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Frozen.py
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Frozen.py
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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import torchvision
import torchvision.transforms
import matplotlib.pyplot as plt
import cv2
from tqdm import tqdm
import requests
from datasets import load_dataset
from torch.utils.tensorboard import SummaryWriter
from transformers import T5Tokenizer, T5ForConditionalGeneration
# forward hook for reading resnet penultimate layer logits
def forward_hook(module, input, output):
global resnet_avgpool_output
resnet_avgpool_output = output
# class for image embeddings - obtained from resnet
class ImageEmbeddings(nn.Module):
def __init__(self, hook_fn, d_model):
super().__init__()
self.resnet = torchvision.models.resnet50()
self.resnet.avgpool.register_forward_hook(hook_fn)
self.proj = nn.Linear(2048, d_model * 2, bias=False) # d_model * 2 because each image is supposed to constitute embeddings of seq_len = 2 (according to the paper)
def forward(self, imgs): # imgs.shape: [b,c,w,h]
batch_size = imgs.shape[0]
_ = self.resnet(imgs)
emb = resnet_avgpool_output # emb.shape: [b, 2048, 1, 1]
emb = emb.flatten(start_dim=1, end_dim=-1) # emb.shape: [b, 2048]
emb = self.proj(emb) # emb.shape: [b, d_model * 2]
emb = emb.reshape(batch_size, d_model, 2)
emb = emb.permute(0, 2, 1) # emb.shape: [batch_size, 2, d_model]
return emb
def slice_datadict(dct, start_idx, end_idx):
slice_dict = {}
keys = list(dct.keys())
for key in keys:
slice_dict[key] = dct[key][start_idx:end_idx]
return slice_dict
def process_batch_(minibatch, img_size):
"""process the url
Parameters
----------
minibatch: Dict
key: ['URL','text']
value: list[URL],list[text]
img_size: int
the size of image
Returns
-------
augmented_imgs: List
length of augmented_imgs: batch
captions: List
length of caption: batch
"""
value_list = list(minibatch.values())
url_list = value_list[0]
captions = value_list[1]
augmented_imgs = []
#processing
for url,cap in zip(url_list,captions):
print(f"processing url: {url}")
# print(f"caption: {cap}")
response = requests.get(url)
if response.status_code == 200:
img_data = response.content
else:
print(f"Failed to fetch image from URL. Status code: {response.status_code}")
continue
# img_data = response.content
img = cv2.imdecode(np.frombuffer(img_data, np.uint8), -1)
resize_shape = (img_size, img_size)
img = cv2.resize(img, resize_shape, interpolation=cv2.INTER_LINEAR)
img = np.float32(img) / 255
img = torch.tensor(img)
img = img.permute(2, 1, 0) # [w, h, c] -> [c, h, w]
transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(int(1.25 * img_size)), # image_size + 1/4 * image_size
torchvision.transforms.RandomResizedCrop(resize_shape, scale=(0.8, 1.0)),
torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # zero mean, unit std
])
img = transforms(img)
augmented_imgs.append(img)
return augmented_imgs, captions
def calculate_loss(imgs,caption,image_encoder,t5_model,tokenizer,device):
"""Calculate Loss
Parameters
----------
imgs: torch.Tensor
shape of imgs `[batch_size, channels, height, width]
caption: Dict
keys: input_ids, attn_mask
values: torch.Tensor
image_encoder: nn.Module
t5_model: nn.Module
Pre-Trained Language Model
tokenizer: Hugging Face Tokenizer
device: torch.device
cuda or cpu
Returns
-------
loss: float
batch_accuracy: float
"""
batch_size = imgs.shape[0]
# obtain img embeddings
img_embs = image_encoder(imgs) # img_embs.shape: [batch_size,2,d_model]
# feed img_embs to t5 encoder to get encoder output
enc_out = t5_model.encoder(
inputs_embeds = img_embs
).last_hidden_state # enc_out.shape: [batch_size, 2, d_model]
# extract cap tokens and attn_mask
cap_tokens, cap_attn_mask = caption.input_ids, caption.attention_mask
# shift cap_tokens right (pre-pend start token) - as input to decoder is
# expected to be right shifted and starting with pad token (used as start token by T5)
start_token_id = tokenizer(tokenizer.pad_token,
return_tensors='pt',
padding=False, truncation=True).input_ids
start_token_id = start_token_id[:, 0] # trim end token appended by the tokenizer
start_token_id = start_token_id.expand(batch_size, -1).to(device) # start_token_id.shape: [batch_size, 1]
cap_tokens_rshifted = torch.cat((start_token_id, cap_tokens), dim=-1) # cap_tokens_rshifted.shape: [batch_size, seq_len+1]
cap_tokens_rshifted = cap_tokens_rshifted[:, :-1] # cap_tokens_rshifted.shape: [batch_size, seq_len]
# feed cap tokens to t5 decoder to get decoder output
dec_out = t5_model.decoder(input_ids=cap_tokens_rshifted,
attention_mask=cap_attn_mask,
encoder_hidden_states=enc_out).last_hidden_state
# dec_out.shape: [batch_size, seq_len, d_model]
# get scores from dec_out
scores = t5_model.lm_head(dec_out) # scores.shape: [batch_size, seq_len, vocab_size]
scores = scores.permute(0, 2, 1) # scores.shape: [batch_size, vocab_size, seq_len] - required for crossEntropyLoss
# create targets = cap_tokens (unshifted)
targets = cap_tokens # targets.shape: [batch_size, seq_len]
# cross entropy loss
criterion = nn.CrossEntropyLoss(reduction='mean')
loss = criterion(scores, targets)
# calculate batch accuracy
pred_cap_tokens = torch.argmax(scores, dim=1) # shape: [batch_size, seq_len]
batch_accuracy = (pred_cap_tokens == cap_tokens).float().mean() * 100
return loss, batch_accuracy,targets,pred_cap_tokens
def ids2text(targets,pre_cap_tokens,tokenizer):
"""convert input_ids to caption
Parameters
----------
targets: torch.Tensor
shape of targets `[batch_size,seq_len]`
pre_cap_tokens: torch.Tensor
shape of pre_cap_tokens `[batch_size,seq_len]`
tokenizer: Hugging Face Tokenizer
T5
Returns
-------
True_Caption: List
Pred_Caption: List
"""
true_caption = []
# true_caption = tokenizer.decode(
# targets,
# skip_special_tokens=True,
# )
batch = targets.shape[0]
for i in range(batch):
true_caption.append(tokenizer.decode(
targets[i],
skip_special_tokens=True,
))
pred_caption = []
for i in range(batch):
pred_caption.append(tokenizer.decode(
pred_cap_tokens[i],
skip_special_tokens=True,
))
return true_caption,pred_caption
## main ##
if __name__ == '__main__':
# hyperparams
img_size = 224 # resize for resnet
d_model = 768 # d_model for T5 (required for resnet proj head)
max_seq_len = 512 # required to init T5 Tokenizer
batch_size = 16
lr = 3e-4
num_epochs = 10
random_seed = 1010
t5_model_name = 't5-base'
checkpoint_path = 'ckpts_frozen_resnet/latest.pt' # path to a save and load checkpoint of the trained resnet
resume_training_from_ckpt = False
# set random seed
np.random.seed(random_seed)
torch.manual_seed(random_seed)
# cuda
# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# device = torch.device("mps")
device = torch.device("cpu")
# init image encoder model (resnet)
image_encoder = ImageEmbeddings(forward_hook, d_model).to(device)
# init T5 tokenizer and transformer model
t5_tokenizer = T5Tokenizer.from_pretrained(t5_model_name, model_max_length=max_seq_len)
t5_model = T5ForConditionalGeneration.from_pretrained(t5_model_name).to(device)
dataset = load_dataset("ChristophSchuhmann/MS_COCO_2017_URL_TEXT")
dataset = dataset['train'][:100]
# init optimizer
optimizer = torch.optim.Adam(params=image_encoder.parameters(), lr=lr, betas=(0.9, 0.95))
#train loop
n = len(dataset["URL"])
caption = []
pre_caption = []
log_dir = './data'
writer = SummaryWriter(log_dir)
for ep in tqdm(range(num_epochs)):
Loss = []
Acc = []
for i in range(0, n, batch_size):
print(f"epoch:{ep+1} | idx:{i}")
sample = slice_datadict(dataset,i,i+batch_size)
value_list = list(sample.values())
url_list = value_list[0]
captions = value_list[1]
augmented_imgs = []
#processing
img_list = []
caption_list = []
for url,_ in zip(url_list,captions):
print(f"processing url: {url}")
# print(f"caption: {cap}")
response = requests.get(url)
if response.status_code == 200:
img_data = response.content
else:
print(f"Failed to fetch image from URL. Status code: {response.status_code}")
continue
# img_data = response.content
img = cv2.imdecode(np.frombuffer(img_data, np.uint8), -1)
resize_shape = (img_size, img_size)
img = cv2.resize(img, resize_shape, interpolation=cv2.INTER_LINEAR)
img = np.float32(img) / 255
img = torch.tensor(img)
img = img.permute(2, 1, 0) # [w, h, c] -> [c, h, w]
transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(int(1.25 * img_size)), # image_size + 1/4 * image_size
torchvision.transforms.RandomResizedCrop(resize_shape, scale=(0.8, 1.0)),
torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # zero mean, unit std
])
img = transforms(img)
augmented_imgs.append(img)
img_list.extend(augmented_imgs)
caption_list.extend(captions)
img = torch.stack(img_list, dim=0).to(device)
cap = t5_tokenizer(caption_list, padding=True, truncation=True, return_tensors="pt")
cap = cap.to(device)
batch_loss, batch_accuracy,targets,pred_cap_tokens = calculate_loss(
imgs=img,
caption=cap,
image_encoder=image_encoder,
t5_model=t5_model,
tokenizer=t5_tokenizer,
device=device,
)
true_cap,pred_cap = ids2text(
targets=targets,
pre_cap_tokens=pred_cap_tokens,
tokenizer=t5_tokenizer,
)
caption.extend(true_cap)
pre_caption.extend(pred_cap)
# update params
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
Loss.append(batch_loss.detach().cpu().item())
Acc.append(batch_accuracy.detach().cpu().item())
loss = np.mean(np.array(Loss))
acc = np.mean(np.array(Acc))
writer.add_scalar("loss",loss,ep)
writer.add_scalar("accuracy",acc,ep)
# print(f"caption: {caption}")
# print(f"pred_caption: {pre_caption}")
model_save_path = './ckpts_frozen_resnet/model_ep30.pth'
torch.save(image_encoder.state_dict(), model_save_path)
caption_array = np.array(caption)
np.savetxt('./data/caption.txt',caption_array,fmt='%s' )
pred_caption_array = np.array(pre_caption)
np.savetxt('./data/pred_caption.txt',pred_caption_array,fmt='%s' )