From ffcb6e42f35b68fad474f71be9cc1e5e0107833a Mon Sep 17 00:00:00 2001 From: huiding Date: Wed, 19 Jul 2023 10:15:49 -0700 Subject: [PATCH] add demo codes --- README.md | 68 ++++---- app.py | 47 ++++++ demo.py | 405 ++++++++++++++++++++++++++++++++++++++++++++++ demo/bear.jpeg | Bin 0 -> 59618 bytes demo/cabin.jpeg | Bin 0 -> 66546 bytes demo/dog.jpg | Bin 0 -> 29781 bytes demo/otta.png | Bin 0 -> 435531 bytes demo/pig.jpeg | Bin 0 -> 48036 bytes demo/pikachu.jpeg | Bin 0 -> 58462 bytes demo/unicorn.jpeg | Bin 0 -> 44719 bytes demo/vases.jpg | Bin 0 -> 80008 bytes 11 files changed, 487 insertions(+), 33 deletions(-) create mode 100644 app.py create mode 100644 demo.py create mode 100644 demo/bear.jpeg create mode 100644 demo/cabin.jpeg create mode 100644 demo/dog.jpg create mode 100644 demo/otta.png create mode 100644 demo/pig.jpeg create mode 100644 demo/pikachu.jpeg create mode 100644 demo/unicorn.jpeg create mode 100644 demo/vases.jpg diff --git a/README.md b/README.md index ea35d52..767334e 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/polyformer-referring-image-segmentation-as/referring-expression-comprehension-on-refcoco-1)](https://paperswithcode.com/sota/referring-expression-comprehension-on-refcoco-1?p=polyformer-referring-image-segmentation-as) -\[[Project Page](https://polyformer.github.io/)\] \[[Paper](https://arxiv.org/abs/2302.07387)\] \[[Demo](https://huggingface.co/spaces/koajoel/PolyFormer)\] +\[[Project Page](https://polyformer.github.io/)\] \[[Paper](https://arxiv.org/abs/2302.07387)\] by [Jiang Liu*](https://joellliu.github.io/), [Hui Ding*](http://www.huiding.org/), [Zhaowei Cai](https://zhaoweicai.github.io/), [Yuting Zhang](https://scholar.google.com/citations?user=9UfZJskAAAAJ&hl=en), [Ravi Kumar Satzoda](https://scholar.google.com.sg/citations?user=4ngycwIAAAAJ&hl=en), [Vijay Mahadevan](https://scholar.google.com/citations?user=n9fRgvkAAAAJ&hl=en), [R. Manmatha](https://ciir.cs.umass.edu/~manmatha/). @@ -28,14 +28,10 @@ PolyFormer is a unified model for referring image segmentation (polygon vertex s ```bash conda create -n polyformer python=3.7.4 conda activate polyformer +pip3 install torch==1.8.1 torchvision==0.9.1 --extra-index-url https://download.pytorch.org/whl/cu113 python -m pip install -r requirements.txt ``` -Note: if you are getting import errors from `fairseq`, try the following: -```bash -python -m pip install pip==21.2.4 -pip uninstall fairseq -pip install -r requirements.txt -``` + ## Datasets ### Prepare Pretraining Data @@ -56,11 +52,11 @@ The workspace directory should be organized like this: PolyFormer/ ├── datasets/ │   ├── images -│   │   ├── flickr30k/*.jpg +│   │   ├── flickr30k/ │   │   ├── mscoco/ -│   │ │  └── train2014/*.jpg -│   │   ├── saiaprtc12/*.jpg -│   │   └── visual-genome/*.jpg +│   │ │  └── train2014/ +│   │   ├── saiaprtc12/ +│   │   └── visual-genome/ │   └── annotations │      └── instances.json └── ... @@ -85,12 +81,12 @@ python data/create_finetuning_data.py ## Pretraining 1. Create the checkpoints folder ```bash -mkdir weights +mkdir pretrained_weights ``` 2. Download pretrain weights of [Swin-base](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth), [Swin-large](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth), -[BERT-base](https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin) -and put the weight files in `./pretrained_weights`. +BERT-base +and put the `pth` files in `./pretrained_weights`. These weights are needed for training to initialize the model. @@ -126,32 +122,38 @@ bash evaluate_polyformer_l_refcoco+.sh bash evaluate_polyformer_l_refcocog.sh ``` + ## Model Zoo -Download the model weights to `./weights` if you want to use our trained models for finetuning and evaluation. +| | Refcoco val|| | Refcoco testA|| | Refcoco testB| || +|------------|------|------|------|------|------|------|------|------|------| +| Model | oIoU | mIoU |Prec@0.5| oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | +|PolyFormerB [Checkpoint](https://drive.google.com/file/d/1K0y-WBO6cL7gBzNnJaHAeNu3pgq4DbJ9/view?usp=share_link) | 74.82| 75.96| 89.73|76.64| 77.09 | 91.73| 71.06| 73.22 | 86.03 | +|PolyFormerL [Checkpoint](https://drive.google.com/file/d/15P6m5RI6HAQE2QXQXMAjw_oBsaPii7b3/view?usp=share_link) | 75.96| 76.94| 90.38|78.29| 78.49 | 92.89| 73.25| 74.83 | 87.16| -| | Refcoco val| | | Refcoco testA| | | Refcoco testB| || -|-------------------------------------------------------------------------------------------------------|------|------|---------|------|-------|------|-----|------|------| -| Model | oIoU | mIoU | Prec@0.5 | oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | -| [PolyFormer-B](https://drive.google.com/file/d/1K0y-WBO6cL7gBzNnJaHAeNu3pgq4DbJ9/view?usp=share_link) | 74.82| 75.96 | 89.73 |76.64| 77.09 | 91.73| 71.06| 73.22 | 86.03 | -| [PolyFormer-L](https://drive.google.com/file/d/15P6m5RI6HAQE2QXQXMAjw_oBsaPii7b3/view?usp=share_link) | 75.96| 76.94 | 90.38 |78.29| 78.49 | 92.89| 73.25| 74.83 | 87.16| +| | Refcoco+ val| || Refcoco+ testA| || Refcoco+ testB| || +|------------|------|------|------|------|------|------|------|------|------| +| Model | oIoU | mIoU |Prec@0.5| oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | +|PolyFormerB [Checkpoint](https://drive.google.com/file/d/12_ylFhsbqGySxDqgeEByn8nKoJtT2n2w/view?usp=share_link) | 67.64| 70.65 | 83.73 | 72.89| 74.51 | 88.60 | 59.33| 64.64 | 76.38 | 67.76| 69.36 | +|PolyFormerL [Checkpoint](https://drive.google.com/file/d/1lUCv7dUPctEz4vEpPr7aI8A8ZmfYCB8y/view?usp=share_link) | 69.33| 72.15 | 84.98 | 74.56| 75.71 | 89.77 | 61.87| 66.73 | 77.97 | 69.20| 71.15 | -| | Refcoco+ val| | | Refcoco+ testA| | | Refcoco+ testB| || -|--------------------------------------------------------------------------------------------------------|------|------|------|------|------|------|------|------|------| -| Model | oIoU | mIoU |Prec@0.5| oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | -| [PolyFormer-B ](https://drive.google.com/file/d/12_ylFhsbqGySxDqgeEByn8nKoJtT2n2w/view?usp=share_link) | 67.64| 70.65 | 83.73 | 72.89| 74.51 | 88.60 | 59.33| 64.64 | 76.38 | 67.76| 69.36 | -| [PolyFormer-L](https://drive.google.com/file/d/1lUCv7dUPctEz4vEpPr7aI8A8ZmfYCB8y/view?usp=share_link) | 69.33| 72.15 | 84.98 | 74.56| 75.71 | 89.77 | 61.87| 66.73 | 77.97 | 69.20| 71.15 | +| | Refcocog val| || Refcocog test| || +|------------|------|------|------|------|------|------| +| Model | oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | +|PolyFormerB [Checkpoint](https://drive.google.com/file/d/12_ylFhsbqGySxDqgeEByn8nKoJtT2n2w/view?usp=share_link) | 67.76| 69.36 | 84.46| 69.05| 69.88 | 84.96 | +|PolyFormerL [Checkpoint](https://drive.google.com/file/d/1lUCv7dUPctEz4vEpPr7aI8A8ZmfYCB8y/view?usp=share_link) | 69.20| 71.15 | 85.83 | 70.19| 71.17 | 85.91| -| | Refcocog val| || | Refcocog test| | -|-------------------------------------------------------------------------------------------------------|------|------|------|------|------|------| -| Model | oIoU | mIoU |Prec@0.5 | oIoU | mIoU |Prec@0.5 | -| [PolyFormer-B](https://drive.google.com/file/d/1am7SKADCJgdOoXcd6z5JNEB3dHlabraA/view?usp=share_link) | 67.76| 69.36 | 84.46| 69.05| 69.88 | 84.96 | -| [PolyFormer-L](https://drive.google.com/file/d/1upjK4YmtQT9b6qcA3yj3DXKnOuI52Pxv/view?usp=share_link) | 69.20| 71.15 | 85.83 | 70.19| 71.17 | 85.91| +* PolyFormerB Pretrain [Checkpoint](https://drive.google.com/file/d/1sAzfChYDdHdaeatB2K14lrJjG4uiXAol/view?usp=share_link) +* PolyFormerL Pretrain [Checkpoint](https://drive.google.com/file/d/1knRxgM1lmEkuZZ-cOm_fmwKP1H0bJGU9/view?usp=share_link) + + +## Run the demo +You can run the demo locally by: +```bash +python app.py +``` -* Pretrained weights: - * [PolyFormer-B](https://drive.google.com/file/d/1sAzfChYDdHdaeatB2K14lrJjG4uiXAol/view?usp=share_link) - * [PolyFormer-L](https://drive.google.com/file/d/1knRxgM1lmEkuZZ-cOm_fmwKP1H0bJGU9/view?usp=share_link) # Acknowlegement This codebase is developed based on [OFA](https://github.com/OFA-Sys/OFA). diff --git a/app.py b/app.py new file mode 100644 index 0000000..845ad9f --- /dev/null +++ b/app.py @@ -0,0 +1,47 @@ +# https://huggingface.co/koajoel/PolyFormer +import os +import torch +import numpy as np +from fairseq import utils,tasks +from utils.checkpoint_utils import load_model_ensemble_and_task +from models.polyformer import PolyFormerModel +import cv2 + +import torch +import numpy as np +from fairseq import utils, tasks +from fairseq import checkpoint_utils +from utils.eval_utils import eval_step +from tasks.refcoco import RefcocoTask +from models.polyformer import PolyFormerModel +from PIL import Image +from torchvision import transforms +import cv2 +import gradio as gr +import math +from io import BytesIO +import base64 +import re +from demo import visual_grounding + +title = "PolyFormer for Visual Grounding" + +description = """

Project Page | Paper | Github Repo

+

Demo of PolyFormer for referring image segmentation and referring expression comprehension. Upload your own image or click any one of the examples, and write a description about a certain object. Then click \"Submit\" and wait for the results.

+""" + +examples = [['demo/vases.jpg', 'the blue vase on the left'], + ['demo/dog.jpg', 'the dog wearing glasses'], + ['demo/bear.jpeg', 'a bear astronaut in the space'], + ['demo/unicorn.jpeg', 'a unicorn doing computer vision research'], + ['demo/pig.jpeg', 'a pig robot preparing a delicious meal'], + ['demo/otta.png', 'a gentleman otter in a 19th century portrait'], + ['demo/pikachu.jpeg', 'a pikachu fine-dining with a view to the Eiffel Tower'], + ['demo/cabin.jpeg', 'a small cabin on top of a snowy mountain in the style of Disney art station'] + ] +io = gr.Interface(fn=visual_grounding, inputs=[gr.inputs.Image(type='pil'), "textbox"], + outputs=[gr.outputs.Image(label="output", type='numpy'), gr.outputs.Image(label="predicted mask", type='numpy')], + title=title, description=description, examples=examples, + allow_flagging=False, allow_screenshot=False, cache_examples=False) +io.launch(share=True) + diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..3b89d95 --- /dev/null +++ b/demo.py @@ -0,0 +1,405 @@ +import torch +import numpy as np +from fairseq import utils,tasks +from utils.checkpoint_utils import load_model_ensemble_and_task +from utils.eval_utils import eval_step +from tasks.refcoco import RefcocoTask +from models.polyformer import PolyFormerModel +from PIL import Image +import cv2 +import math +from skimage import draw + + +tasks.register_task('refcoco', RefcocoTask) + +# turn on cuda if GPU is available +use_cuda = torch.cuda.is_available() +# use fp16 only when GPU is available +use_fp16 = True + +# Load pretrained ckpt & config +overrides={"bpe_dir":"utils/BPE"} +models, cfg, task = load_model_ensemble_and_task( + utils.split_paths('weights/polyformer_l_refcocog.pt'), + arg_overrides=overrides + ) + +cfg.common.seed = 7 +cfg.generation.beam = 5 +cfg.generation.min_len = 12 +cfg.generation.max_len_a = 0 +cfg.generation.max_len_b = 420 +cfg.generation.no_repeat_ngram_size = 3 +cfg.task.patch_image_size = 512 + +from bert.tokenization_bert import BertTokenizer +tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') + +# Fix seed for stochastic decoding +if cfg.common.seed is not None and not cfg.generation.no_seed_provided: + np.random.seed(cfg.common.seed) + utils.set_torch_seed(cfg.common.seed) + + +# Move models to GPU +for model in models: + model.eval() + if use_fp16: + model.half() + if use_cuda and not cfg.distributed_training.pipeline_model_parallel: + model.cuda() + model.prepare_for_inference_(cfg) + +# Initialize generator +generator = task.build_generator(models, cfg.generation) + + +# Image transform +from torchvision import transforms +mean = [0.5, 0.5, 0.5] +std = [0.5, 0.5, 0.5] + +patch_resize_transform = transforms.Compose([ + lambda image: image.convert("RGB"), + transforms.Resize((cfg.task.patch_image_size, cfg.task.patch_image_size), interpolation=Image.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std), +]) + +# Text preprocess +bos_item = torch.LongTensor([task.src_dict.bos()]) +eos_item = torch.LongTensor([task.src_dict.eos()]) +pad_idx = task.src_dict.pad() + + +# Construct input for refcoco task +patch_image_size = cfg.task.patch_image_size +def construct_sample(image: Image, text: str): + w, h = image.size + w_resize_ratio = torch.tensor(patch_image_size / w).unsqueeze(0) + h_resize_ratio = torch.tensor(patch_image_size / h).unsqueeze(0) + patch_image = patch_resize_transform(image).unsqueeze(0) + patch_mask = torch.tensor([True]) + + prompt = ' which region does the text " {} " describe?'.format(text) + tokenized = tokenizer.batch_encode_plus([prompt], padding="longest", return_tensors="pt") + src_tokens = tokenized["input_ids"] + att_masks = tokenized["attention_mask"] + src_lengths = torch.LongTensor(att_masks.ne(0).long().sum()) + + sample = { + "id":np.array(['42']), + "net_input": { + "src_tokens": src_tokens, + "src_lengths": src_lengths, + "att_masks": att_masks, + "patch_images": patch_image, + "patch_masks": patch_mask, + }, + "w_resize_ratios": w_resize_ratio, + "h_resize_ratios": h_resize_ratio, + "region_coords": torch.randn(1, 4), + "label": np.zeros((512,512)), + "poly": 'None', + "text": text + } + return sample + +# Function to turn FP32 to FP16 +def apply_half(t): + if t.dtype is torch.float32: + return t.to(dtype=torch.half) + return t + + +from io import BytesIO +import base64 +import re + +def pre_caption(caption): + caption = caption.lower().lstrip(",.!?*#:;~").replace('-', ' ').replace('/', ' ').replace('', 'person') + + caption = re.sub( + r"\s{2,}", + ' ', + caption, + ) + caption = caption.rstrip('\n') + caption = caption.strip(' ') + return caption + + +def convert_pts(coeffs): + pts = [] + for i in range(len(coeffs) // 2): + pts.append([coeffs[2 * i + 1], coeffs[2 * i]]) # y, x + return np.array(pts, np.int32) + +def get_mask_from_codes(codes, img_size): + masks = [np.zeros(img_size)] + for code in codes: + mask = draw.polygon2mask(img_size, convert_pts(code)) + mask = np.array(mask, np.uint8) + masks.append(mask) + mask = sum(masks) + mask = mask > 0 + return mask.astype(np.uint8) + + +def overlay_predictions(img, mask=None, polygons=None, bbox=None, color_box=(0, 255, 0), color_mask=[255, 102, 102], color_poly=[255, 0, 0], thickness=3, radius=6): + overlayed = img.copy() + if bbox is not None: + overlayed = draw_bbox(overlayed, bbox, color=color_box, thickness=thickness) + if mask is not None: + overlayed = overlay_davis(overlayed, mask, colors=[[0, 0, 0], color_mask]) + if polygons is not None: + overlayed = plot_polygons(overlayed, polygons, color=color_poly, radius=radius) + return overlayed + + +def overlay_davis(image, mask, colors=[[0, 0, 0], [255, 102, 102]], cscale=1, alpha=0.4): # [255, 178, 102] orange [102, 178, 255] red + from scipy.ndimage.morphology import binary_dilation + + colors = np.reshape(colors, (-1, 3)) + colors = np.atleast_2d(colors) * cscale + + im_overlay = image.copy() + object_ids = np.unique(mask) + + h_i, w_i = image.shape[0:2] + h_m, w_m = mask.shape[0:2] + if h_i != h_m: + mask = cv2.resize(mask, [h_i, w_i], interpolation=cv2.INTER_NEAREST) + for object_id in object_ids[1:]: + # Overlay color on binary mask + foreground = image*alpha + np.ones(image.shape)*(1-alpha) * np.array(colors[object_id]) + binary_mask = mask == object_id + + # Compose image + im_overlay[binary_mask] = foreground[binary_mask] + + return im_overlay.astype(image.dtype) + + +def draw_bbox(img, box, color=(0, 255, 0), thickness=2): + x1, y1, x2, y2 = box + return cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, thickness=thickness) + +def plot_polygons(img, polygons, color=(255, 0, 0), radius=7): + for polygon in polygons: + if len(polygon) > 0: + polygon = np.reshape(polygon[:len(polygon)-len(polygon)%2], (len(polygon)//2, 2)).astype(np.int16) + for i, point in enumerate(polygon): + img = cv2.circle(img, point, radius, color, thickness=-1) + img = cv2.circle(img, polygon[0], radius, color, thickness=-1) + return img + +def plot_arrow(img, polygons, color=(128, 128, 128), thickness=3, tip_length=0.3): + for polygon in polygons: + if len(polygon) > 0: + polygon = np.reshape(polygon[:len(polygon)-len(polygon)%2], (len(polygon)//2, 2)).astype(np.int16) + for i, point in enumerate(polygon): + if i > 0: + img = cv2.arrowedLine(img, polygon[i-1], point, color, thickness=thickness, tipLength=tip_length) + return img + +def downsample_polygon(polygon, ds_rate=25): + points = np.array(polygon).reshape(int(len(polygon) / 2), 2) + points = points[::ds_rate] + return list(points.flatten()) + + +def downsample_polygons(polygons, ds_rate=25): + polygons_ds = [] + for polygon in polygons: + polygons_ds.append(downsample_polygon(polygon, ds_rate)) + return polygons_ds + + + +def visual_grounding(image, text): + # Construct input sample & preprocess for GPU if cuda available + sample = construct_sample(image, text.lower()) + sample = utils.move_to_cuda(sample) if use_cuda else sample + sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample + + with torch.no_grad(): + if isinstance(models, list): + model = models[0] + min_len = 6 + max_len = 210 + model.eval() + img = sample["net_input"]["patch_images"] + b = img.shape[0] + prev_output_token_11 = [[0] for _ in range(b)] + prev_output_token_12 = [[0] for _ in range(b)] + prev_output_token_21 = [[0] for _ in range(b)] + prev_output_token_22 = [[0] for _ in range(b)] + delta_x1 = [[0] for _ in range(b)] + delta_y1 = [[0] for _ in range(b)] + delta_x2 = [[1] for _ in range(b)] + delta_y2 = [[1] for _ in range(b)] + + gen_out = [[] for _ in range(b)] + + n_bins = 64 + + unfinish_flag = np.ones(b) + i = 0 + + encoder_out = model.encoder( + sample['net_input']['src_tokens'], + src_lengths=sample['net_input']['src_lengths'], + att_masks=sample['net_input']['att_masks'], + patch_images=sample['net_input']['patch_images'], + patch_masks=sample['net_input']['patch_masks'], + token_embeddings=None, + return_all_hiddens=False, + sample_patch_num=None + ) + attn_masks = [] + while i < max_len and unfinish_flag.any(): + prev_output_tokens_11_tensor = torch.tensor(np.array(prev_output_token_11)).to(img.device).long() + prev_output_tokens_12_tensor = torch.tensor(np.array(prev_output_token_12)).to(img.device).long() + prev_output_tokens_21_tensor = torch.tensor(np.array(prev_output_token_21)).to(img.device).long() + prev_output_tokens_22_tensor = torch.tensor(np.array(prev_output_token_22)).to(img.device).long() + delta_x1_tensor = torch.tensor(np.array(delta_x1)).to(img.device) + delta_x2_tensor = torch.tensor(np.array(delta_x2)).to(img.device) + delta_y1_tensor = torch.tensor(np.array(delta_y1)).to(img.device) + delta_y2_tensor = torch.tensor(np.array(delta_y2)).to(img.device) + + net_output = model.decoder( + prev_output_tokens_11_tensor, + prev_output_tokens_12_tensor, + prev_output_tokens_21_tensor, + prev_output_tokens_22_tensor, + delta_x1_tensor, + delta_y1_tensor, + delta_x2_tensor, + delta_y2_tensor, + code_masks=None, + encoder_out=encoder_out, + features_only=False, + alignment_layer=None, + alignment_heads=None, + src_lengths=sample['net_input']['src_lengths'], + return_all_hiddens=False + ) + + cls_output = net_output[0] + cls_type = torch.argmax(cls_output, 2) + reg_output = net_output[1].squeeze(-1) + attn = net_output[2]['attn'] + attn_arrays = [att.detach().cpu().numpy() for att in attn] + attn_arrays = np.concatenate(attn_arrays, 0) + attn_arrays = np.mean(attn_arrays, 0) + attn_arrays = attn_arrays[i, :256].reshape(16, 16) + h, w = image.size + attn_mask = cv2.resize(attn_arrays.astype(np.float32), (h, w)) + attn_masks.append(attn_mask) + + for j in range(b): + if unfinish_flag[j] == 1: # prediction is not finished + cls_j = cls_type[j, i].item() + if cls_j == 0 or (cls_j == 2 and i < min_len): # 0 for coordinate tokens; 2 for eos + output_j_x, output_j_y = reg_output[j, i].cpu().numpy() + output_j_x = min(output_j_x, 1) + output_j_y = min(output_j_y, 1) + + gen_out[j].extend([output_j_x, output_j_y]) + + output_j_x = output_j_x * (n_bins - 1) + output_j_y = output_j_y * (n_bins - 1) + + output_j_x_floor = math.floor(output_j_x) + output_j_y_floor = math.floor(output_j_y) + output_j_x_ceil = math.ceil(output_j_x) + output_j_y_ceil = math.ceil(output_j_y) + + # convert to token + prev_output_token_11[j].append(output_j_x_floor * n_bins + output_j_y_floor + 4) + prev_output_token_12[j].append(output_j_x_floor * n_bins + output_j_y_ceil + 4) + prev_output_token_21[j].append(output_j_x_ceil * n_bins + output_j_y_floor + 4) + prev_output_token_22[j].append(output_j_x_ceil * n_bins + output_j_y_ceil + 4) + + delta_x = output_j_x - output_j_x_floor + delta_y = output_j_y - output_j_y_floor + elif cls_j == 1: # 1 for separator tokens + gen_out[j].append(2) # insert 2 indicating separator tokens + prev_output_token_11[j].append(3) + prev_output_token_12[j].append(3) + prev_output_token_21[j].append(3) + prev_output_token_22[j].append(3) + delta_x = 0 + delta_y = 0 + else: # eos is predicted and i >= min_len + unfinish_flag[j] = 0 + gen_out[j].append(-1) + prev_output_token_11[j].append(2) # 2 is eos token + prev_output_token_12[j].append(2) # 2 is eos token + prev_output_token_21[j].append(2) # 2 is eos token + prev_output_token_22[j].append(2) # 2 is eos token + delta_x = 0 + delta_y = 0 + else: # prediction is finished + gen_out[j].append(-1) + prev_output_token_11[j].append(1) # 1 is padding token + prev_output_token_12[j].append(1) + prev_output_token_21[j].append(1) + prev_output_token_22[j].append(1) + delta_x = 0 + delta_y = 0 + delta_x1[j].append(delta_x) + delta_y1[j].append(delta_y) + delta_x2[j].append(1 - delta_x) + delta_y2[j].append(1 - delta_y) + i += 1 + print("inference step: ", i) + + hyps = [] + hyps_det = [] + n_poly_pred = [] + b = len(gen_out) + for i in range(b): + gen_out_i = np.array(gen_out[i]) + gen_out_i = gen_out_i[gen_out_i != -1] # excluding eos and padding indices + + + gen_out_i_det = gen_out_i[:4] + w, h = image.size + gen_out_i_det[::2] *= w + gen_out_i_det[1::2] *= h + + polygons_pred = gen_out_i[4:] + polygons_pred = np.append(polygons_pred, [2]) + size = len(polygons_pred) + idx_list = [idx for idx, val in + enumerate(polygons_pred) if val == 2] # 2 indicates separator token + + polygons_pred[::2] *= w + polygons_pred[1::2] *= h + if len(idx_list) > 0: # multiple polygons + polygons = [] + pred_idx = 0 + for idx in idx_list: + cur_idx = idx + if pred_idx == cur_idx or pred_idx == size: + pass + else: + polygons.append(polygons_pred[pred_idx: cur_idx]) + pred_idx = cur_idx + 1 + else: + polygons = [polygons_pred] + + n_poly_pred.append(len(polygons)) + hyps.append(polygons) + hyps_det.append(gen_out_i_det) + + + pred_mask = get_mask_from_codes(hyps[0], (h, w)) + pred_overlayed = overlay_predictions(np.asarray(image), pred_mask, hyps[0], hyps_det[0]) + + return pred_overlayed, np.array(pred_mask*255, dtype=np.uint8) + + diff 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