {"image_id": "1588016177", "caption": "Andechs Abbey and Canola field Bavaria Germany", "id": "1588016177"}, {"image_id": "216578084", "caption": "Snowy mountain and blue sky", "id": "216578084"}, {"image_id": "1588027580", "caption": "A young woman standing in the desert", "id": "1588027580"}, {"image_id": "1587838232", "caption": "Close up of woman smiling", "id": "1587838232"}, {"image_id": "1567866551", "caption": "Senior couple working in garden", "id": "1567866551"}, {"image_id": "1578946055", "caption": "A young woman standing by a swimming pool", "id": "1578946055"}, {"image_id": "1590338984", "caption": "Young girl holding two apples", "id": "1590338984"}, {"image_id": "1872076862", "caption": "Businessman and businesswoman talking in corridor beside office window low section side view", "id": "1872076862"}, {"image_id": "1807085786", "caption": "Couple shopping in department store holding two large piles of towels beside shelf faces obscured background", "id": "1807085786"}, {"image_id": "1578935525", "caption": "Young woman spraying perfume on her neck", "id": "1578935525"}, {"image_id": "1844728919", "caption": "Low angle wide shot of colorful flowers with an elderly couple riding a bicycle in the background", "id": "1844728919"}, {"image_id": "1570154861", "caption": "Arab Fort in Zanzibar City Zanzibar Tanzania Africa", "id": "1570154861"}, {"image_id": "1844729081", "caption": "Vertical shot of an elderly couple kissing in a rowboat on a lake with tall grass", "id": "1844729081"}, {"image_id": "1843609127", "caption": "Vertical portrait of a joyous man crouching and scratching a dog's belly on a sunny day", "id": "1843609127"}, {"image_id": "1857301670", "caption": "Vertical shot of a businessman in sunglasses by airplane with a sun flare in the background", "id": "1857301670"}, {"image_id": "1868720813", "caption": "A vertical Close up shot of big snow covered cow parsley stalks in winter", "id": "1868720813"}, {"image_id": "1857297548", "caption": "Vertical shot of an empty desert road on a sunny day", "id": "1857297548"}, {"image_id": "1587991691", "caption": "Female scientist examining liquid in conical flask", "id": "1587991691"}, {"image_id": "217367597", "caption": "St George's Island and canals Venice Italy", "id": "217367597"}, {"image_id": "1588016237", "caption": "Cars driving on highway in snow with lights on", "id": "1588016237"}, {"image_id": "1586676752", "caption": "Cut Out Of Middle Aged Man Riding Exercise Bike In Gym", "id": "1586676752"}, {"image_id": "1840552898", "caption": "Smiling couple listening to a call at a cafe table", "id": "1840552898"}, {"image_id": "1590361871", "caption": "Close up of assorted Euro banknotes", "id": "1590361871"}, {"image_id": "1840648076", "caption": "A worker with a laptop in the foreground supervising the functioning of a robotic arm on an assembly line in a factory", "id": "1840648076"}, {"image_id": "1587997703", "caption": "High angle view of glass of wine on table next to chair", "id": "1587997703"}, {"image_id": "1590363488", "caption": "Woman walking with bicycle at beach Mahe Seychelles", "id": "1590363488"}, {"image_id": "1859144672", "caption": "Vertical shot of a woman hiking with a backpack in the woods and smiling at the camera", "id": "1859144672"}, {"image_id": "1855882814", "caption": "Horizontal shot of a family of four playing hide and seek in the forest with mother covering her eyes as the rest of family runs to hide in the background", "id": "1855882814"}, {"image_id": "1587149063", "caption": "Detail view of a drink with a glass stirrer", "id": "1587149063"}, {"image_id": "1277269652", "caption": "Teenage girlfriends listening to music on MP3 player sharing headphones whilst relaxing and leaning against beach rocks", "id": "1277269652"}, {"image_id": "1297776653", "caption": "Mother giving daughter piggyback ride outdoors smiling at camera", "id": "1297776653"}, {"image_id": "1866109307", "caption": "Back shot of a farmer with a digital tablet standing in the barley crop field in summer", "id": "1866109307"}, {"image_id": "1297750337", "caption": "Beautiful young woman lying on bed painting her nails", "id": "1297750337"}, {"image_id": "1567863554", "caption": "Woman in bathing suit with umbrella on beach", "id": "1567863554"}, {"image_id": "1590164891", "caption": "Low angle view of teenage students smiling in huddle outdoors", "id": "1590164891"}, {"image_id": "1578216614", "caption": "A young woman wearing a winter hat", "id": "1578216614"}, {"image_id": "1868714486", "caption": "A horizontal portrait of a scientist in a clean suit carrying a container in the silicon wafer manufacturing laboratory", "id": "1868714486"}, {"image_id": "1590339869", "caption": "Young girls playing with ribbons", "id": "1590339869"}, {"image_id": "1590179255", "caption": "Seagull in flight North Island New Zealand", "id": "1590179255"}, {"image_id": "1297741649", "caption": "Retired senior couple driving convertible car with woman stretching arms in air", "id": "1297741649"}, {"image_id": "1766903694", "caption": "Brother and sister with sparklers near Christmas tree", "id": "1766903694"}, {"image_id": "1585877873", "caption": "Close up of glacier Isfjorden Spitsbergen Svalbard Norway Europe", "id": "1585877873"}, {"image_id": "1862083229", "caption": "Portrait of happy Technician standing with crossed arms in a solar panel factory", "id": "1862083229"}, {"image_id": "1588016042", "caption": "A young woman posing with a tropical leaf", "id": "1588016042"}, {"image_id": "1586728145", "caption": "Worker With Rejected Produce In Food Processing Warehouse", "id": "1586728145"}, {"image_id": "1581279620", "caption": "Teenage girls singing and playing guitar in band in garage", "id": "1581279620"}, {"image_id": "1587995945", "caption": "A woman drinking a health drink", "id": "1587995945"}, {"image_id": "216586088", "caption": "Young wheat crop growing in green farm field", "id": "216586088"}, {"image_id": "1851485420", "caption": "Wide shot of a couple flying a kite with their children running in the background at a sunny beach", "id": "1851485420"}, {"image_id": "1859339717", "caption": "A close up shot of a young blonde girl performing experiment in a beaker in a school chemistry laboratory", "id": "1859339717"}, {"image_id": "1844767052", "caption": "Happy business employees involved in conversation", "id": "1844767052"}, {"image_id": "1846707947", "caption": "A full length vertical view of busy surgeons performing a serious operation the operating room", "id": "1846707947"}, {"image_id": "1878695708", "caption": "Portrait shot of a female high school student using an electronic equipment with her teacher", "id": "1878695708"}, {"image_id": "1586683694", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586683694"}, {"image_id": "1587849725", "caption": "Scenic view of beach at Biodola Island of Elba Tuscany Italy", "id": "1587849725"}, {"image_id": "1587980651", "caption": "A male nude midsection", "id": "1587980651"}, {"image_id": "1297788479", "caption": "Man on outdoor walk in autumn countryside smiling at camera", "id": "1297788479"}, {"image_id": "1587992732", "caption": "A male nude flexing bicep muscles", "id": "1587992732"}, {"image_id": "1572515939", "caption": "Close up of flowers studio shot", "id": "1572515939"}, {"image_id": "1869311762", "caption": "Woman with a bicycle looking at the view from the beach", "id": "1869311762"}, {"image_id": "1297775321", "caption": "Smiling young man riding on bike along winter beach", "id": "1297775321"}, {"image_id": "1590161888", "caption": "Waves from storm in the Bay of Biodola with view to Scalieri Elba Tuscany Italy", "id": "1590161888"}, {"image_id": "1581270725", "caption": "Smiling woman pushing shopping trolley in supermarket car park", "id": "1581270725"}, {"image_id": "1865986913", "caption": "Horizontal shot of two children jumping off a jetty into the lake at sunset as the family watches on", "id": "1865986913"}, {"image_id": "1585876499", "caption": "Stormy weather Lardos beach Lindos Rhodes Greece Europe", "id": "1585876499"}, {"image_id": "1586695880", "caption": "Aerial View Of Tractors Baling Hay In Field", "id": "1586695880"}, {"image_id": "1840559657", "caption": "Businessman and worker with clipboard and digital tablet in front of steel tubes discussing business", "id": "1840559657"}, {"image_id": "1586682335", "caption": "Cut Out Of Male Executive With Hands In Pockets", "id": "1586682335"}, {"image_id": "1862131418", "caption": "A portrait shot of a surveyor from backside looking at a co worker through a theodolite at a construction site", "id": "1862131418"}, {"image_id": "1869318137", "caption": "Tractor with a straw baling machine at a rural field", "id": "1869318137"}, {"image_id": "1586693165", "caption": "Smiling high school student cooking pasta in home economics class", "id": "1586693165"}, {"image_id": "1588002695", "caption": "Two young girls at a lemonade stand", "id": "1588002695"}, {"image_id": "1570236731", "caption": "Sundial at Wieskirche Pilgrimage Church of the Scourged Saviour Steingaden Bavaria Germany", "id": "1570236731"}, {"image_id": "1588020011", "caption": "Snow covered street in woods", "id": "1588020011"}, {"image_id": "1571333321", "caption": "View to Stranda Geirangerfjord More og Romsdal Sunnmore region Norway", "id": "1571333321"}, {"image_id": "1859352167", "caption": "A portrait shot of a young boy writing notes while working on a model airplane in a class", "id": "1859352167"}, {"image_id": "1586681459", "caption": "cutout Of Senior Male Doctor Wearing White Coat", "id": "1586681459"}, {"image_id": "1590220082", "caption": "View of rooftops Zurich Canton of Zurich Switzerland", "id": "1590220082"}, {"image_id": "1722076436", "caption": "Portrait of teenagers on skiing holiday Tirol Austria Europe", "id": "1722076436"}, {"image_id": "1590024188", "caption": "A portrait of a senior woman detail of eyes", "id": "1590024188"}, {"image_id": "1590051182", "caption": "A mature woman drinking a bottle of water", "id": "1590051182"}, {"image_id": "1766904309", "caption": "Young man with Swiss flag painted on face", "id": "1766904309"}, {"image_id": "217372028", "caption": "Lavender field Provence France", "id": "217372028"}, {"image_id": "1864613948", "caption": "Little girl and boy peeking from under the covers while their parents are sleeping on the bed in the background", "id": "1864613948"}, {"image_id": "216350201", "caption": "Sun setting in vibrant sky", "id": "216350201"}, {"image_id": "1304263292", "caption": "Sun setting on farm over wheat fields", "id": "1304263292"}, {"image_id": "1590216674", "caption": "Woman looking at boyfriend sleeping in grass outdoors", "id": "1590216674"}, {"image_id": "1846063760", "caption": "A shot of a bright sun shining in a blue sky with clouds", "id": "1846063760"}, {"image_id": "1878869633", "caption": "A young adult Gym teacher in a school gymnasium demonstrating rope climbing while the students raising their hands in excitement with an intent to volunteer", "id": "1878869633"}, {"image_id": "1586691617", "caption": "Exuberant boy jumping for joy over sand dune beach hill", "id": "1586691617"}, {"image_id": "1590347945", "caption": "Detail of businessmen passing the baton", "id": "1590347945"}, {"image_id": "1709253527", "caption": "A young boy climbing on wooden beam", "id": "1709253527"}, {"image_id": "1585878791", "caption": "Northwestern part of Spitsbergen Svalbard Norway Europe", "id": "1585878791"}, {"image_id": "1572542342", "caption": "A man standing on a boardwalk", "id": "1572542342"}, {"image_id": "1578212951", "caption": "Portrait of a young woman looking away smiling", "id": "1578212951"}, {"image_id": "1878694253", "caption": "Home economics teacher and a high school student smiling at each other while cooking pasta", "id": "1878694253"}, {"image_id": "1590203216", "caption": "Close up of water meter", "id": "1590203216"}, {"image_id": "1869318152", "caption": "Wide shot of a rural farm with a combine harvester at the horizon", "id": "1869318152"}, {"image_id": "1586687465", "caption": "Greece Kefalonia Fiskardo view of yachts and sunny coastal harbour", "id": "1586687465"}, {"image_id": "1590053558", "caption": "Businessman and businesswoman traveling waiting in airport or station", "id": "1590053558"}, {"image_id": "1862081366", "caption": "A businessman and a businesswoman talking while sitting in a private jet", "id": "1862081366"}, {"image_id": "1567888442", "caption": "Ylang ylang plantation at Nosy Be Madagascar", "id": "1567888442"}, {"image_id": "1876091939", "caption": "Container ship moored at commercial dock", "id": "1876091939"}, {"image_id": "1590212228", "caption": "Portrait of group of businesswomen", "id": "1590212228"}, {"image_id": "1570362902", "caption": "Statues at Wat Arun Temple Temple of the Dawn Bangkok Thailand", "id": "1570362902"}, {"image_id": "1865999762", "caption": "Horizontal shot of four business colleagues walking side by side in a colonnade on a sunny day", "id": "1865999762"}, {"image_id": "1572544502", "caption": "A mother and son coming home with the shopping", "id": "1572544502"}, {"image_id": "1571589998", "caption": "A man sitting by the sea", "id": "1571589998"}, {"image_id": "1571353205", "caption": "Facade in Ardez Lower Engadine Grisons Switzerland", "id": "1571353205"}, {"image_id": "1587033008", "caption": "Blurred view of carousel lit up at night Covent Garden London United Kingdom", "id": "1587033008"}, {"image_id": "1855878152", "caption": "Young African female athlete with gold medal holding up a relay baton in her raised hand at an athletics event on a bright sunny day at the track", "id": "1855878152"}, {"image_id": "1578217538", "caption": "Portrait of a mid adult woman holding a dried leaf", "id": "1578217538"}, {"image_id": "1710349235", "caption": "Young couple kissing side view", "id": "1710349235"}, {"image_id": "1586682935", "caption": "Senior Male Judging Chrysanthemums At Flower Show", "id": "1586682935"}, {"image_id": "1572515942", "caption": "Face made of vegetables against white background", "id": "1572515942"}, {"image_id": "1587990191", "caption": "A young woman using a cash machine", "id": "1587990191"}, {"image_id": "216388724", "caption": "Low angle view of autumn leaves", "id": "216388724"}, {"image_id": "1846008221", "caption": "A side profile shot of a businessman with Bluetooth headset setting up gps device in his car", "id": "1846008221"}, {"image_id": "1869911621", "caption": "A Close up portrait shot of a businessman looking at a computer in office", "id": "1869911621"}, {"image_id": "1588020902", "caption": "Family in pajamas giving gifts on Christmas", "id": "1588020902"}, {"image_id": "1572529463", "caption": "Tracks and tunnel in Copper Canyon Chihuahua Mexico", "id": "1572529463"}, {"image_id": "1725714131", "caption": "Twin brothers holding hands in park smiling", "id": "1725714131"}, {"image_id": "1839587576", "caption": "Horizontal shot of a young woman working on a laptop over a makeshift desk of boxes in an unfurnished office with copy space", "id": "1839587576"}, {"image_id": "1862067401", "caption": "Low angle shot of bright yellow rapeseeds in a sunny field", "id": "1862067401"}, {"image_id": "1852961699", "caption": "Boy sitting on a merry go round at a playground", "id": "1852961699"}, {"image_id": "1587857330", "caption": "View of a beach at Hacha falls Canaima National Park Venezuela", "id": "1587857330"}, {"image_id": "1578907067", "caption": "Two men playing basketball on a green urban outside court", "id": "1578907067"}, {"image_id": "1816751103", "caption": "Elevated view of a businessman sitting in car beside two opposing arrow signs on road looking at map", "id": "1816751103"}, {"image_id": "1587998798", "caption": "A young boy on a scooter", "id": "1587998798"}, {"image_id": "1878688514", "caption": "Surfer With a prosthetic Leg Sitting On the Beach", "id": "1878688514"}, {"image_id": "1663816598", "caption": "Portrait man lying in bed", "id": "1663816598"}, {"image_id": "1572527837", "caption": "Copper Canyon near Posada Barracas Chihuahua Mexico", "id": "1572527837"}, {"image_id": "1590160451", "caption": "Communications tower under blue sky", "id": "1590160451"}, {"image_id": "1587141173", "caption": "Low angle view of a watering can in a garden", "id": "1587141173"}, {"image_id": "1844724245", "caption": "Vertical shot of a supervisor inspecting boxes at the production line of a distribution warehouse", "id": "1844724245"}, {"image_id": "1859233832", "caption": "A vertical view of skilled Bricklayer adjusting the alignment while lying a brick wall with the use of a trowel", "id": "1859233832"}, {"image_id": "1844196338", "caption": "Close up shot of woman holding apple a nutrition concept", "id": "1844196338"}, {"image_id": "1844196344", "caption": "Close up shot of woman holding apple and banana a nutrition concept", "id": "1844196344"}, {"image_id": "1817411715", "caption": "Backside of a group of friends in swimwear walking along the beach holding surfboards", "id": "1817411715"}, {"image_id": "216389177", "caption": "Yellow autumn leaves over lake", "id": "216389177"}, {"image_id": "1590034337", "caption": "Senior couple embracing next to palm tree at the beach", "id": "1590034337"}, {"image_id": "1277256926", "caption": "Home diy woman cutting plank of wood on workbench with saw renovating improving and decorating home", "id": "1277256926"}, {"image_id": "1590164834", "caption": "Teenage couple reading book in library", "id": "1590164834"}, {"image_id": "1578907016", "caption": "A woman holding an apple", "id": "1578907016"}, {"image_id": "1570348109", "caption": "Demolition in Worthsee Bavaria Germany", "id": "1570348109"}, {"image_id": "1578904220", "caption": "Portrait of a male athlete resting sitting on a running track", "id": "1578904220"}, {"image_id": "217368548", "caption": "Detail view of a flag marking a hole in a golf course", "id": "217368548"}, {"image_id": "1578932780", "caption": "A man sitting at his laptop having a break", "id": "1578932780"}, {"image_id": "1578952937", "caption": "A couple paddling in a lake", "id": "1578952937"}, {"image_id": "1571615228", "caption": "Portrait of a young woman", "id": "1571615228"}, {"image_id": "1572515957", "caption": "Bell peppers against white background", "id": "1572515957"}, {"image_id": "1862115431", "caption": "A rear view of a Pensive businessman with his arms crossed looking out through a glass window in a conference room", "id": "1862115431"}, {"image_id": "1839585533", "caption": "A senior businessman in a wheelchair is waiting for his appointment He has a briefcase containing important paperwork placed on his lap", "id": "1839585533"}, {"image_id": "1586695952", "caption": "Aerial View Of Green English Farm Fields In Herefordshire", "id": "1586695952"}, {"image_id": "1859347283", "caption": "A wide shot of an early green wheat field with a tree in background", "id": "1859347283"}, {"image_id": "1572535175", "caption": "A young woman sitting by a pool", "id": "1572535175"}, {"image_id": "1816211070", "caption": "Side view of three new business partners working in empty office using laptop on file box", "id": "1816211070"}, {"image_id": "1857301673", "caption": "Horizontal shot of a businesswoman by airplane on a runway at the airport", "id": "1857301673"}, {"image_id": "1816211385", "caption": "A happy couple holding keys with selective focus on the keys", "id": "1816211385"}, {"image_id": "1590222950", "caption": "Underwater shot of person upside down in kayak", "id": "1590222950"}, {"image_id": "1572527741", "caption": "Sunrise near Los Islotes Baja California Sur Mexico", "id": "1572527741"}, {"image_id": "1297798697", "caption": "Smiling father and son in showroom sitting in new car at camera", "id": "1297798697"}, {"image_id": "216388823", "caption": "Low angle view of autumn leaves", "id": "216388823"}, {"image_id": "1277240147", "caption": "Girl in sunglasses making snowballs on winter vacation at camera", "id": "1277240147"}, {"image_id": "1571658974", "caption": "A mature businessman waiting in the lobby of a modern office building checking his watch", "id": "1571658974"}, {"image_id": "1578904325", "caption": "Portrait of young African jumping and hanging from a basketball ring", "id": "1578904325"}, {"image_id": "1576745927", "caption": "mother hugging son sitting on a jetty", "id": "1576745927"}, {"image_id": "1869314774", "caption": "Medium shot of a father and son standing in an autumn park and looking at the camera", "id": "1869314774"}, {"image_id": "1852938164", "caption": "Horizontal shot of a boy lying on stomach with a book on bed smiles at the camera", "id": "1852938164"}, {"image_id": "1670340077", "caption": "Female students chatting as they meet studying in coffee cafe working at table", "id": "1670340077"}, {"image_id": "1862081474", "caption": "Business employees discussing while walking away from the plane", "id": "1862081474"}, {"image_id": "1572528638", "caption": "Gray Whale Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528638"}, {"image_id": "1844723918", "caption": "Vertical portrait of a worker with a bin tray looking at the camera while standing at the production line of a distribution warehouse", "id": "1844723918"}, {"image_id": "1590178490", "caption": "Carving on Maori War Canoe Auckland War Memorial Museum New Zealand differential focus", "id": "1590178490"}, {"image_id": "1586684555", "caption": "Female Fishmonger In Fresh Fish Department Of Supermarket", "id": "1586684555"}, {"image_id": "1844766194", "caption": "Family trying to catch fish with fishing nets in the stream below while sitting on a small wooden bridge", "id": "1844766194"}, {"image_id": "1859142956", "caption": "Full shot of a happy couple playing along the shore of a sunny beach", "id": "1859142956"}, {"image_id": "1855878092", "caption": "Close up of a woman looking sideways and smiling on a bright sunny day at the beach", "id": "1855878092"}, {"image_id": "1588020491", "caption": "Child with backpack walking on snowy street", "id": "1588020491"}, {"image_id": "1869908141", "caption": "Overhead shot a businessman and a businesswoman with hands crossed in center looking up at the camera surrounded by their colleagues in a ring formation", "id": "1869908141"}, {"image_id": "1844727554", "caption": "Vertical shot of a fashion designer and a student working on a garment worn by a mannequin", "id": "1844727554"}, {"image_id": "1590160706", "caption": "Scenic view of Dischma Brook view out of Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160706"}, {"image_id": "1590201410", "caption": "Traffic on Michigan Avenue Northbound at night Chicago Illinois United States", "id": "1590201410"}, {"image_id": "1590076868", "caption": "Portrait of young woman with brown hair", "id": "1590076868"}, {"image_id": "1817410335", "caption": "Angled Close up portrait of an active senior man adjusting strap on cycling helmet from low angle", "id": "1817410335"}, {"image_id": "1722076430", "caption": "Teenagers walking on skiing holiday Tirol Austria Europe", "id": "1722076430"}, {"image_id": "1572524393", "caption": "Portrait of young girl smiling", "id": "1572524393"}, {"image_id": "1865894960", "caption": "Business people with luggage waiting in line at the airport terminal", "id": "1865894960"}, {"image_id": "1578922244", "caption": "A young mother kissing her baby tenderly on the cheek", "id": "1578922244"}, {"image_id": "1572529508", "caption": "The Government Palace of Chihuahua Palacio de Gobierno de Chihuahua Chihuahua Mexico", "id": "1572529508"}, {"image_id": "1590361094", "caption": "Close up of assorted US paper currency", "id": "1590361094"}, {"image_id": "1587999299", "caption": "Close up of woman with messy hair and hands up near face", "id": "1587999299"}, {"image_id": "1874803661", "caption": "Surgeon with patient in operating room low angle view", "id": "1874803661"}, {"image_id": "1843588685", "caption": "Smiling middle aged farmer standing on his ploughed field with his hands in his pockets and a tractor and a plough in the background", "id": "1843588685"}, {"image_id": "1590361877", "caption": "Close up of assorted Euro banknotes", "id": "1590361877"}, {"image_id": "1567877690", "caption": "View to Steingaden Bavaria Germany Alps", "id": "1567877690"}, {"image_id": "1590060062", "caption": "A middle aged woman holding a pot of moisturising cream", "id": "1590060062"}, {"image_id": "1297781633", "caption": "Engineers looking at plans standing next to large solar panels", "id": "1297781633"}, {"image_id": "1587982880", "caption": "Close up of two men toasting with wine at restaurant", "id": "1587982880"}, {"image_id": "1586727029", "caption": "Radiographer With Female Patient In Hospital X Ray Department", "id": "1586727029"}, {"image_id": "1859328587", "caption": "A Close up vertical shot of a female technician smiling at camera while holding a microscope in a laboratory", "id": "1859328587"}, {"image_id": "1572528857", "caption": "Red Chairs La Paz Baja California Sur Mexico", "id": "1572528857"}, {"image_id": "1578942629", "caption": "A woman wearing boxing gloves on a beach", "id": "1578942629"}, {"image_id": "1581272681", "caption": "Businesswoman at departure time board in airport lounge with ticket", "id": "1581272681"}, {"image_id": "1570311734", "caption": "View over rooftops of Taormina Sicily Italy", "id": "1570311734"}, {"image_id": "1587811595", "caption": "Close up of flag of Brazil", "id": "1587811595"}, {"image_id": "1578226856", "caption": "A man with a suitcase walking in a desert", "id": "1578226856"}, {"image_id": "1587647738", "caption": "View of ocean waves breaking", "id": "1587647738"}, {"image_id": "1590160397", "caption": "Donkey grazing in field Dischma Valley Davos Graubuenden Grisons Switzerland", "id": "1590160397"}, {"image_id": "1817410404", "caption": "Low angle shot of a businessman walking away from security barrier reflected on the floor with copy space", "id": "1817410404"}, {"image_id": "1277221640", "caption": "Multi generation family doing yard work clearing autumn leaves", "id": "1277221640"}, {"image_id": "1586684645", "caption": "Customer Pushing Shopping Away From Supermarket Checkout", "id": "1586684645"}, {"image_id": "1852963982", "caption": "A smiling waiter preparing coffee in a coffee shop", "id": "1852963982"}, {"image_id": "1297775132", "caption": "Detail of businesswoman using flip chart in office presentation", "id": "1297775132"}, {"image_id": "1844190503", "caption": "Horizontal shot of a young girl holding decorated Easter egg in a green field with a smile on her face", "id": "1844190503"}, {"image_id": "1840648001", "caption": "Worker holding the final packaged box picked up from the assembly line in a large factory", "id": "1840648001"}, {"image_id": "1859323415", "caption": "A Close up shot of a young girl studying a model wind turbine in a classroom", "id": "1859323415"}, {"image_id": "1868723375", "caption": "A vertical shot of a happy blonde woman pointing and showing something to her husband while standing in a wildflower field with bicycles", "id": "1868723375"}, {"image_id": "1587835073", "caption": "Scenic view of city Rome Italy", "id": "1587835073"}, {"image_id": "1665808418", "caption": "Couple sitting on floor next to lit candles", "id": "1665808418"}, {"image_id": "1859339810", "caption": "A low angle portrait shot of a happy senior man pulling his wife's hand while walking in an autumn park", "id": "1859339810"}, {"image_id": "1869046529", "caption": "Horizontal three quarter length shot of a woman having strawberries in breakfast smiles at the camera", "id": "1869046529"}, {"image_id": "1277257643", "caption": "Office worker smiling at camera with time clock on orange wall in casual clothes", "id": "1277257643"}, {"image_id": "1862067368", "caption": "Extreme wide shot of a couple holding hands and walking along the shore of a beach with waves", "id": "1862067368"}, {"image_id": "1859339795", "caption": "A vertical shot of a happy senior woman sitting under a tree and smiling at camera while reading a book in an autumn park", "id": "1859339795"}, {"image_id": "1766907642", "caption": "Businessman walking in snowshoes in snow on mountain", "id": "1766907642"}, {"image_id": "1859143175", "caption": "Medium shot of an elderly couple fishing at the shore of a sunny beach", "id": "1859143175"}, {"image_id": "1590316619", "caption": "Woman with arm on head at beach", "id": "1590316619"}, {"image_id": "1297787702", "caption": "Environmental energy concept of house and wind turbine on glass globe", "id": "1297787702"}, {"image_id": "1587996986", "caption": "Wine and cheese with pitcher on table outdoors", "id": "1587996986"}, {"image_id": "1851405413", "caption": "Horizontal waist up portrait of a young businessman with a marker and businesswoman with a folder by the whiteboard smile at the camera", "id": "1851405413"}, {"image_id": "1570518938", "caption": "Businessman and businesswoman in conversation on coffee break", "id": "1570518938"}, {"image_id": "1840649159", "caption": "A low angle shot of a smiling farmer standing in front of tractor and a plough in the field", "id": "1840649159"}, {"image_id": "1587982586", "caption": "A woman on a beach", "id": "1587982586"}, {"image_id": "1571686652", "caption": "Part of a sailing vessel Ruegen Mecklenburg Western Pommerania Germany", "id": "1571686652"}, {"image_id": "1844765969", "caption": "Young couple using a digital tablet sitting below a tree in an urban park", "id": "1844765969"}, {"image_id": "1297800470", "caption": "Family running down sand dune on beach laughing and holding hands", "id": "1297800470"}, {"image_id": "1277238260", "caption": "Young couple wearing underwear hugging on outdoor balcony at camera", "id": "1277238260"}, {"image_id": "1581272720", "caption": "Girl camping lying inside tent in garden reading book", "id": "1581272720"}, {"image_id": "1590338975", "caption": "Young girls sitting outdoors portrait", "id": "1590338975"}, {"image_id": "1570357136", "caption": "Fish at the market at Lake Tonle Sap Siem Reap Cambodia", "id": "1570357136"}, {"image_id": "1590359279", "caption": "Scenic view of Puerto de la Cruz Tenerife Canary Islands Spain", "id": "1590359279"}, {"image_id": "1844723921", "caption": "Vertical portrait of a worker smiling at the camera while packing boxes at the production line of a distribution warehouse", "id": "1844723921"}, {"image_id": "1586666948", "caption": "Crest of hill on open road in arid landscape with electricity pylons", "id": "1586666948"}, {"image_id": "1766903850", "caption": "Nurse helping boy in hospital while family visits", "id": "1766903850"}, {"image_id": "1588002614", "caption": "Businessman standing at office window drinking a cup of coffee", "id": "1588002614"}, {"image_id": "1297797374", "caption": "Mid air shot of active senior man in fitness clothing exercising", "id": "1297797374"}, {"image_id": "1868703392", "caption": "A view of business people through a glass door reviewing paperwork during a meeting in the conference room", "id": "1868703392"}, {"image_id": "1859176931", "caption": "Portrait shot of an engineer standing next to the turbine engine of a passenger jet at a hangar", "id": "1859176931"}, {"image_id": "1578906944", "caption": "Portrait of a young girl", "id": "1578906944"}, {"image_id": "1578904427", "caption": "A man wearing a headset typing on a laptop", "id": "1578904427"}, {"image_id": "1572459989", "caption": "A businessman in a boardroom", "id": "1572459989"}, {"image_id": "1572544511", "caption": "A surfer standing on a beach", "id": "1572544511"}, {"image_id": "1766920338", "caption": "Young girls sunbathing in bikinis", "id": "1766920338"}, {"image_id": "1876240058", "caption": "Male Brewery Worker Quality Checking Beer Sample", "id": "1876240058"}, {"image_id": "1839588560", "caption": "Horizontal portrait of a young girl giving her mother a pineapple in the kitchen with dad in the background", "id": "1839588560"}, {"image_id": "1868718428", "caption": "A blurry Close up vertical shot of a businessman watching porn on an office computer", "id": "1868718428"}, {"image_id": "1588012475", "caption": "A young woman sitting at a table", "id": "1588012475"}, {"image_id": "1843609325", "caption": "Full length rear view of young people graduating in caps and gowns walking between pillars in the university campus on a sunny day", "id": "1843609325"}, {"image_id": "1839589148", "caption": "Portrait of four young happy surfers in car looking at the camera", "id": "1839589148"}, {"image_id": "1868723474", "caption": "A medium shot of a happy family talking with each other while riding bicycles in a wildflower field", "id": "1868723474"}, {"image_id": "1297794818", "caption": "Cut out of active senior couple in cycle helmets on bike ride kissing", "id": "1297794818"}, {"image_id": "1839580664", "caption": "Handsome young businessman in a black shirt is looking up and smiling in the office corridor", "id": "1839580664"}, {"image_id": "1297783868", "caption": "Young wheat crop growing in green farm field with single tree", "id": "1297783868"}, {"image_id": "1587654083", "caption": "Low angle view of the Loretto chapel Santa Fe New Mexico USA", "id": "1587654083"}, {"image_id": "1873343543", "caption": "Brewery Worker holding the glass up and Checking quality of a Beer Sample", "id": "1873343543"}, {"image_id": "1851485408", "caption": "Medium shot of a man carrying a woman on his back while walking along the shore of a sunny beach", "id": "1851485408"}, {"image_id": "1578935339", "caption": "A senior woman looking angry", "id": "1578935339"}, {"image_id": "1571686625", "caption": "Beach of Ruegen Mecklenburg Western Pommerania Germany", "id": "1571686625"}, {"image_id": "1847201756", "caption": "A vertical view of a smiling student with safety glasses bends down to have a closer look while working on a drill machine in selective focus", "id": "1847201756"}, {"image_id": "1578904211", "caption": "A woman relaxing by a pool", "id": "1578904211"}, {"image_id": "1587849458", "caption": "Close up of nettle plant", "id": "1587849458"}, {"image_id": "217373369", "caption": "Sand dune Namib desert Namibia Africa", "id": "217373369"}, {"image_id": "1570300715", "caption": "View over Lake Koenigssee in winter with mountain panorama Berchtesgaden Bavaria Germany", "id": "1570300715"}, {"image_id": "1587031925", "caption": "View of two men in the bathroom standing at the mirror", "id": "1587031925"}, {"image_id": "1587987866", "caption": "Mother and daughter consulting letting agent about property to let", "id": "1587987866"}, {"image_id": "1297777496", "caption": "Alpine landscape with snow covered mountains and woods", "id": "1297777496"}, {"image_id": "1766922750", "caption": "Young boy with four leaf clover jumping in air", "id": "1766922750"}, {"image_id": "1571351792", "caption": "Group of female soccer players with American Flag walking on field", "id": "1571351792"}, {"image_id": "1586687489", "caption": "Greece Kefalonia Fiskardo view of lamp and yachts in sunny coastal harbour", "id": "1586687489"}, {"image_id": "1665809852", "caption": "Woman standing in front of man laying on bed", "id": "1665809852"}, {"image_id": "1590183797", "caption": "Close up of Vanilla Silique plant Lifou Island Loyalty Islands New Caledonia Overseas Territory of France", "id": "1590183797"}, {"image_id": "216391103", "caption": "Low angle view of green leaves", "id": "216391103"}, {"image_id": "1590222221", "caption": "Couple drinking white wine and smiling at each other indoors", "id": "1590222221"}, {"image_id": "1277237165", "caption": "Woman in bathrobe with cosmetics through bathroom shutters at camera", "id": "1277237165"}, {"image_id": "1670342030", "caption": "Mother washing daughter with sponge in outdoor bath", "id": "1670342030"}, {"image_id": "1862083157", "caption": "Wide shot of a Beekeeper checking honey on the beehive frame in the field full of flowers", "id": "1862083157"}, {"image_id": "1297787645", "caption": "Studio cutout of wind turbine against white background", "id": "1297787645"}, {"image_id": "1587997640", "caption": "Man holding bottle of wine and wine glasses outdoors", "id": "1587997640"}, {"image_id": "1587998810", "caption": "A middle aged woman holding a glass of milk", "id": "1587998810"}, {"image_id": "1873415612", "caption": "Horizontal shot of a senior man assembling a jigsaw puzzle at the table encouraged by a standing home caregiver besides", "id": "1873415612"}, {"image_id": "1766922780", "caption": "Boy in Indian costume jumping in air", "id": "1766922780"}, {"image_id": "1766907072", "caption": "Family in pajamas giving gifts on Christmas", "id": "1766907072"}, {"image_id": "1586659847", "caption": "Two women holding shopping bags Stuttgart Baden Wurttemberg Germany", "id": "1586659847"}, {"image_id": "1590164867", "caption": "Teenage couple studying with laptop in library", "id": "1590164867"}, {"image_id": "1576772033", "caption": "Portrait young woman in bikini top", "id": "1576772033"}, {"image_id": "1873425524", "caption": "A vertical shot of a businesswoman looking up at a clock in office", "id": "1873425524"}, {"image_id": "1859143010", "caption": "Wide shot of a multi generation family at the back of a car beside a lake", "id": "1859143010"}, {"image_id": "1297778846", "caption": "Doctor examining young boy listening to breathing with stethoscope", "id": "1297778846"}, {"image_id": "1590214670", "caption": "Autumn leaves on park bench", "id": "1590214670"}, {"image_id": "1572525116", "caption": "Helmcken Falls Wells Gray Provincial Park British Columbia Canada", "id": "1572525116"}, {"image_id": "1859177009", "caption": "Engineer using a digital tablet with a coworker inspecting the wiring on the ceiling of an empty passenger jet in the background", "id": "1859177009"}, {"image_id": "1570522844", "caption": "Autumn forest and Wuerm River Starnberg Bavaria Germany", "id": "1570522844"}, {"image_id": "1590034391", "caption": "A grandmother and her granddaughters looking at a Christmas pudding", "id": "1590034391"}, {"image_id": "1587845741", "caption": "Coffee grinds in a cup", "id": "1587845741"}, {"image_id": "216583946", "caption": "Person walking next to sheep grazing in winter field", "id": "216583946"}, {"image_id": "1862081327", "caption": "Businesswoman and Businessman holding a digital tablet and discussing in front of a private jet", "id": "1862081327"}, {"image_id": "1843605638", "caption": "A blond woman patient getting her eyes checked by an ophthalmologist using an ophthalmoscope", "id": "1843605638"}, {"image_id": "1847201714", "caption": "A vertical view of Hay being harvested into rolled up straw bales on a vast farm field against the backdrop of a bright blue sky and massive white clouds", "id": "1847201714"}, {"image_id": "1570572263", "caption": "Aerial View of Harbor of Manaus Amazonas Amazon River Brazil", "id": "1570572263"}, {"image_id": "1587997718", "caption": "Close up of corkscrew in wine cork", "id": "1587997718"}, {"image_id": "1670341232", "caption": "Active senior couple wearing sports clothing smiling at camera", "id": "1670341232"}, {"image_id": "1865999675", "caption": "Horizontal shot of a mature couple sitting atop a rock overlooking the Atlantic ocean on a sunny day with copy space", "id": "1865999675"}, {"image_id": "1586724737", "caption": "Business Meeting Around Table In Modern Office", "id": "1586724737"}, {"image_id": "217368896", "caption": "Tropical beach Bora Bora French Polynesia", "id": "217368896"}, {"image_id": "1844731853", "caption": "Medium shot of a happy elderly man riding a bicycle in a meadow full of wildflowers", "id": "1844731853"}, {"image_id": "217365599", "caption": "Banknotes shaped like boats", "id": "217365599"}, {"image_id": "1813181399", "caption": "Vertical shot of a woman holding a set of paper sheets stands beside a white board and some chairs", "id": "1813181399"}, {"image_id": "1571333363", "caption": "Bridge near Finnsnes Troms Norway", "id": "1571333363"}, {"image_id": "1587990047", "caption": "Close up of male scientist with co worker in background", "id": "1587990047"}, {"image_id": "1590179369", "caption": "Life boat on side of passenger ship", "id": "1590179369"}, {"image_id": "1665809579", "caption": "Close up of female couple with one looking at the other", "id": "1665809579"}, {"image_id": "1873340672", "caption": "Railway crossing the road while the vehicles wait for it to pass by", "id": "1873340672"}, {"image_id": "1586687522", "caption": "Greece Kefalonia Assos view of sunny coastal village on hillside", "id": "1586687522"}, {"image_id": "1852924394", "caption": "Young female mechanic standing in her garage with her hands in her pockets and a smile on her face", "id": "1852924394"}, {"image_id": "1590219962", "caption": "Woman wearing bikini at beach", "id": "1590219962"}, {"image_id": "1587835859", "caption": "The Tiber river and Saint Peter's Basilica Rome Italy", "id": "1587835859"}, {"image_id": "1590360719", "caption": "Father and daughter playing at beach", "id": "1590360719"}, {"image_id": "1570236728", "caption": "Doctor walking in corridor of clinic", "id": "1570236728"}, {"image_id": "1297797338", "caption": "cutout of smiling businessman wearing telephone headset at camera", "id": "1297797338"}, {"image_id": "1277224574", "caption": "Businessmen and businesswoman working at computer on desk in desert", "id": "1277224574"}, {"image_id": "1747434198", "caption": "Rolls of assorted Euro coins with businessmen figurines", "id": "1747434198"}, {"image_id": "1840561226", "caption": "Vertical shot of a worker packing a box on conveyor belt at a distribution warehouse", "id": "1840561226"}, {"image_id": "1710363710", "caption": "Midriff of a man standing in a towel", "id": "1710363710"}, {"image_id": "1587839435", "caption": "Senior man and woman at internet cafe", "id": "1587839435"}, {"image_id": "1839580700", "caption": "A confident young businesswoman is standing in the corridor and posing with a laptop in her hand", "id": "1839580700"}, {"image_id": "1572549869", "caption": "A climber climbing a rock face", "id": "1572549869"}, {"image_id": "1570164587", "caption": "Couple walking outdoors in autumn", "id": "1570164587"}, {"image_id": "1859342024", "caption": "A top angle shot of a happy senior couple lying on the grass and holding hands with red autumn leaves in foreground", "id": "1859342024"}, {"image_id": "217369079", "caption": "Tranquil canal streetscape in Burano Venice Italy", "id": "217369079"}, {"image_id": "1587988454", "caption": "Couple looking at woman in foreground", "id": "1587988454"}, {"image_id": "1840650146", "caption": "A man in coveralls standing on a ladder and looking up at ceiling insulation in an attic", "id": "1840650146"}, {"image_id": "1844196821", "caption": "Horizontal shot of a woman holding fresh and juicy tomatoes in kitchen", "id": "1844196821"}, {"image_id": "1587850166", "caption": "View of water wheel in Prague", "id": "1587850166"}, {"image_id": "1847350064", "caption": "A close up shot of a young female worker smiling and showing aluminum light fittings to the camera", "id": "1847350064"}, {"image_id": "1844733053", "caption": "High angle shot of the backside of a group of girls with backpacks sitting on a fence in a green field", "id": "1844733053"}, {"image_id": "1587030206", "caption": "View of construction worker through large pipe hole", "id": "1587030206"}, {"image_id": "1852964087", "caption": "Smiling baker holding a basket of breads in the bakery kitchen", "id": "1852964087"}, {"image_id": "1859181224", "caption": "Extreme wide shot of an engineer inspecting the wing of a passenger jet at a hangar", "id": "1859181224"}, {"image_id": "1590053525", "caption": "A teenage girl watching television", "id": "1590053525"}, {"image_id": "1581292124", "caption": "Mature tourist couple looking at map against blue sky", "id": "1581292124"}, {"image_id": "1585878728", "caption": "Alkhornet mountain Isfjorden Spitsbergen Svalbard Norway Europe", "id": "1585878728"}, {"image_id": "216586760", "caption": "Cobblestone bridge in St Jean de Cole Dordogne France", "id": "216586760"}, {"image_id": "1570154699", "caption": "Ruin of sultan's palace of Moroni Grand Comore Island Ngazidja Comores Africa", "id": "1570154699"}, {"image_id": "1868718506", "caption": "A vertical shot of a young blonde woman in checked shirt adjusting rear view mirror while sitting in driving seat in a car", "id": "1868718506"}, {"image_id": "1859347385", "caption": "Rooftops of idyllic village Bourdeilles Dordogne France", "id": "1859347385"}, {"image_id": "1590359207", "caption": "Young girl jumping in air with balloons", "id": "1590359207"}, {"image_id": "1590067835", "caption": "Man lying down on an outdoor basketball court", "id": "1590067835"}, {"image_id": "1868703266", "caption": "A pensive businesswoman sitting at the table while using a laptop in a conference room", "id": "1868703266"}, {"image_id": "1588010669", "caption": "Businessman using telephone in office", "id": "1588010669"}, {"image_id": "1572474569", "caption": "Portrait of a young businesswoman in an office building", "id": "1572474569"}, {"image_id": "1572527597", "caption": "Punta Colorado Isla San Jose Gulf of California Sea of Cortez Mexico", "id": "1572527597"}, {"image_id": "1878869765", "caption": "high angle view of a proud Chemistry teacher in a school lab smiling at the camera while standing with a group of smart students wearing lab coats and safety goggles", "id": "1878869765"}, {"image_id": "1587119129", "caption": "Man standing holding a leash attached to a woman at his et", "id": "1587119129"}, {"image_id": "1878871331", "caption": "Medium Wide shot of happy laughing couple where the Man is trying to playfully push his girlfriend on a sled on the slopes of a snowy hillside while snowing", "id": "1878871331"}, {"image_id": "1587140861", "caption": "Close up of a leaf floating on water", "id": "1587140861"}, {"image_id": "1844724032", "caption": "Vertical shot of a robotic machinery at a factory with a worker in reflector vest operating it in the foreground", "id": "1844724032"}, {"image_id": "1590149441", "caption": "Businesswoman and businessman shaking hands", "id": "1590149441"}, {"image_id": "1857301637", "caption": "Elevated view of a businessman and businesswoman on a runway at the airport", "id": "1857301637"}, {"image_id": "1859178596", "caption": "Close up shot of an engineer in safety glasses using a micrometer", "id": "1859178596"}, {"image_id": "1590219980", "caption": "Businessman splashing water at beach", "id": "1590219980"}, {"image_id": "1839578906", "caption": "Horizontal facial close up of a young woman with headset at desk in office appear welcoming to the camera with copy space", "id": "1839578906"}, {"image_id": "1878681053", "caption": "Vertical Close up of a smiling businessman and woman sitting in the backseat of a car", "id": "1878681053"}, {"image_id": "1572486317", "caption": "A businesswoman using a laptop", "id": "1572486317"}, {"image_id": "1587058238", "caption": "Active young woman jumping on white background", "id": "1587058238"}, {"image_id": "1297790072", "caption": "Smiling children lying in tent on camping trip at camera", "id": "1297790072"}, {"image_id": "1587992687", "caption": "A woman sitting on a bed meditating", "id": "1587992687"}, {"image_id": "1581272699", "caption": "Family with parked convertible car on mountain road trip at camera", "id": "1581272699"}, {"image_id": "1588016225", "caption": "Figurines of businessmen standing on Euros", "id": "1588016225"}, {"image_id": "1570537112", "caption": "Lumber industry at Breves Channels Brazil", "id": "1570537112"}, {"image_id": "1844731871", "caption": "Vertical shot of a happy elderly couple riding bicycles in a meadow full of wildflowers", "id": "1844731871"}, {"image_id": "1590179330", "caption": "Maori Canoe Waitangi Bay of Islands North Island New Zealand", "id": "1590179330"}, {"image_id": "1587137207", "caption": "Detail view of a hallway with light shining in through doorways", "id": "1587137207"}, {"image_id": "1846038983", "caption": "A man lifting and looking at a golden frame in a frame shop", "id": "1846038983"}, {"image_id": "1570163573", "caption": "Soccer ball floating in night sky next to earth", "id": "1570163573"}, {"image_id": "1878692624", "caption": "High school student throwing a netball during a match in a gym class", "id": "1878692624"}, {"image_id": "1878692867", "caption": "Teacher helping middle school students as they work on digital tablets in a classroom", "id": "1878692867"}, {"image_id": "1570311767", "caption": "Ice hockey players on Lake Woerthsee near Steinebach Bavaria Germany", "id": "1570311767"}, {"image_id": "1578924737", "caption": "A senior couple embracing in autumn time", "id": "1578924737"}, {"image_id": "1862067383", "caption": "High angle vertical shot of colorful wildflowers in a sunny meadow", "id": "1862067383"}, {"image_id": "1277238440", "caption": "Smiling young woman drinking cocktail in outdoor bar at camera", "id": "1277238440"}, {"image_id": "1576745405", "caption": "A climber climbing a rock face", "id": "1576745405"}, {"image_id": "216351764", "caption": "Arrows squeezing British pound symbol", "id": "216351764"}, {"image_id": "1878694376", "caption": "Low angle shot of middle school students playing a flute and saxophone during a music class", "id": "1878694376"}, {"image_id": "1868720591", "caption": "A Close up shot of a young blonde woman sitting on sofa and using a credit for shopping on a tablet while smiling at camera", "id": "1868720591"}, {"image_id": "1587843008", "caption": "Fountain and large castle in Germany", "id": "1587843008"}, {"image_id": "1581279662", "caption": "Portrait of boy having breakfast at table in family kitchen", "id": "1581279662"}, {"image_id": "1588024850", "caption": "Portrait of a young woman showing the side of her face", "id": "1588024850"}, {"image_id": "1766932293", "caption": "Strawberries and champagne glasses strewn on bed next to couple", "id": "1766932293"}, {"image_id": "1859334893", "caption": "A medium shot of a young teacher looking at the monitor while a senior man with gray hair is using the computer", "id": "1859334893"}, {"image_id": "217372502", "caption": "Hot air balloon", "id": "217372502"}, {"image_id": "1578942869", "caption": "Young man with a surfboard", "id": "1578942869"}, {"image_id": "1846039043", "caption": "A backside shot of a woman in bikini watching cruise ship at sunset", "id": "1846039043"}, {"image_id": "1811160929", "caption": "A landscape shot of large waves breaking on the way to shore", "id": "1811160929"}, {"image_id": "1868714288", "caption": "A circuit board being held and examined by an engineer under the magnifying lamp in a laboratory", "id": "1868714288"}, {"image_id": "1572512465", "caption": "Young girl sitting on sofa reading", "id": "1572512465"}, {"image_id": "1839589373", "caption": "Low angle shot of a young woman cyclist in the wilderness strapping on an helmet", "id": "1839589373"}, {"image_id": "1859347319", "caption": "A Close up shot of a man's hand holding a small four leaf clover", "id": "1859347319"}, {"image_id": "1571660513", "caption": "An elderly man playing cards", "id": "1571660513"}, {"image_id": "1572509042", "caption": "A young couple sitting in the desert", "id": "1572509042"}, {"image_id": "1588003502", "caption": "Scenic view of church across body of water Carinthia Austria", "id": "1588003502"}, {"image_id": "1590160412", "caption": "High angle view of many red roses", "id": "1590160412"}, {"image_id": "1576741724", "caption": "A portrait of three business colleagues", "id": "1576741724"}, {"image_id": "1716620831", "caption": "View of teenage girl through turnstile", "id": "1716620831"}, {"image_id": "1878782237", "caption": "A family of four enjoying doing yard work in autumn as the kids are playing with autumn leaves", "id": "1878782237"}, {"image_id": "1873425494", "caption": "A portrait shot of a businesswoman smiling at camera while talking on a telephone in office", "id": "1873425494"}, {"image_id": "1587984323", "caption": "Young woman sitting in a boat trailing her hand in the water", "id": "1587984323"}, {"image_id": "1571687285", "caption": "Beach and a upcoming thunderstorm Bornholm Island Denmark", "id": "1571687285"}, {"image_id": "1587145130", "caption": "View of Christmas decorations in a bowl", "id": "1587145130"}, {"image_id": "1590209393", "caption": "autumn vineyard and sheep in field", "id": "1590209393"}, {"image_id": "1722078368", "caption": "Brother and sister dancing together at home", "id": "1722078368"}, {"image_id": "1570164671", "caption": "College student listening to music on campus in autumn", "id": "1570164671"}, {"image_id": "1859342279", "caption": "A Close up shot of a technician looking up while holding a blueprint with a van and large solar panels in background", "id": "1859342279"}, {"image_id": "1839581477", "caption": "Portrait of a joyous mother and daughter playing in sand with bucket and spade on beach", "id": "1839581477"}, {"image_id": "1590212246", "caption": "Group of businesswomen at meeting", "id": "1590212246"}, {"image_id": "1852965692", "caption": "Medium Close up of a hairdresser spraying hairspray over a woman's hair at a salon", "id": "1852965692"}, {"image_id": "1297798694", "caption": "Couple with salesman looking at new car in showroom", "id": "1297798694"}, {"image_id": "1587024533", "caption": "View of a young woman and her son sitting on a beach", "id": "1587024533"}, {"image_id": "1840650443", "caption": "A two shot of young farmers shearing wool from sheep in a barn", "id": "1840650443"}, {"image_id": "1572530249", "caption": "Young man lying on floor next to woman and scratching head", "id": "1572530249"}, {"image_id": "1716617822", "caption": "Businessman relaxing in park on a chair", "id": "1716617822"}, {"image_id": "1865894951", "caption": "Young confident businesswoman walking and pulling a suitcase in airport baggage claim", "id": "1865894951"}, {"image_id": "1571603078", "caption": "Portrait of a young girl", "id": "1571603078"}, {"image_id": "1590215423", "caption": "Pile of pumpkins in field", "id": "1590215423"}, {"image_id": "1567883168", "caption": "Dhow at the coast of Nosy Be Madagascar", "id": "1567883168"}, {"image_id": "1277240033", "caption": "Young man listening to mp3 player with earphones at camera", "id": "1277240033"}, {"image_id": "1571576828", "caption": "A man snorkeling in the sea", "id": "1571576828"}, {"image_id": "1665810557", "caption": "Senior man on ladder doing diy decorating in home", "id": "1665810557"}, {"image_id": "1588016186", "caption": "Close up of hotel key on nightstand", "id": "1588016186"}, {"image_id": "1277238311", "caption": "Cup of coffee on counter in cafe with customer", "id": "1277238311"}, {"image_id": "1586673371", "caption": "Snow capped mountains in Fimbatal the border between Switzerland and Austria Eschol", "id": "1586673371"}, {"image_id": "1670342096", "caption": "Two young women on winter vacation wearing sunglasses at camera", "id": "1670342096"}, {"image_id": "1590222407", "caption": "Senior couple smiling and hugging indoors", "id": "1590222407"}, {"image_id": "1857297413", "caption": "Low angle view of a man driving a motorhome on road on a sunny day", "id": "1857297413"}, {"image_id": "1588016909", "caption": "Tree in grassy meadow Bavaria Germany", "id": "1588016909"}, {"image_id": "1588025555", "caption": "Portrait of a young woman", "id": "1588025555"}, {"image_id": "1585878788", "caption": "Raudfjorden Northwestern part of Spitsbergen Svalbard Norway Europe", "id": "1585878788"}, {"image_id": "1874804519", "caption": "Young woman sunbathing focus on foreground close up rear view", "id": "1874804519"}, {"image_id": "1855882661", "caption": "Portrait of a young couple holding hands in a park looking back and posing for the camera", "id": "1855882661"}, {"image_id": "1581272747", "caption": "Excited children camping in garden putting up tent at camera", "id": "1581272747"}, {"image_id": "1297780514", "caption": "Boy and girl with snowman in sunny snowy field", "id": "1297780514"}, {"image_id": "1876091906", "caption": "Male athlete with discus midsection", "id": "1876091906"}, {"image_id": "1587994115", "caption": "A young girl with a windmill", "id": "1587994115"}, {"image_id": "1816749552", "caption": "Front view of a senior couple in cycling helmets walking side by side through wood with their bicycles", "id": "1816749552"}, {"image_id": "1587982649", "caption": "Close up of rooftop chimney", "id": "1587982649"}, {"image_id": "1869314600", "caption": "Wide shot of a woman in swimwear sitting at the shore of a sunny beach and looking at the sea", "id": "1869314600"}, {"image_id": "1709394788", "caption": "Two surfers at the beach", "id": "1709394788"}, {"image_id": "1587129488", "caption": "Cabin in snow in front of Karwendel mountains Garmisch Partenkirchen Bavaria Germany Europe", "id": "1587129488"}, {"image_id": "1868718455", "caption": "A Close up vertical shot of a businessman's hand holding a pen near bar graph", "id": "1868718455"}, {"image_id": "1766903781", "caption": "Father and sons putting kite together near lake", "id": "1766903781"}, {"image_id": "1844191904", "caption": "Portrait of a male gymnast performing on gymnastic rings", "id": "1844191904"}, {"image_id": "1586735513", "caption": "Team Of Aero Engineers Working On Aircraft In Hangar", "id": "1586735513"}, {"image_id": "1590067634", "caption": "Senior couple waving to each other at the beach", "id": "1590067634"}, {"image_id": "1857297527", "caption": "Horizontal shot of a businessman with hand on earpiece by car beneath an overpass on a sunny day", "id": "1857297527"}, {"image_id": "1277235530", "caption": "Flower girl hugging grandmother church on wedding day at camera", "id": "1277235530"}, {"image_id": "1843605494", "caption": "Horizontal shot of middle aged hiker man tying the laces on his shoe during a holiday backpacking in the forest", "id": "1843605494"}, {"image_id": "1843586555", "caption": "Sunbeams peeking from behind a big cloud with a lens flare against the background of a blue sky with copy space", "id": "1843586555"}, {"image_id": "1878695885", "caption": "Gym teacher starting a high school basketball game with the tip off", "id": "1878695885"}, {"image_id": "1587851102", "caption": "Close up of Arnica flower", "id": "1587851102"}, {"image_id": "1572524414", "caption": "Young girl smiling and playing with seaweed", "id": "1572524414"}, {"image_id": "1572529481", "caption": "Cable car at Copper Canyon Sierra Tarahumara Chihuahua Mexico", "id": "1572529481"}, {"image_id": "1865999603", "caption": "Horizontal shot of a couple with their two children running towards the parked car on a cliff top overlooking the ocean smile at the camera", "id": "1865999603"}, {"image_id": "1578921164", "caption": "Close up of a young woman sneezing", "id": "1578921164"}, {"image_id": "1570236713", "caption": "Thanksgiving decoration Munich Bavaria Germany", "id": "1570236713"}, {"image_id": "1873346108", "caption": "Wide Aerial view of a kiteboarder kiteboarding on huge waves", "id": "1873346108"}, {"image_id": "1878695765", "caption": "High school students studying in a classroom with their teacher guiding them", "id": "1878695765"}, {"image_id": "1571520317", "caption": "Man relaxing in a chair speaking on a mobile telephone", "id": "1571520317"}, {"image_id": "216580091", "caption": "Close up of tranquil field of blooming buttercups", "id": "216580091"}, {"image_id": "1590344954", "caption": "Young boy putting on necktie", "id": "1590344954"}, {"image_id": "1843607027", "caption": "Close up shot of hands exchanging one hundred dollar bills isolated on black background", "id": "1843607027"}, {"image_id": "1851407597", "caption": "Vertical profile shot of a mature couple wearing white bathrobes standing by the window with woman leaning over the man", "id": "1851407597"}, {"image_id": "1588015328", "caption": "Figurine of couple sitting on park bench on stack on Euros", "id": "1588015328"}, {"image_id": "1587849818", "caption": "Medieval church San Nicolo San Piero in Campo Island of Elba Tuscany Italy", "id": "1587849818"}, {"image_id": "1587811034", "caption": "Street light on foggy night", "id": "1587811034"}, {"image_id": "1587145913", "caption": "View of a colorful hot air balloon against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145913"}, {"image_id": "1839580703", "caption": "A confident young businesswoman is looking straight at the camera with copy space", "id": "1839580703"}, {"image_id": "1588023248", "caption": "A group of teenage friends playing pool in a bar", "id": "1588023248"}, {"image_id": "1277238101", "caption": "Young woman drinking glass of fresh juice in outdoor cafe at camera", "id": "1277238101"}, {"image_id": "1590353420", "caption": "Portrait of happy young woman", "id": "1590353420"}, {"image_id": "1839588440", "caption": "Portrait of a joyous female florist displaying a bouquet to the customer", "id": "1839588440"}, {"image_id": "1587982322", "caption": "Aerial view of lake Woerthsee Mausinsel Island Woerth Bavaria Germany", "id": "1587982322"}, {"image_id": "1817410242", "caption": "Low angle three quarter length shot of a smiling young couple assembling windsurfers on a sandy beach", "id": "1817410242"}, {"image_id": "1572538928", "caption": "A young woman reclining on the beach", "id": "1572538928"}, {"image_id": "1297797278", "caption": "cutout businesswoman wearing telephone headset detail of mouth", "id": "1297797278"}, {"image_id": "1843588220", "caption": "Confident businesswoman in a pink shirt and black skirt standing on one leg with her arms crossed against a white background", "id": "1843588220"}, {"image_id": "1816749675", "caption": "Vertical shot of a joyous couple doing DIY at home with woman drilling hole with power drill on workbench and man assisting", "id": "1816749675"}, {"image_id": "1587830006", "caption": "Close up of senior woman smiling", "id": "1587830006"}, {"image_id": "1570236764", "caption": "Doctor reviewing paperwork in doctor's office", "id": "1570236764"}, {"image_id": "1859328479", "caption": "A portrait shot of young students sitting on a desk and working together on a electronic device with teacher standing in background", "id": "1859328479"}, {"image_id": "1578905438", "caption": "A diver standing on a diving board", "id": "1578905438"}, {"image_id": "1878869591", "caption": "Serious school girl in red uniform sweater writing in a project book while having a model of wind turbine on her desk", "id": "1878869591"}, {"image_id": "1873350971", "caption": "Close up of a Wooden water taxi boats moored on the sunny Grand Canal in front of San Marco Campanile and architectural buildings in Venice Italy", "id": "1873350971"}, {"image_id": "1588015499", "caption": "Figurines of businessmen standing on US Dollars", "id": "1588015499"}, {"image_id": "1297797383", "caption": "Defocused senior couple holding hands on walk in countryside", "id": "1297797383"}, {"image_id": "1277367311", "caption": "Portrait of grandmother granddaughter at home smiling at camera sitting on doorstep and hugging", "id": "1277367311"}, {"image_id": "1710448877", "caption": "Woman playing golf on a sunny day", "id": "1710448877"}, {"image_id": "1297792532", "caption": "Combine harvester emptying harvested wheat grain into tractor trailer", "id": "1297792532"}, {"image_id": "1766908251", "caption": "Couple hugging and smiling at each other in meadow", "id": "1766908251"}, {"image_id": "1585850384", "caption": "Cookie senior woman against white background", "id": "1585850384"}, {"image_id": "1571663726", "caption": "Portrait of a young woman", "id": "1571663726"}, {"image_id": "1572383429", "caption": "Rainforest near Revelstoke British Columbia Canada", "id": "1572383429"}, {"image_id": "1277365424", "caption": "Businessman and businesswoman working in office together", "id": "1277365424"}, {"image_id": "1576780058", "caption": "A bride and groom kissing on a beach", "id": "1576780058"}, {"image_id": "1578902582", "caption": "A businesswoman standing underneath an umbrella", "id": "1578902582"}, {"image_id": "1586685914", "caption": "Farmer Milking Cows In Parlour", "id": "1586685914"}, {"image_id": "1572524333", "caption": "Family digging together on sandy beach", "id": "1572524333"}, {"image_id": "1588012751", "caption": "Little girl holding a birthday cake with lit candles", "id": "1588012751"}, {"image_id": "216109328", "caption": "Close up of canola against blue sky", "id": "216109328"}, {"image_id": "1859202749", "caption": "Senior couple and their family having a picnic in the countryside", "id": "1859202749"}, {"image_id": "1590359150", "caption": "Los Roques rock formation in front of Mount Teide Teide National Park Tenerife Canary Islands Spain", "id": "1590359150"}, {"image_id": "1572396350", "caption": "Cutting tree with chainsaw Latsch Vinschgau Trentino Alto Adige South Tyrol Italy", "id": "1572396350"}, {"image_id": "1865943566", "caption": "Horizontal shot of a senior couple loading suitcases into parked car boot on the driveway in front of the house", "id": "1865943566"}, {"image_id": "1585877429", "caption": "St Paul's Bay where it is reported the apostle landed during a storm Lindos Rhodes Dodecanese Greece Europe", "id": "1585877429"}, {"image_id": "1843609349", "caption": "Selective focus on a tilted red convertible with a senior couple in the background checking maps on a road trip on a sunny day", "id": "1843609349"}, {"image_id": "1588012571", "caption": "A man sitting on rocks", "id": "1588012571"}, {"image_id": "1816211334", "caption": "Angled close up of hairdresser cutting woman's hair in salon with focus on hair hands and scissors", "id": "1816211334"}, {"image_id": "1843588148", "caption": "An old aged farmer standing with his arms crossed in the field on a sunny day with a tractor and a plough in the background", "id": "1843588148"}, {"image_id": "1277252126", "caption": "Smiling girl patient in hospital bed with MP3 player at camera", "id": "1277252126"}, {"image_id": "1864634480", "caption": "car mechanic in red overalls and protective gloves standing below a broken down car and attaching a wheel to it", "id": "1864634480"}, {"image_id": "1570298756", "caption": "A young woman wearing a woolen hat and gloves in the snow", "id": "1570298756"}, {"image_id": "1862083211", "caption": "Portrait of a Technician smiling and operating machinery in a solar panel factory", "id": "1862083211"}, {"image_id": "1852927058", "caption": "Horizontal shot of a seated baby boy and girl looking at each other with the boy s hand on girl's shoulder", "id": "1852927058"}, {"image_id": "1590351017", "caption": "Bust of Frederic Chopin in garden of the Real Cartuja de Valldemossa Mallorca Spain", "id": "1590351017"}, {"image_id": "1570348070", "caption": "Beach umbrellas from Hotel Hermitage Portoferraio Elba Italy", "id": "1570348070"}, {"image_id": "1586693147", "caption": "Portrait smiling middle school student with digital tablet in study hall", "id": "1586693147"}, {"image_id": "1864640876", "caption": "Horizontal profile shot of a mother carrying her daughter in arms on the beach on a sunny day", "id": "1864640876"}, {"image_id": "1847201891", "caption": "A high angle wide view of a large factory with a mature adult man working on a sheet metal press", "id": "1847201891"}, {"image_id": "1864640672", "caption": "Young woman working on a computer at the desk in her home office in front of a big glass window", "id": "1864640672"}, {"image_id": "1869317039", "caption": "Side view of a technician working on a computer in the server room of a data center", "id": "1869317039"}, {"image_id": "1722076412", "caption": "Portrait of teenagers on skiing holiday Tirol Austria Europe", "id": "1722076412"}, {"image_id": "1869043535", "caption": "Young man sitting inside a dome tent with his girlfriend crouching beside a camping stove outside drinking hot drink smile at the camera", "id": "1869043535"}, {"image_id": "1572498467", "caption": "A teenage girl lying in a park", "id": "1572498467"}, {"image_id": "1297725716", "caption": "Young woman driving classic convertible black sports car smiling at camera", "id": "1297725716"}, {"image_id": "1572474500", "caption": "A businessman using a mobile phone", "id": "1572474500"}, {"image_id": "1586735549", "caption": "Portrait Of Businessman On Floor Of Automated Factory", "id": "1586735549"}, {"image_id": "1670374850", "caption": "Male student wearing earphones studying books outside building", "id": "1670374850"}, {"image_id": "1578217487", "caption": "A father teaching his daughter about recycling", "id": "1578217487"}, {"image_id": "1570340042", "caption": "View from Malerwinkel over Konigssee Lake Bavaria Germany", "id": "1570340042"}, {"image_id": "1570154729", "caption": "View to harbor and Friday Mosque Moroni Grand Comore Island Ngazidja Comores Africa", "id": "1570154729"}, {"image_id": "1859323643", "caption": "A medium shot of a smiling woman taking her head out of a car window and looking at the camera", "id": "1859323643"}, {"image_id": "1670340083", "caption": "Girl on home sofa listening to music and playing video game using MP3 player and console", "id": "1670340083"}, {"image_id": "1578922232", "caption": "A young boy walking through a field of daffodils in spring time", "id": "1578922232"}, {"image_id": "1873425416", "caption": "A medium shot of a businessman looking at a laptop while drinking coffee in airport lounge", "id": "1873425416"}, {"image_id": "1859331806", "caption": "A medium shot of an Asian nurse smiling and holding hand of a patient while talking in a hospital room", "id": "1859331806"}, {"image_id": "1839588611", "caption": "Motion blur of a lower section of a waiter zipping past by bar stools in an restaurant", "id": "1839588611"}, {"image_id": "1567888490", "caption": "Banana bunch at Nosy Komba Madagascar", "id": "1567888490"}, {"image_id": "1587137234", "caption": "View of two young female doctors talking in office setting", "id": "1587137234"}, {"image_id": "1590201377", "caption": "Blurred motion shot of ambulance Chicago Illinois United States", "id": "1590201377"}, {"image_id": "1572522944", "caption": "Father carrying boy on shoulders at beach", "id": "1572522944"}, {"image_id": "1585878080", "caption": "Glacier Isfjorden Spitsbergen Svalbard Norway Europe", "id": "1585878080"}, {"image_id": "1578208691", "caption": "Portrait of a young woman wearing a feather shrug looking away", "id": "1578208691"}, {"image_id": "1839581777", "caption": "Portrait of a joyous doctor encouraging a girl in hospital hallway with their reflection on the floor", "id": "1839581777"}, {"image_id": "1862121581", "caption": "A man and woman in formal business suit looking into a digital tablet while sitting at a table in the lobby", "id": "1862121581"}, {"image_id": "1878869645", "caption": "Front view of handsome skier lying in the snow on sunny day with his gear still on", "id": "1878869645"}, {"image_id": "1576738379", "caption": "A young man blowing his nose", "id": "1576738379"}, {"image_id": "1869911705", "caption": "A medium shot of a happy senior farmer inspecting potatoes on a conveyor belt in a rural field with tractor in background", "id": "1869911705"}, {"image_id": "1588020710", "caption": "Woman leaning on wall listening to headphones", "id": "1588020710"}, {"image_id": "1862121650", "caption": "Portrait of a smiling businessman having his arms crossed in a formal suit with colleagues standing in the background", "id": "1862121650"}, {"image_id": "1297780883", "caption": "Exterior of ornate building in Plaza de Espana Seville Spain", "id": "1297780883"}, {"image_id": "1864637795", "caption": "A jubilant father chases his daughter splashing water over her as she runs to save herself at the beach", "id": "1864637795"}, {"image_id": "1570572848", "caption": "Aerial view of Manaus Iranduba Bridge Manaus Amazonas Amazon River Brazil", "id": "1570572848"}, {"image_id": "1865999513", "caption": "Senior businessman in car wearing sunglasses sitting in back seat reads a document", "id": "1865999513"}, {"image_id": "216108563", "caption": "Clouds in blue sky over barley field", "id": "216108563"}, {"image_id": "1865856623", "caption": "Horizontal shot of a group of business people walking and pulling suitcases in the airport on their way to the departure gate", "id": "1865856623"}, {"image_id": "1878688535", "caption": "Close Up Of a Surfer walking On the Beach", "id": "1878688535"}, {"image_id": "1878782285", "caption": "Young girl in a swimming suit standing under a beach umbrella on a sunny day with the ocean in the background", "id": "1878782285"}, {"image_id": "1862081294", "caption": "Business employees sitting in a private jet and having a meeting", "id": "1862081294"}, {"image_id": "1572528572", "caption": "Flag of Mexico San Jose Del Cabo Cabo San Lucas Baja California Sur Mexico", "id": "1572528572"}, {"image_id": "1570300778", "caption": "View over rooftops in the center of Piazza Armerina Sicily Italy", "id": "1570300778"}, {"image_id": "1852653176", "caption": "A high angle view of Nurse and mother looking down at a newborn baby boy in the hospital", "id": "1852653176"}, {"image_id": "1586681501", "caption": "cutout Of Male Tailor", "id": "1586681501"}, {"image_id": "1578902453", "caption": "A couple dressed for an evening out", "id": "1578902453"}, {"image_id": "1297774436", "caption": "Family on winter beach fishing from rocks with nets", "id": "1297774436"}, {"image_id": "1590212186", "caption": "Man carrying deckchair in field", "id": "1590212186"}, {"image_id": "1869046535", "caption": "Horizontal shot of a woman with a bowl of strawberries in breakfast smiles at the camera", "id": "1869046535"}, {"image_id": "1590323564", "caption": "Rock layers at Jebel Harim mountain Musandam peninsula exclave of Oman", "id": "1590323564"}, {"image_id": "1866001007", "caption": "Vertical rear view of a joyous mature couple on a road trip with the man pointing at something on a sunny day", "id": "1866001007"}, {"image_id": "1851407714", "caption": "Horizontal mid length shot of a joyous young woman holding up a dress while shopping in a clothes store with copy space", "id": "1851407714"}, {"image_id": "1587998918", "caption": "A businessman and woman talking in the foyer of an office building", "id": "1587998918"}, {"image_id": "1868722172", "caption": "A medium shot of a senior couple smiling at camera while reading book and having picnic in a field of wildflowers", "id": "1868722172"}, {"image_id": "1587991709", "caption": "Female scientist examining liquid in large vial", "id": "1587991709"}, {"image_id": "1817410308", "caption": "Rear view of a happy senior couple driving in convertible car along country road with woman taking photographs on a camera", "id": "1817410308"}, {"image_id": "1859342276", "caption": "A Close up shot of a large solar panel with two technicians holding a blueprints and standing in background", "id": "1859342276"}, {"image_id": "216573500", "caption": "Clouds in sunny blue sky", "id": "216573500"}, {"image_id": "1862131313", "caption": "A wide shot of two businessmen shaking hands in an airport departure lounge with other passengers waiting in their seats", "id": "1862131313"}, {"image_id": "1859328407", "caption": "A portrait shot of a young girl sawing wood in a vise machine in a classroom", "id": "1859328407"}, {"image_id": "1277237360", "caption": "Businessman with plans smiling at businesswoman with headset in office", "id": "1277237360"}, {"image_id": "1878682883", "caption": "Low angle portrait of a senior man carrying rolled up carpet on the shoulder and moving house", "id": "1878682883"}, {"image_id": "1859323607", "caption": "A Close up shot of a young boy working on a robotic hand in a class", "id": "1859323607"}, {"image_id": "1590213623", "caption": "Doctor talking to patient in office", "id": "1590213623"}, {"image_id": "1855886123", "caption": "Vertical shot of a businessman doing a handstand on board desk in a meeting room as colleagues watch on", "id": "1855886123"}, {"image_id": "1868723489", "caption": "A wide shot of a happy family sitting on a fence and looking at sunflowers in a wildflower field with bicycles kept on a side", "id": "1868723489"}, {"image_id": "1862126078", "caption": "A portrait shot of a tired young boy in school uniform sitting on his desk with books in a classroom", "id": "1862126078"}, {"image_id": "1859176805", "caption": "Vertical shot of an engineer standing on a platform below the tail of a passenger jet at a hangar", "id": "1859176805"}, {"image_id": "1878692618", "caption": "Vertical shot of a gym teacher with a whistle talking to high school students with a netball in a gym", "id": "1878692618"}, {"image_id": "1572535115", "caption": "A woman blowing her nose", "id": "1572535115"}, {"image_id": "1851407570", "caption": "Horizontal shot of a teenage couple sitting in a cafe share a light moment smiling at each other", "id": "1851407570"}, {"image_id": "1766923941", "caption": "Couple's legs dangling over stream", "id": "1766923941"}, {"image_id": "1304264366", "caption": "Family on sofa shopping for furniture in store at camera", "id": "1304264366"}, {"image_id": "1857288770", "caption": "Group of young men and women playing poker at the table in the casino", "id": "1857288770"}, {"image_id": "1570572254", "caption": "Aerial View of Teatro Amazonas Manaus Amazonas Amazon River Brazil", "id": "1570572254"}, {"image_id": "1586684945", "caption": "Farmer Next To Lorry Loaded With Straw Bales", "id": "1586684945"}, {"image_id": "1852967081", "caption": "Medium Close up of the hands of a nail technician applying polish to a woman's fingernails", "id": "1852967081"}, {"image_id": "1586704859", "caption": "Portrait Of IT Technician In Data Centre Standing By Servers", "id": "1586704859"}, {"image_id": "1588020977", "caption": "Young girl pointing at Christmas ornaments", "id": "1588020977"}, {"image_id": "1590160442", "caption": "Close up of folding ruler on table", "id": "1590160442"}, {"image_id": "1297776722", "caption": "Woman on sofa using credit card to shop online with digital tablet", "id": "1297776722"}, {"image_id": "1570280732", "caption": "A young boy taking a photo of a senior woman", "id": "1570280732"}, {"image_id": "1851483932", "caption": "Low angle shot of boy holding a kite and looking at the camera on sunny beach with his parents and sister in the background", "id": "1851483932"}, {"image_id": "1851485621", "caption": "A young boy holding a remote control and pointing it at the televisions in an electronics store", "id": "1851485621"}, {"image_id": "1855915934", "caption": "Three friends playing virtual video game while sitting on a sofa", "id": "1855915934"}, {"image_id": "1864659965", "caption": "Horizontal shot of a woman with car keys smiling at the camera by a car with her husband and salesman shaking hands in the background", "id": "1864659965"}, {"image_id": "1866125960", "caption": "Medium shot of a female pharmacist reading the label on a medicine pot at a pharmacy with a colleague in the foreground", "id": "1866125960"}, {"image_id": "1304266787", "caption": "Portrait of grandfather hugging bridesmaid granddaughter at wedding", "id": "1304266787"}, {"image_id": "216582482", "caption": "Close up of vibrant sunflower", "id": "216582482"}, {"image_id": "1590327461", "caption": "Minaret of Islamic mosque at Nizwa Ad Dakhiliyah Oman", "id": "1590327461"}, {"image_id": "1709388815", "caption": "A climber abseiling on a cliff", "id": "1709388815"}, {"image_id": "1857289067", "caption": "Portrait of mature jewelry shop assistants with a tray of rings in a jewelry shop", "id": "1857289067"}, {"image_id": "1843609193", "caption": "Horizontal shot of a joyous senior couple posing and smiling at the camera in a red convertible on a bright sunny day", "id": "1843609193"}, {"image_id": "1583874674", "caption": "Teenage girl in a bowling alley", "id": "1583874674"}, {"image_id": "1263324989", "caption": "Vehicle tracks through farm field of ripening wheat crop", "id": "1263324989"}, {"image_id": "1855878050", "caption": "midsection of a female baker holding a pie in her hands in the bakery", "id": "1855878050"}, {"image_id": "1840562483", "caption": "Medium close up shot of a technician in safety glasses using measurement probe on the assembly line of a steel bearing manufacturing plant", "id": "1840562483"}, {"image_id": "1571327426", "caption": "Portrait of young girl sitting in park with pink purse", "id": "1571327426"}, {"image_id": "1572381284", "caption": "Johnston Canyon Banff National Park Alberta Canada", "id": "1572381284"}, {"image_id": "1843610651", "caption": "A seated businessman shows his standing colleagues a graph on the monitor in office with the woman taking notes", "id": "1843610651"}, {"image_id": "1865943647", "caption": "Horizontal shot of a father loading camping equipment into a car boot on a driveway with children sitting in a parked car", "id": "1865943647"}, {"image_id": "1277237348", "caption": "Businessman in office with headache dissolving tablets in glass of water", "id": "1277237348"}, {"image_id": "1859328422", "caption": "A Close up vertical shot of a student's hand using wrench to tighten valve on a copper plumbing pipe", "id": "1859328422"}, {"image_id": "1859144498", "caption": "Low angle vertical shot of a girl jumping against the blue sky", "id": "1859144498"}, {"image_id": "1567881275", "caption": "Historical cannon at Port Louis Mauritius Africa", "id": "1567881275"}, {"image_id": "1844765870", "caption": "Vertical portrait of a young man with headphones around his neck texting on his cell phone", "id": "1844765870"}, {"image_id": "1709394749", "caption": "A male nude portrait sad", "id": "1709394749"}, {"image_id": "1590216599", "caption": "Low angle view of man playing guitar at park", "id": "1590216599"}, {"image_id": "1846400849", "caption": "A wide shot of a tractor loading silage into the truck simultaneously on a bright sunny day with clear blue sky in the background", "id": "1846400849"}, {"image_id": "1304266880", "caption": "Smiling senior man riding motor scooter in Balboa Park", "id": "1304266880"}, {"image_id": "1859332616", "caption": "Ornate building in Plaza de Espana Seville Spain", "id": "1859332616"}, {"image_id": "1844765876", "caption": "Vertical portrait of smiling friends busy using digital devices", "id": "1844765876"}, {"image_id": "1725907829", "caption": "A young woman pushing a bicycle with a basket full of shopping", "id": "1725907829"}, {"image_id": "1844196854", "caption": "Horizontal shot of summer bright blue sky with fluffy clouds", "id": "1844196854"}, {"image_id": "216570887", "caption": "Scenic view of countryside", "id": "216570887"}, {"image_id": "1571601872", "caption": "Portrait of a young girl", "id": "1571601872"}, {"image_id": "1277278817", "caption": "Businesswoman walking in center of desert road with briefcase", "id": "1277278817"}, {"image_id": "1859328572", "caption": "A vertical close up shot of a female technician smiling at camera while holding vials in a laboratory", "id": "1859328572"}, {"image_id": "1586728193", "caption": "Portrait Of Excited Family In Airport Departure Lounge", "id": "1586728193"}, {"image_id": "1711058384", "caption": "Woman playing golf wearing a visor", "id": "1711058384"}, {"image_id": "1876239875", "caption": "Woman with basket of vegetables midsection", "id": "1876239875"}, {"image_id": "1277234858", "caption": "Groom and father of the bride rehearsing speech on wedding day", "id": "1277234858"}, {"image_id": "1572512582", "caption": "Boy and girl holding sparklers in snow", "id": "1572512582"}, {"image_id": "1590071315", "caption": "Young man holding two champagne glasses sitting on a bed", "id": "1590071315"}, {"image_id": "1572549791", "caption": "A young boy with a toy bow and arrow", "id": "1572549791"}, {"image_id": "1816751286", "caption": "Elevated Close up shot of a hairdresser shampooing woman's hair in salon", "id": "1816751286"}, {"image_id": "1859353433", "caption": "A low angle medium shot of a happy teacher holding a model wind turbine while her curious students examines it in a classroom", "id": "1859353433"}, {"image_id": "1587982640", "caption": "Close up of blocks of cheese under cloth", "id": "1587982640"}, {"image_id": "1576746071", "caption": "A family on a beach", "id": "1576746071"}, {"image_id": "1878869627", "caption": "A mid shot of smiling school boy in sports uniform on a climbing equipment with his Gym teacher standing next to him", "id": "1878869627"}, {"image_id": "216584522", "caption": "Tourists enjoying waterfront in Seville Spain", "id": "216584522"}, {"image_id": "1843605629", "caption": "A doctor in sharp focus looking through an ophthalmoscope with an eye chart in the background", "id": "1843605629"}, {"image_id": "1277256947", "caption": "Diy woman decorating at home painting wall with test swatches holding paintbrush dipped in red paint", "id": "1277256947"}, {"image_id": "1878692828", "caption": "High school boy sitting with his classmates and using a drawing compass", "id": "1878692828"}, {"image_id": "1588014245", "caption": "Businessman sitting cross legged on floor using telephone", "id": "1588014245"}, {"image_id": "1277239442", "caption": "Portrait of mature woman in pajamas lying on bed at home", "id": "1277239442"}, {"image_id": "1587150074", "caption": "Detail view of various bottles in a bathroom", "id": "1587150074"}, {"image_id": "1725907877", "caption": "A young woman resting up against a tree trunk", "id": "1725907877"}, {"image_id": "216351116", "caption": "Clouds in blue sky", "id": "216351116"}, {"image_id": "1586691842", "caption": "Quality control workers inspecting and packing ripe red tomatoes in food processing plant", "id": "1586691842"}, {"image_id": "1844190341", "caption": "Low angle view male gymnast performing on pommel horse isolated on black background", "id": "1844190341"}, {"image_id": "1590349244", "caption": "Women holding nets on pier", "id": "1590349244"}, {"image_id": "1766916984", "caption": "Triplet babies sitting on potty chairs", "id": "1766916984"}, {"image_id": "1716620855", "caption": "Executive leaping in front of glass wall", "id": "1716620855"}, {"image_id": "1587990137", "caption": "A woman in evening dress walking in a car park", "id": "1587990137"}, {"image_id": "1570219568", "caption": "Clock tower and buildings in Susch Lower Engadin Engadin Grisons Switzerland", "id": "1570219568"}, {"image_id": "1868705411", "caption": "A qualified technician in scrub suit guiding a patient into the MRI scanner at the hospital", "id": "1868705411"}, {"image_id": "1263322079", "caption": "Farm field of wheat crop against blue summer sky", "id": "1263322079"}, {"image_id": "1725713018", "caption": "Twin blowing bubbles at brother in park", "id": "1725713018"}, {"image_id": "1586693789", "caption": "Combine Harvester Filling Trailer With Wheat", "id": "1586693789"}, {"image_id": "1852927223", "caption": "Vertical shot of a jubilant seated baby boy playing with abacus", "id": "1852927223"}, {"image_id": "1844731622", "caption": "Two engineers with a file discussing paperwork in the control room of a nuclear power station with computers in the foreground", "id": "1844731622"}, {"image_id": "1747441329", "caption": "Figurines of bride and groom surrounded by hearts", "id": "1747441329"}, {"image_id": "1581273491", "caption": "Grandmother and granddaughter with kebab at multi generation barbecue", "id": "1581273491"}, {"image_id": "217370285", "caption": "Forest path near Chantilly France", "id": "217370285"}, {"image_id": "1859144576", "caption": "Wide shot of a father lifting his daughter on a sunny beach", "id": "1859144576"}, {"image_id": "1839579068", "caption": "Vertical shot of a joyous mother and young daughter gardening kneeling in soil and smiling at the camera", "id": "1839579068"}, {"image_id": "1588016045", "caption": "A businesswoman in waiting room typing on a laptop", "id": "1588016045"}, {"image_id": "1578916337", "caption": "A Young Woman Holding A Birthday Cake With A Candle", "id": "1578916337"}, {"image_id": "1859176919", "caption": "Engineers in uniforms inspecting the engine of passenger jet with a torch at a hangar", "id": "1859176919"}, {"image_id": "1590161801", "caption": "Close up of gladioli flowers outdoors", "id": "1590161801"}, {"image_id": "1590178646", "caption": "Aerial view of Marsden Point North Island New Zealand", "id": "1590178646"}, {"image_id": "1590027383", "caption": "Little girl sitting in an ornate chair wearing a party dress", "id": "1590027383"}, {"image_id": "1590361847", "caption": "Close up of assorted Euro banknotes", "id": "1590361847"}, {"image_id": "1571351789", "caption": "View to Lindau Island Lindau Lake Constance Bavaria Germany", "id": "1571351789"}, {"image_id": "1862083532", "caption": "Beekeeper using smoker to check beehives in the field full of flowers", "id": "1862083532"}, {"image_id": "1847350094", "caption": "A portrait shot of a young female worker working on an aluminum light fittings on the production line with other workers", "id": "1847350094"}, {"image_id": "1859144702", "caption": "Vertical shot of a woman standing with a mountain bike in the woods", "id": "1859144702"}, {"image_id": "1710363743", "caption": "A couple about to kiss", "id": "1710363743"}, {"image_id": "216573575", "caption": "Clouds in sunny blue sky", "id": "216573575"}, {"image_id": "1571344901", "caption": "Close up of woman with band aid on toe after hiking", "id": "1571344901"}, {"image_id": "1586682233", "caption": "cutout Of Female Executive Holding Folder", "id": "1586682233"}, {"image_id": "1839584162", "caption": "Close up of a young businessman looking away from the camera in a suit and yellow tie", "id": "1839584162"}, {"image_id": "1860742169", "caption": "Horizontal shot of a mature woman and friend in swimsuits in shallow waters on the beach", "id": "1860742169"}, {"image_id": "1873350980", "caption": "Ornate Venetian masks for Venice Carnival on display in the shop window with architectural reflection Italy", "id": "1873350980"}, {"image_id": "1586668460", "caption": "A senior couple talking to a medical consultant", "id": "1586668460"}, {"image_id": "1572549851", "caption": "A senior couple in a sports car", "id": "1572549851"}, {"image_id": "1868703098", "caption": "A full length view of a serious businessman in a formal suit sitting at the laptop in the conference room", "id": "1868703098"}, {"image_id": "1865999810", "caption": "Horizontal low angle tilted shot of a smiling senior couple standing side by side in an autumn garden", "id": "1865999810"}, {"image_id": "1868716595", "caption": "A low angle wide shot of an engineer working on a metal machinery in a warehouse", "id": "1868716595"}, {"image_id": "1859347238", "caption": "A wide shot of a happy family standing together and smiling at camera with a large solar panel in background", "id": "1859347238"}, {"image_id": "1852922912", "caption": "Young woman standing with her dog at the beach on a bright sunny day", "id": "1852922912"}, {"image_id": "1572536684", "caption": "Young man flossing his teeth", "id": "1572536684"}, {"image_id": "1840553930", "caption": "Portrait of confident salesman leaning on car in a car dealership showroom", "id": "1840553930"}, {"image_id": "1297792586", "caption": "Exposed insulation in ceiling of house under construction", "id": "1297792586"}, {"image_id": "1570343048", "caption": "Be Happy sign in front of wooden logs and chair", "id": "1570343048"}, {"image_id": "1576738508", "caption": "A woman writing Christmas cards", "id": "1576738508"}, {"image_id": "1590179858", "caption": "Scenic view of the Nature Reserve of East Diamond Island Australia", "id": "1590179858"}, {"image_id": "1873296680", "caption": "High angle shot of a businessman leaning against a stack of containers at a potato food processing plant", "id": "1873296680"}, {"image_id": "1844729132", "caption": "Vertical shot of two elderly women having lunch and drinking from wine glasses at the patio table", "id": "1844729132"}, {"image_id": "1590363470", "caption": "Young woman swimming on back in swimming pool differential focus", "id": "1590363470"}, {"image_id": "1567877705", "caption": "Red poppy field", "id": "1567877705"}, {"image_id": "1865943602", "caption": "Vertical shot of a young girl carrying a soccer ball and tennis racquets with family loading camping equipment into a car boot in the background", "id": "1865943602"}, {"image_id": "1588020896", "caption": "Father and young daughter with gift on Christmas", "id": "1588020896"}, {"image_id": "1572528581", "caption": "Land's End southern tip of the Baja California peninsula Baja California Sur Mexico", "id": "1572528581"}, {"image_id": "1576774418", "caption": "Portrait of a serious looking man in a tropical garden", "id": "1576774418"}, {"image_id": "1578921188", "caption": "A portrait of a young woman sneezing", "id": "1578921188"}, {"image_id": "1586722964", "caption": "Businesspeople Having Informal Meeting In Modern Office", "id": "1586722964"}, {"image_id": "1865999660", "caption": "Horizontal shot of a daughter helping her father load groceries into car boot in a supermarket s car park", "id": "1865999660"}, {"image_id": "1570355474", "caption": "Stairs leading up to a doorway at Angkor Wat Angkor Siem Reap Cambodia", "id": "1570355474"}, {"image_id": "1277237339", "caption": "Businessman working late with by takeaway food carton on desk", "id": "1277237339"}, {"image_id": "1570559126", "caption": "View to Manaus Amazonas Amazon River Brazil", "id": "1570559126"}, {"image_id": "1590349214", "caption": "Woman with hands in hair at beach", "id": "1590349214"}, {"image_id": "1571665559", "caption": "A woman with a migraine fingers pressed to forehead", "id": "1571665559"}, {"image_id": "1277256725", "caption": "Teenage girl patient sitting in dentist chair having check up in dental surgery", "id": "1277256725"}, {"image_id": "1572544451", "caption": "Beauty portrait of a woman in a tropical setting", "id": "1572544451"}, {"image_id": "1297777316", "caption": "Female high school student in uniform in computer science lesson", "id": "1297777316"}, {"image_id": "216347945", "caption": "Clouds in sunset sky", "id": "216347945"}, {"image_id": "1571341775", "caption": "Statue of The Smith of Kochel Kochel am See Bavaria Germany", "id": "1571341775"}, {"image_id": "1588023983", "caption": "Young girl in her bedroom hugging a teddy bear", "id": "1588023983"}, {"image_id": "1873296803", "caption": "Medium shot of a middle school student painting a cloth over a canvas during an art class", "id": "1873296803"}, {"image_id": "1570574012", "caption": "Amazon River at Iquitos Peru", "id": "1570574012"}, {"image_id": "1587982679", "caption": "Boats in marina St Jean de Luz Biarritz France", "id": "1587982679"}, {"image_id": "1587996167", "caption": "Couple eating opposite ends of large sandwich", "id": "1587996167"}, {"image_id": "1859233796", "caption": "Close up of the hands of a bricklayer holding mortar on a trowel in front of a brick wall", "id": "1859233796"}, {"image_id": "1839588644", "caption": "Low angle tilted full length shot of a joyous senior couple with bicycles outdoors", "id": "1839588644"}, {"image_id": "1846707968", "caption": "A team of skilled surgeons cautiously performing an operation in the operation theatre", "id": "1846707968"}, {"image_id": "1865986910", "caption": "Horizontal rear view of a multi generational family on a jetty at sunset with two children jumping into a lake watched by family members", "id": "1865986910"}, {"image_id": "1817411568", "caption": "Family playing disc in park with boy throwing disc to parents and girl in middle", "id": "1817411568"}, {"image_id": "1722076442", "caption": "Teenagers having snowball fight on skiing holiday Tirol Austria Europe", "id": "1722076442"}, {"image_id": "1590224780", "caption": "Woman smiling and holding bouquet of flowers outdoors", "id": "1590224780"}, {"image_id": "1864643783", "caption": "Young man sitting at his desk in his home office and leaning on the table while wearing a dressing gown and smiling", "id": "1864643783"}, {"image_id": "1587849554", "caption": "Cars driving in the rain", "id": "1587849554"}, {"image_id": "1852959848", "caption": "Boys blowing bubbles sitting on monkey bars at a playground", "id": "1852959848"}, {"image_id": "1846708055", "caption": "A nurse in a blue scrub suit applying hand sanitizer from a small spray bottle while two surgeons talking to each other in the background", "id": "1846708055"}, {"image_id": "1851481436", "caption": "Father and son cycling in the forest", "id": "1851481436"}, {"image_id": "1868720606", "caption": "A medium shot of a blonde woman smiling at camera and holding a small teddy bear while sitting on sofa", "id": "1868720606"}, {"image_id": "1747441221", "caption": "Figurines of bride and groom in front of house", "id": "1747441221"}, {"image_id": "1590178124", "caption": "Person walking under umbrella at beach", "id": "1590178124"}, {"image_id": "1590070331", "caption": "A family on a beach", "id": "1590070331"}, {"image_id": "1859349041", "caption": "A Close up shot of a boy s hands filling soil in a pot with different vegetables kept on the table", "id": "1859349041"}, {"image_id": "1587811016", "caption": "View through broken window to bedroom", "id": "1587811016"}, {"image_id": "1766918292", "caption": "African woman holding world championship soccer ball", "id": "1766918292"}, {"image_id": "1570236659", "caption": "Woman doing yoga in forest", "id": "1570236659"}, {"image_id": "1585868765", "caption": "Cruiser in Kongsfjorden Spitsbergen Svalbard Norway Europe", "id": "1585868765"}, {"image_id": "1572524273", "caption": "Family lying together on beach", "id": "1572524273"}, {"image_id": "1874804252", "caption": "Container ship with cargo containers moored at commercial dock", "id": "1874804252"}, {"image_id": "1587991058", "caption": "Female scientist smiling in laboratory", "id": "1587991058"}, {"image_id": "1865943800", "caption": "Vertical shot of a family relaxing on the deck of a sailing boat out at sea with the father standing at the helm", "id": "1865943800"}, {"image_id": "1586687555", "caption": "Greece Kefalonia view of beach and ocean", "id": "1586687555"}, {"image_id": "1868703311", "caption": "A smiling businesswoman closely looking at the adhesive notes on the glass wall of a meeting room in an office", "id": "1868703311"}, {"image_id": "1590315356", "caption": "Pile of One Euro coins", "id": "1590315356"}, {"image_id": "1852646654", "caption": "A portrait of a confident pharmacist in lab coat proudly standing in the pharmacy with her arms crossed", "id": "1852646654"}, {"image_id": "1844764352", "caption": "Daughter clicking a picture of her family on a hill near the sea beside a parked car", "id": "1844764352"}, {"image_id": "1851488726", "caption": "Father carrying his daughter high in the air while the mother is watching sitting on the wooden pier of the lake", "id": "1851488726"}, {"image_id": "1878695711", "caption": "Teacher guiding female high school student with electronics in shop class", "id": "1878695711"}, {"image_id": "1855878059", "caption": "Close up of a young female baker with a basket full of baguettes standing in her bakery and smiling", "id": "1855878059"}, {"image_id": "1865999666", "caption": "Vertical shot of a mature couple hiking on a mountain trail with the woman holding a map smiles at the camera", "id": "1865999666"}, {"image_id": "1860726701", "caption": "Wide shot of a pole vault athlete going over bar with a clear blue sky and lens flare in the background", "id": "1860726701"}, {"image_id": "1722078383", "caption": "Mother embracing son and daughter on rug in living room", "id": "1722078383"}, {"image_id": "1590178541", "caption": "Pond and footbridge in garden North Island New Zealand", "id": "1590178541"}, {"image_id": "1590061787", "caption": "A young woman sitting on a bed with a laptop", "id": "1590061787"}, {"image_id": "1855882634", "caption": "Horizontal shot of a young attractive couple holding hands in a park on a bright sunny day", "id": "1855882634"}, {"image_id": "1846752404", "caption": "A wide view of an extensive meadow of summer flowers and fresh green grass under the bright blue sky with cluster of white clouds", "id": "1846752404"}, {"image_id": "1578924734", "caption": "A senior couple holding hands in autumn time", "id": "1578924734"}, {"image_id": "1586685902", "caption": "Farmer Hand Rearing Orphaned Calf", "id": "1586685902"}, {"image_id": "1277271530", "caption": "Family riding bikes in countryside on motorhome vacation", "id": "1277271530"}, {"image_id": "1587654818", "caption": "Detail view of Christmas decorations", "id": "1587654818"}, {"image_id": "1862086877", "caption": "Vertical shot of a couple drinking wine while on a picnic and smiling at each other", "id": "1862086877"}, {"image_id": "1873346279", "caption": "Teacher and middle school students learning gardening in plant greenhouse", "id": "1873346279"}, {"image_id": "1590317645", "caption": "Close up of tea plants at tea plantation Mahe Seychelles", "id": "1590317645"}, {"image_id": "1572537380", "caption": "A man splashing in the sea", "id": "1572537380"}, {"image_id": "1864637690", "caption": "Vertical shot of a teenage couple running down a sand dune with girl holding towel and boy a ball", "id": "1864637690"}, {"image_id": "1590363326", "caption": "Sunset over rock formations at beach Anse Takamaka Mahe Seychelles", "id": "1590363326"}, {"image_id": "1864643828", "caption": "Close up of a young businesswoman looking at the camera with confidence while her colleagues are working in the background", "id": "1864643828"}, {"image_id": "1855886288", "caption": "Horizontal shot of the usher bridesmaid and flower girl waving at a senior bride and groom preparing to board a helicopter outdoors with copy space", "id": "1855886288"}, {"image_id": "1590363899", "caption": "Rocks in sand Police Bay Mahe Seychelles", "id": "1590363899"}, {"image_id": "1570327499", "caption": "A man wearing a Santa costume standing in the snow", "id": "1570327499"}, {"image_id": "1872076820", "caption": "Woman decorating at home painting wall with paint brush Close up side view", "id": "1872076820"}, {"image_id": "1813179776", "caption": "Two kids in the kitchen preparing cake batter in a glass bowl while their father watches them", "id": "1813179776"}, {"image_id": "1576771940", "caption": "Young woman standing on a beach", "id": "1576771940"}, {"image_id": "216573521", "caption": "Clouds in blue sky over countryside", "id": "216573521"}, {"image_id": "1576771976", "caption": "Young businesswoman in an office using a mobile phone", "id": "1576771976"}, {"image_id": "1572474563", "caption": "Young couple eating food with chopsticks", "id": "1572474563"}, {"image_id": "1571544905", "caption": "A businesswoman reading a text message", "id": "1571544905"}, {"image_id": "1859349056", "caption": "A portrait shot of a young smiling boy showing his dirty muddy hands to the camera with teacher and other students standing in background", "id": "1859349056"}, {"image_id": "1859352068", "caption": "A side profile close up shot of a happy metalworker using a drill in a workshop", "id": "1859352068"}, {"image_id": "1869046541", "caption": "Couple relaxing on bed at home with the man using a laptop and wife holding a large cup of coffee smiles at the camera", "id": "1869046541"}, {"image_id": "1590217352", "caption": "Senior biker couple pointing with motorcycle in background in rural area", "id": "1590217352"}, {"image_id": "1297781663", "caption": "Beautiful sun setting over tranquil sea", "id": "1297781663"}, {"image_id": "1571579153", "caption": "A couple relaxing by a pool", "id": "1571579153"}, {"image_id": "1862115425", "caption": "A rear view of a Pensive businessman looking out through a glass window in a conference room", "id": "1862115425"}, {"image_id": "1865986829", "caption": "Low angle shot of a father and son jumping off a jetty into the lake at sunset as the daughter and mother cheers on", "id": "1865986829"}, {"image_id": "1766916966", "caption": "Figurines of father showing car to family", "id": "1766916966"}, {"image_id": "1578907046", "caption": "Portrait of a smiling woman", "id": "1578907046"}, {"image_id": "1852967096", "caption": "Low angle shot of a girl hula hooping with two plastic hoops", "id": "1852967096"}, {"image_id": "1874256560", "caption": "Crane loading cargo containers on container ship at commercial dock", "id": "1874256560"}, {"image_id": "1843607138", "caption": "Vertical head and shoulder portrait of a salesman leaning to shine a car with his tie reflected on the bonnet in a showroom with copy space", "id": "1843607138"}, {"image_id": "1587982616", "caption": "A woman walking in the desert holding a bunch of balloons", "id": "1587982616"}, {"image_id": "1869908210", "caption": "Overhead shot of two groups of businesspeople holding a large jigsaw piece coming together to merge with one group smiling at the camera", "id": "1869908210"}, {"image_id": "217368335", "caption": "Gentoo Penguin and chicks Hannah Point Livingston Island Antarctica", "id": "217368335"}, {"image_id": "1297781453", "caption": "Family on winter vacation sitting on sled on mountain top smiling at camera", "id": "1297781453"}, {"image_id": "1813178489", "caption": "Close up shot of a boy looking at the camera while hugging her mother", "id": "1813178489"}, {"image_id": "1572528545", "caption": "Cargo train on the line of El Chepe State of Chihuahua Mexico", "id": "1572528545"}, {"image_id": "1578224411", "caption": "A mid adult woman holding a handful of redcurrants", "id": "1578224411"}, {"image_id": "216589541", "caption": "Rooftops of idyllic village Bourdeilles Dordogne France", "id": "216589541"}, {"image_id": "1873425389", "caption": "A vertical shot of a businessman from waist down standing with a suitcase while holding a passport", "id": "1873425389"}, {"image_id": "1297793864", "caption": "Young wheat crop growing in farm field with blue sky with single tree", "id": "1297793864"}, {"image_id": "1297741778", "caption": "Man at home decorating christmas tree", "id": "1297741778"}, {"image_id": "1844724194", "caption": "Supervisor with a digital tablet watching worker scan paperwork and a box at the production line of a distribution warehouse", "id": "1844724194"}, {"image_id": "1585936838", "caption": "Woman trying perfume in organic grocery store", "id": "1585936838"}, {"image_id": "1844192033", "caption": "Girl in ballerina costume covering her face with a bunch of flowers on stage with the daylight coming through the window in the background", "id": "1844192033"}, {"image_id": "1864634687", "caption": "Horizontal shot of multi generation family sitting on the sofa with a senior woman cutting the birthday cake", "id": "1864634687"}, {"image_id": "1590179417", "caption": "Scenic view over Kingston Golf Course to Phillip Island Norfolk Island External Territory of Australia", "id": "1590179417"}, {"image_id": "1590149663", "caption": "Chimney sweep and church figurines in snow globe", "id": "1590149663"}, {"image_id": "1650251657", "caption": "Watering can on garden lawn house in background focus on foreground", "id": "1650251657"}, {"image_id": "1571664809", "caption": "Portrait of a young boy", "id": "1571664809"}, {"image_id": "1586683772", "caption": "Senior Couple Working In Beautiful Cottage Flower Garden", "id": "1586683772"}, {"image_id": "1865993645", "caption": "Horizontal shot of a multi generational family jumping up in the air in delight look at the camera during hiking on a mountain trail", "id": "1865993645"}, {"image_id": "1868722085", "caption": "A medium shot of a happy senior couple toasting wine glasses while having a picnic in a field of wildflowers", "id": "1868722085"}, {"image_id": "1572537353", "caption": "Father Christmas Santa Claus and a woman drinking champagne", "id": "1572537353"}, {"image_id": "1590206120", "caption": "Illuminated Church of the dear Lady in winter Frauenkirch near Davos Grisons Switzerland", "id": "1590206120"}, {"image_id": "1590202145", "caption": "Woman using Nordic walking poles in park", "id": "1590202145"}, {"image_id": "1864614104", "caption": "Young mother feeding milk to her baby girl from a bottle in bed and smiling", "id": "1864614104"}, {"image_id": "1570568129", "caption": "Aerial View of part of Manaus Amazonas Amazon River Brazil", "id": "1570568129"}, {"image_id": "1590363473", "caption": "Young woman swimming on back in swimming pool differential focus", "id": "1590363473"}, {"image_id": "1869318146", "caption": "Tractor attached to a baling machine running over a straw field at a rural farm", "id": "1869318146"}, {"image_id": "1588012886", "caption": "Little girl in a ballet pose", "id": "1588012886"}, {"image_id": "1587142604", "caption": "Detail view of the River Wuerm in autumn near Starnberg Upper Bavaria Germany", "id": "1587142604"}, {"image_id": "1868718335", "caption": "A Close up shot of a young blonde woman adjusting thread on a sewing machine", "id": "1868718335"}, {"image_id": "1277231348", "caption": "Underwater view of woman wearing goggles in swimming pool at camera", "id": "1277231348"}, {"image_id": "1843605623", "caption": "Close up of smiling blond woman doctor putting a stethoscope up to her ears preparing to examine a patient", "id": "1843605623"}, {"image_id": "1586659880", "caption": "Businessman talking on cell phone standing next to businesswoman Stuttgart Baden Wurttemberg Germany", "id": "1586659880"}, {"image_id": "1864640828", "caption": "Horizontal shot of three girls sitting on a school wall having lunch with copy space", "id": "1864640828"}, {"image_id": "1868714429", "caption": "A horizontal view of a scientist in clean suit closely examining the silicon wafer while standing next to a microscope in a laboratory", "id": "1868714429"}, {"image_id": "1839578894", "caption": "Horizontal head and shoulder shot of a young woman with headset and hand on chin in office looks at the camera with copy space", "id": "1839578894"}, {"image_id": "1277253347", "caption": "Active senior couple on summer countryside bicycle ride checking map sitting on rock beside bicycles taking a break", "id": "1277253347"}, {"image_id": "1865986907", "caption": "Horizontal shot of a young man swinging off a rope above the lake on a sunny day with copy space", "id": "1865986907"}, {"image_id": "1766928474", "caption": "Couple on beach in Seychelles", "id": "1766928474"}, {"image_id": "1572528563", "caption": "Humpback Whales Gorda Banks Baja California Sur Mexico", "id": "1572528563"}, {"image_id": "1852963973", "caption": "Wide shot of the interior of a wine shop", "id": "1852963973"}, {"image_id": "1586690804", "caption": "Portrait of smiling workers at food packaging production line", "id": "1586690804"}, {"image_id": "1590350009", "caption": "Helicopter in blue sky with white clouds", "id": "1590350009"}, {"image_id": "1590216815", "caption": "Low angle view of senior couple on motorcycle", "id": "1590216815"}, {"image_id": "1846008026", "caption": "A shot of an inspector shaking hands with bakery owner in a bakery with loaves of bread in the foreground", "id": "1846008026"}, {"image_id": "1766928987", "caption": "Group of people practicing yoga Kleinwalsertal Allgau Germany", "id": "1766928987"}, {"image_id": "1847350085", "caption": "A Close up shot of a young female worker smiling while drilling aluminum light fittings", "id": "1847350085"}, {"image_id": "1851485459", "caption": "Full shot of a couple holding hands and walking along a sunny beach", "id": "1851485459"}, {"image_id": "1578225881", "caption": "A mid adult woman holding a small pumpkin", "id": "1578225881"}, {"image_id": "1873340669", "caption": "Close up of a Brown trout in river jumping to eat a fishing fly", "id": "1873340669"}, {"image_id": "1572477422", "caption": "A bride standing on a beach", "id": "1572477422"}, {"image_id": "1587987743", "caption": "Portrait of a young man", "id": "1587987743"}, {"image_id": "1766918364", "caption": "Young family running in park", "id": "1766918364"}, {"image_id": "1839581558", "caption": "Rock cairn made on a rocky beach on a warm sunny day with the waves of the ocean splashing the bottom of the cairn", "id": "1839581558"}, {"image_id": "216788837", "caption": "A view of a beach", "id": "216788837"}, {"image_id": "1711088057", "caption": "A graduate holding his diploma Close up", "id": "1711088057"}, {"image_id": "1570154711", "caption": "Place de l Europe and Friday Mosque Moroni Grand Comore Island Ngazidja Comores Africa", "id": "1570154711"}, {"image_id": "1572542144", "caption": "A surfer sitting on a beach", "id": "1572542144"}, {"image_id": "1590056546", "caption": "Little girl asleep on the floor next to her birthday presents", "id": "1590056546"}, {"image_id": "1844194406", "caption": "Colorful clouds in the rays of the setting sun", "id": "1844194406"}, {"image_id": "1590341303", "caption": "North Coast of Jersey Channel Islands Great Britain UK", "id": "1590341303"}, {"image_id": "1590102662", "caption": "Couple sitting on the side of a wooden jetty by the sea holding hands", "id": "1590102662"}, {"image_id": "1586672516", "caption": "Happy mature woman sitting in mountains on winter day", "id": "1586672516"}, {"image_id": "1587994226", "caption": "Portrait of a young woman in a bath", "id": "1587994226"}, {"image_id": "1766920407", "caption": "Sisters listening to music together", "id": "1766920407"}, {"image_id": "1590360671", "caption": "Portrait of woman in park", "id": "1590360671"}, {"image_id": "1862115728", "caption": "A view of business people through a glass window while having a meeting in the conference room", "id": "1862115728"}, {"image_id": "216578222", "caption": "View of snowy mountain range", "id": "216578222"}, {"image_id": "1576782659", "caption": "Portrait of three adults standing in a row with man smiling", "id": "1576782659"}, {"image_id": "1868722196", "caption": "A medium shot of a smiling senior couple drinking wine while sitting on a blanket and having picnic in a field of wildflowers", "id": "1868722196"}, {"image_id": "1840559678", "caption": "Businessman using digital tablet with steel tubes in background in a warehouse", "id": "1840559678"}, {"image_id": "1868722040", "caption": "A Close up portrait shot of beautiful frost covered cow parsley stalks in winter with parsley in background", "id": "1868722040"}, {"image_id": "1586725763", "caption": "Businesswoman Mother On Phone With Daughter Outside Office", "id": "1586725763"}, {"image_id": "1590161825", "caption": "View of across the Inn River to Wasserburg am Inn with Imaginaeres Museum City Gate and Red Bridge Brucktor Bavaria Germany", "id": "1590161825"}, {"image_id": "1571580047", "caption": "Young man in bed reading papers Close up", "id": "1571580047"}, {"image_id": "1590327266", "caption": "Rear view of mid adult man sailing in Key West Florida USA", "id": "1590327266"}, {"image_id": "1588012268", "caption": "Two businessmen cheering in office", "id": "1588012268"}, {"image_id": "1588025525", "caption": "Detail of woman's face showing left eye with blue iris", "id": "1588025525"}, {"image_id": "1840648007", "caption": "Front view of happy logistic worker holding a clipboard in one hand while pulling the hand truck carrying big boxes of inventory in a large warehouse", "id": "1840648007"}, {"image_id": "1844733197", "caption": "Girl with a backpack smiling at the camera while climbing over a fence in a green field with a group on friends in the background", "id": "1844733197"}, {"image_id": "1572474467", "caption": "Portrait of a girl leaning against a blue painted wall", "id": "1572474467"}, {"image_id": "1570162160", "caption": "Coco de Mer with fruit at Vallee de Mai Nature Reserve Praslin Island Seychelles", "id": "1570162160"}, {"image_id": "1840562888", "caption": "Medium shot of a businessman with a clipboard and a businesswoman with digital tablet having a discussion in a manufacturing plant", "id": "1840562888"}, {"image_id": "1766904339", "caption": "Mother putting sunscreen on daughter at beach", "id": "1766904339"}, {"image_id": "1277234753", "caption": "Young tourist couple on city break vacation with guidebook on wall", "id": "1277234753"}, {"image_id": "1665810491", "caption": "Two men hugging and posing", "id": "1665810491"}, {"image_id": "1571579090", "caption": "Mother taking photograph of husband and child", "id": "1571579090"}, {"image_id": "1817411028", "caption": "Wide shot of a hand holding a watermelon with the sky in the background", "id": "1817411028"}, {"image_id": "1578922445", "caption": "A young girl walking through daffodils with a basket full of Easter eggs", "id": "1578922445"}, {"image_id": "1665809720", "caption": "Close up of woman's hands", "id": "1665809720"}, {"image_id": "1862121443", "caption": "A portrait of a smart businessman in formal suit presenting on a flipchart in the conference room", "id": "1862121443"}, {"image_id": "1876240118", "caption": "Cranes unloading container ship at commercial dock", "id": "1876240118"}, {"image_id": "1844733068", "caption": "Medium shot of the backside of a group of girls with backpacks walking across a green field", "id": "1844733068"}, {"image_id": "1664820587", "caption": "Portrait of two children playing in the snow", "id": "1664820587"}, {"image_id": "1852925555", "caption": "Horizontal profile shot of two baby boys with one smiling with hand in mouth", "id": "1852925555"}, {"image_id": "1586673506", "caption": "Mature woman holding bowl of lemons and oranges on winter day", "id": "1586673506"}, {"image_id": "1590327443", "caption": "Dromedaries in desert near Ibra Ash Sharqiyah Region Oman", "id": "1590327443"}, {"image_id": "1571338100", "caption": "Galavatnet near Gala Gudbrandsdalen Oppland Norway", "id": "1571338100"}, {"image_id": "1297787474", "caption": "Active senior man standing by running machines in gym smiling at camera", "id": "1297787474"}, {"image_id": "1725722138", "caption": "Man doing push ups in grass", "id": "1725722138"}, {"image_id": "1590061802", "caption": "Side profile portrait of a businessman looking happy", "id": "1590061802"}, {"image_id": "1588016993", "caption": "Close up of wallet with Euros sticking out", "id": "1588016993"}, {"image_id": "1578212882", "caption": "Portrait of a young woman with blue eyes looking up", "id": "1578212882"}, {"image_id": "1587990374", "caption": "Shop assistant and a customer", "id": "1587990374"}, {"image_id": "1572537389", "caption": "A woman relaxing in the sun", "id": "1572537389"}, {"image_id": "1846406198", "caption": "A full grown grass in selective focus to be cut for silage with the help of a mowing tractor", "id": "1846406198"}, {"image_id": "1570537124", "caption": "Lumber industry at Breves Channels Brazil", "id": "1570537124"}, {"image_id": "1844194412", "caption": "Dense clouds in twilight sky in the winter evening", "id": "1844194412"}, {"image_id": "1578930431", "caption": "A businessman on a walkway of large modern office building riding a scooter", "id": "1578930431"}, {"image_id": "1587843002", "caption": "Low angle view of castle in Germany", "id": "1587843002"}, {"image_id": "1844764175", "caption": "Vertical portrait of a happy joint family sitting on a wooden bridge over a stream", "id": "1844764175"}, {"image_id": "1570388246", "caption": "View from North Boat Quay over Singapore river Singapore Republic of Singapore", "id": "1570388246"}, {"image_id": "1590341246", "caption": "Young woman holding European Union flag and soccer ball", "id": "1590341246"}, {"image_id": "1813180574", "caption": "Close up shot of a volleyball net with blurred palm trees is the background", "id": "1813180574"}, {"image_id": "1587857351", "caption": "View of a young woman flamenco dancing", "id": "1587857351"}, {"image_id": "1862083313", "caption": "Technician arranging solar cells to form a solar panel on the production line", "id": "1862083313"}, {"image_id": "1857297377", "caption": "Horizontal shot of a businesswoman with folder flanked by colleagues on a runway at the airport", "id": "1857297377"}, {"image_id": "1816212906", "caption": "Medium shot of a male dentist standing with his coat on and a dental chair light in the foreground", "id": "1816212906"}, {"image_id": "1587140894", "caption": "Still life view of a bunch of different colored pumpkins", "id": "1587140894"}, {"image_id": "1572512624", "caption": "Woman in woolly hat and scarf in snow", "id": "1572512624"}, {"image_id": "1590024140", "caption": "Portrait of young woman holding her hand beside her face eye closed", "id": "1590024140"}, {"image_id": "1650268304", "caption": "Surfboard floating in pool outside on a sunny day", "id": "1650268304"}, {"image_id": "1586659856", "caption": "Two women sitting at outdoor cafe_ looking at computer Stuttgart Baden Wurttemberg Germany", "id": "1586659856"}, {"image_id": "1813180649", "caption": "A female teenager in front of a mirror trying out new clothes in the trial room", "id": "1813180649"}, {"image_id": "1846400816", "caption": "Woman smiling and hugging her boyfriend while sitting on a snowy slope with skis in the background", "id": "1846400816"}, {"image_id": "1878784238", "caption": "Tight shot of a man calling for a roadside assistance to fix the broken car", "id": "1878784238"}, {"image_id": "1576738475", "caption": "Portrait of a senior man flexing his biceps", "id": "1576738475"}, {"image_id": "1657958753", "caption": "Children looking at Christmas decorations of candles in apples near window", "id": "1657958753"}, {"image_id": "217371068", "caption": "Lone Creek Waterfall near Sabie Mpumalanga South Africa", "id": "217371068"}, {"image_id": "1590079850", "caption": "A teenage boy playing basketball", "id": "1590079850"}, {"image_id": "1570405538", "caption": "Roots of Tetrameles Nudiflora in Ta Prohm temple Angkor Siem Reap Cambodia", "id": "1570405538"}, {"image_id": "1590070535", "caption": "Portrait of two office colleagues smiling", "id": "1590070535"}, {"image_id": "1766908248", "caption": "Group of businessmen paddling in whitewater raft", "id": "1766908248"}, {"image_id": "1878784349", "caption": "Full length low angle portrait of a young woman with hands joined above the head and stretching low towards a side indoors", "id": "1878784349"}, {"image_id": "1572524321", "caption": "Family running together on beach", "id": "1572524321"}, {"image_id": "1567879937", "caption": "Doctor and nurse talking in doctor's office", "id": "1567879937"}, {"image_id": "1587110966", "caption": "Children baking with their parents", "id": "1587110966"}, {"image_id": "1747434312", "caption": "Stack of One Euro coins next to bull figurine", "id": "1747434312"}, {"image_id": "1586672585", "caption": "Senior couple walking together near frozen stream on winter day", "id": "1586672585"}, {"image_id": "1587990080", "caption": "Boy jumping over man lying on the grass", "id": "1587990080"}, {"image_id": "1590361295", "caption": "Close up of US one hundred dollar bill", "id": "1590361295"}, {"image_id": "1587996980", "caption": "Close up of grapes and wine on table outdoors", "id": "1587996980"}, {"image_id": "1847350223", "caption": "A portrait shot of a senior cheese maker smiling and holding a sample of a farmhouse cheddar on a cheese iron", "id": "1847350223"}, {"image_id": "1590183653", "caption": "Sunset and small island near Ile Des Pins New Caledonia Overseas Territory of France", "id": "1590183653"}, {"image_id": "1587139106", "caption": "Portrait of a young businesswoman", "id": "1587139106"}, {"image_id": "1590073427", "caption": "A young man applying deodorant", "id": "1590073427"}, {"image_id": "1868716577", "caption": "A medium shot of two engineers in coverall cleaning a big cylindrical machinery in a factory", "id": "1868716577"}, {"image_id": "1844765882", "caption": "Close up portrait of serious young man with headphones around his neck", "id": "1844765882"}, {"image_id": "1590352295", "caption": "Fishing lodges at the beach of Cala Llombards Mallorca Spain", "id": "1590352295"}, {"image_id": "1874803595", "caption": "Male rock climber hanging from rock with arm outstretched", "id": "1874803595"}, {"image_id": "1587996218", "caption": "Three men hugging in kitchen", "id": "1587996218"}, {"image_id": "1590201260", "caption": "Electric meters on side of house Key West Florida United States", "id": "1590201260"}, {"image_id": "215268662", "caption": "Bicycles leaning against tree in wood Close up low angle view", "id": "215268662"}, {"image_id": "1586730020", "caption": "Silhouette Of Man Standing On Mountain Peak At Sunset", "id": "1586730020"}, {"image_id": "1570573481", "caption": "Amazon River near Panelas Brazil", "id": "1570573481"}, {"image_id": "1585878749", "caption": "Magdalenafjorden with trapper hut Spitsbergen Svalbard Norway Europe", "id": "1585878749"}, {"image_id": "1843609256", "caption": "Horizontal shot of a salesman s hand handing the keys to a joyous man of a new red convertible in a showroom", "id": "1843609256"}, {"image_id": "1297774229", "caption": "Senior man polishing paintwork of classic sports car", "id": "1297774229"}, {"image_id": "1839579029", "caption": "Low angle three quarter length portrait of a businesswoman working on an electronic organizer looking sideways standing around a pillar", "id": "1839579029"}, {"image_id": "1725719297", "caption": "Mother helping daughter with homework at dining room table", "id": "1725719297"}, {"image_id": "1590224792", "caption": "Woman in sportswear stretching in urban scene at night", "id": "1590224792"}, {"image_id": "1859342231", "caption": "A medium shot of an engineer and a technician holding a blueprint and talking to each other with a large solar panel in background", "id": "1859342231"}, {"image_id": "1586728001", "caption": "Mother Washing Baby Son In Plastic Bath On Nursery Table", "id": "1586728001"}, {"image_id": "216392381", "caption": "Low angle view of autumn leaves", "id": "216392381"}, {"image_id": "1587980666", "caption": "A teenage girl using a computer", "id": "1587980666"}, {"image_id": "1578942671", "caption": "A young woman walking along the shore", "id": "1578942671"}, {"image_id": "1585868780", "caption": "Kongsfjorden Spitsbergen Svalbard Norway Europe", "id": "1585868780"}, {"image_id": "1847201729", "caption": "A Close up of fresh green grapes growing on vine with softly blurred background and sunbeam shining through", "id": "1847201729"}, {"image_id": "1590349253", "caption": "Multi ethnic women in a row at beach", "id": "1590349253"}, {"image_id": "1864660097", "caption": "Vertical shot of a senior man sitting on a rock with a road map smiles at the camera on a sunny day", "id": "1864660097"}, {"image_id": "1587989924", "caption": "Scientist examining substance in Petri dish", "id": "1587989924"}, {"image_id": "1277237186", "caption": "Smiling young woman in bathrobe brushing teeth at camera", "id": "1277237186"}, {"image_id": "1578922346", "caption": "A portrait of a young girl holding a bunch of daffodils smiling", "id": "1578922346"}, {"image_id": "1578946166", "caption": "Woman in a white bikini standing on a yacht on the sea", "id": "1578946166"}, {"image_id": "1766928930", "caption": "Senior couple hiking Kleinwalsertal Allgau Germany", "id": "1766928930"}, {"image_id": "1588006622", "caption": "Portrait of a smiling senior woman in a tropical dune setting", "id": "1588006622"}, {"image_id": "1576738559", "caption": "A young woman using a laptop", "id": "1576738559"}, {"image_id": "1766922816", "caption": "Young princess kissing frog in mid air", "id": "1766922816"}, {"image_id": "1578215057", "caption": "A young woman looking at a glass bauble", "id": "1578215057"}, {"image_id": "1590351095", "caption": "Real Cartuja de Valldemossa Mallorca Spain", "id": "1590351095"}, {"image_id": "1588014650", "caption": "A businessman holding a mobile phone and briefcase", "id": "1588014650"}, {"image_id": "1670340020", "caption": "Group of young adults in swimwear on wooden lake jetty at camera", "id": "1670340020"}, {"image_id": "1576738358", "caption": "A young woman using a laptop", "id": "1576738358"}, {"image_id": "1578252497", "caption": "A young man camping pouring water on his toothbrush", "id": "1578252497"}, {"image_id": "1581286382", "caption": "motorhome driving on open road through countryside", "id": "1581286382"}, {"image_id": "1866109355", "caption": "Patient undergoing a mammogram at x ray machine", "id": "1866109355"}, {"image_id": "1586684186", "caption": "Senior Man Working In Cottage Vegetable Garden", "id": "1586684186"}, {"image_id": "1865993414", "caption": "Vertical shot of a young woman leaning on a parked on a cliff overlooking an ocean smiles at the camera", "id": "1865993414"}, {"image_id": "1817410236", "caption": "Horizontal profile shot of a young couple sitting beside windsurfers on sandy beach with copy space", "id": "1817410236"}, {"image_id": "1277253083", "caption": "Portrait of doctor and nurse walking in hospital corridor carrying clipboard", "id": "1277253083"}, {"image_id": "1576745969", "caption": "A woman choosing a dress to wear", "id": "1576745969"}, {"image_id": "1576741856", "caption": "A man relaxing in a canoe", "id": "1576741856"}, {"image_id": "1297732649", "caption": "A mature woman sitting at a garden bench drinking orange juice", "id": "1297732649"}, {"image_id": "1587851087", "caption": "View of castle in Prague", "id": "1587851087"}, {"image_id": "1846749353", "caption": "A qualified and caring radiologist helps a smiling patient with the X ray machine in a clinic", "id": "1846749353"}, {"image_id": "1570230932", "caption": "View to Forte Falcone Portoferraio Island of Elba Province of Livorno Tuscany Italy", "id": "1570230932"}, {"image_id": "1852924232", "caption": "Senior couple standing arm in arm in the lawn outside their home on a bright sunny day", "id": "1852924232"}, {"image_id": "1878690347", "caption": "High school student reading sheet music and playing a saxophone with a music teacher playing piano in the foreground", "id": "1878690347"}, {"image_id": "1571688560", "caption": "Duomo Cathedral of Pisa Piazza del Duomo Cathedral Square Pisa Tuscany Italy", "id": "1571688560"}, {"image_id": "1590222758", "caption": "Couple drinking white wine and smiling at each other indoors", "id": "1590222758"}, {"image_id": "216586124", "caption": "Trees and field in snow covered winter landscape", "id": "216586124"}, {"image_id": "1590339833", "caption": "Young girl looking at goldfish bowl outdoors", "id": "1590339833"}, {"image_id": "1587039911", "caption": "Baby girl 3 months old", "id": "1587039911"}, {"image_id": "1587850181", "caption": "View of Charles Bridge in Prague", "id": "1587850181"}, {"image_id": "1873415597", "caption": "Horizontal shot of a senior man applying glue to a model sailboat indoors", "id": "1873415597"}, {"image_id": "1590216818", "caption": "Woman smiling with man on motorcycle in background", "id": "1590216818"}, {"image_id": "1277235539", "caption": "Beautiful bride holding up bouquet of flowers on wedding day at camera", "id": "1277235539"}, {"image_id": "1578927203", "caption": "A senior couple smiling at each other", "id": "1578927203"}, {"image_id": "1852961792", "caption": "Woman holding a cup of coffee and working on a laptop", "id": "1852961792"}, {"image_id": "1578921071", "caption": "A portrait of a young blonde woman", "id": "1578921071"}, {"image_id": "1590352154", "caption": "Bathing Beach at Cabo de Formentor Mallorca Spain", "id": "1590352154"}, {"image_id": "1587982943", "caption": "Mallard duck sitting in potted plant", "id": "1587982943"}, {"image_id": "1873351028", "caption": "Water taxi and moored boat on the sunny Grand Canal in front of San Marco Campanile and architectural buildings in Venice Italy", "id": "1873351028"}, {"image_id": "1586687435", "caption": "Greece Kefalonia Fiskardo view of sunny harbour from boat", "id": "1586687435"}, {"image_id": "1277246315", "caption": "Mother and daughter jumping on bed in pajamas at home laughing", "id": "1277246315"}, {"image_id": "1570573547", "caption": "Sunset over Amazon River near Uara Brazil", "id": "1570573547"}, {"image_id": "1590200840", "caption": "Close up of the American flag", "id": "1590200840"}, {"image_id": "1297799795", "caption": "Annoyed senior couple at home not speaking after argument", "id": "1297799795"}, {"image_id": "1590023114", "caption": "A businessman in an office three colleagues in background having a discussion", "id": "1590023114"}, {"image_id": "1570350344", "caption": "Weiler im Allgau Weiler Simmerberg Swabia Bavaria Germany", "id": "1570350344"}, {"image_id": "1297774214", "caption": "Young man exercising on cross training equipment in gym", "id": "1297774214"}, {"image_id": "1571610902", "caption": "Portrait of a senior man standing on a beach with arms outstretched", "id": "1571610902"}, {"image_id": "1874803976", "caption": "Farm worker shearing sheep for wool with traditional hand shears", "id": "1874803976"}, {"image_id": "1868722133", "caption": "A vertical shot of a happy senior man holding his wife's hand from behind while carrying a picnic basket through a field of wildflowers", "id": "1868722133"}, {"image_id": "1586685833", "caption": "Pharmacist smiling with hands in pockets in pharmacy", "id": "1586685833"}, {"image_id": "1572527816", "caption": "View from Cerro del Gallego to Urique Canyon Mexico", "id": "1572527816"}, {"image_id": "1839578912", "caption": "Horizontal upper section Close up profile shot of a young woman with headset at desk in office with copy space", "id": "1839578912"}, {"image_id": "1578207887", "caption": "Portrait of a young woman wearing a grey woollen hat smiling", "id": "1578207887"}, {"image_id": "1578927221", "caption": "A young couple pushing a bicycle in autumn time", "id": "1578927221"}, {"image_id": "1571676467", "caption": "Broken Solar Power Photovoltaic Panels Repperndorf Bavaria Germany", "id": "1571676467"}, {"image_id": "1570363838", "caption": "Fountain and Massachusetts State House in the snow Boston Massachusetts USA", "id": "1570363838"}, {"image_id": "1581273401", "caption": "Grandmother with daughter and granddaughter sitting on swing in garden", "id": "1581273401"}, {"image_id": "1710272555", "caption": "Portrait of a woman smiling", "id": "1710272555"}, {"image_id": "1586704871", "caption": "White Clouds Against Blue Summer Sky", "id": "1586704871"}, {"image_id": "1844196158", "caption": "Mature farmer looking with satisfaction at his cultivated field and having car of wheat after a working day", "id": "1844196158"}, {"image_id": "1859347163", "caption": "A portrait shot of a teacher talking with a young girl while using a lathe machine with a young boy working in foreground", "id": "1859347163"}, {"image_id": "1859182766", "caption": "Vertical shot of a male rock climber holding on to the edge of a rock", "id": "1859182766"}, {"image_id": "1857285944", "caption": "Senior couple standing in the middle of an electronics appliances store while shopping for a new computer", "id": "1857285944"}, {"image_id": "1587145085", "caption": "Low angle view of hot air balloons against blue sky Balloon Festival Albuquerque New Mexico USA", "id": "1587145085"}, {"image_id": "1709394794", "caption": "A climber helping another climber on a cliff", "id": "1709394794"}, {"image_id": "1868716469", "caption": "A high angle vertical shot of a senior farmer watching potatoes filling into a trailer in a sunny rural field", "id": "1868716469"}, {"image_id": "1844767133", "caption": "Doctor and nurse running down the hospital corridor", "id": "1844767133"}, {"image_id": "1587137267", "caption": "View of a male doctor checking a young woman's lungs", "id": "1587137267"}, {"image_id": "1862121509", "caption": "Portrait of a confident businessman having his arms crossed in a formal suit with colleagues standing in the background", "id": "1862121509"}, {"image_id": "1844733182", "caption": "Close up shot of a girl holding a compass and map in a green field with group on friends in the background", "id": "1844733182"}, {"image_id": "1869314783", "caption": "Wide shot of a couple playfully throwing leaves in an autumn park", "id": "1869314783"}, {"image_id": "1587840452", "caption": "Close up of woman smiling", "id": "1587840452"}, {"image_id": "1587839228", "caption": "Close up of man with hands to mouth", "id": "1587839228"}, {"image_id": "1864613918", "caption": "Rear view of a man carrying a tray containing breakfast for his wife who is resting on the bed in the bedroom", "id": "1864613918"}, {"image_id": "1578205904", "caption": "A young woman lying on a massage table under a tree in blossom eyes closed", "id": "1578205904"}, {"image_id": "1567881320", "caption": "Lotus at Garden near Port Louis Mauritius Africa", "id": "1567881320"}, {"image_id": "1587984359", "caption": "A young woman applying face cream", "id": "1587984359"}, {"image_id": "1578208646", "caption": "A young woman wearing a grey woollen hat trying to keep warm", "id": "1578208646"}, {"image_id": "1571663738", "caption": "A man relaxing in a pool", "id": "1571663738"}, {"image_id": "1766904423", "caption": "Sad sports fans with painted faces", "id": "1766904423"}, {"image_id": "1855878230", "caption": "Close up of a male athlete passing a relay baton to his teammate on a bright sunny day at an athletics competition", "id": "1855878230"}, {"image_id": "1868716592", "caption": "A medium shot of an engineer standing with a tablet in front of big solar panels", "id": "1868716592"}, {"image_id": "1588012913", "caption": "Portrait of a smiling senior woman in front of tropical foliage", "id": "1588012913"}, {"image_id": "1570308302", "caption": "Boats in the harbor at Syracuse Sicily Italy", "id": "1570308302"}, {"image_id": "1297800338", "caption": "Male gymnast performing on pommel horse in competition", "id": "1297800338"}, {"image_id": "1567878608", "caption": "Flock of sheep on hiking trail Davos Grisons Switzerland", "id": "1567878608"}, {"image_id": "1839585650", "caption": "Vertical shot of an African family enjoying their day out a sunny autumn day with the kids walking their dog on a leash", "id": "1839585650"}, {"image_id": "1584665534", "caption": "A young woman using a laptop", "id": "1584665534"}, {"image_id": "1590351053", "caption": "Frederic Chopin s garden at the Real Cartuja de Valldemossa Mallorca Spain", "id": "1590351053"}, {"image_id": "1576745906", "caption": "Portrait of a girl leaning against a traffic meter", "id": "1576745906"}, {"image_id": "1839580658", "caption": "Close up shot of a businessman wearing glasses and adjusting his tie with copy space available", "id": "1839580658"}, {"image_id": "1567883174", "caption": "Bamboo rainforest Nosy Mangabe Madagascar", "id": "1567883174"}, {"image_id": "1570154795", "caption": "Ostriches at Tsavo East National Park Kenya Africa", "id": "1570154795"}, {"image_id": "1586676731", "caption": "cutout Of Female Executive Using Mobile Phone", "id": "1586676731"}, {"image_id": "1572528704", "caption": "Gray Whales Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528704"}, {"image_id": "1588006658", "caption": "Mother holding young daughter smiling", "id": "1588006658"}, {"image_id": "1846771184", "caption": "Sports scientist with a digital tablet monitoring a senior runner with mask on a treadmill in a laboratory", "id": "1846771184"}, {"image_id": "1572515984", "caption": "Brightly colored flowers against white background", "id": "1572515984"}, {"image_id": "1590222773", "caption": "Two men drinking wine at wine tasting", "id": "1590222773"}, {"image_id": "1572396332", "caption": "Wooden Jetty in empty Lake of Reschen Upper Vinschgau South Tyrol Italy", "id": "1572396332"}, {"image_id": "1654572809", "caption": "Group of teenagers 12 15 standing on sandy beach", "id": "1654572809"}, {"image_id": "1817411712", "caption": "Medium shot of the Backside of a group of friends in swimwear walking along the beach holding surfboards", "id": "1817411712"}, {"image_id": "1571679299", "caption": "Carillon of the French Cathedral Berlin Germany", "id": "1571679299"}, {"image_id": "1570573988", "caption": "Little channel of Amazon River near Panelas Brazil", "id": "1570573988"}, {"image_id": "216584528", "caption": "Palm trees and walkway Alcazar Seville Spain", "id": "216584528"}, {"image_id": "1578922283", "caption": "A young woman sitting amongst daffodils", "id": "1578922283"}, {"image_id": "1839582731", "caption": "Wide shot of a stranded woman is trying to flag down another car next to her broken down car on a cold snowy day to help her out", "id": "1839582731"}, {"image_id": "1590178439", "caption": "Industrial machinery and wood shavings North Island New Zealand", "id": "1590178439"}, {"image_id": "1570405550", "caption": "Entry towers and tree at Banteay Kdei Angkor Siem Reap Cambodia", "id": "1570405550"}, {"image_id": "216578048", "caption": "Snowy mountain range and blue sky", "id": "216578048"}, {"image_id": "1588012544", "caption": "A young boy with a bunch of balloons", "id": "1588012544"}, {"image_id": "1710331514", "caption": "Man kissing his girlfriend s hand", "id": "1710331514"}, {"image_id": "1839587453", "caption": "Horizontal shot of a joyous senior couple taking a selfie on a digital camera near couch in the living room", "id": "1839587453"}, {"image_id": "1590363332", "caption": "Crucifix on rock formation Port Launay Marine National Park Mahe Seychelles", "id": "1590363332"}, {"image_id": "1588000760", "caption": "Female pharmacist giving medication to customer", "id": "1588000760"}, {"image_id": "1297790003", "caption": "Senior man checking lettuce in vegetable garden", "id": "1297790003"}, {"image_id": "1869041441", "caption": "Horizontal shot of a car salesman with a brochure showing senior couple new car in the showroom", "id": "1869041441"}, {"image_id": "1590357704", "caption": "Cathedral of Our Blessed Lady Towers of Frauenkirche Munich Germany", "id": "1590357704"}, {"image_id": "1860726971", "caption": "Low angle view of a businessman standing by a briefcase using a laptop in the desert", "id": "1860726971"}, {"image_id": "1844191892", "caption": "Side view of a male gymnast performing on parallel bars", "id": "1844191892"}, {"image_id": "1571577482", "caption": "A couple embracing by a pool", "id": "1571577482"}, {"image_id": "1578924929", "caption": "A portrait of a young mother holding her baby", "id": "1578924929"}, {"image_id": "1851405533", "caption": "Horizontal head and shoulder profile shot of a young woman in bathrobe leaning against a wall looks at the camera with copy space", "id": "1851405533"}, {"image_id": "1839589109", "caption": "A wide rear view shot of a girl with a monkey rucksack leaning and looking through a large window in an airport lounge watching an airplane taking off", "id": "1839589109"}, {"image_id": "1571662124", "caption": "A man wearing a headset", "id": "1571662124"}, {"image_id": "1586673464", "caption": "Young couple running in mountains on winter day", "id": "1586673464"}, {"image_id": "1590359342", "caption": "Los Gigantes cliffs and resort along water Tenerife Canary Islands Spain", "id": "1590359342"}, {"image_id": "1847201939", "caption": "A vertical view of a romantic couple enjoying the snowfall while standing under a red umbrella on a snowy slope of a mountain", "id": "1847201939"}, {"image_id": "1572549797", "caption": "A portrait of an attractive middle aged woman smiling", "id": "1572549797"}, {"image_id": "1578933038", "caption": "A young businessman in a waiting room reading a newspaper", "id": "1578933038"}, {"image_id": "1859328542", "caption": "A medium shot of a teacher watching a young girl and a young boy working on a wooden airplane model in a class", "id": "1859328542"}, {"image_id": "1588027631", "caption": "A young woman looking in a shop window", "id": "1588027631"}, {"image_id": "1878688736", "caption": "Vertical shot of a technician looking at the wires in the data center", "id": "1878688736"}, {"image_id": "1578927386", "caption": "Close up of woman crying", "id": "1578927386"}, {"image_id": "1590361124", "caption": "Woman practicing yoga at beach", "id": "1590361124"}, {"image_id": "1572529421", "caption": "Cactus at Urique Canyon Copper Canyon Chihuahua Mexico", "id": "1572529421"}, {"image_id": "1588000736", "caption": "Female pharmacist with medication using cell phone", "id": "1588000736"}, {"image_id": "1578915809", "caption": "A Young Woman Holding A Bowl Of Salad", "id": "1578915809"}, {"image_id": "1570160897", "caption": "Fishing boat in Zanzibar Channel Zanzibar Tanzania Africa", "id": "1570160897"}, {"image_id": "1297778792", "caption": "Nurse with boy in hospital bed smiling at camera", "id": "1297778792"}, {"image_id": "1585874039", "caption": "View from Gornercrest Zermatt Valais Switzerland Europe", "id": "1585874039"}, {"image_id": "1586704868", "caption": "Portrait Of Businesswoman Working In Busy Modern Office", "id": "1586704868"}, {"image_id": "1590070451", "caption": "Cropped portrait of a senior woman in a swimsuit at the beach holding a straw hat", "id": "1590070451"}, {"image_id": "1304265908", "caption": "Active senior man warming up with stretches before outdoor exercise", "id": "1304265908"}, {"image_id": "1840562621", "caption": "Medium wide shot of a worker standing in the aisle of a manufacturing plant with a broom and looking at the camera", "id": "1840562621"}, {"image_id": "1567876856", "caption": "Dock at Klopeiner See near Klagenfurth Carinthia Austria", "id": "1567876856"}, {"image_id": "1572546812", "caption": "A businessman with a briefcase at a modern office building", "id": "1572546812"}, {"image_id": "1590327203", "caption": "Camel market at the oasis Al Ain Emirate of Abu Dhabi United Arab Emirates", "id": "1590327203"}, {"image_id": "1586682890", "caption": "Exhibitor With Prize Winning Potatoes At Agricultural Show", "id": "1586682890"}, {"image_id": "1587854147", "caption": "View of a clock hanging from a sign post outside a building Gmuend Kaernten Austria", "id": "1587854147"}, {"image_id": "1859178449", "caption": "Vertical shot of an engineer looking up at a passenger jet at a hangar", "id": "1859178449"}, {"image_id": "1852922723", "caption": "Close up of a little girl holding up a jar containing a starfish kept in water while her brother is watching her in the background", "id": "1852922723"}, {"image_id": "1581301721", "caption": "Distribution warehouse worker pulling boxes on pallet truck", "id": "1581301721"}, {"image_id": "1864634477", "caption": "car mechanic in red overalls and protective gloves looking up at the floor of a broken down car on a hydraulic platform in an auto repair shop", "id": "1864634477"}, {"image_id": "1297741637", "caption": "Studio portrait of beautiful smiling woman with long hair in profile", "id": "1297741637"}, {"image_id": "1869317087", "caption": "Wide shot of a technician kneeling next to a cabinet and working on a laptop in a server room", "id": "1869317087"}, {"image_id": "1571368193", "caption": "Ukrainian fans at soccer game in Cape Town South Africa", "id": "1571368193"}, {"image_id": "1585868912", "caption": "Coastline Hornsund Spitsbergen Svalbard Norway Europe", "id": "1585868912"}, {"image_id": "1855880600", "caption": "Low angle shot of a tourist couple smiling at each other with shopping bags on a sunny day", "id": "1855880600"}, {"image_id": "1665810011", "caption": "Two men walking down steps", "id": "1665810011"}, {"image_id": "1586727173", "caption": "Mother And Baby Son Playing In Outdoor Paddling Pool Together", "id": "1586727173"}, {"image_id": "1590339797", "caption": "Young woman with French flag painted on face", "id": "1590339797"}, {"image_id": "1570405553", "caption": "Roots of Tetrameles Nudiflora in Ta Prohm temple Angkor Siem Reap Cambodia", "id": "1570405553"}, {"image_id": "1844196263", "caption": "Butcher in uniform behind the counter selling meat to customer", "id": "1844196263"}, {"image_id": "1878692834", "caption": "High school student completing his homework in the study hall", "id": "1878692834"}, {"image_id": "216096473", "caption": "Fishing rod on beach", "id": "216096473"}, {"image_id": "1868716514", "caption": "A wide shot of engineers standing in a forged steel foundry", "id": "1868716514"}, {"image_id": "1588015346", "caption": "Figurines of businessmen standing on Euros", "id": "1588015346"}, {"image_id": "1855878026", "caption": "Horizontal close up of a woman with hands around piles of gambling chips on the table during a game of poker at the casino", "id": "1855878026"}, {"image_id": "1864659869", "caption": "Vertical shot of a woman driving a red convertible car on a sunny day smiles at the camera", "id": "1864659869"}, {"image_id": "1586659184", "caption": "Portrait of mother putting daisies in her daughter's hair studio shot", "id": "1586659184"}, {"image_id": "1878690515", "caption": "Vertical shot of a high school student with a helix DNA model speaking to a science teacher", "id": "1878690515"}, {"image_id": "1865895200", "caption": "Middle aged woman looking over shoulder and smiling at the camera while serving herself with her friends laughing and talking to each other in the background", "id": "1865895200"}, {"image_id": "1859323709", "caption": "A wide shot of a happy senior man giving his wife a piggyback ride while smiling at camera in a park in autumn", "id": "1859323709"}, {"image_id": "1572528707", "caption": "Gray Whale Eschrichtius robustus Boca de la Soledad Baja California Sur Mexico", "id": "1572528707"}, {"image_id": "1590213677", "caption": "Doctor talking to patient in office", "id": "1590213677"}, {"image_id": "1576745204", "caption": "Close up portrait of a man chin and shoulders", "id": "1576745204"}, {"image_id": "1297775108", "caption": "Funny studio of senior businessman with large glasses smiling at camera", "id": "1297775108"}, {"image_id": "1567864766", "caption": "Boy learning to lay bricks on construction site", "id": "1567864766"}, {"image_id": "1572510344", "caption": "Young girl feeding strawberries to mother", "id": "1572510344"}, {"image_id": "1590225344", "caption": "Male teenage student sleeping on stack of books in library", "id": "1590225344"}, {"image_id": "1844196851", "caption": "Natural blue sky with clouds scene of sunset or sunrise", "id": "1844196851"}, {"image_id": "1846008134", "caption": "A portrait shot of happy girls gambling at a roulette table in a casino", "id": "1846008134"}, {"image_id": "1590350795", "caption": "Marina and illuminated Cathedral of Palma de Mallorca at night Mallorca Spain", "id": "1590350795"}, {"image_id": "1571689451", "caption": "Sultan Ahmed Mosque Blue Mosque Istanbul Turkey", "id": "1571689451"}, {"image_id": "1864622684", "caption": "Senior woman using a mobile phone outdoors and smiling at the camera while her husband is checking the engine of a car in the background", "id": "1864622684"}, {"image_id": "1587031388", "caption": "View of a fountain with historical landmark in the background Mirabell garden Fort Hohensalzburg Salzburg Austria", "id": "1587031388"}, {"image_id": "1663683845", "caption": "Teenage boys training with dumbbells in gym portrait", "id": "1663683845"}, {"image_id": "1578933023", "caption": "A young businessman in a waiting room holding a briefcase", "id": "1578933023"}, {"image_id": "1843605521", "caption": "Smiling doctor sitting with an arm placed on the desk in his office with a stethoscope around his neck and a computer in the background", "id": "1843605521"}, {"image_id": "1581270515", "caption": "Girl in school art class holding paintbrush at camera smiling at camera", "id": "1581270515"}, {"image_id": "1722076439", "caption": "Teenagers having snowball fight on skiing holiday Tirol Austria Europe", "id": "1722076439"}, {"image_id": "1590314042", "caption": "Close up of wine bottles", "id": "1590314042"}, {"image_id": "1590352280", "caption": "Lighthouse at Cap de Ses Salines Mallorca Spain", "id": "1590352280"}, {"image_id": "1572529604", "caption": "View of engine and wing on commercial airplane", "id": "1572529604"}, {"image_id": "1766903739", "caption": "Father and son cooking fish on campfire near remote lake", "id": "1766903739"}, {"image_id": "1711241600", "caption": "A Christmas place setting on a nicely set dining table", "id": "1711241600"}, {"image_id": "1576777139", "caption": "Woman holding a glass of water", "id": "1576777139"}, {"image_id": "1297776713", "caption": "Woman on sofa using credit card to shop online with digital tablet", "id": "1297776713"}, {"image_id": "1586691857", "caption": "Quality control worker checking tomatoes at production line in food processing plant", "id": "1586691857"}, {"image_id": "1855878098", "caption": "Family of four flying a kite on the beach on an overcast windy day", "id": "1855878098"}, {"image_id": "1277235635", "caption": "Businessmen and business women applauding and congratulating colleague", "id": "1277235635"}, {"image_id": "1865999579", "caption": "Selective focus on a woman holding a map hiking on a mountain trail with her husband in the background", "id": "1865999579"}, {"image_id": "1725714137", "caption": "Portrait of twin brothers smiling in park", "id": "1725714137"}, {"image_id": "1851407723", "caption": "Horizontal portrait of two cheerful young women shopping in a clothes store with one looking at the camera", "id": "1851407723"}, {"image_id": "1567889348", "caption": "Village at Nosy Komba Madagascar", "id": "1567889348"}, {"image_id": "1572524381", "caption": "Family wrapped in blanket on beach", "id": "1572524381"}, {"image_id": "1586691008", "caption": "Portrait confident quality control worker at production line in cheese processing plant", "id": "1586691008"}, {"image_id": "1587038501", "caption": "Costume Carnival participant in Venice Italy", "id": "1587038501"}, {"image_id": "1585868954", "caption": "Directional sign with caution polar bear sign Longyearbyen Spitsbergen Svalbard Norway Europe", "id": "1585868954"}, {"image_id": "1844190491", "caption": "Horizontal shot of a young girl's hand holding decorated Easter eggs", "id": "1844190491"}], "images": [{"id": "1587140930"}, {"id": "1297794716"}, {"id": "1873405220"}, {"id": "1811160950"}, {"id": "1572486425"}, {"id": "1583609237"}, {"id": "1297776779"}, {"id": "1866123341"}, {"id": "1297792622"}, {"id": "1868703086"}, {"id": "1766819739"}, {"id": "1277230520"}, {"id": "1865999636"}, {"id": "1277237108"}, {"id": "1869314615"}, {"id": "1586659883"}, {"id": "1586648897"}, {"id": "1844190626"}, {"id": "1572538823"}, {"id": "1657956392"}, {"id": "1839578876"}, {"id": "1578208685"}, {"id": "1813181234"}, {"id": "1587056594"}, {"id": "1844724134"}, {"id": "1590351173"}, {"id": "1571576855"}, {"id": "1572381272"}, {"id": "1586681510"}, {"id": "1572482078"}, {"id": "1587144479"}, {"id": "1578225338"}, {"id": "1709219594"}, {"id": "1578947231"}, {"id": "1590209309"}, {"id": "1873350869"}, {"id": "1859233562"}, {"id": "1277240015"}, {"id": "1859237084"}, {"id": "1590056570"}, {"id": "216582038"}, {"id": "1866109439"}, {"id": "1586728034"}, {"id": "1576745393"}, {"id": "1869278936"}, {"id": "1709207771"}, {"id": "1590337340"}, {"id": "1571337356"}, {"id": "1578954437"}, {"id": "1839588557"}, {"id": "1590024095"}, {"id": "1816212777"}, {"id": "1297798532"}, {"id": "1590314084"}, {"id": "1297799963"}, {"id": "1576741895"}, {"id": "1839589178"}, {"id": "1570154705"}, {"id": "1869041345"}, {"id": "1556812026"}, {"id": "1578922343"}, {"id": "1840650371"}, {"id": "1844723999"}, {"id": "1590160415"}, {"id": "1297778825"}, {"id": "1844731844"}, {"id": "1846038902"}, {"id": "1586684384"}, {"id": "1587030983"}, {"id": "1277229257"}, {"id": "1586673356"}, {"id": "1570162253"}, {"id": "1878688547"}, {"id": "1570568135"}, {"id": "1876650716"}, {"id": "1855878236"}, {"id": "1590317678"}, {"id": "1839580559"}, {"id": "1586672642"}, {"id": "1878681089"}, {"id": "1590164825"}, {"id": "1572524360"}, {"id": "1587855641"}, {"id": "1587845312"}, {"id": "1297725746"}, {"id": "217368656"}, {"id": "1578207233"}, {"id": "1590201239"}, {"id": "216586940"}, {"id": "1844729123"}, {"id": "1588003460"}, {"id": "1587144287"}, {"id": "1851485540"}, {"id": "1725713111"}, {"id": "1846059800"}, {"id": "1845993980"}, {"id": "1587857354"}, {"id": "1847335727"}, {"id": "1587998411"}, {"id": "1844731739"}, {"id": "1571338088"}, {"id": "1578924920"}, {"id": "1844766173"}, {"id": "1846752473"}, {"id": "1572528599"}, {"id": "1590361103"}, {"id": "1570154798"}, {"id": "216387086"}, {"id": "1590213614"}, {"id": "1572508223"}, {"id": "1859237117"}, {"id": "1873346399"}, {"id": "1878682946"}, {"id": "1277365355"}, {"id": "1576774415"}, {"id": "1766928966"}, {"id": "1572486347"}, {"id": "1663818179"}, {"id": "1847201882"}, {"id": "1586729777"}, {"id": "1590179393"}, {"id": "1590224714"}, {"id": "1873296866"}, {"id": "216580118"}, {"id": "216587720"}, {"id": "1873296701"}, {"id": "1277239433"}, {"id": "1578924710"}, {"id": "1297741703"}, {"id": "1864656257"}, {"id": "1859342048"}, {"id": "1572463142"}, {"id": "1576774421"}, {"id": "1587148154"}, {"id": "1873296719"}, {"id": "1576741802"}, {"id": "1872077435"}, {"id": "1587642974"}, {"id": "216582452"}, {"id": "1572538727"}, {"id": "1581271640"}, {"id": "1869316937"}, {"id": "1869316970"}, {"id": "1590210725"}, {"id": "1586648876"}, {"id": "1859331860"}, {"id": "1578207290"}, {"id": "217375592"}, {"id": "1843607165"}, {"id": "1866109304"}, {"id": "1869314603"}, {"id": "1297725806"}, {"id": "1588011569"}, {"id": "1590179312"}, {"id": "1670342054"}, {"id": "1878695720"}, {"id": "1839589106"}, {"id": "1587038600"}, {"id": "1844196359"}, {"id": "1297799906"}, {"id": "1572512522"}, {"id": "1590214457"}, {"id": "1572535112"}, {"id": "1843607000"}, {"id": "1862126084"}, {"id": "1590314159"}, {"id": "1859337146"}, {"id": "1869314585"}, {"id": "1839582923"}, {"id": "1587849773"}, {"id": "1846749455"}, {"id": "1857286055"}, {"id": "1572478493"}, {"id": "1277260814"}, {"id": "1844731667"}, {"id": "1572661478"}, {"id": "1859176814"}, {"id": "1586685053"}, {"id": "1587982730"}, {"id": "1576780013"}, {"id": "1878695747"}, {"id": "1590059978"}, {"id": "1766928975"}, {"id": "1587850169"}, {"id": "1766923872"}, {"id": "216584114"}, {"id": "1587838187"}, {"id": "1578902381"}, {"id": "1590352277"}, {"id": "1711214267"}, {"id": "1590209144"}, {"id": "1840559624"}, {"id": "1590337334"}, {"id": "1277230598"}, {"id": "1862063180"}, {"id": "1277231528"}, {"id": "1590315398"}, {"id": "1859181176"}, {"id": "1587144335"}, {"id": "1572528818"}, {"id": "1590209141"}, {"id": "1844194310"}, {"id": "1576774220"}, {"id": "1587033782"}, {"id": "1297732715"}, {"id": "216395267"}, {"id": "1766920365"}, {"id": "1578947168"}, {"id": "1590363419"}, {"id": "1297781540"}, {"id": "1869316979"}, {"id": "1586684477"}, {"id": "1578924779"}, {"id": "1852967162"}, {"id": "1297781495"}, {"id": "1874804267"}, {"id": "1766925084"}, {"id": "1571664311"}, {"id": "217362053"}, {"id": "1590177932"}, {"id": "1816749579"}, {"id": "1587031418"}, {"id": "1586723015"}, {"id": "1570350353"}, {"id": "1878778730"}, {"id": "1586684492"}, {"id": "1572542282"}, {"id": "1590216746"}, {"id": "1590163853"}, {"id": "1590070607"}, {"id": "1571663771"}, {"id": "1813179749"}, {"id": "1852925516"}, {"id": "216108530"}, {"id": "1587121172"}, {"id": "1586681936"}, {"id": "1878871124"}, {"id": "1583881124"}, {"id": "1587031040"}, {"id": "1868722163"}, {"id": "1844724236"}, {"id": "1297757135"}, {"id": "1570236710"}, {"id": "1576745915"}, {"id": "1878692735"}, {"id": "1590056627"}, {"id": "1865895140"}, {"id": "1277230358"}, {"id": "1650271805"}, {"id": "1297725704"}, {"id": "1571610938"}, {"id": "1862131307"}, {"id": "216587690"}, {"id": "1297786298"}, {"id": "1590222326"}, {"id": "1869911636"}, {"id": "1813180613"}, {"id": "1844769152"}, {"id": "1277238296"}, {"id": "1816211349"}, {"id": "1590061796"}, {"id": "1576738283"}, {"id": "1586685137"}, {"id": "1570230875"}, {"id": "1710280361"}, {"id": "1587992660"}, {"id": "1590352079"}, {"id": "1857288803"}, {"id": "1587031427"}, {"id": "1567889369"}, {"id": "1859144537"}, {"id": "1670340041"}, {"id": "1572390290"}, {"id": "1878688580"}, {"id": "1866123407"}, {"id": "1277233040"}, {"id": "1587830111"}, {"id": "1839580517"}, {"id": "1847335655"}, {"id": "1844769308"}, {"id": "1578920300"}, {"id": "1590181361"}, {"id": "1586735573"}, {"id": "1846060094"}, {"id": "1869911600"}, {"id": "1586683907"}, {"id": "1571576939"}, {"id": "1277240855"}, {"id": "1572388625"}, {"id": "1572396362"}, {"id": "1862081210"}, {"id": "1852646675"}, {"id": "1864656356"}, {"id": "1813179758"}, {"id": "1586693111"}, {"id": "1590344036"}, {"id": "1297775138"}, {"id": "1586684123"}, {"id": "1570354679"}, {"id": "1851481535"}, {"id": "1878869711"}, {"id": "1578935504"}, {"id": "1590338072"}, {"id": "216387074"}, {"id": "1586664884"}, {"id": "1587984401"}, {"id": "1570300724"}, {"id": "1669107905"}, {"id": "1304265911"}, {"id": "1578927377"}, {"id": "1572460034"}, {"id": "1586724557"}, {"id": "1578224426"}, {"id": "1840555061"}, {"id": "1277235527"}, {"id": "1843605716"}, {"id": "1570162070"}, {"id": "1846749176"}, {"id": "1839586319"}, {"id": "1590216704"}, {"id": "1277234600"}, {"id": "1873351076"}, {"id": "1578942938"}, {"id": "1587149465"}, {"id": "1586693144"}, {"id": "1578225890"}, {"id": "1578921338"}, {"id": "1710028655"}, {"id": "1590214532"}, {"id": "1581286463"}, {"id": "1587149051"}, {"id": "1590067880"}, {"id": "1578921332"}, {"id": "1587642941"}, {"id": "1844196023"}, {"id": "1670341739"}, {"id": "1766918385"}, {"id": "1590163829"}, {"id": "217367033"}, {"id": "1663817651"}, {"id": "1590360539"}, {"id": "1862067386"}, {"id": "1572546803"}, {"id": "1844765963"}, {"id": "1567877678"}, {"id": "1304266772"}, {"id": "1576772051"}, {"id": "1588002839"}, {"id": "1576774451"}, {"id": "1590149384"}, {"id": "1571518013"}, {"id": "1670339453"}, {"id": "1572527729"}, {"id": "1852935032"}, {"id": "1590327425"}, {"id": "1878694193"}, {"id": "1840553813"}, {"id": "1857297359"}, {"id": "1570154768"}, {"id": "1572537347"}, {"id": "1663817615"}, {"id": "1845993911"}, {"id": "1852961738"}, {"id": "1866109394"}, {"id": "1855916000"}, {"id": "1859334752"}, {"id": "1839585464"}, {"id": "1590210734"}, {"id": "1878695792"}, {"id": "1852961639"}, {"id": "1852924409"}, {"id": "1846752437"}, {"id": "1588008341"}, {"id": "1839587396"}, {"id": "1588002716"}, {"id": "1868714402"}, {"id": "1556812050"}, {"id": "1862081204"}, {"id": "1585879976"}, {"id": "1570365635"}, {"id": "1567879982"}, {"id": "1650267014"}, {"id": "1865856611"}, {"id": "1590215483"}, {"id": "1817410962"}, {"id": "1578927281"}, {"id": "1865993540"}, {"id": "1895443064"}, {"id": "1571366138"}, {"id": "1587139034"}, {"id": "1876650545"}, {"id": "1304264360"}, {"id": "1865993435"}, {"id": "1587642908"}, {"id": "1277235731"}, {"id": "1578927338"}, {"id": "1578906938"}, {"id": "1657966331"}, {"id": "216348173"}, {"id": "1572387446"}, {"id": "1570552790"}, {"id": "1859178629"}, {"id": "1587032009"}, {"id": "1862131412"}, {"id": "216105776"}, {"id": "1873340963"}, {"id": "1844731772"}, {"id": "1869314846"}, {"id": "1865986619"}, {"id": "1586682860"}, {"id": "1869041636"}, {"id": "1840561337"}, {"id": "1587997037"}, {"id": "1578946934"}, {"id": "1588020107"}, {"id": "1590359501"}, {"id": "1869314531"}, {"id": "1590206117"}, {"id": "1859176985"}, {"id": "1277230664"}, {"id": "1839588356"}, {"id": "1571580875"}, {"id": "1710181409"}, {"id": "1859177120"}, {"id": "1813180589"}, {"id": "1571687333"}, {"id": "1297790012"}, {"id": "1852927010"}, {"id": "1572536705"}, {"id": "1297775231"}, {"id": "1588014299"}, {"id": "1852926914"}, {"id": "1570405532"}, {"id": "1590358562"}, {"id": "1852938179"}, {"id": "1277247644"}, {"id": "405737462"}, {"id": "1572536708"}, {"id": "1878695879"}, {"id": "1590360704"}, {"id": "1587983375"}, {"id": "1859178434"}, {"id": "1864651058"}, {"id": "1586736506"}, {"id": "1840648082"}, {"id": "1587839444"}, {"id": "1576777112"}, {"id": "1581275180"}, {"id": "1840559663"}, {"id": "1590220544"}, {"id": "1878688646"}, {"id": "1859353367"}, {"id": "1587114347"}, {"id": "1908116630"}, {"id": "1857301733"}, {"id": "1868714408"}, {"id": "1588025624"}, {"id": "1571660534"}, {"id": "1587137231"}, {"id": "1843610627"}, {"id": "1869314786"}, {"id": "1570154891"}, {"id": "1588010654"}, {"id": "1852646636"}, {"id": "1866088610"}, {"id": "1840649672"}, {"id": "1869278909"}, {"id": "1297776590"}, {"id": "1587056555"}, {"id": "1722076466"}, {"id": "1570357052"}, {"id": "216587576"}, {"id": "1844194106"}, {"id": "1587982706"}, {"id": "1859339825"}, {"id": "1277252000"}, {"id": "1852959650"}, {"id": "1576746137"}, {"id": "1670346359"}, {"id": "1869907973"}, {"id": "1843609253"}, {"id": "1859334704"}, {"id": "1844190614"}, {"id": "216348149"}, {"id": "1572509090"}, {"id": "1670340668"}, {"id": "1590161795"}, {"id": "1590353468"}, {"id": "1586684429"}, {"id": "1587849824"}, {"id": "1590220499"}, {"id": "1868714348"}, {"id": "1277240063"}, {"id": "1586683733"}, {"id": "1855886183"}, {"id": "1578239030"}, {"id": "1588019078"}, {"id": "1581271655"}, {"id": "1590347597"}, {"id": "1874805098"}, {"id": "1590358559"}, {"id": "1570537148"}, {"id": "1864651007"}, {"id": "1862131448"}, {"id": "1586672651"}, {"id": "1590359507"}, {"id": "1586736467"}, {"id": "1590327437"}, {"id": "1578930485"}, {"id": "1846708154"}, {"id": "1766915472"}, {"id": "1817411673"}, {"id": "1297790066"}, {"id": "1581299096"}, {"id": "1665810884"}, {"id": "1587032267"}, {"id": "1811160932"}, {"id": "1813172246"}, {"id": "1716617837"}, {"id": "1846771055"}, {"id": "1572512429"}, {"id": "1590314078"}, {"id": "1567876805"}, {"id": "1567878509"}, {"id": "1864637657"}, {"id": "1664820515"}, {"id": "1878871115"}, {"id": "1844731643"}, {"id": "1587990323"}, {"id": "1588006856"}, {"id": "1813172240"}, {"id": "1590053357"}, {"id": "1590079862"}, {"id": "1297725731"}, {"id": "1578226835"}, {"id": "1844766209"}, {"id": "1766920434"}, {"id": "1587642932"}, {"id": "1576777307"}, {"id": "1590076856"}, {"id": "1844724179"}, {"id": "1590361106"}, {"id": "1590201227"}, {"id": "1586725778"}, {"id": "1587032270"}, {"id": "1865986811"}, {"id": "1665809573"}, {"id": "1725907826"}, {"id": "1670340671"}, {"id": "1588002842"}, {"id": "1578225329"}, {"id": "1874366402"}, {"id": "1570559111"}, {"id": "1851481529"}, {"id": "1590065399"}, {"id": "1846771040"}, {"id": "1852965386"}, {"id": "1839580637"}, {"id": "1590317615"}, {"id": "1590341225"}, {"id": "1590183779"}, {"id": "1590329693"}, {"id": "1878695750"}, {"id": "1590323561"}, {"id": "1585874003"}, {"id": "1859181137"}, {"id": "1585868897"}, {"id": "1578205946"}, {"id": "1817411529"}, {"id": "1297781582"}, {"id": "1571337425"}, {"id": "1590216794"}, {"id": "1586684582"}, {"id": "1297790018"}, {"id": "1277234690"}, {"id": "1843605758"}, {"id": "1587140855"}, {"id": "1587990356"}, {"id": "1571337404"}, {"id": "1585876535"}, {"id": "1766915469"}, {"id": "216587585"}, {"id": "1297792514"}, {"id": "1865943857"}, {"id": "1590363329"}, {"id": "1588008386"}, {"id": "1590338969"}, {"id": "1571687297"}, {"id": "1588014656"}, {"id": "1586666900"}, {"id": "1590361196"}, {"id": "1567881305"}, {"id": "1878688574"}, {"id": "1851487421"}, {"id": "216582812"}, {"id": "1297780919"}, {"id": "1843605509"}, {"id": "1839589145"}, {"id": "1586736530"}, {"id": "1865943548"}, {"id": "1571333384"}, {"id": "1304265932"}, {"id": "216351173"}, {"id": "1866112205"}, {"id": "1587856106"}, {"id": "1586684942"}, {"id": "216573359"}, {"id": "1878871253"}, {"id": "1840561301"}, {"id": "1576780175"}, {"id": "1578216593"}, {"id": "1571335163"}, {"id": "1864613927"}, {"id": "1710319457"}, {"id": "1571330813"}, {"id": "1859144684"}, {"id": "1588000721"}, {"id": "1578942776"}, {"id": "1587110885"}, {"id": "1766923860"}, {"id": "1816749549"}, {"id": "1725720734"}, {"id": "1297776776"}, {"id": "1570311758"}, {"id": "216777662"}, {"id": "1297787465"}, {"id": "1586689166"}, {"id": "1840650098"}, {"id": "1855878203"}, {"id": "1862081342"}, {"id": "1588002794"}, {"id": "1868716631"}, {"id": "1590339005"}, {"id": "1878694235"}, {"id": "1839589319"}, {"id": "1586672552"}, {"id": "1813172156"}, {"id": "1722076475"}, {"id": "1571663795"}, {"id": "1586683493"}, {"id": "1663816031"}, {"id": "1590216545"}, {"id": "1862121647"}, {"id": "1586684042"}, {"id": "1571688776"}, {"id": "1847201678"}, {"id": "1843588208"}, {"id": "1766903742"}, {"id": "1578930494"}, {"id": "1590363905"}, {"id": "1710181358"}, {"id": "1878694184"}, {"id": "1859233541"}, {"id": "1844767097"}, {"id": "1590044063"}, {"id": "1664813837"}, {"id": "1576739279"}, {"id": "1277230511"}, {"id": "1864637696"}, {"id": "1587996017"}, {"id": "1590056663"}, {"id": "1588017305"}, {"id": "1590329636"}, {"id": "1725720938"}, {"id": "1586723807"}, {"id": "1278037145"}, {"id": "1571358260"}, {"id": "1859143166"}, {"id": "1766923923"}, {"id": "1586683718"}, {"id": "1297792583"}, {"id": "1578207224"}, {"id": "1844764208"}, {"id": "1587994169"}, {"id": "1844764439"}, {"id": "1840562471"}, {"id": "1865943617"}, {"id": "1587142622"}, {"id": "1578915653"}, {"id": "1590217499"}, {"id": "1873343708"}, {"id": "1813172102"}, {"id": "1587128807"}, {"id": "1846400639"}, {"id": "1586671511"}, {"id": "1860726794"}, {"id": "1864640822"}, {"id": "1590047591"}, {"id": "1878688604"}, {"id": "1570388285"}, {"id": "1840555073"}, {"id": "1590031937"}, {"id": "1576782539"}, {"id": "1297750277"}, {"id": "1859347433"}, {"id": "1587141254"}, {"id": "1585870481"}, {"id": "1586682941"}, {"id": "1570161005"}, {"id": "1576745237"}, {"id": "1571580080"}, {"id": "1297789817"}, {"id": "1572542231"}, {"id": "1576771997"}, {"id": "1578252488"}, {"id": "1588016063"}, {"id": "216355505"}, {"id": "1590357695"}, {"id": "1844764262"}, {"id": "1844765960"}, {"id": "1571338076"}, {"id": "1587833804"}, {"id": "1590214550"}, {"id": "1578225314"}, {"id": "1840649675"}, {"id": "1840559828"}, {"id": "1277278862"}, {"id": "1878690233"}, {"id": "1586724593"}, {"id": "1571664791"}, {"id": "1570340039"}, {"id": "1586724719"}, {"id": "1852924400"}, {"id": "1859347268"}, {"id": "1868703314"}, {"id": "1277221451"}, {"id": "1572387347"}, {"id": "1590352148"}, {"id": "1567876850"}, {"id": "1297787669"}, {"id": "1874256497"}, {"id": "1572486329"}, {"id": "1868714447"}, {"id": "1860742205"}, {"id": "1571353247"}, {"id": "1873346102"}, {"id": "1297775348"}, {"id": "1859328512"}, {"id": "1864651091"}, {"id": "1585850105"}, {"id": "1586683775"}, {"id": "1586735687"}, {"id": "1590352241"}, {"id": "1878869585"}, {"id": "1297777556"}, {"id": "1588012403"}, {"id": "1851401546"}, {"id": "1586666138"}, {"id": "1588015142"}, {"id": "1844196815"}, {"id": "1586691776"}, {"id": "1590361265"}, {"id": "1813172162"}, {"id": "1840560551"}, {"id": "1572530273"}, {"id": "1844724128"}, {"id": "1671841082"}, {"id": "1297798640"}, {"id": "1859176898"}, {"id": "1587048545"}, {"id": "1297774418"}, {"id": "1571668397"}, {"id": "1587991700"}, {"id": "1277239451"}, {"id": "1297781579"}, {"id": "1297800467"}, {"id": "1859182757"}, {"id": "1859331785"}, {"id": "1846038956"}, {"id": "1878695849"}, {"id": "1572527786"}, {"id": "1878688562"}, {"id": "1859144675"}, {"id": "1277235458"}, {"id": "1586682896"}, {"id": "1866001004"}, {"id": "1587654119"}, {"id": "1570160951"}, {"id": "1590053348"}, {"id": "1587851459"}, {"id": "1587110903"}, {"id": "1576745486"}, {"id": "1844194409"}, {"id": "1571663798"}, {"id": "1859204663"}, {"id": "1590164234"}, {"id": "1864656248"}, {"id": "1572381332"}, {"id": "1766904489"}, {"id": "1859349074"}, {"id": "1570298798"}, {"id": "1588007585"}, {"id": "216108605"}, {"id": "1571353220"}, {"id": "1655771105"}, {"id": "1572530309"}, {"id": "1844194400"}, {"id": "1680424568"}, {"id": "1716611657"}, {"id": "1869318149"}, {"id": "1586685143"}, {"id": "1844769257"}, {"id": "216581234"}, {"id": "1866091451"}, {"id": "1840562315"}, {"id": "1868705204"}, {"id": "1862067377"}, {"id": "1851483824"}, {"id": "1585878731"}, {"id": "1586693288"}, {"id": "1857297389"}, {"id": "1857301709"}, {"id": "1590206162"}, {"id": "1572528611"}, {"id": "1588014647"}, {"id": "1297781651"}, {"id": "1576772045"}, {"id": "1590317528"}, {"id": "1571335133"}, {"id": "1578939221"}, {"id": "1578922427"}, {"id": "1570160996"}, {"id": "1711241162"}, {"id": "1811163218"}, {"id": "1590315305"}, {"id": "1590164864"}, {"id": "1864660103"}, {"id": "1859349170"}, {"id": "1650246044"}, {"id": "1587839042"}, {"id": "1572537425"}, {"id": "1711021526"}, {"id": "1839588581"}, {"id": "1586727959"}, {"id": "1860761720"}, {"id": "1572521447"}, {"id": "1586681570"}, {"id": "1590027443"}, {"id": "1817410956"}, {"id": "1572495314"}, {"id": "1585878797"}, {"id": "1576779983"}, {"id": "1665809801"}, {"id": "216108977"}, {"id": "1859331758"}, {"id": "1578946052"}, {"id": "1297775372"}, {"id": "1588015193"}, {"id": "1813178675"}, {"id": "1844769341"}, {"id": "1868720555"}, {"id": "1862067344"}, {"id": "1572515972"}, {"id": "1869911579"}, {"id": "1590361133"}, {"id": "1571519090"}, {"id": "1813181345"}, {"id": "1813180616"}, {"id": "1874804492"}, {"id": "1839581555"}, {"id": "1766921538"}, {"id": "1766929023"}, {"id": "1710413957"}, {"id": "1878692699"}, {"id": "1840560677"}, {"id": "1578932849"}, {"id": "1851483962"}, {"id": "1840560515"}, {"id": "1839588359"}, {"id": "1865894900"}, {"id": "1859181425"}, {"id": "1590341198"}, {"id": "1590065480"}, {"id": "1590336119"}, {"id": "1588015490"}, {"id": "216581957"}, {"id": "1571327438"}, {"id": "1859182823"}, {"id": "1859347298"}, {"id": "1844733092"}, {"id": "1581286460"}, {"id": "1817411565"}, {"id": "1570363811"}, {"id": "1572549815"}, {"id": "1864640723"}, {"id": "1590222959"}, {"id": "1843607069"}, {"id": "1590317537"}, {"id": "1664820671"}, {"id": "1570160765"}, {"id": "1590363392"}, {"id": "1766931678"}, {"id": "1725719324"}, {"id": "1670341637"}, {"id": "1578217556"}, {"id": "1590352094"}, {"id": "1843605782"}, {"id": "1587996977"}, {"id": "1578922517"}, {"id": "1852922645"}, {"id": "1576779974"}, {"id": "1587148994"}, {"id": "217372079"}, {"id": "1277230658"}, {"id": "1570552859"}, {"id": "1572529418"}, {"id": "1587032276"}, {"id": "1590323567"}, {"id": "1587831080"}, {"id": "1878688691"}, {"id": "216586736"}, {"id": "1304266766"}, {"id": "216109472"}, {"id": "1766904435"}, {"id": "1586722013"}, {"id": "1839580775"}, {"id": "1570541396"}, {"id": "1864651172"}, {"id": "1587032975"}, {"id": "1869314711"}, {"id": "1578946964"}, {"id": "1578922406"}, {"id": "1578932987"}, {"id": "1571660516"}, {"id": "1859334905"}, {"id": "1587649253"}, {"id": "216108536"}, {"id": "1586682404"}, {"id": "216581945"}, {"id": "1588012256"}, {"id": "1840553876"}, {"id": "1864643660"}, {"id": "1587992627"}, {"id": "1844194196"}, {"id": "1587132728"}, {"id": "1663818032"}, {"id": "1570154843"}, {"id": "1866091295"}, {"id": "1862131478"}, {"id": "1840650479"}, {"id": "1654577039"}, {"id": "1587646382"}, {"id": "1844769155"}, {"id": "1576777178"}, {"id": "1586684648"}, {"id": "1590181373"}, {"id": "1868718527"}, {"id": "1844196317"}, {"id": "1587032339"}, {"id": "1670342624"}, {"id": "1710363722"}, {"id": "1590329510"}, {"id": "1571579156"}, {"id": "1590102629"}, {"id": "1587982937"}, {"id": "216580475"}, {"id": "1844196239"}, {"id": "1587144266"}, {"id": "1590317651"}, {"id": "1670246444"}, {"id": "1578922382"}, {"id": "1570154807"}, {"id": "1878782306"}, {"id": "1590355016"}, {"id": "1807085765"}, {"id": "1865999723"}, {"id": "1857285956"}, {"id": "1297750457"}, {"id": "1852961912"}, {"id": "1578225902"}, {"id": "1590053474"}, {"id": "1297775183"}, {"id": "1766925108"}, {"id": "1868709398"}, {"id": "1571580872"}, {"id": "1578947120"}, {"id": "1590323627"}, {"id": "1650253274"}, {"id": "1571347802"}, {"id": "1766929032"}, {"id": "1590027392"}, {"id": "1590347606"}, {"id": "1277225282"}, {"id": "1587845321"}, {"id": "1578947102"}, {"id": "1586664137"}, {"id": "1578915716"}, {"id": "1590352109"}, {"id": "1588016972"}, {"id": "1590163835"}, {"id": "1869317015"}, {"id": "1839585569"}, {"id": "1855878308"}, {"id": "1868705180"}, {"id": "1664815124"}, {"id": "1839580601"}, {"id": "1587046664"}, {"id": "1663818551"}, {"id": "1590060182"}, {"id": "1277235470"}, {"id": "1711241957"}, {"id": "1851481526"}, {"id": "1654652111"}, {"id": "1866109295"}, {"id": "1570572920"}, {"id": "1581281498"}, {"id": "1297775360"}, {"id": "1297741688"}, {"id": "1587849752"}, {"id": "216388871"}, {"id": "1571353241"}, {"id": "1571602997"}, {"id": "1572496661"}, {"id": "1590070328"}, {"id": "1859353421"}, {"id": "1297775204"}, {"id": "1588012250"}, {"id": "1571356397"}, {"id": "1590202241"}, {"id": "217368329"}, {"id": "1590034421"}, {"id": "1876239929"}, {"id": "1878871268"}, {"id": "1581271658"}, {"id": "1576774250"}, {"id": "1586681534"}, {"id": "1845993758"}, {"id": "1586722967"}, {"id": "1586652284"}, {"id": "1587843029"}, {"id": "1864637789"}, {"id": "1868718347"}, {"id": "1766903802"}, {"id": "1578225272"}, {"id": "1873425311"}, {"id": "1297793675"}, {"id": "1859347232"}, {"id": "1583611289"}, {"id": "1590329666"}, {"id": "1590349904"}, {"id": "1844194451"}, {"id": "1588012274"}, {"id": "1571335145"}, {"id": "1571333327"}, {"id": "1671843695"}, {"id": "1868705375"}, {"id": "1587039884"}, {"id": "1869911615"}, {"id": "1710181361"}, {"id": "1277365364"}, {"id": "1588012607"}, {"id": "1722076472"}, {"id": "1860727016"}, {"id": "1817411589"}, {"id": "1663818017"}, {"id": "1813181198"}, {"id": "1590359933"}, {"id": "1576746005"}, {"id": "1587647735"}, {"id": "1572549899"}, {"id": "1277235653"}, {"id": "1571330861"}, {"id": "1664820650"}, {"id": "1661486624"}, {"id": "1587987797"}, {"id": "1297787624"}, {"id": "1813178642"}, {"id": "1571520359"}, {"id": "1847350133"}, {"id": "1878778658"}, {"id": "1859339603"}, {"id": "1869911459"}, {"id": "1277231429"}, {"id": "1590363518"}, {"id": "1277214128"}, {"id": "1587992657"}, {"id": "1766928495"}, {"id": "1859347193"}, {"id": "1873350878"}, {"id": "1844769095"}, {"id": "1839585584"}, {"id": "1578216596"}, {"id": "1590160370"}, {"id": "1585874798"}, {"id": "1864651028"}, {"id": "1570160942"}, {"id": "1844731748"}, {"id": "1665809786"}, {"id": "1840647806"}, {"id": "1586693087"}, {"id": "1570522946"}, {"id": "1588027652"}, {"id": "1840647827"}, {"id": "1766903679"}, {"id": "1277235689"}, {"id": "1586663900"}, {"id": "1587051902"}, {"id": "1587997706"}, {"id": "1572527732"}, {"id": "1846406135"}, {"id": "1586736392"}, {"id": "1839580721"}, {"id": "1277232758"}, {"id": "1585873994"}, {"id": "1590071285"}, {"id": "1572528845"}, {"id": "1874803700"}, {"id": "1578932864"}, {"id": "1572469544"}, {"id": "1572522950"}, {"id": "1839580670"}, {"id": "1590353585"}, {"id": "217368554"}, {"id": "1839581768"}, {"id": "1277240144"}, {"id": "1766903859"}, {"id": "1839585527"}, {"id": "1578909968"}, {"id": "1586727209"}, {"id": "1669108721"}, {"id": "1587835922"}, {"id": "1872076997"}, {"id": "1844769239"}, {"id": "1590051068"}, {"id": "1859331836"}, {"id": "1590337247"}, {"id": "1578924698"}, {"id": "1851475637"}, {"id": "1578932834"}, {"id": "1590163859"}, {"id": "1716618329"}, {"id": "1587121178"}, {"id": "1578939155"}, {"id": "1844728907"}, {"id": "1813172048"}, {"id": "1587982505"}, {"id": "1587137246"}, {"id": "1859352206"}, {"id": "1572482039"}, {"id": "1590200753"}, {"id": "1572661466"}, {"id": "1859144666"}, {"id": "1711231061"}, {"id": "1590355031"}, {"id": "1878695789"}, {"id": "1297800488"}, {"id": "1866123266"}, {"id": "1864613879"}, {"id": "1862121671"}, {"id": "1578212909"}, {"id": "1851488738"}, {"id": "1590361820"}, {"id": "1590347672"}, {"id": "1590060053"}, {"id": "1586672525"}, {"id": "1878692813"}, {"id": "1576779989"}, {"id": "1839589223"}, {"id": "1851483983"}, {"id": "1846008092"}, {"id": "1851481568"}, {"id": "1857286265"}, {"id": "1859331869"}, {"id": "1585868900"}, {"id": "217367066"}, {"id": "1865999534"}, {"id": "1585850123"}, {"id": "1866109511"}, {"id": "1851488744"}, {"id": "1852922861"}, {"id": "1578225863"}, {"id": "1576772060"}, {"id": "1590200738"}, {"id": "1839588572"}, {"id": "1590323588"}, {"id": "1571664314"}, {"id": "1864651037"}, {"id": "1572549887"}, {"id": "1869318053"}, {"id": "1587031196"}, {"id": "1868714480"}, {"id": "1590347594"}, {"id": "1570552784"}, {"id": "1813180646"}, {"id": "1297797335"}, {"id": "1844196050"}, {"id": "1859334866"}, {"id": "1851488735"}, {"id": "1670340674"}, {"id": "1590056606"}, {"id": "1586683922"}, {"id": "1859181131"}, {"id": "216582731"}, {"id": "1570405586"}, {"id": "1862086757"}, {"id": "1869908249"}, {"id": "1873340927"}, {"id": "1587031376"}, {"id": "1590200669"}, {"id": "1297792571"}, {"id": "1840562306"}, {"id": "1878688538"}, {"id": "1845922283"}, {"id": "1590179438"}, {"id": "1590315725"}, {"id": "1570363868"}, {"id": "1587145145"}, {"id": "1572538721"}, {"id": "1297800530"}, {"id": "1590056609"}, {"id": "1766918226"}, {"id": "1851483926"}, {"id": "217368578"}, {"id": "1297798526"}, {"id": "1840648490"}, {"id": "1665808490"}, {"id": "1855886150"}, {"id": "1590044066"}, {"id": "1844194343"}, {"id": "1578946151"}, {"id": "1670246459"}, {"id": "1868723351"}, {"id": "1844727506"}, {"id": "1586687297"}, {"id": "1590164798"}, {"id": "1586724662"}, {"id": "1671848261"}, {"id": "1567863530"}, {"id": "1586675249"}, {"id": "1873296698"}, {"id": "1855878032"}, {"id": "1840552718"}, {"id": "1590070310"}, {"id": "1587835871"}, {"id": "1576774367"}, {"id": "216576980"}, {"id": "1572535982"}, {"id": "1586730098"}, {"id": "1860726761"}, {"id": "1590347909"}, {"id": "1859143124"}, {"id": "1878692708"}, {"id": "1866001040"}, {"id": "1590149657"}, {"id": "1297794659"}, {"id": "216389351"}, {"id": "1571518010"}, {"id": "1878869618"}, {"id": "1852963967"}, {"id": "1297775291"}, {"id": "1868723399"}, {"id": "217374353"}, {"id": "1578942875"}, {"id": "1862083478"}, {"id": "1297788770"}, {"id": "1587997637"}, {"id": "216570866"}, {"id": "1576739426"}, {"id": "1586685776"}, {"id": "1588012880"}, {"id": "1587984248"}, {"id": "1840553963"}, {"id": "1859176955"}, {"id": "1587656774"}, {"id": "1663815677"}, {"id": "1869278816"}, {"id": "1710418406"}, {"id": "1839581495"}, {"id": "1852965587"}, {"id": "1578947198"}, {"id": "1864634696"}, {"id": "1859201045"}, {"id": "1586721830"}, {"id": "1590149381"}, {"id": "1587058253"}, {"id": "1844764277"}, {"id": "1297780814"}, {"id": "1587987839"}, {"id": "1711067282"}, {"id": "1576745264"}, {"id": "1864634702"}, {"id": "1570350299"}, {"id": "1864622714"}, {"id": "1576746143"}, {"id": "1650274619"}, {"id": "1859342204"}, {"id": "1571687294"}, {"id": "217367375"}, {"id": "1840647824"}, {"id": "1590224696"}, {"id": "1590359906"}, {"id": "1587995873"}, {"id": "1297797461"}, {"id": "1587131942"}, {"id": "1852965713"}, {"id": "216120050"}, {"id": "1873351118"}, {"id": "1590354869"}, {"id": "1590319592"}, {"id": "1590046034"}, {"id": "1586693084"}, {"id": "1576771991"}, {"id": "1587857282"}, {"id": "1844767151"}, {"id": "1572388604"}, {"id": "1587850184"}, {"id": "1586684546"}, {"id": "1868709506"}, {"id": "1570280723"}, {"id": "217368494"}, {"id": "1846708202"}, {"id": "1590160817"}, {"id": "1869046598"}, {"id": "1588020797"}, {"id": "1852967129"}, {"id": "1872076856"}, {"id": "1572510440"}, {"id": "1665809783"}, {"id": "1857288746"}, {"id": "1570537094"}, {"id": "1587145154"}, {"id": "1571610899"}, {"id": "1570365641"}, {"id": "1590045962"}, {"id": "1665810146"}, {"id": "1587139088"}, {"id": "1590209231"}, {"id": "1588025585"}, {"id": "1587992681"}, {"id": "1844194397"}, {"id": "1297793786"}, {"id": "1587990152"}, {"id": "1840648595"}, {"id": "1590216773"}, {"id": "1586722985"}, {"id": "1665810860"}, {"id": "217367000"}, {"id": "1570300802"}, {"id": "1587133091"}, {"id": "1576777214"}, {"id": "1590319625"}, {"id": "1865993573"}, {"id": "1590349859"}, {"id": "1588008413"}, {"id": "1587982595"}, {"id": "1587038534"}, {"id": "1590220502"}, {"id": "1263320693"}, {"id": "1590215495"}, {"id": "1297792502"}, {"id": "1846752431"}, {"id": "1587141272"}, {"id": "1590102587"}, {"id": "1297793753"}, {"id": "1570350278"}, {"id": "1571579096"}, {"id": "1840553861"}, {"id": "1710280346"}, {"id": "1277234618"}, {"id": "1571333324"}, {"id": "1590047366"}, {"id": "1587148190"}, {"id": "1873296923"}, {"id": "1572661535"}, {"id": "1570357124"}, {"id": "1846708256"}, {"id": "1277367191"}, {"id": "1716617834"}, {"id": "1846771121"}, {"id": "1590179375"}, {"id": "1297732865"}, {"id": "1588010177"}, {"id": "1859331779"}, {"id": "1857297398"}, {"id": "1873346294"}, {"id": "1843586648"}, {"id": "1840562420"}, {"id": "1864640831"}, {"id": "1747441218"}, {"id": "1578906830"}, {"id": "1571337401"}, {"id": "1590359903"}, {"id": "1852961618"}, {"id": "1816749642"}, {"id": "1587811604"}, {"id": "1587139100"}, {"id": "1570559144"}, {"id": "1866088826"}, {"id": "1576741859"}, {"id": "1844723924"}, {"id": "1578212861"}, {"id": "1873425437"}, {"id": "1277238344"}, {"id": "1277252066"}, {"id": "1587121256"}, {"id": "216348866"}, {"id": "1857297566"}, {"id": "1844194112"}, {"id": "1839589334"}, {"id": "1852925660"}, {"id": "1570537100"}, {"id": "1860743072"}, {"id": "1590338246"}, {"id": "1876650680"}, {"id": "1590202223"}, {"id": "1578942641"}, {"id": "1869316976"}, {"id": "1277231417"}, {"id": "1855882715"}, {"id": "1587142646"}, {"id": "1586672624"}, {"id": "1868703248"}, {"id": "1587825011"}, {"id": "1839588467"}, {"id": "1570348097"}, {"id": "1590316565"}, {"id": "1840559672"}, {"id": "1571520335"}, {"id": "1864651109"}, {"id": "1586664161"}, {"id": "1852959794"}, {"id": "1586666192"}, {"id": "1840563596"}, {"id": "1844733080"}, {"id": "216390656"}, {"id": "1571658911"}, {"id": "1587999317"}, {"id": "1578225827"}, {"id": "1852964030"}, {"id": "1844727695"}, {"id": "1588020971"}, {"id": "1590181229"}, {"id": "1570537085"}, {"id": "1572537401"}, {"id": "1813179956"}, {"id": "1869314819"}, {"id": "1766906310"}, {"id": "1587850103"}, {"id": "1297789934"}, {"id": "1855882694"}, {"id": "1588020458"}, {"id": "1571664065"}, {"id": "1297792517"}, {"id": "1840561322"}, {"id": "1277238272"}, {"id": "1590061580"}, {"id": "1578907001"}, {"id": "1572550082"}, {"id": "1725713087"}, {"id": "1590225530"}, {"id": "1578916778"}, {"id": "1866104786"}, {"id": "1852938092"}, {"id": "1588003487"}, {"id": "1862121458"}, {"id": "1588015202"}, {"id": "1572537374"}, {"id": "1866123557"}, {"id": "1567889354"}, {"id": "1586735456"}, {"id": "1587825008"}, {"id": "1297750442"}, {"id": "1590201311"}, {"id": "1843586543"}, {"id": "1590209306"}, {"id": "1590056537"}, {"id": "1587142634"}, {"id": "1587149084"}, {"id": "1578942626"}, {"id": "1578904175"}, {"id": "1570160999"}, {"id": "1587128762"}, {"id": "1864637525"}, {"id": "1868718413"}, {"id": "1862063159"}, {"id": "1587139037"}, {"id": "1851485501"}, {"id": "1297793720"}, {"id": "1590219863"}, {"id": "1587983462"}, {"id": "1846059797"}, {"id": "1587842939"}, {"id": "1587855617"}, {"id": "1586672588"}, {"id": "1587046703"}, {"id": "1869043580"}, {"id": "1578207215"}, {"id": "1572475913"}, {"id": "1586666927"}, {"id": "1571579078"}, {"id": "1766908257"}, {"id": "1716610052"}, {"id": "1590352163"}, {"id": "1587126701"}, {"id": "1844196368"}, {"id": "1855880564"}, {"id": "216584132"}, {"id": "1844766959"}, {"id": "1878692816"}, {"id": "1578905444"}, {"id": "1664821145"}, {"id": "1570308335"}, {"id": "1587030212"}, {"id": "1571677088"}, {"id": "1277229362"}, {"id": "1578952919"}, {"id": "1590355028"}, {"id": "1588000682"}, {"id": "1587811568"}, {"id": "1851405509"}, {"id": "1570574048"}, {"id": "1807085645"}, {"id": "1868723465"}, {"id": "1533007542"}, {"id": "1868722220"}, {"id": "1586693717"}, {"id": "1670264750"}, {"id": "1811160812"}, {"id": "1590339857"}, {"id": "1590034403"}, {"id": "1590341195"}, {"id": "1586684441"}, {"id": "1572538895"}, {"id": "1860761729"}, {"id": "1297786268"}, {"id": "1859201150"}, {"id": "1817410446"}, {"id": "1663817606"}, {"id": "1844731841"}, {"id": "1665809846"}, {"id": "1590361874"}, {"id": "1587103877"}, {"id": "1586723585"}, {"id": "1572535172"}, {"id": "1587651290"}, {"id": "1590338237"}, {"id": "1570327478"}, {"id": "1852936727"}, {"id": "1869317066"}, {"id": "1860726674"}, {"id": "1586652266"}, {"id": "1862121422"}, {"id": "1578212924"}, {"id": "1586693114"}, {"id": "1586684012"}, {"id": "1590352211"}, {"id": "1590209321"}, {"id": "1859237018"}, {"id": "1572527696"}, {"id": "1277235599"}, {"id": "1766925072"}, {"id": "1844196305"}, {"id": "1570572305"}, {"id": "1571342708"}, {"id": "1571658905"}, {"id": "1852925762"}, {"id": "1855882751"}, {"id": "1851485396"}, {"id": "1862083244"}, {"id": "1590315329"}, {"id": "1590360536"}, {"id": "1859349095"}, {"id": "1817411535"}, {"id": "1571685638"}, {"id": "1586691593"}, {"id": "1586683766"}, {"id": "1862086844"}, {"id": "1586673278"}, {"id": "1572381359"}, {"id": "1868714531"}, {"id": "1657965941"}, {"id": "1878869768"}, {"id": "1587128837"}, {"id": "1862086772"}, {"id": "1588024022"}, {"id": "1571664776"}, {"id": "1851483929"}, {"id": "1571342681"}, {"id": "1670342045"}, {"id": "1865943779"}, {"id": "1586685212"}, {"id": "1297776791"}, {"id": "1578205928"}, {"id": "1590316793"}, {"id": "1590319490"}, {"id": "1590183785"}, {"id": "1839586298"}, {"id": "1869318026"}, {"id": "1766918283"}, {"id": "1851471998"}, {"id": "1586690789"}, {"id": "1590350069"}, {"id": "1852961633"}, {"id": "1590214487"}, {"id": "1588000232"}, {"id": "1846038875"}, {"id": "1644223652"}, {"id": "1866128471"}, {"id": "1862131415"}, {"id": "1297775246"}, {"id": "1844764340"}, {"id": "1852938059"}, {"id": "1590217379"}, {"id": "1297757039"}, {"id": "1868709494"}, {"id": "1670341712"}, {"id": "1586659874"}, {"id": "1846400813"}, {"id": "1578920087"}, {"id": "1766932269"}, {"id": "1590178118"}, {"id": "1859178500"}, {"id": "1852925582"}, {"id": "1586693093"}, {"id": "1578208655"}, {"id": "1572535151"}, {"id": "1297775255"}, {"id": "1851472028"}, {"id": "1851485570"}, {"id": "1586723021"}, {"id": "1297793813"}, {"id": "1586727146"}, {"id": "1588014248"}, {"id": "216586112"}, {"id": "1852938101"}, {"id": "1587810977"}, {"id": "1710331553"}, {"id": "1588004099"}, {"id": "1586724698"}, {"id": "1590025229"}, {"id": "1570162076"}, {"id": "1852925822"}, {"id": "1297774343"}, {"id": "1571368178"}, {"id": "1578906950"}, {"id": "1581296936"}, {"id": "1873296875"}, {"id": "1813181363"}, {"id": "1664815649"}, {"id": "1570311791"}, {"id": "1859347427"}, {"id": "1747435521"}, {"id": "1570311782"}, {"id": "1590352226"}, {"id": "1846771283"}, {"id": "1576738319"}, {"id": "1571608289"}, {"id": "1587991013"}, {"id": "1670343479"}, {"id": "1862067380"}, {"id": "1570300736"}, {"id": "1710028550"}, {"id": "1873343684"}, {"id": "1571686643"}, {"id": "1665810209"}, {"id": "1572527765"}, {"id": "1590076970"}, {"id": "1570298723"}, {"id": "1571691458"}, {"id": "1869314801"}, {"id": "1590353639"}, {"id": "1590222452"}, {"id": "1570552793"}, {"id": "1846771256"}, {"id": "1570219553"}, {"id": "1590359192"}, {"id": "1857286268"}, {"id": "1586681435"}, {"id": "1859202755"}, {"id": "1297788749"}, {"id": "1766919177"}, {"id": "1670343398"}, {"id": "1663817609"}, {"id": "1571691161"}, {"id": "1868723288"}, {"id": "1571337416"}, {"id": "1570368221"}, {"id": "1590164849"}, {"id": "1874804642"}, {"id": "1578902414"}, {"id": "1297796600"}, {"id": "1766932272"}, {"id": "1588001735"}, {"id": "1297725863"}, {"id": "1578927176"}, {"id": "1590102626"}, {"id": "1862086811"}, {"id": "1572523001"}, {"id": "1874804258"}, {"id": "1587982907"}, {"id": "1571544890"}, {"id": "1571690498"}, {"id": "1847335562"}, {"id": "1588006730"}, {"id": "216580106"}, {"id": "1588021769"}, {"id": "1852961783"}, {"id": "1590360548"}, {"id": "1864660052"}, {"id": "1590160391"}, {"id": "1844767067"}, {"id": "1571690534"}, {"id": "1587137291"}, {"id": "1859331851"}, {"id": "1587990338"}, {"id": "1576741970"}, {"id": "1578905387"}, {"id": "1571660432"}, {"id": "1572527747"}, {"id": "1570161014"}, {"id": "1578927227"}, {"id": "1588003499"}, {"id": "1578924989"}, {"id": "1297732829"}, {"id": "1859334686"}, {"id": "1868703374"}, {"id": "1846038974"}, {"id": "1878694181"}, {"id": "1590323594"}, {"id": "1860761597"}, {"id": "1874803628"}, {"id": "1590337367"}, {"id": "1847350067"}, {"id": "1590164219"}, {"id": "1586683304"}, {"id": "1766922819"}, {"id": "1304266763"}, {"id": "1816749639"}, {"id": "1587826367"}, {"id": "1846060076"}, {"id": "1571686586"}, {"id": "1571342630"}, {"id": "1588026293"}, {"id": "1859339567"}, {"id": "1578942683"}, {"id": "1868714525"}, {"id": "1587988841"}, {"id": "1570154741"}, {"id": "1875313676"}, {"id": "1669107884"}, {"id": "1570572266"}, {"id": "1869279014"}, {"id": "1587128831"}, {"id": "1665810212"}, {"id": "1578942902"}, {"id": "1263322061"}, {"id": "1869908156"}, {"id": "1590079901"}, {"id": "1570522838"}, {"id": "1576739411"}, {"id": "1590053546"}, {"id": "1572509105"}, {"id": "1572512639"}, {"id": "1590047606"}, {"id": "1586652242"}, {"id": "1297788785"}, {"id": "1590200759"}, {"id": "1852935113"}, {"id": "1578942800"}, {"id": "1878688712"}, {"id": "1895443046"}, {"id": "1587825056"}, {"id": "1860726872"}, {"id": "1587836972"}, {"id": "1571590034"}, {"id": "1578927440"}, {"id": "1868703395"}, {"id": "1860742193"}, {"id": "1571676431"}, {"id": "1868709308"}, {"id": "1587831152"}, {"id": "1578946178"}, {"id": "1878695690"}, {"id": "1844729147"}, {"id": "1277246411"}, {"id": "1860726905"}, {"id": "1586722916"}, {"id": "1590202217"}, {"id": "1709253596"}, {"id": "1840559801"}, {"id": "1586703938"}, {"id": "1572549863"}, {"id": "1576782434"}, {"id": "1587982622"}, {"id": "1859176808"}, {"id": "1576738316"}, {"id": "1859201021"}, {"id": "1868709320"}, {"id": "1840555082"}, {"id": "1586722523"}, {"id": "1878871256"}, {"id": "1297780787"}, {"id": "1297781444"}, {"id": "1567889321"}, {"id": "1586681417"}, {"id": "1297800368"}, {"id": "1586690921"}, {"id": "1586668637"}, {"id": "1587842135"}, {"id": "1590355022"}, {"id": "1590164885"}, {"id": "1590352256"}, {"id": "1860742115"}, {"id": "1650257255"}, {"id": "1578915785"}, {"id": "1844733107"}, {"id": "1587139070"}, {"id": "1587144365"}, {"id": "1588001747"}, {"id": "1864650950"}, {"id": "1570300739"}, {"id": "1588016273"}, {"id": "1578935441"}, {"id": "1670340653"}, {"id": "1843586507"}, {"id": "1581271643"}, {"id": "1571330852"}, {"id": "1873346264"}, {"id": "1873296911"}, {"id": "1578226802"}, {"id": "1840555148"}, {"id": "217371155"}, {"id": "1578920069"}, {"id": "1588027574"}, {"id": "1869314642"}, {"id": "1590353372"}, {"id": "1572524384"}, {"id": "1846400555"}, {"id": "1576745252"}, {"id": "1590178448"}, {"id": "216567248"}, {"id": "1277235701"}, {"id": "1587857279"}, {"id": "1844728922"}, {"id": "1297790081"}, {"id": "1277255717"}, {"id": "1572463103"}, {"id": "1859323601"}, {"id": "1588026551"}, {"id": "1590359156"}, {"id": "1587033716"}, {"id": "1587982634"}, {"id": "1869314669"}, {"id": "1567864778"}, {"id": "1587984380"}, {"id": "1587141221"}, {"id": "216347807"}, {"id": "1578927179"}, {"id": "1846039067"}, {"id": "1650267005"}, {"id": "1578932831"}, {"id": "1844191958"}, {"id": "1571615264"}, {"id": "1590350873"}, {"id": "1570161023"}, {"id": "1859328347"}, {"id": "1297787513"}, {"id": "1576782482"}, {"id": "1878688532"}, {"id": "1571356379"}, {"id": "1590044102"}, {"id": "1571615219"}, {"id": "1816211364"}, {"id": "1586663702"}, {"id": "1590314939"}, {"id": "1585935884"}, {"id": "1570365638"}, {"id": "1590073406"}, {"id": "1581288719"}, {"id": "1851407603"}, {"id": "1590347654"}, {"id": "1844196365"}, {"id": "1844190368"}, {"id": "1843610555"}, {"id": "1587024569"}, {"id": "1586688353"}, {"id": "1578939119"}, {"id": "1868703371"}, {"id": "1587843926"}, {"id": "1587138296"}, {"id": "1587997616"}, {"id": "1297787534"}, {"id": "1839589349"}, {"id": "1859353355"}, {"id": "1844194103"}, {"id": "1587855620"}, {"id": "1570348067"}, {"id": "1578921359"}, {"id": "1297797329"}, {"id": "1587649259"}, {"id": "1587145118"}, {"id": "1725717722"}, {"id": "1297800548"}, {"id": "1840559834"}, {"id": "1663817603"}, {"id": "1571668373"}, {"id": "1578930578"}, {"id": "1277235632"}, {"id": "1670077610"}, {"id": "1869318125"}, {"id": "1570573415"}, {"id": "1277229104"}, {"id": "1570552811"}, {"id": "1852961663"}, {"id": "1844764172"}, {"id": "1590209096"}, {"id": "1859144561"}, {"id": "1590314951"}, {"id": "1864630883"}, {"id": "1590361277"}, {"id": "1587031187"}, {"id": "1578939224"}, {"id": "1590337232"}, {"id": "1590034352"}, {"id": "1578212900"}, {"id": "1297780868"}, {"id": "1586676668"}, {"id": "217369628"}, {"id": "1868709551"}, {"id": "1586689157"}, {"id": "1859201000"}, {"id": "1590329714"}, {"id": "1571665529"}, {"id": "1588002734"}, {"id": "1866123515"}, {"id": "1846059953"}, {"id": "1578904409"}, {"id": "1852963985"}, {"id": "1851401351"}, {"id": "1670342024"}, {"id": "1817411595"}, {"id": "1874804231"}, {"id": "1869911510"}, {"id": "1710417455"}, {"id": "1873351055"}, {"id": "1586735732"}, {"id": "1872077420"}, {"id": "1670339996"}, {"id": "1725717716"}, {"id": "1671831644"}, {"id": "1725719261"}, {"id": "1857288779"}, {"id": "1578909998"}, {"id": "1590160763"}, {"id": "1864613981"}, {"id": "1587048509"}, {"id": "1587030803"}, {"id": "1862121440"}, {"id": "1586684510"}, {"id": "1587651248"}, {"id": "1869911477"}, {"id": "1586693060"}, {"id": "1840560434"}, {"id": "1277235605"}, {"id": "1587856019"}, {"id": "1843609379"}, {"id": "1587145064"}, {"id": "1855915937"}, {"id": "1590210545"}, {"id": "1866104699"}, {"id": "1844764238"}, {"id": "1857288830"}, {"id": "1878688475"}, {"id": "1567876838"}, {"id": "1878784283"}, {"id": "1587986996"}, {"id": "1590067814"}, {"id": "1590336032"}, {"id": "1572522962"}, {"id": "216573533"}, {"id": "1862067158"}, {"id": "1725719318"}, {"id": "1866123422"}, {"id": "1587983372"}, {"id": "1297786244"}, {"id": "1572536693"}, {"id": "1590025187"}, {"id": "1578927392"}, {"id": "1855886135"}, {"id": "1852936730"}, {"id": "1588016180"}, {"id": "1587838199"}, {"id": "1859142974"}, {"id": "1590353426"}, {"id": "1816751121"}, {"id": "1567881278"}, {"id": "1588002611"}, {"id": "1865943716"}, {"id": "216350816"}, {"id": "1665809504"}, {"id": "1862067185"}, {"id": "1297774193"}, {"id": "1873346150"}, {"id": "1297800287"}, {"id": "1852924217"}, {"id": "1277231360"}, {"id": "1866123377"}, {"id": "1567864799"}, {"id": "1669206395"}, {"id": "1587842165"}, {"id": "1572528557"}, {"id": "1866109442"}, {"id": "1878688703"}, {"id": "1587048542"}, {"id": "1587142637"}, {"id": "1864634402"}, {"id": "1664813651"}, {"id": "1567878596"}, {"id": "1570348118"}, {"id": "1844190584"}, {"id": "1859331989"}, {"id": "1588000238"}, {"id": "1297750301"}, {"id": "1590047402"}, {"id": "1663818029"}, {"id": "1297789991"}, {"id": "1873351046"}, {"id": "1297750286"}, {"id": "1586693159"}, {"id": "1587982904"}, {"id": "1586691764"}, {"id": "1590179387"}, {"id": "1570518995"}, {"id": "1878688592"}, {"id": "1847201867"}, {"id": "1576746026"}, {"id": "1878692714"}, {"id": "217368851"}, {"id": "1873296770"}, {"id": "1570354589"}, {"id": "1843609292"}, {"id": "1277240018"}, {"id": "1572383423"}, {"id": "1878688745"}, {"id": "1587851081"}, {"id": "1590210563"}, {"id": "1570235120"}, {"id": "1590345056"}, {"id": "1844764379"}, {"id": "1844766956"}, {"id": "1570160792"}, {"id": "1868709590"}, {"id": "216582401"}, {"id": "1587028808"}, {"id": "1586687480"}, {"id": "1586696012"}, {"id": "1570572842"}, {"id": "1587990092"}, {"id": "1711190648"}, {"id": "1725713012"}, {"id": "1590209396"}, {"id": "217372085"}, {"id": "1585850393"}, {"id": "1587833729"}, {"id": "1878778766"}, {"id": "1570219565"}, {"id": "1816210998"}, {"id": "1590215609"}, {"id": "1572661520"}, {"id": "1586682350"}, {"id": "1865895095"}, {"id": "1576738535"}, {"id": "1865999789"}, {"id": "1277233118"}, {"id": "1862083241"}, {"id": "1304264975"}, {"id": "1570316318"}, {"id": "1588020749"}, {"id": "1865993660"}, {"id": "1571664818"}, {"id": "1840649711"}, {"id": "1576745363"}, {"id": "1297781504"}, {"id": "1585850102"}, {"id": "1572527849"}, {"id": "1586682917"}, {"id": "1846008038"}, {"id": "1564940417"}, {"id": "1860742049"}, {"id": "1811163215"}, {"id": "1588015148"}, {"id": "1571686592"}, {"id": "1813171970"}, {"id": "1590061676"}, {"id": "1587032324"}, {"id": "1843605590"}, {"id": "217361570"}, {"id": "1570365626"}, {"id": "1576777148"}, {"id": "1571347718"}, {"id": "1586683958"}, {"id": "1587144251"}, {"id": "1587850202"}, {"id": "1862081297"}, {"id": "1590067799"}, {"id": "1571353223"}, {"id": "1844727620"}, {"id": "1588004078"}, {"id": "1264213472"}, {"id": "1869314678"}, {"id": "1866109235"}, {"id": "1581272372"}, {"id": "1578922295"}, {"id": "1581281519"}, {"id": "1570568192"}, {"id": "1571615213"}, {"id": "1844765840"}, {"id": "1766919147"}, {"id": "1860742052"}, {"id": "1816751115"}, {"id": "1840561370"}, {"id": "1572512645"}, {"id": "1766923878"}, {"id": "1571337422"}, {"id": "1576739477"}, {"id": "1297794872"}, {"id": "1587842132"}, {"id": "1304265980"}, {"id": "1572538757"}, {"id": "1578253478"}, {"id": "1846771271"}, {"id": "1586669624"}, {"id": "1570552826"}, {"id": "1587830120"}, {"id": "1586693321"}, {"id": "1840560482"}, {"id": "1844765879"}, {"id": "1878871265"}, {"id": "1588010693"}, {"id": "1586673437"}, {"id": "1576746128"}, {"id": "1813180643"}, {"id": "1583609624"}, {"id": "1570573520"}, {"id": "1844765930"}, {"id": "1578939293"}, {"id": "1846708118"}, {"id": "1878688754"}, {"id": "1865993417"}, {"id": "1869314822"}, {"id": "1874804843"}, {"id": "1587830096"}, {"id": "1844196782"}, {"id": "1844731592"}, {"id": "1851481589"}, {"id": "1864631039"}, {"id": "1590160463"}, {"id": "1590350951"}, {"id": "1869043463"}, {"id": "1868709602"}, {"id": "1264115609"}, {"id": "1297786319"}, {"id": "1590164264"}, {"id": "1572495872"}, {"id": "1716620870"}, {"id": "1571666165"}, {"id": "1578236732"}, {"id": "1865943743"}, {"id": "1878694289"}, {"id": "1590223049"}, {"id": "1588002851"}, {"id": "1846749416"}, {"id": "1578916826"}, {"id": "1859237132"}, {"id": "1277256896"}, {"id": "1590317663"}, {"id": "1857286271"}, {"id": "1840648079"}, {"id": "1578935330"}, {"id": "1859339816"}, {"id": "1588010675"}, {"id": "1847201708"}, {"id": "1586672612"}, {"id": "1571331545"}, {"id": "1570292714"}, {"id": "1840648583"}, {"id": "1586723816"}, {"id": "1590350093"}, {"id": "1586673368"}, {"id": "1586693990"}, {"id": "1586735534"}, {"id": "1873340894"}, {"id": "1590213665"}, {"id": "1277231648"}, {"id": "1585878071"}, {"id": "1670342075"}, {"id": "1297786340"}, {"id": "1851401390"}, {"id": "1873350941"}, {"id": "1572528755"}, {"id": "1878695834"}, {"id": "1851481580"}, {"id": "1572524330"}, {"id": "1587028136"}, {"id": "1578212969"}, {"id": "1297774187"}, {"id": "1865986955"}, {"id": "1578924755"}, {"id": "1590336047"}, {"id": "1567876817"}, {"id": "1766918214"}, {"id": "1588012814"}, {"id": "1586685119"}, {"id": "1570164665"}, {"id": "1590352142"}, {"id": "1839580727"}, {"id": "1588011629"}, {"id": "1857288767"}, {"id": "216356894"}, {"id": "1844190422"}, {"id": "1277238107"}, {"id": "1813180760"}, {"id": "1571337437"}, {"id": "1297732676"}, {"id": "1578942659"}, {"id": "1587996212"}, {"id": "1862126105"}, {"id": "1840648592"}, {"id": "1590178595"}, {"id": "1571691158"}, {"id": "217368557"}, {"id": "1844731859"}, {"id": "1657996766"}, {"id": "1590214643"}, {"id": "1851401237"}, {"id": "1571337443"}, {"id": "1571686631"}, {"id": "1277228987"}, {"id": "1586683499"}, {"id": "1590178592"}, {"id": "1590073445"}, {"id": "1576780151"}, {"id": "1572661505"}, {"id": "1587048551"}, {"id": "1304263382"}, {"id": "1587838010"}, {"id": "1578930443"}, {"id": "1570388279"}, {"id": "1571518034"}, {"id": "1578922415"}, {"id": "1590023090"}, {"id": "1846708181"}, {"id": "1570327397"}, {"id": "1570160798"}, {"id": "1587138344"}, {"id": "1587849455"}, {"id": "1297796729"}, {"id": "1576772057"}, {"id": "1590070412"}, {"id": "1859202752"}, {"id": "1663683863"}, {"id": "1872077114"}, {"id": "1572527672"}, {"id": "1590339008"}, {"id": "1297783715"}, {"id": "1590027413"}, {"id": "1843607216"}, {"id": "1859182868"}, {"id": "1588023989"}, {"id": "1851471929"}, {"id": "1859323628"}, {"id": "1860742184"}, {"id": "1304266796"}, {"id": "1571686661"}, {"id": "1590343523"}, {"id": "1725907856"}, {"id": "1587991697"}, {"id": "1588025561"}, {"id": "1583609294"}, {"id": "1869314750"}, {"id": "1590215531"}, {"id": "1587987647"}, {"id": "1590220547"}, {"id": "1572509003"}, {"id": "1587997634"}, {"id": "1844729084"}, {"id": "1587060989"}, {"id": "1572388631"}, {"id": "1588000322"}, {"id": "1839578870"}, {"id": "1587119147"}, {"id": "1710247385"}, {"id": "1590179291"}, {"id": "1862086763"}, {"id": "1865993597"}, {"id": "1571664752"}, {"id": "1587857291"}, {"id": "1590323633"}, {"id": "1277256734"}, {"id": "1878694187"}, {"id": "1590319388"}, {"id": "1571351795"}, {"id": "1851407780"}, {"id": "1571601965"}, {"id": "1578935336"}, {"id": "1868723420"}, {"id": "1843605506"}, {"id": "1840555037"}, {"id": "1869311939"}, {"id": "1873296704"}, {"id": "1587028127"}, {"id": "1586690990"}, {"id": "1587982256"}, {"id": "1586682932"}, {"id": "1852927187"}, {"id": "1297799915"}, {"id": "1855916165"}, {"id": "1578226847"}, {"id": "1843605515"}, {"id": "1587119117"}, {"id": "1865999621"}, {"id": "1297788671"}, {"id": "1864634420"}, {"id": "1857286046"}, {"id": "1587649232"}, {"id": "1588016240"}, {"id": "1572527714"}, {"id": "1297750358"}, {"id": "1859339615"}, {"id": "1587838220"}, {"id": "1873296878"}, {"id": "1839579071"}, {"id": "1586676692"}, {"id": "1590338879"}, {"id": "1570311800"}, {"id": "1590164792"}, {"id": "1845993722"}, {"id": "1587149030"}, {"id": "216387149"}, {"id": "1571517989"}, {"id": "1839580598"}, {"id": "1570235681"}, {"id": "1873415564"}, {"id": "1590102605"}, {"id": "1868703239"}, {"id": "1587865385"}, {"id": "1572395051"}, {"id": "1578904442"}, {"id": "1590338870"}, {"id": "1578920285"}, {"id": "1813180016"}, {"id": "1851483722"}, {"id": "1844196266"}, {"id": "1587982898"}, {"id": "1878871142"}, {"id": "1576780049"}, {"id": "1570162181"}, {"id": "1807085666"}, {"id": "1297750196"}, {"id": "1586681303"}, {"id": "1578935492"}, {"id": "1586738528"}, {"id": "1587855632"}, {"id": "1587031433"}, {"id": "1587110939"}, {"id": "1840650458"}, {"id": "1859178383"}, {"id": "1655856812"}, {"id": "1585868759"}, {"id": "1572508304"}, {"id": "1865856617"}, {"id": "1571686685"}, {"id": "1859143181"}, {"id": "1840648532"}, {"id": "1277260760"}, {"id": "1852646690"}, {"id": "1864656119"}, {"id": "1839588641"}, {"id": "216779531"}, {"id": "1844724098"}, {"id": "1868723291"}, {"id": "1586668580"}, {"id": "1844733188"}, {"id": "1869317168"}, {"id": "1590347663"}, {"id": "1570235027"}, {"id": "1846060046"}, {"id": "1859339783"}, {"id": "1817411649"}, {"id": "1766918361"}, {"id": "1570355504"}, {"id": "1586736533"}, {"id": "1670339441"}, {"id": "1869317177"}, {"id": "1590220055"}, {"id": "1590161897"}, {"id": "1588017254"}, {"id": "1587992639"}, {"id": "1852935128"}, {"id": "1852967069"}, {"id": "1297741907"}, {"id": "1862067146"}, {"id": "1590202229"}, {"id": "1590179900"}, {"id": "1587826376"}, {"id": "1586672558"}, {"id": "1844196362"}, {"id": "1571576858"}, {"id": "1570316207"}, {"id": "1576779914"}, {"id": "1859339570"}, {"id": "1817410167"}, {"id": "1864640705"}, {"id": "1572390227"}, {"id": "1588004090"}, {"id": "1852646705"}, {"id": "1304266799"}, {"id": "1588024832"}, {"id": "1865895089"}, {"id": "1297789835"}, {"id": "1859237114"}, {"id": "1590341132"}, {"id": "1868723498"}, {"id": "1277255708"}, {"id": "1859323736"}, {"id": "1587128399"}, {"id": "1578212948"}, {"id": "1586727047"}, {"id": "1851471974"}, {"id": "1590031952"}, {"id": "1570355486"}, {"id": "1578252518"}, {"id": "1840648535"}, {"id": "1572383462"}, {"id": "1840563155"}, {"id": "1586663939"}, {"id": "216583037"}, {"id": "1844766023"}, {"id": "1862121449"}, {"id": "1297794593"}, {"id": "1846059965"}, {"id": "1852935203"}, {"id": "1576782605"}, {"id": "1868716364"}, {"id": "1817410197"}, {"id": "1840561325"}, {"id": "1843610573"}, {"id": "1866091286"}, {"id": "1572515954"}, {"id": "1844723984"}, {"id": "1297776785"}, {"id": "1878695831"}, {"id": "1869314699"}, {"id": "1578910088"}, {"id": "1663685435"}, {"id": "1570219580"}, {"id": "1297774409"}, {"id": "1588004072"}, {"id": "1868716628"}, {"id": "1585874846"}, {"id": "1851487436"}, {"id": "1571356433"}, {"id": "1581271664"}, {"id": "1817410293"}, {"id": "1840555169"}, {"id": "1865986901"}, {"id": "1860726929"}, {"id": "1859182799"}, {"id": "1658726105"}, {"id": "1844196209"}, {"id": "1585871138"}, {"id": "1297794782"}, {"id": "1571353160"}, {"id": "1669207298"}, {"id": "1297781591"}, {"id": "1869317189"}, {"id": "1590341258"}, {"id": "1840560557"}, {"id": "1590316556"}, {"id": "1878692939"}, {"id": "1586723018"}, {"id": "1570568150"}, {"id": "1590047414"}, {"id": "1813178627"}, {"id": "1869908177"}, {"id": "1590361211"}, {"id": "1878695645"}, {"id": "1590209345"}, {"id": "1846752701"}, {"id": "1865993426"}, {"id": "1852959830"}, {"id": "1865986748"}, {"id": "1587048476"}, {"id": "1590338972"}, {"id": "1297787657"}, {"id": "1868718245"}, {"id": "1840552994"}, {"id": "1590358655"}, {"id": "1590059975"}, {"id": "1571577515"}, {"id": "1586673398"}, {"id": "1586681456"}, {"id": "1297750208"}, {"id": "1590355007"}, {"id": "1859328602"}, {"id": "1846749374"}, {"id": "1571602994"}, {"id": "1846749428"}, {"id": "1846749287"}, {"id": "1859353397"}, {"id": "1868703176"}, {"id": "1839582782"}, {"id": "1590061574"}, {"id": "1864656173"}, {"id": "1857285962"}, {"id": "1590206171"}, {"id": "1587132776"}, {"id": "1277231591"}, {"id": "1587990275"}, {"id": "1855882772"}, {"id": "1866091574"}, {"id": "1586691629"}, {"id": "1865986865"}, {"id": "216580229"}, {"id": "1588008527"}, {"id": "1859144609"}, {"id": "1869314507"}, {"id": "1840650428"}, {"id": "1570154864"}, {"id": "1590179306"}, {"id": "1590359936"}, {"id": "1297793582"}, {"id": "1846749431"}, {"id": "1578946118"}, {"id": "1585869983"}, {"id": "1878692822"}, {"id": "1851483791"}, {"id": "1297792520"}, {"id": "1586735516"}, {"id": "1859144495"}, {"id": "1581280190"}, {"id": "1855882733"}, {"id": "1585935404"}, {"id": "1843609178"}, {"id": "1843586669"}, {"id": "1570340063"}, {"id": "1855880507"}, {"id": "1297789943"}, {"id": "1839578909"}, {"id": "1852961708"}, {"id": "1578224369"}, {"id": "1586675237"}, {"id": "1570327403"}, {"id": "1571686673"}, {"id": "1855878071"}, {"id": "1844764214"}, {"id": "1587811031"}, {"id": "1587859688"}, {"id": "1570354577"}, {"id": "1570355459"}, {"id": "1277282483"}, {"id": "1588000670"}, {"id": "1710331535"}, {"id": "1578207191"}, {"id": "1297776680"}, {"id": "1590224888"}, {"id": "1859323616"}, {"id": "1865999612"}, {"id": "1844727527"}, {"id": "1866091556"}, {"id": "1877642744"}, {"id": "1588015208"}, {"id": "1866109220"}, {"id": "1587845762"}, {"id": "1578927167"}, {"id": "1578904343"}, {"id": "1725718001"}, {"id": "1908116561"}, {"id": "1587030203"}, {"id": "1865993663"}, {"id": "1845993893"}, {"id": "1578932960"}, {"id": "1578902531"}, {"id": "1571353142"}, {"id": "1869278966"}, {"id": "1587032360"}, {"id": "1578921110"}, {"id": "1571330879"}, {"id": "1297775297"}, {"id": "1587034640"}, {"id": "1869279005"}, {"id": "1865999801"}, {"id": "1587983366"}, {"id": "1876092134"}, {"id": "1874256509"}, {"id": "1851401333"}, {"id": "1590183773"}, {"id": "1570572863"}, {"id": "1583872835"}, {"id": "1590070469"}, {"id": "1859347388"}, {"id": "1869314594"}, {"id": "1839589307"}, {"id": "1590359498"}, {"id": "1277239424"}, {"id": "1711228529"}, {"id": "1587144485"}, {"id": "1859328524"}, {"id": "1766902725"}, {"id": "1590327452"}, {"id": "1859178506"}, {"id": "1588026224"}, {"id": "1844766206"}, {"id": "1578947159"}, {"id": "1571356355"}, {"id": "1873346435"}, {"id": "1840561154"}, {"id": "1866105002"}, {"id": "1578942890"}, {"id": "1583613611"}, {"id": "1590337379"}, {"id": "1590220535"}, {"id": "1587139085"}, {"id": "1586685818"}, {"id": "1859337137"}, {"id": "1587842954"}, {"id": "1578236738"}, {"id": "1590209300"}, {"id": "1277235614"}, {"id": "1572528641"}, {"id": "1587987800"}, {"id": "1846008230"}, {"id": "1859352107"}, {"id": "1586683916"}, {"id": "1587833114"}, {"id": "1570327496"}, {"id": "1868703281"}, {"id": "1873346210"}, {"id": "1576774214"}, {"id": "1590352166"}, {"id": "1572538937"}, {"id": "1586672522"}, {"id": "1878869621"}, {"id": "1277367176"}, {"id": "1866104783"}, {"id": "1859233553"}, {"id": "1571666207"}, {"id": "1297732898"}, {"id": "1840562300"}, {"id": "1868703350"}, {"id": "1663817693"}, {"id": "1297781492"}, {"id": "1766907600"}, {"id": "1263322085"}, {"id": "1807085759"}, {"id": "1857286262"}, {"id": "1578906977"}, {"id": "1590067721"}, {"id": "1578922436"}, {"id": "1578921158"}, {"id": "1816212813"}, {"id": "1588014347"}, {"id": "1869314672"}, {"id": "1277238416"}, {"id": "1859143217"}, {"id": "1578212888"}, {"id": "1876092107"}, {"id": "1586690948"}, {"id": "1587845309"}, {"id": "1855878131"}, {"id": "1576774271"}, {"id": "1572537386"}, {"id": "1852959839"}, {"id": "1657956410"}, {"id": "1586684951"}, {"id": "1590217349"}, {"id": "1873346048"}, {"id": "1297789916"}, {"id": "1571353211"}, {"id": "1878778784"}, {"id": "1572463127"}, {"id": "1586729936"}, {"id": "1840560518"}, {"id": "217368203"}, {"id": "1590361829"}, {"id": "1570363877"}, {"id": "1277238182"}, {"id": "1843607021"}, {"id": "1840563197"}, {"id": "1578207899"}, {"id": "1572463157"}, {"id": "1669206413"}, {"id": "1868705297"}, {"id": "1297794791"}, {"id": "1878688568"}, {"id": "1586682908"}, {"id": "216384743"}, {"id": "1571690459"}, {"id": "1862121551"}, {"id": "1571353250"}, {"id": "1868720807"}, {"id": "1817410332"}, {"id": "1571519105"}, {"id": "1586666978"}, {"id": "1590047585"}, {"id": "216111272"}, {"id": "1590316601"}, {"id": "1571358248"}, {"id": "1570574054"}, {"id": "1844194277"}, {"id": "1587106484"}, {"id": "1572538784"}, {"id": "1859144516"}, {"id": "1869046586"}, {"id": "1277246378"}, {"id": "1578953099"}, {"id": "1277231384"}, {"id": "1869317195"}, {"id": "1572538877"}, {"id": "1571590010"}, {"id": "1588016036"}, {"id": "1586684501"}, {"id": "1859200907"}, {"id": "1873340819"}, {"id": "1586696027"}, {"id": "1590160808"}, {"id": "1587982958"}, {"id": "1570316282"}, {"id": "217365923"}, {"id": "216583802"}, {"id": "1586684489"}, {"id": "1860727001"}, {"id": "1567877750"}, {"id": "1587833111"}, {"id": "1663598228"}, {"id": "1869041678"}, {"id": "1590053363"}, {"id": "1590338147"}, {"id": "1576777160"}, {"id": "1590316706"}, {"id": "1576741820"}, {"id": "1587056546"}, {"id": "1852959764"}, {"id": "1576738349"}, {"id": "1578954401"}, {"id": "1860742235"}, {"id": "1843610576"}, {"id": "1590347615"}, {"id": "1873296815"}, {"id": "1590327398"}, {"id": "1852936895"}, {"id": "1862086931"}, {"id": "1895443025"}, {"id": "1588010672"}, {"id": "1570388225"}, {"id": "1590053426"}, {"id": "216583907"}, {"id": "1587996005"}, {"id": "1878778787"}, {"id": "1586738522"}, {"id": "1875313754"}, {"id": "1277234552"}, {"id": "1576780076"}, {"id": "1570574009"}, {"id": "1567877720"}, {"id": "1846406201"}, {"id": "1590224939"}, {"id": "1581271697"}, {"id": "1766931735"}, {"id": "1878869777"}, {"id": "1578207293"}, {"id": "1839586325"}, {"id": "1587811022"}, {"id": "1587833696"}, {"id": "1571579087"}, {"id": "1868722022"}, {"id": "1588014260"}, {"id": "1590341156"}, {"id": "1590317492"}, {"id": "1868716649"}, {"id": "1572524303"}, {"id": "1590315326"}, {"id": "216578399"}, {"id": "1665809519"}, {"id": "1570573502"}, {"id": "1578946274"}, {"id": "1852965686"}, {"id": "1590222764"}, {"id": "1590056501"}, {"id": "1277230667"}, {"id": "1590222707"}, {"id": "1839585545"}, {"id": "1277257658"}, {"id": "1297796597"}, {"id": "1297793861"}, {"id": "1570363850"}, {"id": "1590213575"}, {"id": "1844727530"}, {"id": "1663683767"}, {"id": "1570343000"}, {"id": "1587145121"}, {"id": "1586666129"}, {"id": "1572530288"}, {"id": "1865993699"}, {"id": "1297725689"}, {"id": "1839581621"}, {"id": "1581271601"}, {"id": "1590024167"}, {"id": "1576774178"}, {"id": "1817411604"}, {"id": "1570537091"}, {"id": "1578916895"}, {"id": "1570572878"}, {"id": "1873398569"}, {"id": "1590160358"}, {"id": "1868703242"}, {"id": "1571664305"}, {"id": "1263325031"}, {"id": "1844196173"}, {"id": "1587051863"}, {"id": "1588015331"}, {"id": "1572383468"}, {"id": "1846708268"}, {"id": "1670341259"}, {"id": "1578212828"}, {"id": "1578212957"}, {"id": "1866091550"}, {"id": "1297774244"}, {"id": "1570164641"}, {"id": "1868714303"}, {"id": "1578217439"}, {"id": "1586682302"}, {"id": "1851401387"}, {"id": "217371089"}, {"id": "1852926920"}, {"id": "1572508163"}, {"id": "1578930542"}, {"id": "1570572272"}, {"id": "1587854102"}, {"id": "1868723300"}, {"id": "1864634705"}, {"id": "1567881233"}, {"id": "1874803328"}, {"id": "1851475895"}, {"id": "1590315302"}, {"id": "1865895197"}, {"id": "1570340051"}, {"id": "1590056351"}, {"id": "1572542207"}, {"id": "1766904372"}, {"id": "1567878614"}, {"id": "1570308371"}, {"id": "1297789868"}, {"id": "1576779953"}, {"id": "1590314108"}, {"id": "1572535178"}, {"id": "1844767142"}, {"id": "1590061685"}, {"id": "1586664152"}, {"id": "1590160778"}, {"id": "1590076853"}, {"id": "1869318110"}, {"id": "1864650971"}, {"id": "1852959845"}, {"id": "1297797308"}, {"id": "1868703188"}, {"id": "1859178539"}, {"id": "1572477389"}, {"id": "1277239163"}, {"id": "1868714507"}, {"id": "1847350106"}, {"id": "1869318134"}, {"id": "1588016057"}, {"id": "1277226113"}, {"id": "1587136481"}, {"id": "1868716532"}, {"id": "1709253587"}, {"id": "1852924244"}, {"id": "1855880474"}, {"id": "1859328392"}, {"id": "1578207938"}, {"id": "217361843"}, {"id": "1586690963"}, {"id": "1587137216"}, {"id": "1590337241"}, {"id": "1852924364"}, {"id": "1878694163"}, {"id": "1852961777"}, {"id": "1844764331"}, {"id": "1864637798"}, {"id": "1570552823"}, {"id": "1859204651"}, {"id": "1586695922"}, {"id": "1869318047"}, {"id": "1588025612"}, {"id": "1864622669"}, {"id": "1578225866"}, {"id": "1570552781"}, {"id": "1843609040"}, {"id": "1590102614"}, {"id": "1571580044"}, {"id": "1587145067"}, {"id": "1859339849"}, {"id": "1587119126"}, {"id": "1297784891"}, {"id": "1586666987"}, {"id": "1590065486"}, {"id": "1855880516"}, {"id": "1576741955"}, {"id": "1570348043"}, {"id": "1843606994"}, {"id": "1572516008"}, {"id": "1588015154"}, {"id": "1587105251"}, {"id": "1868721908"}, {"id": "1586682428"}, {"id": "1590220031"}, {"id": "1852936706"}, {"id": "1581281513"}, {"id": "1859328461"}, {"id": "1766922825"}, {"id": "1908116594"}, {"id": "1855878299"}, {"id": "1865943701"}, {"id": "1570573508"}, {"id": "1590025169"}, {"id": "1843610615"}, {"id": "1840559729"}, {"id": "1587995966"}, {"id": "1587983438"}, {"id": "1277229206"}, {"id": "1587984170"}, {"id": "1840563086"}, {"id": "216584810"}, {"id": "1670340023"}, {"id": "1817411658"}, {"id": "1586666930"}, {"id": "1587997730"}, {"id": "1570574069"}, {"id": "1572486326"}, {"id": "1578924776"}, {"id": "1297744721"}, {"id": "1587138233"}, {"id": "1587108923"}, {"id": "1869317978"}, {"id": "1844191970"}, {"id": "1851487394"}, {"id": "1859331995"}, {"id": "1664813738"}, {"id": "1868705261"}, {"id": "1852925702"}, {"id": "1586683970"}, {"id": "1277260994"}, {"id": "1868718338"}, {"id": "1844194457"}, {"id": "1590341201"}, {"id": "1571353238"}, {"id": "1843607282"}, {"id": "1576745306"}, {"id": "1864631120"}, {"id": "1816749606"}, {"id": "1578208625"}, {"id": "1807085660"}, {"id": "1572542327"}, {"id": "1572388703"}, {"id": "1570154684"}, {"id": "1586703428"}, {"id": "1590222923"}, {"id": "1821544410"}, {"id": "1586693282"}, {"id": "1571338151"}, {"id": "1590223010"}, {"id": "1587048473"}, {"id": "216581339"}, {"id": "1572527852"}, {"id": "1578922253"}, {"id": "1586690792"}, {"id": "1590160802"}, {"id": "1844727692"}, {"id": "1576739231"}, {"id": "1587141179"}, {"id": "1277235521"}, {"id": "1588023242"}, {"id": "1304264969"}, {"id": "1587810959"}, {"id": "1852925561"}, {"id": "1572528683"}, {"id": "1843609370"}, {"id": "1844731754"}, {"id": "1587031385"}, {"id": "1586649095"}, {"id": "1590070526"}, {"id": "1572527873"}, {"id": "1865999588"}, {"id": "1840648520"}, {"id": "1578904400"}, {"id": "1587126692"}, {"id": "1578212987"}, {"id": "1588020461"}, {"id": "1587997661"}, {"id": "1587827696"}, {"id": "1869907961"}, {"id": "1876239746"}, {"id": "1817411073"}, {"id": "1587145898"}, {"id": "1571687243"}, {"id": "1277242526"}, {"id": "1869278822"}, {"id": "1587999008"}, {"id": "1578236798"}, {"id": "1570154900"}, {"id": "1866118388"}, {"id": "1585871555"}, {"id": "1576782479"}, {"id": "216584888"}, {"id": "1578207902"}, {"id": "1578927161"}, {"id": "1587991070"}, {"id": "1846752470"}, {"id": "1277234585"}, {"id": "1586676686"}, {"id": "1587031193"}, {"id": "1588004081"}, {"id": "1497687810"}, {"id": "1868709419"}, {"id": "1572529490"}, {"id": "1588015442"}, {"id": "1590353636"}, {"id": "1590067796"}, {"id": "216582896"}, {"id": "1813180529"}, {"id": "1878869609"}, {"id": "1297776674"}, {"id": "1860742307"}, {"id": "1844731889"}, {"id": "1590317717"}, {"id": "217378889"}, {"id": "1571519093"}, {"id": "1576774370"}, {"id": "1297780760"}, {"id": "1578922439"}, {"id": "1297775225"}, {"id": "1576774154"}, {"id": "1297789940"}, {"id": "1587145865"}, {"id": "1587148172"}, {"id": "1590061625"}, {"id": "1869908126"}, {"id": "1585874819"}, {"id": "1590201374"}, {"id": "1587994214"}, {"id": "1578904172"}, {"id": "1297774292"}, {"id": "1839578930"}, {"id": "1864613831"}, {"id": "1862121692"}, {"id": "1859201084"}, {"id": "1297781585"}, {"id": "1844731661"}, {"id": "1590361274"}, {"id": "1862081231"}, {"id": "1811160824"}, {"id": "1859181455"}, {"id": "1846038914"}, {"id": "1587990869"}, {"id": "1852925792"}, {"id": "1590213653"}, {"id": "1766918367"}, {"id": "1590345065"}, {"id": "1817411646"}, {"id": "1590351212"}, {"id": "1864613921"}, {"id": "1587843905"}, {"id": "1578921287"}, {"id": "1840647995"}, {"id": "1670342087"}, {"id": "1868718242"}, {"id": "1571343290"}, {"id": "1588002836"}, {"id": "1586659112"}, {"id": "1587980747"}, {"id": "1590161891"}, {"id": "1277229026"}, {"id": "1587990005"}, {"id": "1297798604"}, {"id": "1851483968"}, {"id": "1588011563"}, {"id": "1277226029"}, {"id": "1844196218"}, {"id": "1578946313"}, {"id": "1570161008"}, {"id": "1570357049"}, {"id": "1864640585"}, {"id": "1571691083"}, {"id": "1587842129"}, {"id": "1859342006"}, {"id": "1297794623"}, {"id": "1572524267"}, {"id": "1852924382"}, {"id": "1590214580"}, {"id": "1839579032"}, {"id": "1657956374"}, {"id": "1576782452"}, {"id": "1581270692"}, {"id": "1722076460"}, {"id": "1586666096"}, {"id": "1277233181"}, {"id": "1587851105"}, {"id": "1587112889"}, {"id": "1587850196"}, {"id": "1873340771"}, {"id": "1297741847"}, {"id": "1570298747"}, {"id": "1572527594"}, {"id": "1484419698"}, {"id": "1578922400"}, {"id": "1590363338"}, {"id": "1872077147"}, {"id": "1567881296"}, {"id": "1852924349"}, {"id": "1587846149"}, {"id": "1571603033"}, {"id": "1578935381"}, {"id": "1590222719"}, {"id": "1571353172"}, {"id": "1586693309"}, {"id": "1587652148"}, {"id": "1578208673"}, {"id": "1587058163"}, {"id": "1587988457"}, {"id": "1670341253"}, {"id": "1297750334"}, {"id": "1843609076"}, {"id": "1587849449"}, {"id": "1585936934"}, {"id": "1587034607"}, {"id": "1670343470"}, {"id": "1590065444"}, {"id": "1865999624"}, {"id": "1590315683"}, {"id": "1277231426"}, {"id": "217368263"}, {"id": "1571691170"}, {"id": "1586666123"}, {"id": "1590349982"}, {"id": "1578915794"}, {"id": "1570343042"}, {"id": "1859201003"}, {"id": "1840560638"}, {"id": "1846400540"}, {"id": "1588000277"}, {"id": "1865986958"}, {"id": "1588014437"}, {"id": "1576738499"}, {"id": "1766923893"}, {"id": "1586683979"}, {"id": "1570559081"}, {"id": "1578953114"}, {"id": "1590216821"}, {"id": "1862081243"}, {"id": "1587982265"}, {"id": "1844194469"}, {"id": "1586684471"}, {"id": "1590316760"}, {"id": "1852936796"}, {"id": "1571579141"}, {"id": "1587040553"}, {"id": "1590360683"}, {"id": "1878688433"}, {"id": "1297797464"}, {"id": "1578215051"}, {"id": "1864650977"}, {"id": "1590222224"}, {"id": "1587031229"}, {"id": "1588021781"}, {"id": "1844727578"}, {"id": "1862081408"}, {"id": "1839580643"}, {"id": "1852959470"}, {"id": "1590051185"}, {"id": "1851485399"}, {"id": "1590317666"}, {"id": "216111254"}, {"id": "1859177141"}, {"id": "1586668664"}, {"id": "1590313958"}, {"id": "1570162247"}, {"id": "1590360017"}, {"id": "1587990881"}, {"id": "1590351167"}, {"id": "1844766083"}, {"id": "1865993438"}, {"id": "1722076895"}, {"id": "1587839462"}, {"id": "1844766962"}, {"id": "1590024107"}, {"id": "1710319400"}, {"id": "1578916802"}, {"id": "1654572791"}, {"id": "1586664194"}, {"id": "1766903664"}, {"id": "1590060056"}, {"id": "1869041666"}, {"id": "1587138239"}, {"id": "1851405326"}, {"id": "1868720804"}, {"id": "1277247470"}, {"id": "1816211379"}, {"id": "1277237102"}, {"id": "1586687441"}, {"id": "1263322046"}, {"id": "1859331812"}, {"id": "1766906286"}, {"id": "1851471923"}, {"id": "1844728997"}, {"id": "1588026629"}, {"id": "1571679275"}, {"id": "217378091"}, {"id": "1816749465"}, {"id": "1860742043"}, {"id": "1586684165"}, {"id": "1843607267"}, {"id": "1572509054"}, {"id": "1766904432"}, {"id": "1851471938"}, {"id": "1865999657"}, {"id": "1868714276"}, {"id": "1297797359"}, {"id": "1657955663"}, {"id": "1571590127"}, {"id": "1587842159"}, {"id": "1576738346"}, {"id": "1864656440"}, {"id": "1576741751"}, {"id": "1571668358"}, {"id": "1585871510"}, {"id": "1587148232"}, {"id": "1578932870"}, {"id": "1840649684"}, {"id": "1840562480"}, {"id": "1590161879"}, {"id": "1590161783"}, {"id": "1590343499"}, {"id": "1716620837"}, {"id": "1588017308"}, {"id": "1304265968"}, {"id": "1297794803"}, {"id": "1843586504"}, {"id": "1859143223"}, {"id": "1725713093"}, {"id": "1846752464"}, {"id": "1572510428"}, {"id": "1585939355"}, {"id": "1590317534"}, {"id": "1297799918"}, {"id": "1590183647"}, {"id": "1590053402"}, {"id": "1844724173"}, {"id": "1671842753"}, {"id": "1846038959"}, {"id": "1590065450"}, {"id": "1807085741"}, {"id": "1869312014"}, {"id": "1846771328"}, {"id": "1572550373"}, {"id": "1590061697"}, {"id": "1878682910"}, {"id": "1590053516"}, {"id": "1839578933"}, {"id": "1587031694"}, {"id": "1587831044"}, {"id": "1571686589"}, {"id": "1277235542"}, {"id": "1297786262"}, {"id": "1571664764"}, {"id": "1571615177"}, {"id": "1851488750"}, {"id": "1587141251"}, {"id": "1587995813"}, {"id": "1710285014"}, {"id": "1277242601"}, {"id": "1864631111"}, {"id": "1590317675"}, {"id": "1590347903"}, {"id": "1576774388"}, {"id": "217361663"}, {"id": "1590183839"}, {"id": "1862125916"}, {"id": "1277368427"}, {"id": "1586726966"}, {"id": "1571544896"}, {"id": "1844766935"}, {"id": "1590314120"}, {"id": "1844196827"}, {"id": "1859144624"}, {"id": "1766928984"}, {"id": "1590350798"}, {"id": "1297794713"}, {"id": "1587642905"}, {"id": "1571366066"}, {"id": "1587030965"}, {"id": "1587835895"}, {"id": "1590353621"}, {"id": "1868703278"}, {"id": "1581270530"}, {"id": "1570355477"}, {"id": "1586735663"}, {"id": "1817410899"}, {"id": "1855878269"}, {"id": "1590316826"}, {"id": "1840560419"}, {"id": "1840559585"}, {"id": "1852963970"}, {"id": "1587031670"}, {"id": "1817411052"}, {"id": "1590317759"}, {"id": "1588003538"}, {"id": "1585876529"}, {"id": "1852938173"}, {"id": "1277255792"}, {"id": "1844764391"}, {"id": "1585850426"}, {"id": "1866091370"}, {"id": "217370885"}, {"id": "1570363742"}, {"id": "1567883183"}, {"id": "1868724128"}, {"id": "1297800443"}, {"id": "1650247151"}, {"id": "1864634678"}, {"id": "1572538775"}, {"id": "1664820536"}, {"id": "1590317510"}, {"id": "1844769077"}, {"id": "1839579038"}, {"id": "1586690837"}, {"id": "1851485519"}, {"id": "1585876439"}, {"id": "1277235620"}, {"id": "1869314726"}, {"id": "1859143274"}, {"id": "1859331728"}, {"id": "1587149099"}, {"id": "1590215360"}, {"id": "1711247564"}, {"id": "1590200795"}, {"id": "1578217502"}, {"id": "1586722919"}, {"id": "1844733110"}, {"id": "1590201440"}, {"id": "1590053486"}, {"id": "1587825038"}, {"id": "1868709527"}, {"id": "1844731634"}, {"id": "1670374844"}, {"id": "1839588617"}, {"id": "1878681158"}, {"id": "1747434315"}, {"id": "1586693267"}, {"id": "1571330825"}, {"id": "1277240942"}, {"id": "1576745900"}, {"id": "1852925666"}, {"id": "1571608364"}, {"id": "1578942848"}, {"id": "1859342081"}, {"id": "1766908170"}, {"id": "1590178103"}, {"id": "1277239211"}, {"id": "1587037112"}, {"id": "1578924866"}, {"id": "1578921266"}, {"id": "1878681026"}, {"id": "1670343416"}, {"id": "1873346105"}, {"id": "1587149060"}, {"id": "1586693330"}, {"id": "1571353139"}, {"id": "1851483860"}, {"id": "1297787678"}, {"id": "1578922271"}, {"id": "1578236783"}, {"id": "1590219848"}, {"id": "1839589169"}, {"id": "1816749447"}, {"id": "1725717704"}, {"id": "1725714212"}, {"id": "1855882769"}, {"id": "1869311858"}, {"id": "1570574210"}, {"id": "1878695783"}, {"id": "1851481466"}, {"id": "1578922451"}, {"id": "1571338163"}, {"id": "1766902656"}, {"id": "1868723252"}, {"id": "1873346033"}, {"id": "1586690978"}, {"id": "1844766215"}, {"id": "1669109912"}, {"id": "216352226"}, {"id": "1576777208"}, {"id": "1576774253"}, {"id": "1570160810"}, {"id": "1586648888"}, {"id": "1813180688"}, {"id": "1578952916"}, {"id": "1590056591"}, {"id": "1572524300"}, {"id": "1586725784"}, {"id": "1868714261"}, {"id": "1590339788"}, {"id": "1587987830"}, {"id": "1570568201"}, {"id": "1839588416"}, {"id": "1586730125"}, {"id": "1576745309"}, {"id": "1862067299"}, {"id": "1587984143"}, {"id": "1860742292"}, {"id": "1587998453"}, {"id": "1578906959"}, {"id": "1816212861"}, {"id": "1297787504"}, {"id": "1571660525"}, {"id": "1840649402"}, {"id": "1277239415"}, {"id": "1297780790"}, {"id": "1852646720"}, {"id": "1766918223"}, {"id": "1571338169"}, {"id": "1588016024"}, {"id": "1588005185"}, {"id": "1587833708"}, {"id": "1852922900"}, {"id": "1590200648"}, {"id": "1590216629"}, {"id": "1851407534"}, {"id": "1587845750"}, {"id": "1590317561"}, {"id": "1590338030"}, {"id": "1586684936"}, {"id": "1590178133"}, {"id": "1570348088"}, {"id": "1570357133"}, {"id": "1297776620"}, {"id": "1859178650"}, {"id": "1670342012"}, {"id": "1862083520"}, {"id": "1864650875"}, {"id": "1586668472"}, {"id": "1590214433"}, {"id": "1590319631"}, {"id": "1590200624"}, {"id": "1583880827"}, {"id": "1846059956"}, {"id": "1570230914"}, {"id": "1878695732"}, {"id": "1586729939"}, {"id": "1590224903"}, {"id": "1865993558"}, {"id": "1587060059"}, {"id": "1581272369"}, {"id": "1664821070"}, {"id": "1297776536"}, {"id": "1586723048"}, {"id": "1869908195"}, {"id": "1576777103"}, {"id": "1277234687"}, {"id": "1578946946"}, {"id": "1665809729"}, {"id": "1578902441"}, {"id": "1587850121"}, {"id": "1843588691"}, {"id": "1843607054"}, {"id": "1587107774"}, {"id": "1670342558"}, {"id": "1571576867"}, {"id": "1570308374"}, {"id": "1578906872"}, {"id": "1277224523"}, {"id": "1847201897"}, {"id": "1588001771"}, {"id": "1864640678"}, {"id": "1590338186"}, {"id": "1590178571"}, {"id": "1663683764"}, {"id": "1664820506"}, {"id": "1844764268"}, {"id": "1297800545"}, {"id": "1588001801"}, {"id": "1588012928"}, {"id": "1567876811"}, {"id": "1844194190"}, {"id": "1586667011"}, {"id": "1297781597"}, {"id": "1576771910"}, {"id": "1590327362"}, {"id": "1590216665"}, {"id": "216581375"}, {"id": "1297797536"}, {"id": "1587128735"}, {"id": "1869314612"}, {"id": "1572529430"}, {"id": "1665809777"}, {"id": "1587851096"}, {"id": "1297788536"}, {"id": "1572528773"}, {"id": "1766907087"}, {"id": "1586684444"}, {"id": "1859331746"}, {"id": "1590177977"}, {"id": "1576746167"}, {"id": "1587839045"}, {"id": "1570154762"}, {"id": "1868716490"}, {"id": "1571358266"}, {"id": "1846771259"}, {"id": "1576774391"}, {"id": "1587033803"}, {"id": "1590338909"}, {"id": "1864614092"}, {"id": "1590359519"}, {"id": "1586727920"}, {"id": "1588000286"}, {"id": "1874803349"}, {"id": "1588007699"}, {"id": "1588012247"}, {"id": "1590032003"}, {"id": "1844724050"}, {"id": "217365818"}, {"id": "1567863545"}, {"id": "1766905812"}, {"id": "1572524327"}, {"id": "1725908234"}, {"id": "1670341265"}, {"id": "1843588463"}, {"id": "1864640774"}, {"id": "1585876502"}, {"id": "1588016168"}, {"id": "1859339714"}, {"id": "1859339681"}, {"id": "1839578951"}, {"id": "1570518947"}, {"id": "1864613909"}, {"id": "1586672675"}, {"id": "1277367290"}, {"id": "1813180748"}, {"id": "1578942668"}, {"id": "1297732700"}, {"id": "1578924830"}, {"id": "1663818554"}, {"id": "1866109523"}, {"id": "1874366513"}, {"id": "1484411595"}, {"id": "1587987527"}, {"id": "1590314081"}, {"id": "1855882781"}, {"id": "1572527657"}, {"id": "1587849806"}, {"id": "1572524276"}, {"id": "1586724545"}, {"id": "1873425302"}, {"id": "1277238185"}, {"id": "1590351206"}, {"id": "1590337208"}, {"id": "1578217529"}, {"id": "1588012919"}, {"id": "1840562369"}, {"id": "1670340044"}, {"id": "1590350849"}, {"id": "1587989972"}, {"id": "1878690434"}, {"id": "1766920431"}, {"id": "1859201009"}, {"id": "1586693138"}, {"id": "1588025564"}, {"id": "1571652938"}, {"id": "1586663876"}, {"id": "1587144470"}, {"id": "1578212801"}, {"id": "1572537398"}, {"id": "1860742262"}, {"id": "1851481595"}, {"id": "1586672609"}, {"id": "1766919183"}, {"id": "1851487418"}, {"id": "1572524177"}, {"id": "1586724572"}, {"id": "1567864808"}, {"id": "1869314573"}, {"id": "1878688448"}, {"id": "1864643861"}, {"id": "1571343287"}, {"id": "1587145139"}, {"id": "1586687552"}, {"id": "1576745993"}, {"id": "1567888433"}, {"id": "1572538769"}, {"id": "1567879988"}, {"id": "1710359954"}, {"id": "1857297539"}, {"id": "1585850141"}, {"id": "1570568159"}, {"id": "1590355037"}, {"id": "1304264864"}, {"id": "1297775135"}, {"id": "1570552856"}, {"id": "1840563473"}, {"id": "1578927152"}, {"id": "1851471950"}, {"id": "1873398467"}, {"id": "1811161121"}, {"id": "1710349247"}, {"id": "1277238239"}, {"id": "1590363344"}, {"id": "1570573556"}, {"id": "1855882679"}, {"id": "1590201389"}, {"id": "1587028223"}, {"id": "1766919096"}, {"id": "1852961744"}, {"id": "1576771907"}, {"id": "1840553990"}, {"id": "1859331773"}, {"id": "1840553969"}, {"id": "1590213842"}, {"id": "1843610531"}, {"id": "1572508340"}, {"id": "1663479167"}, {"id": "1587031439"}, {"id": "1578939263"}, {"id": "216584000"}, {"id": "1859336927"}, {"id": "1766919162"}, {"id": "1878688688"}, {"id": "1590206096"}, {"id": "1588026590"}, {"id": "1725713027"}, {"id": "1297725734"}, {"id": "1813180544"}, {"id": "1840562510"}, {"id": "1570573439"}, {"id": "1277274902"}, {"id": "1590352244"}, {"id": "1297794857"}, {"id": "1587148982"}, {"id": "1857288785"}, {"id": "1571347796"}, {"id": "1578224366"}, {"id": "1571662058"}, {"id": "1570568132"}, {"id": "1587849833"}, {"id": "1859353370"}, {"id": "217365902"}, {"id": "1297744766"}, {"id": "1586693708"}, {"id": "1859201162"}, {"id": "1844727464"}, {"id": "1578947252"}, {"id": "1844764334"}, {"id": "1844194508"}, {"id": "1590351203"}, {"id": "216385226"}, {"id": "1590352268"}, {"id": "1590200714"}, {"id": "1576741841"}, {"id": "1844191910"}, {"id": "1859181377"}, {"id": "1571658881"}, {"id": "1766903751"}, {"id": "1816212954"}, {"id": "216362525"}, {"id": "1572527618"}, {"id": "1570161017"}, {"id": "1588027688"}, {"id": "1590314045"}, {"id": "1587110891"}, {"id": "1847201888"}, {"id": "1578922265"}, {"id": "1587148145"}, {"id": "1844190608"}, {"id": "1868716655"}, {"id": "1840553852"}, {"id": "1663816028"}, {"id": "1576745885"}, {"id": "1297787696"}, {"id": "1868709305"}, {"id": "1590362510"}, {"id": "1570573559"}, {"id": "1590178526"}, {"id": "1813180598"}, {"id": "1277234750"}, {"id": "1590164285"}, {"id": "217368161"}, {"id": "1846771193"}, {"id": "1578927197"}, {"id": "1586673386"}, {"id": "1587998405"}, {"id": "1839585641"}, {"id": "1869279071"}, {"id": "1581297476"}, {"id": "1846059977"}, {"id": "1860761594"}, {"id": "1587140837"}, {"id": "1868714462"}, {"id": "1590224924"}, {"id": "1813181258"}, {"id": "1908116555"}, {"id": "1571615156"}, {"id": "1862083217"}, {"id": "1588000748"}, {"id": "216587951"}, {"id": "1710331592"}, {"id": "1590362681"}, {"id": "1578907034"}, {"id": "1571327411"}, {"id": "1587998450"}, {"id": "1587843944"}, {"id": "1852961627"}, {"id": "1588012868"}, {"id": "1873346063"}, {"id": "1297796477"}, {"id": "1297777367"}, {"id": "1572542147"}, {"id": "1862115743"}, {"id": "1590344402"}, {"id": "1846749125"}, {"id": "1570219556"}, {"id": "1846771241"}, {"id": "1869318029"}, {"id": "1588006577"}, {"id": "1878694238"}, {"id": "1869318182"}, {"id": "1868705207"}, {"id": "1578942653"}, {"id": "1586682257"}, {"id": "1766920428"}, {"id": "1297787516"}, {"id": "1576741697"}, {"id": "1710331607"}, {"id": "1578215039"}, {"id": "216348806"}, {"id": "216111293"}, {"id": "1576738436"}, {"id": "1590178505"}, {"id": "1857297395"}, {"id": "1587128840"}, {"id": "1587034619"}, {"id": "1587997733"}, {"id": "1277367173"}, {"id": "1840648523"}, {"id": "1590224753"}, {"id": "1277282846"}, {"id": "1860726947"}, {"id": "1766904456"}, {"id": "1483531473"}, {"id": "1590046007"}, {"id": "1571676473"}, {"id": "1725722108"}, {"id": "1658731673"}, {"id": "1654637597"}, {"id": "1846771217"}, {"id": "1839580550"}, {"id": "1578922268"}, {"id": "1846749089"}, {"id": "1571691164"}, {"id": "1297757156"}, {"id": "1590315341"}, {"id": "1297732862"}, {"id": "1588016957"}, {"id": "1590201947"}, {"id": "1587038573"}, {"id": "1586681531"}, {"id": "1868703194"}, {"id": "1852926878"}, {"id": "1581271661"}, {"id": "1587646385"}, {"id": "1857301826"}, {"id": "1277238212"}, {"id": "1878688451"}, {"id": "1570316255"}, {"id": "1277271518"}, {"id": "1586695751"}, {"id": "1844196206"}, {"id": "1576782389"}, {"id": "1572528848"}, {"id": "1578933041"}, {"id": "1851471914"}, {"id": "1866000989"}, {"id": "1864643801"}, {"id": "1588003523"}, {"id": "216390821"}, {"id": "1587998984"}, {"id": "1263325007"}, {"id": "1587046658"}, {"id": "1578225275"}, {"id": "1590070313"}, {"id": "1588016177"}, {"id": "216578084"}, {"id": "1588027580"}, {"id": "1587838232"}, {"id": "1567866551"}, {"id": "1578946055"}, {"id": "1590338984"}, {"id": "1872076862"}, {"id": "1807085786"}, {"id": "1578935525"}, {"id": "1844728919"}, {"id": "1570154861"}, {"id": "1844729081"}, {"id": "1843609127"}, {"id": "1857301670"}, {"id": "1868720813"}, {"id": "1857297548"}, {"id": "1587991691"}, {"id": "217367597"}, {"id": "1588016237"}, {"id": "1586676752"}, {"id": "1840552898"}, {"id": "1590361871"}, {"id": "1840648076"}, {"id": "1587997703"}, {"id": "1590363488"}, {"id": "1859144672"}, {"id": "1855882814"}, {"id": "1587149063"}, {"id": "1277269652"}, {"id": "1297776653"}, {"id": "1866109307"}, {"id": "1297750337"}, {"id": "1567863554"}, {"id": "1590164891"}, {"id": "1578216614"}, {"id": "1868714486"}, {"id": "1590339869"}, {"id": "1590179255"}, {"id": "1297741649"}, {"id": "1766903694"}, {"id": "1585877873"}, {"id": "1862083229"}, {"id": "1588016042"}, {"id": "1586728145"}, {"id": "1581279620"}, {"id": "1587995945"}, {"id": "216586088"}, {"id": "1851485420"}, {"id": "1859339717"}, {"id": "1844767052"}, {"id": "1846707947"}, {"id": "1878695708"}, {"id": "1586683694"}, {"id": "1587849725"}, {"id": "1587980651"}, {"id": "1297788479"}, {"id": "1587992732"}, {"id": "1572515939"}, {"id": "1869311762"}, {"id": "1297775321"}, {"id": "1590161888"}, {"id": "1581270725"}, {"id": "1865986913"}, {"id": "1585876499"}, {"id": "1586695880"}, {"id": "1840559657"}, {"id": "1586682335"}, {"id": "1862131418"}, {"id": "1869318137"}, {"id": "1586693165"}, {"id": "1588002695"}, {"id": "1570236731"}, {"id": "1588020011"}, {"id": "1571333321"}, {"id": "1859352167"}, {"id": "1586681459"}, {"id": "1590220082"}, {"id": "1722076436"}, {"id": "1590024188"}, {"id": "1590051182"}, {"id": "1766904309"}, {"id": "217372028"}, {"id": "1864613948"}, {"id": "216350201"}, {"id": "1304263292"}, {"id": "1590216674"}, {"id": "1846063760"}, {"id": "1878869633"}, {"id": "1586691617"}, {"id": "1590347945"}, {"id": "1709253527"}, {"id": "1585878791"}, {"id": "1572542342"}, {"id": "1578212951"}, {"id": "1878694253"}, {"id": "1590203216"}, {"id": "1869318152"}, {"id": "1586687465"}, {"id": "1590053558"}, {"id": "1862081366"}, {"id": "1567888442"}, {"id": "1876091939"}, {"id": "1590212228"}, {"id": "1570362902"}, {"id": "1865999762"}, {"id": "1572544502"}, {"id": "1571589998"}, {"id": "1571353205"}, {"id": "1587033008"}, {"id": "1855878152"}, {"id": "1578217538"}, {"id": "1710349235"}, {"id": "1586682935"}, {"id": "1572515942"}, {"id": "1587990191"}, {"id": "216388724"}, {"id": "1846008221"}, {"id": "1869911621"}, {"id": "1588020902"}, {"id": "1572529463"}, {"id": "1725714131"}, {"id": "1839587576"}, {"id": "1862067401"}, {"id": "1852961699"}, {"id": "1587857330"}, {"id": "1578907067"}, {"id": "1816751103"}, {"id": "1587998798"}, {"id": "1878688514"}, {"id": "1663816598"}, {"id": "1572527837"}, {"id": "1590160451"}, {"id": "1587141173"}, {"id": "1844724245"}, {"id": "1859233832"}, {"id": "1844196338"}, {"id": "1844196344"}, {"id": "1817411715"}, {"id": "216389177"}, {"id": "1590034337"}, {"id": "1277256926"}, {"id": "1590164834"}, {"id": "1578907016"}, {"id": "1570348109"}, {"id": "1578904220"}, {"id": "217368548"}, {"id": "1578932780"}, {"id": "1578952937"}, {"id": "1571615228"}, {"id": "1572515957"}, {"id": "1862115431"}, {"id": "1839585533"}, {"id": "1586695952"}, {"id": "1859347283"}, {"id": "1572535175"}, {"id": "1816211070"}, {"id": "1857301673"}, {"id": "1816211385"}, {"id": "1590222950"}, {"id": "1572527741"}, {"id": "1297798697"}, {"id": "216388823"}, {"id": "1277240147"}, {"id": "1571658974"}, {"id": "1578904325"}, {"id": "1576745927"}, {"id": "1869314774"}, {"id": "1852938164"}, {"id": "1670340077"}, {"id": "1862081474"}, {"id": "1572528638"}, {"id": "1844723918"}, {"id": "1590178490"}, {"id": "1586684555"}, {"id": "1844766194"}, {"id": "1859142956"}, {"id": "1855878092"}, {"id": "1588020491"}, {"id": "1869908141"}, {"id": "1844727554"}, {"id": "1590160706"}, {"id": "1590201410"}, {"id": "1590076868"}, {"id": "1817410335"}, {"id": "1722076430"}, {"id": "1572524393"}, {"id": "1865894960"}, {"id": "1578922244"}, {"id": "1572529508"}, {"id": "1590361094"}, {"id": "1587999299"}, {"id": "1874803661"}, {"id": "1843588685"}, {"id": "1590361877"}, {"id": "1567877690"}, {"id": "1590060062"}, {"id": "1297781633"}, {"id": "1587982880"}, {"id": "1586727029"}, {"id": "1859328587"}, {"id": "1572528857"}, {"id": "1578942629"}, {"id": "1581272681"}, {"id": "1570311734"}, {"id": "1587811595"}, {"id": "1578226856"}, {"id": "1587647738"}, {"id": "1590160397"}, {"id": "1817410404"}, {"id": "1277221640"}, {"id": "1586684645"}, {"id": "1852963982"}, {"id": "1297775132"}, {"id": "1844190503"}, {"id": "1840648001"}, {"id": "1859323415"}, {"id": "1868723375"}, {"id": "1587835073"}, {"id": "1665808418"}, {"id": "1859339810"}, {"id": "1869046529"}, {"id": "1277257643"}, {"id": "1862067368"}, {"id": "1859339795"}, {"id": "1766907642"}, {"id": "1859143175"}, {"id": "1590316619"}, {"id": "1297787702"}, {"id": "1587996986"}, {"id": "1851405413"}, {"id": "1570518938"}, {"id": "1840649159"}, {"id": "1587982586"}, {"id": "1571686652"}, {"id": "1844765969"}, {"id": "1297800470"}, {"id": "1277238260"}, {"id": "1581272720"}, {"id": "1590338975"}, {"id": "1570357136"}, {"id": "1590359279"}, {"id": "1844723921"}, {"id": "1586666948"}, {"id": "1766903850"}, {"id": "1588002614"}, {"id": "1297797374"}, {"id": "1868703392"}, {"id": "1859176931"}, {"id": "1578906944"}, {"id": "1578904427"}, {"id": "1572459989"}, {"id": "1572544511"}, {"id": "1766920338"}, {"id": "1876240058"}, {"id": "1839588560"}, {"id": "1868718428"}, {"id": "1588012475"}, {"id": "1843609325"}, {"id": "1839589148"}, {"id": "1868723474"}, {"id": "1297794818"}, {"id": "1839580664"}, {"id": "1297783868"}, {"id": "1587654083"}, {"id": "1873343543"}, {"id": "1851485408"}, {"id": "1578935339"}, {"id": "1571686625"}, {"id": "1847201756"}, {"id": "1578904211"}, {"id": "1587849458"}, {"id": "217373369"}, {"id": "1570300715"}, {"id": "1587031925"}, {"id": "1587987866"}, {"id": "1297777496"}, {"id": "1766922750"}, {"id": "1571351792"}, {"id": "1586687489"}, {"id": "1665809852"}, {"id": "1590183797"}, {"id": "216391103"}, {"id": "1590222221"}, {"id": "1277237165"}, {"id": "1670342030"}, {"id": "1862083157"}, {"id": "1297787645"}, {"id": "1587997640"}, {"id": "1587998810"}, {"id": "1873415612"}, {"id": "1766922780"}, {"id": "1766907072"}, {"id": "1586659847"}, {"id": "1590164867"}, {"id": "1576772033"}, {"id": "1873425524"}, {"id": "1859143010"}, {"id": "1297778846"}, {"id": "1590214670"}, {"id": "1572525116"}, {"id": "1859177009"}, {"id": "1570522844"}, {"id": "1590034391"}, {"id": "1587845741"}, {"id": "216583946"}, {"id": "1862081327"}, {"id": "1843605638"}, {"id": "1847201714"}, {"id": "1570572263"}, {"id": "1587997718"}, {"id": "1670341232"}, {"id": "1865999675"}, {"id": "1586724737"}, {"id": "217368896"}, {"id": "1844731853"}, {"id": "217365599"}, {"id": "1813181399"}, {"id": "1571333363"}, {"id": "1587990047"}, {"id": "1590179369"}, {"id": "1665809579"}, {"id": "1873340672"}, {"id": "1586687522"}, {"id": "1852924394"}, {"id": "1590219962"}, {"id": "1587835859"}, {"id": "1590360719"}, {"id": "1570236728"}, {"id": "1297797338"}, {"id": "1277224574"}, {"id": "1747434198"}, {"id": "1840561226"}, {"id": "1710363710"}, {"id": "1587839435"}, {"id": "1839580700"}, {"id": "1572549869"}, {"id": "1570164587"}, {"id": "1859342024"}, {"id": "217369079"}, {"id": "1587988454"}, {"id": "1840650146"}, {"id": "1844196821"}, {"id": "1587850166"}, {"id": "1847350064"}, {"id": "1844733053"}, {"id": "1587030206"}, {"id": "1852964087"}, {"id": "1859181224"}, {"id": "1590053525"}, {"id": "1581292124"}, {"id": "1585878728"}, {"id": "216586760"}, {"id": "1570154699"}, {"id": "1868718506"}, {"id": "1859347385"}, {"id": "1590359207"}, {"id": "1590067835"}, {"id": "1868703266"}, {"id": "1588010669"}, {"id": "1572474569"}, {"id": "1572527597"}, {"id": "1878869765"}, {"id": "1587119129"}, {"id": "1878871331"}, {"id": "1587140861"}, {"id": "1844724032"}, {"id": "1590149441"}, {"id": "1857301637"}, {"id": "1859178596"}, {"id": "1590219980"}, {"id": "1839578906"}, {"id": "1878681053"}, {"id": "1572486317"}, {"id": "1587058238"}, {"id": "1297790072"}, {"id": "1587992687"}, {"id": "1581272699"}, {"id": "1588016225"}, {"id": "1570537112"}, {"id": "1844731871"}, {"id": "1590179330"}, {"id": "1587137207"}, {"id": "1846038983"}, {"id": "1570163573"}, {"id": "1878692624"}, {"id": "1878692867"}, {"id": "1570311767"}, {"id": "1578924737"}, {"id": "1862067383"}, {"id": "1277238440"}, {"id": "1576745405"}, {"id": "216351764"}, {"id": "1878694376"}, {"id": "1868720591"}, {"id": "1587843008"}, {"id": "1581279662"}, {"id": "1588024850"}, {"id": "1766932293"}, {"id": "1859334893"}, {"id": "217372502"}, {"id": "1578942869"}, {"id": "1846039043"}, {"id": "1811160929"}, {"id": "1868714288"}, {"id": "1572512465"}, {"id": "1839589373"}, {"id": "1859347319"}, {"id": "1571660513"}, {"id": "1572509042"}, {"id": "1588003502"}, {"id": "1590160412"}, {"id": "1576741724"}, {"id": "1716620831"}, {"id": "1878782237"}, {"id": "1873425494"}, {"id": "1587984323"}, {"id": "1571687285"}, {"id": "1587145130"}, {"id": "1590209393"}, {"id": "1722078368"}, {"id": "1570164671"}, {"id": "1859342279"}, {"id": "1839581477"}, {"id": "1590212246"}, {"id": "1852965692"}, {"id": "1297798694"}, {"id": "1587024533"}, {"id": "1840650443"}, {"id": "1572530249"}, {"id": "1716617822"}, {"id": "1865894951"}, {"id": "1571603078"}, {"id": "1590215423"}, {"id": "1567883168"}, {"id": "1277240033"}, {"id": "1571576828"}, {"id": "1665810557"}, {"id": "1588016186"}, {"id": "1277238311"}, {"id": "1586673371"}, {"id": "1670342096"}, {"id": "1590222407"}, {"id": "1857297413"}, {"id": "1588016909"}, {"id": "1588025555"}, {"id": "1585878788"}, {"id": "1874804519"}, {"id": "1855882661"}, {"id": "1581272747"}, {"id": "1297780514"}, {"id": "1876091906"}, {"id": "1587994115"}, {"id": "1816749552"}, {"id": "1587982649"}, {"id": "1869314600"}, {"id": "1709394788"}, {"id": "1587129488"}, {"id": "1868718455"}, {"id": "1766903781"}, {"id": "1844191904"}, {"id": "1586735513"}, {"id": "1590067634"}, {"id": "1857297527"}, {"id": "1277235530"}, {"id": "1843605494"}, {"id": "1843586555"}, {"id": "1878695885"}, {"id": "1587851102"}, {"id": "1572524414"}, {"id": "1572529481"}, {"id": "1865999603"}, {"id": "1578921164"}, {"id": "1570236713"}, {"id": "1873346108"}, {"id": "1878695765"}, {"id": "1571520317"}, {"id": "216580091"}, {"id": "1590344954"}, {"id": "1843607027"}, {"id": "1851407597"}, {"id": "1588015328"}, {"id": "1587849818"}, {"id": "1587811034"}, {"id": "1587145913"}, {"id": "1839580703"}, {"id": "1588023248"}, {"id": "1277238101"}, {"id": "1590353420"}, {"id": "1839588440"}, {"id": "1587982322"}, {"id": "1817410242"}, {"id": "1572538928"}, {"id": "1297797278"}, {"id": "1843588220"}, {"id": "1816749675"}, {"id": "1587830006"}, {"id": "1570236764"}, {"id": "1859328479"}, {"id": "1578905438"}, {"id": "1878869591"}, {"id": "1873350971"}, {"id": "1588015499"}, {"id": "1297797383"}, {"id": "1277367311"}, {"id": "1710448877"}, {"id": "1297792532"}, {"id": "1766908251"}, {"id": "1585850384"}, {"id": "1571663726"}, {"id": "1572383429"}, {"id": "1277365424"}, {"id": "1576780058"}, {"id": "1578902582"}, {"id": "1586685914"}, {"id": "1572524333"}, {"id": "1588012751"}, {"id": "216109328"}, {"id": "1859202749"}, {"id": "1590359150"}, {"id": "1572396350"}, {"id": "1865943566"}, {"id": "1585877429"}, {"id": "1843609349"}, {"id": "1588012571"}, {"id": "1816211334"}, {"id": "1843588148"}, {"id": "1277252126"}, {"id": "1864634480"}, {"id": "1570298756"}, {"id": "1862083211"}, {"id": "1852927058"}, {"id": "1590351017"}, {"id": "1570348070"}, {"id": "1586693147"}, {"id": "1864640876"}, {"id": "1847201891"}, {"id": "1864640672"}, {"id": "1869317039"}, {"id": "1722076412"}, {"id": "1869043535"}, {"id": "1572498467"}, {"id": "1297725716"}, {"id": "1572474500"}, {"id": "1586735549"}, {"id": "1670374850"}, {"id": "1578217487"}, {"id": "1570340042"}, {"id": "1570154729"}, {"id": "1859323643"}, {"id": "1670340083"}, {"id": "1578922232"}, {"id": "1873425416"}, {"id": "1859331806"}, {"id": "1839588611"}, {"id": "1567888490"}, {"id": "1587137234"}, {"id": "1590201377"}, {"id": "1572522944"}, {"id": "1585878080"}, {"id": "1578208691"}, {"id": "1839581777"}, {"id": "1862121581"}, {"id": "1878869645"}, {"id": "1576738379"}, {"id": "1869911705"}, {"id": "1588020710"}, {"id": "1862121650"}, {"id": "1297780883"}, {"id": "1864637795"}, {"id": "1570572848"}, {"id": "1865999513"}, {"id": "216108563"}, {"id": "1865856623"}, {"id": "1878688535"}, {"id": "1878782285"}, {"id": "1862081294"}, {"id": "1572528572"}, {"id": "1570300778"}, {"id": "1852653176"}, {"id": "1586681501"}, {"id": "1578902453"}, {"id": "1297774436"}, {"id": "1590212186"}, {"id": "1869046535"}, {"id": "1590323564"}, {"id": "1866001007"}, {"id": "1851407714"}, {"id": "1587998918"}, {"id": "1868722172"}, {"id": "1587991709"}, {"id": "1817410308"}, {"id": "1859342276"}, {"id": "216573500"}, {"id": "1862131313"}, {"id": "1859328407"}, {"id": "1277237360"}, {"id": "1878682883"}, {"id": "1859323607"}, {"id": "1590213623"}, {"id": "1855886123"}, {"id": "1868723489"}, {"id": "1862126078"}, {"id": "1859176805"}, {"id": "1878692618"}, {"id": "1572535115"}, {"id": "1851407570"}, {"id": "1766923941"}, {"id": "1304264366"}, {"id": "1857288770"}, {"id": "1570572254"}, {"id": "1586684945"}, {"id": "1852967081"}, {"id": "1586704859"}, {"id": "1588020977"}, {"id": "1590160442"}, {"id": "1297776722"}, {"id": "1570280732"}, {"id": "1851483932"}, {"id": "1851485621"}, {"id": "1855915934"}, {"id": "1864659965"}, {"id": "1866125960"}, {"id": "1304266787"}, {"id": "216582482"}, {"id": "1590327461"}, {"id": "1709388815"}, {"id": "1857289067"}, {"id": "1843609193"}, {"id": "1583874674"}, {"id": "1263324989"}, {"id": "1855878050"}, {"id": "1840562483"}, {"id": "1571327426"}, {"id": "1572381284"}, {"id": "1843610651"}, {"id": "1865943647"}, {"id": "1277237348"}, {"id": "1859328422"}, {"id": "1859144498"}, {"id": "1567881275"}, {"id": "1844765870"}, {"id": "1709394749"}, {"id": "1590216599"}, {"id": "1846400849"}, {"id": "1304266880"}, {"id": "1859332616"}, {"id": "1844765876"}, {"id": "1725907829"}, {"id": "1844196854"}, {"id": "216570887"}, {"id": "1571601872"}, {"id": "1277278817"}, {"id": "1859328572"}, {"id": "1586728193"}, {"id": "1711058384"}, {"id": "1876239875"}, {"id": "1277234858"}, {"id": "1572512582"}, {"id": "1590071315"}, {"id": "1572549791"}, {"id": "1816751286"}, {"id": "1859353433"}, {"id": "1587982640"}, {"id": "1576746071"}, {"id": "1878869627"}, {"id": "216584522"}, {"id": "1843605629"}, {"id": "1277256947"}, {"id": "1878692828"}, {"id": "1588014245"}, {"id": "1277239442"}, {"id": "1587150074"}, {"id": "1725907877"}, {"id": "216351116"}, {"id": "1586691842"}, {"id": "1844190341"}, {"id": "1590349244"}, {"id": "1766916984"}, {"id": "1716620855"}, {"id": "1587990137"}, {"id": "1570219568"}, {"id": "1868705411"}, {"id": "1263322079"}, {"id": "1725713018"}, {"id": "1586693789"}, {"id": "1852927223"}, {"id": "1844731622"}, {"id": "1747441329"}, {"id": "1581273491"}, {"id": "217370285"}, {"id": "1859144576"}, {"id": "1839579068"}, {"id": "1588016045"}, {"id": "1578916337"}, {"id": "1859176919"}, {"id": "1590161801"}, {"id": "1590178646"}, {"id": "1590027383"}, {"id": "1590361847"}, {"id": "1571351789"}, {"id": "1862083532"}, {"id": "1847350094"}, {"id": "1859144702"}, {"id": "1710363743"}, {"id": "216573575"}, {"id": "1571344901"}, {"id": "1586682233"}, {"id": "1839584162"}, {"id": "1860742169"}, {"id": "1873350980"}, {"id": "1586668460"}, {"id": "1572549851"}, {"id": "1868703098"}, {"id": "1865999810"}, {"id": "1868716595"}, {"id": "1859347238"}, {"id": "1852922912"}, {"id": "1572536684"}, {"id": "1840553930"}, {"id": "1297792586"}, {"id": "1570343048"}, {"id": "1576738508"}, {"id": "1590179858"}, {"id": "1873296680"}, {"id": "1844729132"}, {"id": "1590363470"}, {"id": "1567877705"}, {"id": "1865943602"}, {"id": "1588020896"}, {"id": "1572528581"}, {"id": "1576774418"}, {"id": "1578921188"}, {"id": "1586722964"}, {"id": "1865999660"}, {"id": "1570355474"}, {"id": "1277237339"}, {"id": "1570559126"}, {"id": "1590349214"}, {"id": "1571665559"}, {"id": "1277256725"}, {"id": "1572544451"}, {"id": "1297777316"}, {"id": "216347945"}, {"id": "1571341775"}, {"id": "1588023983"}, {"id": "1873296803"}, {"id": "1570574012"}, {"id": "1587982679"}, {"id": "1587996167"}, {"id": "1859233796"}, {"id": "1839588644"}, {"id": "1846707968"}, {"id": "1865986910"}, {"id": "1817411568"}, {"id": "1722076442"}, {"id": "1590224780"}, {"id": "1864643783"}, {"id": "1587849554"}, {"id": "1852959848"}, {"id": "1846708055"}, {"id": "1851481436"}, {"id": "1868720606"}, {"id": "1747441221"}, {"id": "1590178124"}, {"id": "1590070331"}, {"id": "1859349041"}, {"id": "1587811016"}, {"id": "1766918292"}, {"id": "1570236659"}, {"id": "1585868765"}, {"id": "1572524273"}, {"id": "1874804252"}, {"id": "1587991058"}, {"id": "1865943800"}, {"id": "1586687555"}, {"id": "1868703311"}, {"id": "1590315356"}, {"id": "1852646654"}, {"id": "1844764352"}, {"id": "1851488726"}, {"id": "1878695711"}, {"id": "1855878059"}, {"id": "1865999666"}, {"id": "1860726701"}, {"id": "1722078383"}, {"id": "1590178541"}, {"id": "1590061787"}, {"id": "1855882634"}, {"id": "1846752404"}, {"id": "1578924734"}, {"id": "1586685902"}, {"id": "1277271530"}, {"id": "1587654818"}, {"id": "1862086877"}, {"id": "1873346279"}, {"id": "1590317645"}, {"id": "1572537380"}, {"id": "1864637690"}, {"id": "1590363326"}, {"id": "1864643828"}, {"id": "1855886288"}, {"id": "1590363899"}, {"id": "1570327499"}, {"id": "1872076820"}, {"id": "1813179776"}, {"id": "1576771940"}, {"id": "216573521"}, {"id": "1576771976"}, {"id": "1572474563"}, {"id": "1571544905"}, {"id": "1859349056"}, {"id": "1859352068"}, {"id": "1869046541"}, {"id": "1590217352"}, {"id": "1297781663"}, {"id": "1571579153"}, {"id": "1862115425"}, {"id": "1865986829"}, {"id": "1766916966"}, {"id": "1578907046"}, {"id": "1852967096"}, {"id": "1874256560"}, {"id": "1843607138"}, {"id": "1587982616"}, {"id": "1869908210"}, {"id": "217368335"}, {"id": "1297781453"}, {"id": "1813178489"}, {"id": "1572528545"}, {"id": "1578224411"}, {"id": "216589541"}, {"id": "1873425389"}, {"id": "1297793864"}, {"id": "1297741778"}, {"id": "1844724194"}, {"id": "1585936838"}, {"id": "1844192033"}, {"id": "1864634687"}, {"id": "1590179417"}, {"id": "1590149663"}, {"id": "1650251657"}, {"id": "1571664809"}, {"id": "1586683772"}, {"id": "1865993645"}, {"id": "1868722085"}, {"id": "1572537353"}, {"id": "1590206120"}, {"id": "1590202145"}, {"id": "1864614104"}, {"id": "1570568129"}, {"id": "1590363473"}, {"id": "1869318146"}, {"id": "1588012886"}, {"id": "1587142604"}, {"id": "1868718335"}, {"id": "1277231348"}, {"id": "1843605623"}, {"id": "1586659880"}, {"id": "1864640828"}, {"id": "1868714429"}, {"id": "1839578894"}, {"id": "1277253347"}, {"id": "1865986907"}, {"id": "1766928474"}, {"id": "1572528563"}, {"id": "1852963973"}, {"id": "1586690804"}, {"id": "1590350009"}, {"id": "1590216815"}, {"id": "1846008026"}, {"id": "1766928987"}, {"id": "1847350085"}, {"id": "1851485459"}, {"id": "1578225881"}, {"id": "1873340669"}, {"id": "1572477422"}, {"id": "1587987743"}, {"id": "1766918364"}, {"id": "1839581558"}, {"id": "216788837"}, {"id": "1711088057"}, {"id": "1570154711"}, {"id": "1572542144"}, {"id": "1590056546"}, {"id": "1844194406"}, {"id": "1590341303"}, {"id": "1590102662"}, {"id": "1586672516"}, {"id": "1587994226"}, {"id": "1766920407"}, {"id": "1590360671"}, {"id": "1862115728"}, {"id": "216578222"}, {"id": "1576782659"}, {"id": "1868722196"}, {"id": "1840559678"}, {"id": "1868722040"}, {"id": "1586725763"}, {"id": "1590161825"}, {"id": "1571580047"}, {"id": "1590327266"}, {"id": "1588012268"}, {"id": "1588025525"}, {"id": "1840648007"}, {"id": "1844733197"}, {"id": "1572474467"}, {"id": "1570162160"}, {"id": "1840562888"}, {"id": "1766904339"}, {"id": "1277234753"}, {"id": "1665810491"}, {"id": "1571579090"}, {"id": "1817411028"}, {"id": "1578922445"}, {"id": "1665809720"}, {"id": "1862121443"}, {"id": "1876240118"}, {"id": "1844733068"}, {"id": "1664820587"}, {"id": "1852925555"}, {"id": "1586673506"}, {"id": "1590327443"}, {"id": "1571338100"}, {"id": "1297787474"}, {"id": "1725722138"}, {"id": "1590061802"}, {"id": "1588016993"}, {"id": "1578212882"}, {"id": "1587990374"}, {"id": "1572537389"}, {"id": "1846406198"}, {"id": "1570537124"}, {"id": "1844194412"}, {"id": "1578930431"}, {"id": "1587843002"}, {"id": "1844764175"}, {"id": "1570388246"}, {"id": "1590341246"}, {"id": "1813180574"}, {"id": "1587857351"}, {"id": "1862083313"}, {"id": "1857297377"}, {"id": "1816212906"}, {"id": "1587140894"}, {"id": "1572512624"}, {"id": "1590024140"}, {"id": "1650268304"}, {"id": "1586659856"}, {"id": "1813180649"}, {"id": "1846400816"}, {"id": "1878784238"}, {"id": "1576738475"}, {"id": "1657958753"}, {"id": "217371068"}, {"id": "1590079850"}, {"id": "1570405538"}, {"id": "1590070535"}, {"id": "1766908248"}, {"id": "1878784349"}, {"id": "1572524321"}, {"id": "1567879937"}, {"id": "1587110966"}, {"id": "1747434312"}, {"id": "1586672585"}, {"id": "1587990080"}, {"id": "1590361295"}, {"id": "1587996980"}, {"id": "1847350223"}, {"id": "1590183653"}, {"id": "1587139106"}, {"id": "1590073427"}, {"id": "1868716577"}, {"id": "1844765882"}, {"id": "1590352295"}, {"id": "1874803595"}, {"id": "1587996218"}, {"id": "1590201260"}, {"id": "215268662"}, {"id": "1586730020"}, {"id": "1570573481"}, {"id": "1585878749"}, {"id": "1843609256"}, {"id": "1297774229"}, {"id": "1839579029"}, {"id": "1725719297"}, {"id": "1590224792"}, {"id": "1859342231"}, {"id": "1586728001"}, {"id": "216392381"}, {"id": "1587980666"}, {"id": "1578942671"}, {"id": "1585868780"}, {"id": "1847201729"}, {"id": "1590349253"}, {"id": "1864660097"}, {"id": "1587989924"}, {"id": "1277237186"}, {"id": "1578922346"}, {"id": "1578946166"}, {"id": "1766928930"}, {"id": "1588006622"}, {"id": "1576738559"}, {"id": "1766922816"}, {"id": "1578215057"}, {"id": "1590351095"}, {"id": "1588014650"}, {"id": "1670340020"}, {"id": "1576738358"}, {"id": "1578252497"}, {"id": "1581286382"}, {"id": "1866109355"}, {"id": "1586684186"}, {"id": "1865993414"}, {"id": "1817410236"}, {"id": "1277253083"}, {"id": "1576745969"}, {"id": "1576741856"}, {"id": "1297732649"}, {"id": "1587851087"}, {"id": "1846749353"}, {"id": "1570230932"}, {"id": "1852924232"}, {"id": "1878690347"}, {"id": "1571688560"}, {"id": "1590222758"}, {"id": "216586124"}, {"id": "1590339833"}, {"id": "1587039911"}, {"id": "1587850181"}, {"id": "1873415597"}, {"id": "1590216818"}, {"id": "1277235539"}, {"id": "1578927203"}, {"id": "1852961792"}, {"id": "1578921071"}, {"id": "1590352154"}, {"id": "1587982943"}, {"id": "1873351028"}, {"id": "1586687435"}, {"id": "1277246315"}, {"id": "1570573547"}, {"id": "1590200840"}, {"id": "1297799795"}, {"id": "1590023114"}, {"id": "1570350344"}, {"id": "1297774214"}, {"id": "1571610902"}, {"id": "1874803976"}, {"id": "1868722133"}, {"id": "1586685833"}, {"id": "1572527816"}, {"id": "1839578912"}, {"id": "1578207887"}, {"id": "1578927221"}, {"id": "1571676467"}, {"id": "1570363838"}, {"id": "1581273401"}, {"id": "1710272555"}, {"id": "1586704871"}, {"id": "1844196158"}, {"id": "1859347163"}, {"id": "1859182766"}, {"id": "1857285944"}, {"id": "1587145085"}, {"id": "1709394794"}, {"id": "1868716469"}, {"id": "1844767133"}, {"id": "1587137267"}, {"id": "1862121509"}, {"id": "1844733182"}, {"id": "1869314783"}, {"id": "1587840452"}, {"id": "1587839228"}, {"id": "1864613918"}, {"id": "1578205904"}, {"id": "1567881320"}, {"id": "1587984359"}, {"id": "1578208646"}, {"id": "1571663738"}, {"id": "1766904423"}, {"id": "1855878230"}, {"id": "1868716592"}, {"id": "1588012913"}, {"id": "1570308302"}, {"id": "1297800338"}, {"id": "1567878608"}, {"id": "1839585650"}, {"id": "1584665534"}, {"id": "1590351053"}, {"id": "1576745906"}, {"id": "1839580658"}, {"id": "1567883174"}, {"id": "1570154795"}, {"id": "1586676731"}, {"id": "1572528704"}, {"id": "1588006658"}, {"id": "1846771184"}, {"id": "1572515984"}, {"id": "1590222773"}, {"id": "1572396332"}, {"id": "1654572809"}, {"id": "1817411712"}, {"id": "1571679299"}, {"id": "1570573988"}, {"id": "216584528"}, {"id": "1578922283"}, {"id": "1839582731"}, {"id": "1590178439"}, {"id": "1570405550"}, {"id": "216578048"}, {"id": "1588012544"}, {"id": "1710331514"}, {"id": "1839587453"}, {"id": "1590363332"}, {"id": "1588000760"}, {"id": "1297790003"}, {"id": "1869041441"}, {"id": "1590357704"}, {"id": "1860726971"}, {"id": "1844191892"}, {"id": "1571577482"}, {"id": "1578924929"}, {"id": "1851405533"}, {"id": "1839589109"}, {"id": "1571662124"}, {"id": "1586673464"}, {"id": "1590359342"}, {"id": "1847201939"}, {"id": "1572549797"}, {"id": "1578933038"}, {"id": "1859328542"}, {"id": "1588027631"}, {"id": "1878688736"}, {"id": "1578927386"}, {"id": "1590361124"}, {"id": "1572529421"}, {"id": "1588000736"}, {"id": "1578915809"}, {"id": "1570160897"}, {"id": "1297778792"}, {"id": "1585874039"}, {"id": "1586704868"}, {"id": "1590070451"}, {"id": "1304265908"}, {"id": "1840562621"}, {"id": "1567876856"}, {"id": "1572546812"}, {"id": "1590327203"}, {"id": "1586682890"}, {"id": "1587854147"}, {"id": "1859178449"}, {"id": "1852922723"}, {"id": "1581301721"}, {"id": "1864634477"}, {"id": "1297741637"}, {"id": "1869317087"}, {"id": "1571368193"}, {"id": "1585868912"}, {"id": "1855880600"}, {"id": "1665810011"}, {"id": "1586727173"}, {"id": "1590339797"}, {"id": "1570405553"}, {"id": "1844196263"}, {"id": "1878692834"}, {"id": "216096473"}, {"id": "1868716514"}, {"id": "1588015346"}, {"id": "1855878026"}, {"id": "1864659869"}, {"id": "1586659184"}, {"id": "1878690515"}, {"id": "1865895200"}, {"id": "1859323709"}, {"id": "1572528707"}, {"id": "1590213677"}, {"id": "1576745204"}, {"id": "1297775108"}, {"id": "1567864766"}, {"id": "1572510344"}, {"id": "1590225344"}, {"id": "1844196851"}, {"id": "1846008134"}, {"id": "1590350795"}, {"id": "1571689451"}, {"id": "1864622684"}, {"id": "1587031388"}, {"id": "1663683845"}, {"id": "1578933023"}, {"id": "1843605521"}, {"id": "1581270515"}, {"id": "1722076439"}, {"id": "1590314042"}, {"id": "1590352280"}, {"id": "1572529604"}, {"id": "1766903739"}, {"id": "1711241600"}, {"id": "1576777139"}, {"id": "1297776713"}, {"id": "1586691857"}, {"id": "1855878098"}, {"id": "1277235635"}, {"id": "1865999579"}, {"id": "1725714137"}, {"id": "1851407723"}, {"id": "1567889348"}, {"id": "1572524381"}, {"id": "1586691008"}, {"id": "1587038501"}, {"id": "1585868954"}, {"id": "1844190491"}]} \ No newline at end of file diff --git a/inference.ipynb b/inference.ipynb new file mode 100755 index 0000000..771f40d --- /dev/null +++ b/inference.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import csv\n", + "# with open('output_caption/average.json', 'r') as f:\n", + "# data = json.load(f)\n", + "\n", + "# with open('pred.csv', 'w') as f:\n", + "# w = csv.writer(f)\n", + "# w.writerows(data.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "tokenization...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Apr 11, 2023 3:43:15 AM edu.stanford.nlp.process.PTBLexer next\n", + "WARNING: Untokenizable: � (U+FFFD, decimal: 65533)\n", + "PTBTokenizer tokenized 61388 tokens at 354984.88 tokens per second.\n", + "PTBTokenizer tokenized 54451 tokens at 478619.41 tokens per second.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting up scorers...\n", + "computing Bleu score...\n", + "{'testlen': 49427, 'reflen': 56389, 'guess': [49427, 44427, 39427, 34427], 'correct': [16461, 4481, 1576, 572]}\n", + "ratio: 0.8765362038695336\n", + "Bleu_1: 0.289\n", + "Bleu_2: 0.159\n", + "Bleu_3: 0.096\n", + "Bleu_4: 0.060\n", + "computing METEOR score...\n", + "METEOR: 0.121\n", + "computing Rouge score...\n", + "ROUGE_L: 0.275\n", + "computing CIDEr score...\n", + "CIDEr: 0.681\n" + ] + } + ], + "source": [ + "import json\n", + "from pycocoevalcap.eval import COCOEvalCap\n", + "from pycocotools.coco import COCO\n", + "\n", + "def preprocess(infile, outfile):\n", + " gr = {}\n", + " with open(infile,'r') as f:\n", + " json_data = json.load(f)\n", + "\n", + " gr[\"annotations\"] = []\n", + " gr[\"images\"] = []\n", + "\n", + " for key, value in json_data.items():\n", + " temp_2, temp_3 = {}, {}\n", + " temp_2[\"image_id\"] = key\n", + " temp_2[\"caption\"] = value\n", + " temp_2[\"id\"] = key\n", + " temp_3[\"id\"] = key\n", + " gr[\"annotations\"].append(temp_2)\n", + " gr[\"images\"].append(temp_3)\n", + "\n", + " with open(outfile, 'w') as f:\n", + " json.dump(gr, f)\n", + "\n", + "file_name = 'average_sqrt2'\n", + "infile = './output_caption/' + file_name + '.json'\n", + "outfile = './for_inference/' + file_name + '.json'\n", + "\n", + "preprocess(infile=infile, outfile=outfile)\n", + "\n", + "coco_eval = COCOEvalCap(COCO('for_inference/nice_gt.json'), COCO(outfile))\n", + "coco_eval.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "tokenization...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Apr 11, 2023 3:43:34 AM edu.stanford.nlp.process.PTBLexer next\n", + "WARNING: Untokenizable: � (U+FFFD, decimal: 65533)\n", + "PTBTokenizer tokenized 61388 tokens at 374796.81 tokens per second.\n", + "PTBTokenizer tokenized 54451 tokens at 473598.05 tokens per second.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting up scorers...\n", + "computing Bleu score...\n", + "{'testlen': 49427, 'reflen': 56389, 'guess': [49427, 44427, 39427, 34427], 'correct': [16461, 4481, 1576, 572]}\n", + "ratio: 0.8765362038695336\n", + "Bleu_1: 0.289\n", + "Bleu_2: 0.159\n", + "Bleu_3: 0.096\n", + "Bleu_4: 0.060\n", + "computing METEOR score...\n", + "METEOR: 0.121\n", + "computing Rouge score...\n", + "ROUGE_L: 0.275\n", + "computing CIDEr score...\n", + "CIDEr: 0.681\n" + ] + } + ], + "source": [ + "file_name = 'average_sqrt3'\n", + "infile = './output_caption/' + file_name + '.json'\n", + "outfile = './for_inference/' + file_name + '.json'\n", + "\n", + "preprocess(infile=infile, outfile=outfile)\n", + "\n", + "coco_eval = COCOEvalCap(COCO('for_inference/nice_gt.json'), COCO(outfile))\n", + "coco_eval.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "tokenization...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Apr 11, 2023 4:34:30 AM edu.stanford.nlp.process.PTBLexer next\n", + "WARNING: Untokenizable: � (U+FFFD, decimal: 65533)\n", + "PTBTokenizer tokenized 61388 tokens at 377905.36 tokens per second.\n", + "PTBTokenizer tokenized 54451 tokens at 488382.22 tokens per second.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setting up scorers...\n", + "computing Bleu score...\n", + "{'testlen': 49427, 'reflen': 56389, 'guess': [49427, 44427, 39427, 34427], 'correct': [16461, 4481, 1576, 572]}\n", + "ratio: 0.8765362038695336\n", + "Bleu_1: 0.289\n", + "Bleu_2: 0.159\n", + "Bleu_3: 0.096\n", + "Bleu_4: 0.060\n", + "computing METEOR score...\n", + "METEOR: 0.121\n", + "computing Rouge score...\n", + "ROUGE_L: 0.275\n", + "computing CIDEr score...\n", + "CIDEr: 0.681\n" + ] + } + ], + "source": [ + "file_name = 'average'\n", + "infile = './output_caption/' + file_name + '.json'\n", + "outfile = './for_inference/' + file_name + '.json'\n", + "\n", + "preprocess(infile=infile, outfile=outfile)\n", + "\n", + "coco_eval = COCOEvalCap(COCO('for_inference/nice_gt.json'), COCO(outfile))\n", + "coco_eval.evaluate()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "clip_prefix_caption", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/inference.py b/inference.py new file mode 100755 index 0000000..8fb997d --- /dev/null +++ b/inference.py @@ -0,0 +1,102 @@ +import os, torch, json, argparse +from tqdm import tqdm +from modeling_opt_pp import OPTForCausalLM +from transformers import AutoTokenizer +import copy + +OPT_MODEL = "facebook/opt-2.7b" + +def generate( + model, tokenizer, prefix_embed, + use_nucleus_sampling=False, + num_beams=5, + max_length=30, + min_length=1, + top_p=0.9, + repetition_penalty=1.0, + length_penalty=1.0, + num_captions=1, + temperature=1, + prompt="", + device=torch.device('cuda:0'), + ): + + with torch.cuda.amp.autocast( + enabled=(prefix_embed.device != torch.device("cpu")) + ): + eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0] + atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device) + opt_tokens = tokenizer([prompt], return_tensors='pt').to(device) + input_ids = opt_tokens.input_ids + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = model.generate( + input_ids=input_ids, + query_embeds=prefix_embed, + attention_mask=attention_mask, + do_sample=use_nucleus_sampling, + top_p=top_p, + temperature=temperature, + num_beams=num_beams, + max_new_tokens=max_length, + min_length=min_length, + eos_token_id=eos_token_id, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + num_return_sequences=num_captions, + ) + + prompt_length = input_ids.shape[1] + output_text = tokenizer.batch_decode( + outputs[:, prompt_length:], skip_special_tokens=True + ) + output_text = [text.strip() for text in output_text] + + return output_text[0] + +parser = argparse.ArgumentParser() +parser.add_argument('--device', type=str, default=3) +parser.add_argument('--ofile', type=str, default='snow') +parser.add_argument('--prompt', type=str, default="a photo of") +args = parser.parse_args() + +device = torch.device('cuda:' + args.device) +prompt = args.prompt + +clipcap_path = os.path.abspath('/data1/IC/nice_val_features/clipcap_ori') +blip2_path = os.path.abspath('/data1/IC/nice_val_features/blip2OPT') + +feature_flist = os.listdir(clipcap_path) + +output_folder = './output_caption' +output_file = args.ofile + '.json' +os.makedirs(output_folder, exist_ok=True) + +opt_model = OPTForCausalLM.from_pretrained(OPT_MODEL, torch_dtype=torch.float16) +tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL, use_fast=False) + +opt_model.model.decoder.setting_device(device1 = device, device2 = device, device3 = device) +opt_model.eval() + +data = {} +for feature_file in tqdm(feature_flist): + clipcap_feature = torch.load(os.path.join(clipcap_path, feature_file)).to(device) + blip2_feature = torch.load(os.path.join(blip2_path, feature_file)).to(device) + feature = (clipcap_feature + blip2_feature)/2 #*torch.sqrt(torch.tensor(3)) + + # feature = torch.load(os.path.join(blip2_path, feature_file)).to(device) + # feature_shuffle = copy.deepcopy(feature) + # indices = torch.randperm(32) + # feature_shuffle = feature_shuffle[indices] + + # feature = feature.unsqueeze(0) + # feature_shuffle = feature_shuffle.unsqueeze(0) + + # generated = generate(opt_model, tokenizer, feature) + # generated_shuffle = generate(opt_model, tokenizer, feature_shuffle) + # data[int(feature_file[:-3])] = [indices, generated, generated_shuffle] + + generated_caption = generate(model=opt_model, tokenizer=tokenizer, prefix_embed=blip2_feature, prompt=prompt, device=device) + data[int(feature_file[:-3])] = generated_caption +with open(os.path.join(output_folder, output_file), 'w') as fp: + json.dump(data, fp, default=str) \ No newline at end of file diff --git a/modeling_opt_pp.py b/modeling_opt_pp.py old mode 100644 new mode 100755 index 014ebe3..1fc1c9a --- a/modeling_opt_pp.py +++ b/modeling_opt_pp.py @@ -569,7 +569,7 @@ def __init__(self, config: OPTConfig): # Initialize weights and apply final processing self.post_init() - def setting_device(self, device1, device2, device3, pn1, pn2): + def setting_device(self, device1, device2, device3, pn1=4, pn2=9): self.device1 = device1 self.device2 = device2 self.device3 = device3 @@ -1083,11 +1083,12 @@ def forward( loss = None if labels is not None: - logits = logits[:, -labels.size(1) :, :] + #labels.shape : 1 x 56, logits.shape : 1 x 56 x 50272 + logits = logits[:, -labels.size(1) :, :] # 1 x 56 x 50272 # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() + shift_logits = logits[..., :-1, :].contiguous() # 1 x 55 x 50272 + shift_labels = labels[..., 1:].contiguous() # 1 x 55 # Flatten the tokens loss_fct = CrossEntropyLoss(reduction=reduction) loss = loss_fct( diff --git a/predict.py b/predict.py old mode 100644 new mode 100755 index 4f4bdb7..59c36ca --- a/predict.py +++ b/predict.py @@ -33,6 +33,8 @@ TSN = Optional[TS] TA = Union[T, ARRAY] +weights = "/data1/checkpoint/clipcap/model_coco_prefix-009.pt" #.pt" + def direct_weight_paths(language_model): if language_model == 'gpt2': WEIGHTS_PATHS = { @@ -43,8 +45,8 @@ def direct_weight_paths(language_model): return WEIGHTS_PATHS elif language_model == 'opt': WEIGHTS_PATHS = { - "opt_000": "/data/IC/clipcap/model_coco_prefix-000.pt", - "opt_001": "/data/IC/clipcap/model_coco_prefix-001.pt", + "opt_000": weights, + "opt_001": weights, } print('your language model is : OPT') return WEIGHTS_PATHS @@ -60,7 +62,7 @@ class Predictor(cog.Predictor): def setup(self, args): """Load the model into memory to make running multiple predictions efficient""" # self.device = torch.device("cuda") - self.device1 = make_device(args)[0] + self.device1 = make_device_pn(device=args.device, pn=args.pn)[0] self.clip_model, self.preprocess = clip.load( "ViT-B/32", device=self.device1, jit=False ) @@ -69,17 +71,17 @@ def setup(self, args): if self.args.language_model == 'gpt2': self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2") elif self.args.language_model == 'opt': - self.tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL) + self.tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL, use_fast=False) self.models = {} self.prefix_length = args.prefix_length for key, weights_path in WEIGHTS_PATHS.items(): - - model = ClipCaptionModel(args) - model.load_state_dict(torch.load(weights_path, map_location=CPU)) - model = model.eval() - # model = model.to(self.device) - self.models[key] = model + if args.checkpoint == key[-3:]: + model = ClipCaptionModel(args) + model.load_state_dict(torch.load(weights_path, map_location=CPU)) + model = model.eval() + # model = model.to(self.device) + self.models[key] = model @cog.input("image", type=cog.Path, help="Input image") @cog.input( @@ -95,7 +97,7 @@ def setup(self, args): default=False, help="Whether to apply beam search to generate the output text", ) - def predict(self, image, model, use_beam_search): + def predict(self, image, model): """Run a single prediction on the model""" image = io.imread(image) model = self.models[model] @@ -107,7 +109,7 @@ def predict(self, image, model, use_beam_search): ) prefix_embed = model.clip_project(prefix).reshape(1, self.prefix_length, -1) - return generate(model, self.tokenizer, prefix_embed, self.device1) + return generate(model, self.tokenizer, prefix_embed, self.device1), prefix_embed.detach().to(CPU) class MlpTransformer(nn.Module): @@ -260,15 +262,14 @@ def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512 self.prefix_size = prefix_size self.clip_length = clip_length self.num_layers = num_layers - self.device1, device2, device3 = make_device(args) - pn1, pn2 = int(args.pn[0]), int(args.pn[1:]) + self.device1, device2, device3, pn1, pn2 = make_device_pn(device=args.device, pn=args.pn) if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] elif self.args.language_model == 'opt': print('clipcaption - LM : OPT') - self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) + self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) #, torch_dtype=torch.float16) self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] self.gpt.model.decoder.setting_device(device1=self.device1, device2=device2, device3=device3, pn1=pn1, pn2=pn2) @@ -286,64 +287,79 @@ def train(self, mode: bool = True): self.gpt.eval() return self -def make_device(args): - device_num = len(args.device) +def make_device_pn(device, pn): + device_num = len(device) devices = [] for i in range(device_num): - device = "cuda:" + args.device[i] - devices.append(torch.device(device)) + device_name = "cuda:" + device[i] + devices.append(torch.device(device_name)) assert len(devices) < 4 + assert len(pn) < 5 + if len(devices) == 1: devices *= 3 device1, device2, device3 = devices + pn1, pn2 = 12, 12 elif len(devices) == 2: device1 = devices[0] device2 = devices[1] device3 = devices[1] + pn1, pn2 = int(pn), 12 else: device1, device2, device3 = devices - return device1, device2, device3 - - -def generate(model, tokenizer, prefix_embed, device1, - use_nucleus_sampling=False, - num_beams=5, - max_length=30, - min_length=1, - top_p=0.9, - repetition_penalty=1.5, - length_penalty=1.0, - num_captions=1, - temperature=1, - prompt=""): - - atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device1) - opt_tokens = tokenizer([prompt], return_tensors='pt').to(device1) - input_ids = opt_tokens.input_ids - query_embeds = prefix_embed - attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) - - outputs = model.gpt.generate( - input_ids=input_ids, - query_embeds=query_embeds, - attention_mask=attention_mask, - do_sample=use_nucleus_sampling, - top_p=top_p, - temperature=temperature, - num_beams=num_beams, - max_new_tokens=max_length, - min_length=min_length, - eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], - repetition_penalty=repetition_penalty, - length_penalty=length_penalty, - num_return_sequences=num_captions, - ) - - prompt_length = input_ids.shape[1] - output_text = tokenizer.batch_decode( - outputs[:, prompt_length:], skip_special_tokens=True - ) - output_text = [text.strip() for text in output_text] + length = len(pn) + if length < 4: + pn1, pn2 = int(pn[0]), int(pn[1:]) + else: + pn1, pn2 = int(pn[:2]), int(pn[2:]) + + return device1, device2, device3, pn1, pn2 + +def generate( + model, tokenizer, prefix_embed, device1, + use_nucleus_sampling=False, + num_beams=5, + max_length=30, + min_length=1, + top_p=0.9, + repetition_penalty=1.0, + length_penalty=1.0, + num_captions=1, + temperature=1, + prompt="a photo of" + ): - return output_text[0] \ No newline at end of file + with torch.cuda.amp.autocast( + enabled=(prefix_embed.device != torch.device("cpu")) + ): + eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0] + atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device1) + opt_tokens = tokenizer([prompt], return_tensors='pt').to(device1) + input_ids = opt_tokens.input_ids + query_embeds = prefix_embed + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = model.gpt.generate( + input_ids=input_ids, + query_embeds=query_embeds, + attention_mask=attention_mask, + do_sample=use_nucleus_sampling, + top_p=top_p, + temperature=temperature, + num_beams=num_beams, + max_new_tokens=max_length, + min_length=min_length, + eos_token_id=eos_token_id, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + num_return_sequences=num_captions, + ) + + prompt_length = input_ids.shape[1] + output_text = tokenizer.batch_decode( + outputs[:, prompt_length:], skip_special_tokens=True + ) + output_text = [text.strip() for text in output_text] + + return output_text[0] \ No newline at end of file diff --git a/predict_nice.py b/predict_nice.py old mode 100644 new mode 100755 index 0a43b5a..51d9a72 --- a/predict_nice.py +++ b/predict_nice.py @@ -3,7 +3,7 @@ import os from datetime import datetime -import pandas as pd +# import pandas as pd from tqdm import tqdm from predict import * @@ -12,17 +12,40 @@ parser = argparse.ArgumentParser() parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') parser.add_argument('--prefix_length', type=int, default=32, help='must match prefix_length of your trained model') -parser.add_argument('--checkpoint', type=int, default='001', help='checkpoint weight path') +parser.add_argument('--checkpoint', type=str, default='001', help='checkpoint weight path') +parser.add_argument('--ofile', type=str, default='clipcap') parser.add_argument('--device', default='123') parser.add_argument('--pn', default='111') args = parser.parse_args() # file path : CVPR2023challenge -fpath_nice = os.path.join('/data/IC/nice-eval', 'images') -flist_nice = os.listdir(fpath_nice) -annot_csv = pd.read_csv(os.path.join('/data/IC/nice-eval', 'nice-val-5k.csv')) -output_file = f'./output_caption/{datetime.now().strftime("%Y%m%d-%H%M%S")}' -os.makedirs(output_file, exist_ok=True) + +# fpath_nice = os.path.join('/data1/IC/nice-eval', 'images') +# flist_nice = os.listdir(fpath_nice) +# output_folder = './output_caption' +# output_file = args.ofile + '.json' +# clipcap_path_nice = os.path.abspath('/data1/IC/nice_val_features/clipcap') +# os.makedirs(output_folder, exist_ok=True) + +# annot_csv = pd.read_csv(os.path.join('/data/IC/nice-eval', 'nice-val-5k.csv')) +# output_file = f'./output_caption/{datetime.now().strftime("%Y%m%d-%H%M%S")}' +# os.makedirs(output_file, exist_ok=True) + + +fpath_nice_test = '/data1/IC/nice-test/images' +flist_nice_test = os.listdir(fpath_nice_test) +output_folder = './output_caption' +output_file = args.ofile + '.json' +os.makedirs(output_folder, exist_ok=True) + + + +# folder = 'val2017' +# fpath_coco = os.path.join('/data1/IC/coco/images', folder) +# flist_coco = os.listdir(fpath_coco) +# clipcap_path_coco = os.path.join('/data1/IC/coco_features/clipcap', folder[:-4]) + + OPT_MODEL = 'facebook/opt-2.7b' @@ -31,82 +54,30 @@ predict.setup(args) print('Ready to predict captions of CVPR2023-NICE dataset') -# p_model = ClipCaptionModel(args) -# p_model.load_state_dict(torch.load("/data/IC/clipcap/model_coco_prefix-000.pt", map_location=CPU)) -# p_tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL) -# p_model = p_model.eval() -# p_device1 = make_device(args)[0] -# p_prefix_length = args.prefix_length -# p_clip_model, p_preprocess = clip.load("ViT-B/32", device=p_device1, jit=False) - -# example -# for i in [0, 1, 2, 3]: -# print(i) -# image = os.path.join(fpath_nice, flist_nice[i]) - -# image = io.imread(image) -# model = p_model; tokenizer = p_tokenizer -# pil_image = PIL.Image.fromarray(image) -# image = p_preprocess(pil_image).unsqueeze(0).to(p_device1) -# with torch.no_grad(): -# prefix = p_clip_model.encode_image(image).to( -# p_device1, dtype=torch.float32 -# ) -# prefix_embed = model.clip_project(prefix).reshape(1, p_prefix_length, -1) - -# use_nucleus_sampling=False -# num_beams=5 -# max_length=30 -# min_length=1 -# top_p=0.9 -# repetition_penalty=1.5 -# length_penalty=1.0 -# num_captions=1 -# temperature=1 - -# atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(p_device1) -# opt_tokens = tokenizer([""], return_tensors='pt').to(p_device1) -# input_ids = opt_tokens.input_ids -# query_embeds = prefix_embed #.repeat_interleave(num_beams, dim=0) -# attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) - -# outputs = model.gpt.generate( -# input_ids=input_ids, -# query_embeds=query_embeds, -# attention_mask=attention_mask, -# do_sample=use_nucleus_sampling, -# top_p=top_p, -# temperature=temperature, -# num_beams=num_beams, -# max_new_tokens=max_length, -# min_length=min_length, -# eos_token_id=tokenizer('\n', add_special_tokens=False).input_ids[0], -# repetition_penalty=repetition_penalty, -# length_penalty=length_penalty, -# num_return_sequences=num_captions, -# ) - -# prompt_length = input_ids.shape[1] -# output_text = tokenizer.batch_decode( -# outputs[:, prompt_length:], skip_special_tokens=True -# ) -# output_text = [text.strip() for text in output_text] -# print(output_text) - -# generated_caption_coco_2 = p_predict(image=image, model='coco', use_beam_search=True) -# print("Exammple Caption :", generated_caption_coco_2) # start generating captions data= {} -for img_nice in tqdm(flist_nice): - image = os.path.join(fpath_nice, img_nice) +for img_nice in tqdm(flist_nice_test): + image = os.path.join(fpath_nice_test, img_nice) + + generated_caption, _ = predict.predict(image=image, model=f'opt_{args.checkpoint}') - generated_caption= predict.predict(image=image, model=f'opt_{args.checkpoint}', use_beam_search=False) + # torch.save(prefix_embed, os.path.join(clipcap_path_nice, img_nice[:-4] + '.pt')) + # target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() - target_caption = annot_csv[annot_csv['public_id']==int(img_nice[:-4])]['caption_gt'].item() + data[int(img_nice[:-4])] = generated_caption + + +# data= {} +# for img_coco in tqdm(flist_coco): +# image = os.path.join(fpath_coco, img_coco) - data[int(img_nice[:-4])] = [target_caption, generated_caption] +# _, prefix_embed = predict.predict(image=image, model=f'opt_{args.checkpoint}') +# torch.save(prefix_embed, os.path.join(clipcap_path_coco, img_coco[:-4] + '.pt')) + + + # save generated caption # with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}.json'), 'w') as fp: # json.dump(data_coco_2, fp) @@ -114,5 +85,5 @@ # json.dump(data_coco_beam, fp) # save generated caption -with open(os.path.join(output_file, f'clipcap_2_opt13b_{args.language_model}_{args.checkpoint}.json'), 'w') as fp: - json.dump(data, fp) \ No newline at end of file +with open(os.path.join(output_folder, output_file), 'w') as fp: + json.dump(data, fp, default=str) \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000..8a66840 --- /dev/null +++ b/train.py @@ -0,0 +1,209 @@ +import os, torch, random, json, argparse #, yaml, wandb +import numpy as np +import torch.backends.cudnn as cudnn +from tqdm import tqdm, trange +from train_models import ConnectLayer, CaptionDataset_WithFeature +from torch.utils.data import DataLoader +from transformers import get_linear_schedule_with_warmup +from datetime import datetime +from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer +from pycocoevalcap.cider.cider import Cider + +SEED = 27 + +def setup_seeds(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + cudnn.benchmark = False + cudnn.deterministic = True + random.seed(seed) + +def save_config(args: argparse.Namespace, output_dir): + config = {} + for key, item in args._get_kwargs(): + if key in ["total_epoch", "batch_size", "lr"]: + config[key] = item + out_path = os.path.join(output_dir, "config.json") + with open(out_path, 'w') as outfile: + json.dump(config, outfile) + +def generate( + model, tokenizer, prefix_embed, + use_nucleus_sampling=False, + num_beams=5, + max_length=30, + min_length=1, + top_p=0.9, + repetition_penalty=1.0, + length_penalty=1.0, + num_captions=1, + temperature=1, + prompt="", + device=torch.device('cuda:0'), + ): + + prefix_embed = prefix_embed.to(device) + with torch.cuda.amp.autocast( + enabled=(prefix_embed.device != torch.device("cpu")) + ): + eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0] + atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device) + opt_tokens = tokenizer([prompt], return_tensors='pt').to(device) + input_ids = opt_tokens.input_ids + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = model.generate( + input_ids=input_ids, + query_embeds=prefix_embed, + attention_mask=attention_mask, + do_sample=use_nucleus_sampling, + top_p=top_p, + temperature=temperature, + num_beams=num_beams, + max_new_tokens=max_length, + min_length=min_length, + eos_token_id=eos_token_id, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + num_return_sequences=num_captions, + ) + + prompt_length = input_ids.shape[1] + output_text = tokenizer.batch_decode( + outputs[:, prompt_length:], skip_special_tokens=True + ) + output_text = [text.strip() for text in output_text] + + return output_text[0] + +def main(): + + parser = argparse.ArgumentParser() + parser.add_argument('--model_key', type=str, default='cnn') + parser.add_argument('--output_folder', type=str, default='/data1/checkpoint/connect_layer') + parser.add_argument('--total_epoch', type=int, default=10) + parser.add_argument('--batch_size', type=int, default=16) + parser.add_argument('--lr', type=float, default=5e-4) + parser.add_argument('--device', type=str, default='123') + parser.add_argument('--pn', type=str, default='411', help='splitting OPT layer for pipeline parallelization') + args = parser.parse_args() + + model_key = args.model_key + output_folder = args.output_folder + total_epoch = args.total_epoch + batch_size = args.batch_size + lr = args.lr + device = args.device + pn = args.pn + + setup_seeds(SEED) + # wandb.init(project='connect_layer : ' + model_key) + + blip2_feature_path = '/data1/IC/coco_features/blip2OPT/' + clipcap_feature_path = '/data1/IC/coco_features/clipcap_ori/' + coco_ann_path = '/data1/IC/coco/annotations/' + + folder_time = datetime.now().strftime("%Y-%m-%d_%I-%M-%S_%p") + output_dir = os.path.join(output_folder, model_key, folder_time) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + save_config(args=args, output_dir=output_dir) + + train_feature_paths = [blip2_feature_path + 'train', clipcap_feature_path + 'train'] + train_ann_paths = [coco_ann_path + 'coco_karpathy_train.json'] + + val_feature_paths = [blip2_feature_path + 'val', clipcap_feature_path + 'val'] + val_ann_paths = [coco_ann_path + 'coco_karpathy_val.json'] + + model = ConnectLayer( + connect_model_key=model_key, num_feature=len(train_feature_paths), batch_size=batch_size, device=device, pn=pn + ) + model.train() + + optimizer = torch.optim.AdamW(model.connect_model.parameters(), lr=lr) + + train_dataset = CaptionDataset_WithFeature(feature_paths=train_feature_paths, ann_paths=train_ann_paths) + val_dataset = CaptionDataset_WithFeature(feature_paths=val_feature_paths, ann_paths=val_ann_paths) + + + train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) + val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False, drop_last=False) + + # scheduler = get_linear_schedule_with_warmup( + # optimizer=optimizer, num_training_steps=warmup_steps, num_training_steps=total_epoch*len(train_loader) + # ) + + cider_tokenizer = PTBTokenizer() + best_cider = 0 + for cur_epoch in trange(total_epoch): + print(f">>>>>> epoch : {cur_epoch}") + print(">>> train") + progress_train = tqdm(total=len(train_loader), desc="train") + for idx, samples in enumerate(train_loader): + model.zero_grad() + loss = model(samples=samples) + loss.backward() + optimizer.step() + # scheduler.step() + optimizer.zero_grad() + # wandb.log({'loss' : loss.item()}) + progress_train.set_postfix({"loss" : loss.item()}) + progress_train.update() + progress_train.close() + + with torch.no_grad(): + print(">>> val") + + gts = {} + res = {} + + progress_val = tqdm(total=len(val_loader), desc="validation") + for samples in val_loader: + features = samples["features"] + features = features.to(model.device1) + if model.model_key == "cnn": + feature = model.connect_model(features) + elif model.model_key == "hprod": + feature = model.connect_model(features) + elif model.model_key == "mix_qdim": + features = features.permute(1, 2).view( + model.batch_size, + model.num_query_token, + model.num_query_token * model.query_dimension + ) + feature = model.connect_model(features).unsqueeze(1) + + targets = samples['text_input'] + + for idx, imgId in enumerate(samples["image_id"]): + generated_caption = generate( + model=model.opt_model, tokenizer=model.opt_tokenizer, prefix_embed=feature[idx], prompt=model.prompt, device=model.device1 + ) + gts[imgId] = [{"image_id" : imgId, "caption" : targets[idx], "id" : imgId}] + res[imgId] = [{"image_id" : imgId, "caption" : generated_caption, "id" : imgId}] + + gts = cider_tokenizer.tokenize(gts) + res = cider_tokenizer.tokenize(res) + new_cider = Cider().compute_score(gts, res)[0] + progress_val.close() + + if best_cider < new_cider: + torch.save( + model.connect_model.state_dict(), os.path.join(output_dir, f"model_{model_key}_max_cider_{cur_epoch:03d}.pt") + ) + + torch.save( + model.connect_model.state_dict(), os.path.join(output_dir, f"model_{model_key}.pt") + ) + # torch.save( + # scheduler.state_dict(), os.path.join(output_dir, f"schedular_{model_key}.pt") + # ) + torch.save( + optimizer.state_dict(), os.path.join(output_dir, f"optimizer_{model_key}.pt") + ) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train_OPT.py b/train_OPT.py old mode 100644 new mode 100755 index 1ef588a..a4f03d9 --- a/train_OPT.py +++ b/train_OPT.py @@ -3,8 +3,7 @@ from torch.nn import functional as nnf from torch.utils.data import Dataset, DataLoader from enum import Enum -from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup -from transformers import AdamW, get_linear_schedule_with_warmup +from transformers import GPT2LMHeadModel, get_linear_schedule_with_warmup from modeling_opt_pp import OPTForCausalLM from transformers import AutoTokenizer from tqdm import tqdm @@ -15,7 +14,7 @@ import json from typing import Tuple, Optional, Union -import wandb +# import wandb OPT_MODEL = 'facebook/opt-2.7b' @@ -55,7 +54,7 @@ def __getitem__(self, item: int) -> Tuple[torch.Tensor, ...]: def __init__(self, data_path: str, prefix_length: int, gpt2_type: str = OPT_MODEL, # edit normalize_prefix=False): # self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type) - self.tokenizer = AutoTokenizer.from_pretrained(gpt2_type, use_fast=True) + self.tokenizer = AutoTokenizer.from_pretrained(gpt2_type, use_fast=False) self.prefix_length = prefix_length self.normalize_prefix = normalize_prefix with open(data_path, 'rb') as f: @@ -231,17 +230,20 @@ def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None): - if self.args.language_model == 'gpt2': - embedding_text = self.gpt.transformer.wte(tokens) - elif self.args.language_model == 'opt': - embedding_text = self.gpt.model.decoder.embed_tokens(tokens) - prefix_projections = self.clip_project(prefix).view(-1, self.args.prefix_length, self.gpt_embedding_size) - embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) - if labels is not None: - dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) - labels = torch.cat((dummy_token, tokens), dim=1) - out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) - return out + with torch.cuda.amp.autocast( + enabled=(tokens.device != torch.device("cpu")) + ): + if self.args.language_model == 'gpt2': + embedding_text = self.gpt.transformer.wte(tokens) + elif self.args.language_model == 'opt': + embedding_text = self.gpt.model.decoder.embed_tokens(tokens) + prefix_projections = self.clip_project(prefix).view(-1, self.args.prefix_length, self.gpt_embedding_size) + embedding_cat = torch.cat((prefix_projections, embedding_text.to(self.device1)), dim=1) + if labels is not None: + dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device) + labels = torch.cat((dummy_token, tokens), dim=1) + out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask) + return out def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512, num_layers: int = 8): super(ClipCaptionModel, self).__init__() @@ -249,15 +251,14 @@ def __init__(self, args, clip_length: Optional[int] = 32, prefix_size: int = 512 self.prefix_size = prefix_size self.clip_length = clip_length self.num_layers = num_layers - self.device1, device2, device3 = make_device(args) - pn1, pn2 = int(args.pn[0]), int(args.pn[1:]) + self.device1, device2, device3, pn1, pn2 = make_device_pn(device=args.device, pn=args.pn) if self.args.language_model == 'gpt2': self.gpt = GPT2LMHeadModel.from_pretrained('gpt2') self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1] elif self.args.language_model == 'opt': print('clipcaption - LM : OPT') - self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL) + self.gpt = OPTForCausalLM.from_pretrained(OPT_MODEL, torch_dtype=torch.float16) self.gpt_embedding_size = self.gpt.model.decoder.embed_tokens.weight.shape[1] self.gpt.model.decoder.setting_device(device1=self.device1, device2=device2, device3=device3, pn1=pn1, pn2=pn2) @@ -275,13 +276,6 @@ def train(self, mode: bool = True): self.gpt.eval() return self -def make_device(args): - device_num = len(args.device) - devices = [] - for i in range(device_num): - device = "cuda:" + args.device[i] - devices.append(torch.device(device)) - return devices def save_config(args: argparse.Namespace): config = {} @@ -323,19 +317,20 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, os.makedirs(output_dir) # model = model.to(device) model.train() - optimizer = AdamW(model.parameters(), lr=lr) + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=warmup_steps, num_training_steps=epochs * len(train_dataloader) ) + # minimum_loss = None # save_config(args) for epoch in range(epochs): print(f">>> Training epoch {epoch}") sys.stdout.flush() progress = tqdm(total=len(train_dataloader), desc=output_prefix) - for idx, (tokens, mask, prefix) in enumerate(train_dataloader): + for idx, (tokens, mask, prefix) in enumerate(train_dataloader): # 41, 512, 73 model.zero_grad() - tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32) + tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float16) outputs = model(tokens, prefix, mask) logits = outputs.logits[:, dataset.prefix_length - 1: -1].to(device) loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0) @@ -344,75 +339,94 @@ def train(dataset: ClipCocoDataset, model: ClipCaptionModel, args, scheduler.step() optimizer.zero_grad() - wandb.log({'loss':loss.item()}) + # wandb.log({'loss':loss.item()}) progress.set_postfix({"loss": loss.item()}) progress.update() - if (idx + 1) % 10000 == 0: - torch.save( - model.state_dict(), - os.path.join(output_dir, f"{output_prefix}_latest.pt"), - ) + + # if (idx + 1) % 10000 == 0: + # torch.save( + # model.state_dict(), + # os.path.join(output_dir, f"{output_prefix}_latest.pt"), + # ) + # if not minimum_loss: + # minimum_loss = loss.item() + # elif loss.item() < minimum_loss: + # torch.save( + # model.state_dict(), + # os.path.join(output_dir, f"model_{output_prefix}_min_loss.pt"), + # ) progress.close() if epoch % args.save_every == 0 or epoch == epochs - 1: torch.save( model.state_dict(), - os.path.join(output_dir, f"model_{output_prefix}-{epoch:03d}.pt"), + os.path.join(output_dir, f"model_{output_prefix}.pt"), ) torch.save( scheduler.state_dict(), - os.path.join(output_dir, f"schedular_{output_prefix}-{epoch:03d}.pt"), + os.path.join(output_dir, f"schedular_{output_prefix}.pt"), ) torch.save( optimizer.state_dict(), - os.path.join(output_dir, f"optimizer_{output_prefix}-{epoch:03d}.pt"), + os.path.join(output_dir, f"optimizer_{output_prefix}.pt"), ) return model -def make_device(args): - device_num = len(args.device) +def make_device_pn(device, pn): + device_num = len(device) devices = [] + for i in range(device_num): - device = "cuda:" + args.device[i] - devices.append(torch.device(device)) + device_name = "cuda:" + device[i] + devices.append(torch.device(device_name)) assert len(devices) < 4 + assert len(pn) < 5 + if len(devices) == 1: devices *= 3 device1, device2, device3 = devices + pn1, pn2 = 12, 12 elif len(devices) == 2: device1 = devices[0] device2 = devices[1] device3 = devices[1] + pn1, pn2 = int(pn), 12 else: device1, device2, device3 = devices - return device1, device2, device3 + length = len(pn) + if length < 4: + pn1, pn2 = int(pn[0]), int(pn[1:]) + else: + pn1, pn2 = int(pn[:2]), int(pn[2:]) + + return device1, device2, device3, pn1, pn2 def main(): parser = argparse.ArgumentParser() parser.add_argument('--data', default='./data/coco/oscar_split_ViT-B_32_train.pkl') - parser.add_argument('--out_dir', default='./checkpoints') + parser.add_argument('--out_dir', default='/data1/checkpoint/clipcap') parser.add_argument('--prefix', default='coco_prefix', help='prefix for saved filenames') parser.add_argument('--epochs', type=int, default=10) parser.add_argument('--save_every', type=int, default=1) parser.add_argument('--prefix_length', type=int, default=32) - parser.add_argument('--prefix_length_clip', type=int, default=32) + # parser.add_argument('--prefix_length_clip', type=int, default=32) parser.add_argument('--bs', type=int, default=40) parser.add_argument('--only_prefix', dest='only_prefix', action='store_true') parser.add_argument('--mapping_type', type=str, default='transformer', help='mlp/transformer') parser.add_argument('--num_layers', type=int, default=8) parser.add_argument('--is_rn', dest='is_rn', action='store_true') parser.add_argument('--normalize_prefix', dest='normalize_prefix', action='store_true') - parser.add_argument('--device', default='12') + parser.add_argument('--device', type=str, default='12') parser.add_argument('--language_model', type=str, default='opt', help='gpt2/opt') - parser.add_argument('--pn', default='47', help='splitting OPT layer for pipeline parallelization') + parser.add_argument('--pn', default='411', help='splitting OPT layer for pipeline parallelization') args = parser.parse_args() prefix_length = args.prefix_length - - wandb.init(project='ClipCap') - + + # wandb.init(project='ClipCap') + dataset = ClipCocoDataset(args.data, prefix_length, normalize_prefix=args.normalize_prefix) prefix_dim = 640 if args.is_rn else 512 args.mapping_type = {'mlp': MappingType.MLP, 'transformer': MappingType.Transformer}[args.mapping_type] diff --git a/train_models.py b/train_models.py new file mode 100755 index 0000000..e6da7d9 --- /dev/null +++ b/train_models.py @@ -0,0 +1,270 @@ +import torch, os, json +import torch.nn as nn +from torch.utils.data.dataloader import default_collate +from torch.utils.data import Dataset +from transformers import AutoTokenizer +from modeling_opt_pp import OPTForCausalLM +from collections import OrderedDict + + +""" +FeatureDataset = +Feauture = Dataloader( Feature) +optimizer = AdamW +lr = + +""" + +class __DisplMixin: + def displ_item(self, index): + sample, ann = self.__getitem__(index), self.annotation[index] + + return OrderedDict( + { + "file": ann["image"], + "caption": ann["caption"], + "image": sample["image"], + } + ) + +class BaseDataset(Dataset): + def __init__( + self, vis_processor=None, text_processor=None, vis_root=None, ann_paths=[] + ): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + """ + self.vis_root = vis_root + + self.annotation = [] + for ann_path in ann_paths: + self.annotation.extend(json.load(open(ann_path, "r"))) + + self.vis_processor = vis_processor + self.text_processor = text_processor + + self._add_instance_ids() + + def __len__(self): + return len(self.annotation) + + def collater(self, samples): + return default_collate(samples) + + def set_processors(self, vis_processor, text_processor): + self.vis_processor = vis_processor + self.text_processor = text_processor + + def _add_instance_ids(self, key="instance_id"): + for idx, ann in enumerate(self.annotation): + ann[key] = str(idx) + +class Hprod(nn.Module): + def __init__(self, num_feature, num_query_token, query_dimension, device): + self.weights = nn.Parameter(torch.Tensor(1, num_feature, num_query_token, query_dimension)).to(device) + + def forward(self, features): + prod = features * self.weights + output = torch.sum(prod, dim=1, keepdim=True) + return output + +OPT_MODEL = 'facebook/opt-2.7b' +MODEL = { + "cnn" : nn.Conv2d, + "hprod" : Hprod, + "mix_qdim" : nn.Linear, + } + +class ConnectLayer(nn.Module): + def __init__( + self, + connect_model_key = "cnn", + num_feature = 3, + num_query_token = 32, + query_dimension = 2560, + batch_size = 16, + prompt="a photo of", + max_txt_len=32, + device = "123", + pn = "411", + ): + super().__init__() + + self.model_key = connect_model_key + assert self.model_key in MODEL.keys() + self.device1, device2, device3, pn1, pn2 = make_device_pn(device=device, pn=pn) + assert pn1>0 + + if self.model_key == "cnn": + self.connect_model = MODEL[self.model_key]( + in_channels=2, + out_channels=1, + kernel_size=1, + stride=1, + padding=0, + bias=False, + device=self.device1 + ) + elif self.model_key == "hprod": + self.connect_model = MODEL[self.model_key]( + num_feature=num_feature, + num_query_token=num_query_token, + query_dimension=query_dimension, + device=self.device1 + ) + elif self.model_key == "mix_qdim": + self.connect_model = MODEL[self.model_key]( + in_feature=num_query_token * query_dimension, + out_features=query_dimension, + bias=False, + device=self.device1 + ) + + self.opt_model = OPTForCausalLM.from_pretrained(OPT_MODEL, torch_dtype=torch.float16) + self.opt_tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL, use_fast=False) + + self.opt_model.model.decoder.setting_device(device1=self.device1, device2=device2, device3=device3, pn1=pn1, pn2=pn2) + + for _, param in self.opt_model.named_parameters(): + param.requires_grad = False + self.eos_token_id = self.opt_tokenizer( + "\n", add_special_tokens=False + ).input_ids[0] + + self.prompt = prompt + prompt_tokens = self.opt_tokenizer(self.prompt, return_tensors="pt") + self.prompt_length = prompt_tokens.attention_mask.sum(1) + self.batch_size = batch_size + self.num_query_token = num_query_token + self.query_dimension = query_dimension + self.max_txt_len = max_txt_len + + def forward(self, samples): + features = samples["features"] + features = features.to(self.device1) + if self.model_key == "cnn": + query_embeds = self.connect_model(features) + elif self.model_key == "hprod": + query_embeds = self.connect_model(features) + elif self.model_key == "mix_qdim": + features = features.permute(1, 2).view( + self.batch_size, + self.num_query_token, + self.num_query_token * self.query_dimension + ) + query_embeds = self.connect_model(features).unsqueeze(1) + + atts_opt = torch.ones(query_embeds.size()[:-1], dtype=torch.long).to(features.device) + + self.opt_tokenizer.padding_side = "right" + + text = [samples["text_input"] + "\n"] + # text = [t + "\n" for t in [samples["text_input"]]] + + opt_tokens = self.opt_tokenizer( + text, + return_tensors="pt", + padding="longest", + truncation=True, + max_length=self.max_txt_len, + ).to(features.device) + + targets = opt_tokens.input_ids.masked_fill( + opt_tokens.input_ids == self.opt_tokenizer.pad_token_id, -100 + ) + if self.prompt: + targets[:, : self.prompt_length] = -100 # do not apply loss to the prompt + + empty_targets = ( + torch.ones(atts_opt.size(), dtype=torch.long).to(features.device).fill_(-100) + ) + targets = torch.cat([empty_targets, targets], dim=1) + + inputs_embeds = self.opt_model.model.decoder.embed_tokens(opt_tokens.input_ids) + inputs_embeds = torch.cat([query_embeds, inputs_embeds], dim=1) + attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) + + outputs = self.opt_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + return_dict=True, + labels=targets, + ) + # loss = outputs.loss + + return outputs.loss #{"loss": loss} + + +class CaptionDataset_WithFeature(BaseDataset, __DisplMixin): + def __init__(self, feature_paths, ann_paths, vis_processor=None, text_processor=None, vis_root=None): + """ + vis_root (string): Root directory of images (e.g. coco/images/) + ann_root (string): directory to store the annotation file + """ + super().__init__(vis_processor, text_processor, vis_root, ann_paths) + # ann_paths = ['/data1/IC/coco/annotations/coco_karpathy_train.json'] + self.feature_paths = feature_paths + self.img_ids = {} + n = 0 + for ann in self.annotation: + img_id = ann["image_id"] + if img_id not in self.img_ids.keys(): + self.img_ids[img_id] = n + n += 1 + + def __getitem__(self, index): + + # TODO this assumes image input, not general enough + ann = self.annotation[index] + + _, jpg_file = os.path.split(ann['image']) + file_name, _ = os.path.splitext(jpg_file) + pt_file = file_name + '.pt' + + # feature_paths = ['/data1/IC/coco_features/blip2OPT', '/data1/IC/coco_features/clipcap'] + + features = [ torch.load( os.path.join(path, pt_file) ).squeeze(0) for path in self.feature_paths ] # [1 x 32 x 2560 이 num_features 만큼] + features = torch.stack(features) # num_features x 32 x 2560 + + # image_path = os.path.join(self.vis_root, ann["image"]) + # image = Image.open(image_path).convert("RGB") + + # image = self.vis_processor(image)/ + caption = ann["caption"] ## ann["caption"] == caption 인데 그러면 저 self.text_processor는 왜 있는거지? + ## self.text_processor = lavis.processors.blip_processors.BlipCaptionProcessor + + return { + "features": features, + "text_input": caption, + "image_id": self.img_ids[ann["image_id"]], + } + +def make_device_pn(device, pn): + device_num = len(device) + devices = [] + for i in range(device_num): + device_name = "cuda:" + device[i] + devices.append(torch.device(device_name)) + + assert len(devices) < 4 + assert len(pn) < 5 + + if len(devices) == 1: + devices *= 3 + device1, device2, device3 = devices + pn1, pn2 = 12, 12 + elif len(devices) == 2: + device1 = devices[0] + device2 = devices[1] + device3 = devices[1] + pn1, pn2 = int(pn), 12 + else: + device1, device2, device3 = devices + length = len(pn) + if length < 4: + pn1, pn2 = int(pn[0]), int(pn[1:]) + else: + pn1, pn2 = int(pn[:2]), int(pn[2:]) + + return device1, device2, device3, pn1, pn2 From d6e7302e9bc65fa122d69df35adbb9056b21c5e2 Mon Sep 17 00:00:00 2001 From: snow-parkis Date: Fri, 14 Apr 2023 09:14:30 +0900 Subject: [PATCH 25/25] train bug fix --- modeling_opt_pp.py | 132 +++++++++++++++++++++++---------------------- train.py | 109 +++++++++++++++++++------------------ train_models.py | 59 ++++++++++---------- 3 files changed, 156 insertions(+), 144 deletions(-) diff --git a/modeling_opt_pp.py b/modeling_opt_pp.py index 1fc1c9a..3b19ecf 100755 --- a/modeling_opt_pp.py +++ b/modeling_opt_pp.py @@ -352,60 +352,60 @@ def forward( """ residual = hidden_states + with torch.autocast("cuda"): + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + past_key_value=past_key_value, + attention_mask=attention_mask, + layer_head_mask=layer_head_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout( + hidden_states, p=self.dropout, training=self.training + ) + hidden_states = residual + hidden_states - # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention - if self.do_layer_norm_before: - hidden_states = self.self_attn_layer_norm(hidden_states) - - # Self Attention - hidden_states, self_attn_weights, present_key_value = self.self_attn( - hidden_states=hidden_states, - past_key_value=past_key_value, - attention_mask=attention_mask, - layer_head_mask=layer_head_mask, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout( - hidden_states, p=self.dropout, training=self.training - ) - hidden_states = residual + hidden_states - - # 350m applies layer norm AFTER attention - if not self.do_layer_norm_before: - hidden_states = self.self_attn_layer_norm(hidden_states) + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) - # Fully Connected - hidden_states_shape = hidden_states.shape - hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) - residual = hidden_states + # Fully Connected + hidden_states_shape = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) + residual = hidden_states - # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention - if self.do_layer_norm_before: - hidden_states = self.final_layer_norm(hidden_states) + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) - hidden_states = self.fc1(hidden_states) - hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) - hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout( - hidden_states, p=self.dropout, training=self.training - ) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout( + hidden_states, p=self.dropout, training=self.training + ) - hidden_states = (residual + hidden_states).view(hidden_states_shape) + hidden_states = (residual + hidden_states).view(hidden_states_shape) - # 350m applies layer norm AFTER attention - if not self.do_layer_norm_before: - hidden_states = self.final_layer_norm(hidden_states) + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) - outputs = (hidden_states,) + outputs = (hidden_states,) - if output_attentions: - outputs += (self_attn_weights,) + if output_attentions: + outputs += (self_attn_weights,) - if use_cache: - outputs += (present_key_value,) + if use_cache: + outputs += (present_key_value,) - return outputs + return outputs OPT_START_DOCSTRING = r""" @@ -817,31 +817,32 @@ def custom_forward(*inputs): if output_attentions: all_self_attns += (layer_outputs[1],) - if self.final_layer_norm is not None: - self.final_layer_norm.to(self.device3) - hidden_states = self.final_layer_norm(hidden_states) + with torch.autocast("cuda"): + if self.final_layer_norm is not None: + self.final_layer_norm.to(self.device3) + hidden_states = self.final_layer_norm(hidden_states) - if self.project_out is not None: - self.project_out.to(self.device3) - hidden_states = self.project_out(hidden_states) + if self.project_out is not None: + self.project_out.to(self.device3) + hidden_states = self.project_out(hidden_states) - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) - next_cache = next_decoder_cache if use_cache else None - if not return_dict: - return tuple( - v - for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] - if v is not None + next_cache = next_decoder_cache if use_cache else None + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] + if v is not None + ) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, ) - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - ) @add_start_docstrings( @@ -1079,7 +1080,8 @@ def forward( return_dict=return_dict, ) device = self.model.decoder.device1 - logits = self.lm_head(outputs[0].to(device)).contiguous() + with torch.autocast("cuda"): + logits = self.lm_head(outputs[0].to(device)).contiguous() loss = None if labels is not None: diff --git a/train.py b/train.py index 8a66840..ecf23a4 100644 --- a/train.py +++ b/train.py @@ -2,7 +2,7 @@ import numpy as np import torch.backends.cudnn as cudnn from tqdm import tqdm, trange -from train_models import ConnectLayer, CaptionDataset_WithFeature +from train_models import ConnectLayer, CaptionDataset_WithFeature, CNN, HPROD, MIX from torch.utils.data import DataLoader from transformers import get_linear_schedule_with_warmup from datetime import datetime @@ -43,14 +43,10 @@ def generate( prompt="", device=torch.device('cuda:0'), ): - - prefix_embed = prefix_embed.to(device) - with torch.cuda.amp.autocast( - enabled=(prefix_embed.device != torch.device("cpu")) - ): + with torch.autocast("cuda"): eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0] atts_opt = torch.ones(prefix_embed.size()[:-1], dtype=torch.long).to(device) - opt_tokens = tokenizer([prompt], return_tensors='pt').to(device) + opt_tokens = tokenizer([prompt]*prefix_embed.shape[0], return_tensors='pt').to(device) input_ids = opt_tokens.input_ids attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1) @@ -76,15 +72,16 @@ def generate( ) output_text = [text.strip() for text in output_text] - return output_text[0] + return output_text def main(): parser = argparse.ArgumentParser() - parser.add_argument('--model_key', type=str, default='cnn') + parser.add_argument('--model_key', type=str, default='cnn', help='cnn, hprod, mix-qdim') parser.add_argument('--output_folder', type=str, default='/data1/checkpoint/connect_layer') parser.add_argument('--total_epoch', type=int, default=10) - parser.add_argument('--batch_size', type=int, default=16) + parser.add_argument('--warmup_steps', type=int, default=5000) + parser.add_argument('--batch_size', type=int, default=32) parser.add_argument('--lr', type=float, default=5e-4) parser.add_argument('--device', type=str, default='123') parser.add_argument('--pn', type=str, default='411', help='splitting OPT layer for pipeline parallelization') @@ -93,6 +90,7 @@ def main(): model_key = args.model_key output_folder = args.output_folder total_epoch = args.total_epoch + warmup_steps = args.warmup_steps batch_size = args.batch_size lr = args.lr device = args.device @@ -119,10 +117,10 @@ def main(): val_ann_paths = [coco_ann_path + 'coco_karpathy_val.json'] model = ConnectLayer( - connect_model_key=model_key, num_feature=len(train_feature_paths), batch_size=batch_size, device=device, pn=pn + connect_model_key=model_key, num_feature=len(train_feature_paths), device=device, pn=pn ) model.train() - + optimizer = torch.optim.AdamW(model.connect_model.parameters(), lr=lr) train_dataset = CaptionDataset_WithFeature(feature_paths=train_feature_paths, ann_paths=train_ann_paths) @@ -132,27 +130,28 @@ def main(): train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False, drop_last=False) - # scheduler = get_linear_schedule_with_warmup( - # optimizer=optimizer, num_training_steps=warmup_steps, num_training_steps=total_epoch*len(train_loader) - # ) - + scheduler = get_linear_schedule_with_warmup( + optimizer=optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_epoch*len(train_loader) + ) + cider_tokenizer = PTBTokenizer() best_cider = 0 + best_epoch = 0 for cur_epoch in trange(total_epoch): - print(f">>>>>> epoch : {cur_epoch}") - print(">>> train") - progress_train = tqdm(total=len(train_loader), desc="train") - for idx, samples in enumerate(train_loader): - model.zero_grad() - loss = model(samples=samples) - loss.backward() - optimizer.step() - # scheduler.step() - optimizer.zero_grad() - # wandb.log({'loss' : loss.item()}) - progress_train.set_postfix({"loss" : loss.item()}) - progress_train.update() - progress_train.close() + # print(f">>>>>> epoch : {cur_epoch}") + # print(">>> train") + # progress_train = tqdm(total=len(train_loader), desc=f"epoch {cur_epoch} train") + # for idx, samples in enumerate(train_loader): + # model.zero_grad() + # loss = model(samples=samples) + # loss.backward() + # optimizer.step() + # scheduler.step() + # optimizer.zero_grad() + # # wandb.log({'loss' : loss.item()}) + # progress_train.set_postfix({"loss" : loss.item()}) + # progress_train.update() + # progress_train.close() with torch.no_grad(): print(">>> val") @@ -160,50 +159,56 @@ def main(): gts = {} res = {} - progress_val = tqdm(total=len(val_loader), desc="validation") + progress_val = tqdm(total=len(val_loader), desc=f"epoch {cur_epoch} validation") for samples in val_loader: features = samples["features"] features = features.to(model.device1) - if model.model_key == "cnn": - feature = model.connect_model(features) - elif model.model_key == "hprod": - feature = model.connect_model(features) - elif model.model_key == "mix_qdim": - features = features.permute(1, 2).view( - model.batch_size, - model.num_query_token, - model.num_query_token * model.query_dimension - ) - feature = model.connect_model(features).unsqueeze(1) + with torch.autocast("cuda"): + if model.model_key == CNN: + feature = model.connect_model(features).squeeze(1) + elif model.model_key == HPROD: + feature = model.connect_model(features) + elif model.model_key == MIX: + features = features.transpose(1, 2).reshape( + features.shape[0], + model.num_query_token, + model.num_feature * model.query_dimension + ) + feature = model.connect_model(features) targets = samples['text_input'] - + generated_caption = generate( + model=model.opt_model, tokenizer=model.opt_tokenizer, prefix_embed=feature, prompt=model.prompt, device=model.device1 + ) for idx, imgId in enumerate(samples["image_id"]): - generated_caption = generate( - model=model.opt_model, tokenizer=model.opt_tokenizer, prefix_embed=feature[idx], prompt=model.prompt, device=model.device1 - ) gts[imgId] = [{"image_id" : imgId, "caption" : targets[idx], "id" : imgId}] - res[imgId] = [{"image_id" : imgId, "caption" : generated_caption, "id" : imgId}] - + res[imgId] = [{"image_id" : imgId, "caption" : generated_caption[idx], "id" : imgId}] + + progress_val.update() gts = cider_tokenizer.tokenize(gts) res = cider_tokenizer.tokenize(res) new_cider = Cider().compute_score(gts, res)[0] progress_val.close() if best_cider < new_cider: + best_epoch = cur_epoch torch.save( - model.connect_model.state_dict(), os.path.join(output_dir, f"model_{model_key}_max_cider_{cur_epoch:03d}.pt") + model.connect_model.state_dict(), os.path.join(output_dir, f"model_{model_key}_max_cider.pt") ) + with open(os.path.join(output_dir, 'generated_caption.json'), 'w') as f: + json.dump(res, f) torch.save( model.connect_model.state_dict(), os.path.join(output_dir, f"model_{model_key}.pt") ) - # torch.save( - # scheduler.state_dict(), os.path.join(output_dir, f"schedular_{model_key}.pt") - # ) + torch.save( + scheduler.state_dict(), os.path.join(output_dir, f"schedular_{model_key}.pt") + ) torch.save( optimizer.state_dict(), os.path.join(output_dir, f"optimizer_{model_key}.pt") ) + print(f"best cider : {best_cider} at epoch {best_epoch}") + if __name__ == "__main__": main() \ No newline at end of file diff --git a/train_models.py b/train_models.py index e6da7d9..32e4a23 100755 --- a/train_models.py +++ b/train_models.py @@ -62,28 +62,32 @@ def _add_instance_ids(self, key="instance_id"): class Hprod(nn.Module): def __init__(self, num_feature, num_query_token, query_dimension, device): - self.weights = nn.Parameter(torch.Tensor(1, num_feature, num_query_token, query_dimension)).to(device) - + super().__init__() + self.weights = nn.Parameter(torch.randn(1, num_feature, num_query_token, query_dimension, device=device)) + def forward(self, features): prod = features * self.weights - output = torch.sum(prod, dim=1, keepdim=True) + output = torch.sum(prod, dim=1) return output OPT_MODEL = 'facebook/opt-2.7b' MODEL = { "cnn" : nn.Conv2d, "hprod" : Hprod, - "mix_qdim" : nn.Linear, + "mix-qdim" : nn.Linear, } +CNN = [*MODEL][0] +HPROD = [*MODEL][1] +MIX = [*MODEL][2] + class ConnectLayer(nn.Module): def __init__( self, - connect_model_key = "cnn", + connect_model_key = CNN, num_feature = 3, num_query_token = 32, query_dimension = 2560, - batch_size = 16, prompt="a photo of", max_txt_len=32, device = "123", @@ -96,9 +100,9 @@ def __init__( self.device1, device2, device3, pn1, pn2 = make_device_pn(device=device, pn=pn) assert pn1>0 - if self.model_key == "cnn": + if self.model_key == CNN: self.connect_model = MODEL[self.model_key]( - in_channels=2, + in_channels=num_feature, out_channels=1, kernel_size=1, stride=1, @@ -106,21 +110,21 @@ def __init__( bias=False, device=self.device1 ) - elif self.model_key == "hprod": + elif self.model_key == HPROD: self.connect_model = MODEL[self.model_key]( num_feature=num_feature, num_query_token=num_query_token, query_dimension=query_dimension, device=self.device1 ) - elif self.model_key == "mix_qdim": + elif self.model_key == MIX: self.connect_model = MODEL[self.model_key]( - in_feature=num_query_token * query_dimension, + in_features=num_feature * query_dimension, out_features=query_dimension, bias=False, device=self.device1 ) - + self.opt_model = OPTForCausalLM.from_pretrained(OPT_MODEL, torch_dtype=torch.float16) self.opt_tokenizer = AutoTokenizer.from_pretrained(OPT_MODEL, use_fast=False) @@ -135,7 +139,7 @@ def __init__( self.prompt = prompt prompt_tokens = self.opt_tokenizer(self.prompt, return_tensors="pt") self.prompt_length = prompt_tokens.attention_mask.sum(1) - self.batch_size = batch_size + self.num_feature = num_feature self.num_query_token = num_query_token self.query_dimension = query_dimension self.max_txt_len = max_txt_len @@ -143,24 +147,24 @@ def __init__( def forward(self, samples): features = samples["features"] features = features.to(self.device1) - if self.model_key == "cnn": - query_embeds = self.connect_model(features) - elif self.model_key == "hprod": - query_embeds = self.connect_model(features) - elif self.model_key == "mix_qdim": - features = features.permute(1, 2).view( - self.batch_size, - self.num_query_token, - self.num_query_token * self.query_dimension - ) - query_embeds = self.connect_model(features).unsqueeze(1) + with torch.autocast("cuda"): + if self.model_key == CNN: + query_embeds = self.connect_model(features).squeeze(1) + elif self.model_key == HPROD: + query_embeds = self.connect_model(features).squeeze(1) + elif self.model_key == MIX: + features = features.transpose(1, 2).reshape( + features.shape[0], + self.num_query_token, + self.num_feature * self.query_dimension + ) + query_embeds = self.connect_model(features) atts_opt = torch.ones(query_embeds.size()[:-1], dtype=torch.long).to(features.device) self.opt_tokenizer.padding_side = "right" - text = [samples["text_input"] + "\n"] - # text = [t + "\n" for t in [samples["text_input"]]] + text = [t + "\n" for t in samples["text_input"]] opt_tokens = self.opt_tokenizer( text, @@ -224,7 +228,8 @@ def __getitem__(self, index): # feature_paths = ['/data1/IC/coco_features/blip2OPT', '/data1/IC/coco_features/clipcap'] - features = [ torch.load( os.path.join(path, pt_file) ).squeeze(0) for path in self.feature_paths ] # [1 x 32 x 2560 이 num_features 만큼] + load_features = [ torch.load( os.path.join(path, pt_file) ).to(torch.device("cpu")) for path in self.feature_paths ] + features = [feature.squeeze(0) if len(feature.shape) == 3 else feature for feature in load_features] # [1 x 32 x 2560 이 num_features 만큼] features = torch.stack(features) # num_features x 32 x 2560 # image_path = os.path.join(self.vis_root, ann["image"])