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| import os | |
| os.system('cd fairseq;' | |
| 'pip install ./; cd ..') | |
| os.system('ls -l') | |
| 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.mm_tasks.refcoco import RefcocoTask | |
| from models.ofa import OFAModel | |
| from PIL import Image | |
| from torchvision import transforms | |
| import cv2 | |
| import gradio as gr | |
| # Register refcoco task | |
| 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 = False | |
| os.system('wget https://ofa-silicon.oss-us-west-1.aliyuncs.com/checkpoints/refcocog_large_best.pt; ' | |
| 'mkdir -p checkpoints; mv refcocog_large_best.pt checkpoints/refcocog.pt') | |
| # Load pretrained ckpt & config | |
| overrides = {"bpe_dir": "utils/BPE", "eval_cider": False, "beam": 5, | |
| "max_len_b": 16, "no_repeat_ngram_size": 3, "seed": 7} | |
| models, cfg, task = checkpoint_utils.load_model_ensemble_and_task( | |
| utils.split_paths('checkpoints/refcocog.pt'), | |
| arg_overrides=overrides | |
| ) | |
| cfg.common.seed = 7 | |
| cfg.generation.beam = 5 | |
| cfg.generation.min_len = 4 | |
| cfg.generation.max_len_a = 0 | |
| cfg.generation.max_len_b = 4 | |
| cfg.generation.no_repeat_ngram_size = 3 | |
| # 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) | |
| 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() | |
| def encode_text(text, length=None, append_bos=False, append_eos=False): | |
| s = task.tgt_dict.encode_line( | |
| line=task.bpe.encode(text), | |
| add_if_not_exist=False, | |
| append_eos=False | |
| ).long() | |
| if length is not None: | |
| s = s[:length] | |
| if append_bos: | |
| s = torch.cat([bos_item, s]) | |
| if append_eos: | |
| s = torch.cat([s, eos_item]) | |
| return s | |
| 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]) | |
| src_text = encode_text(' which region does the text " {} " describe?'.format(text), append_bos=True, | |
| append_eos=True).unsqueeze(0) | |
| src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text]) | |
| sample = { | |
| "id": np.array(['42']), | |
| "net_input": { | |
| "src_tokens": src_text, | |
| "src_lengths": src_length, | |
| "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) | |
| } | |
| 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 | |
| # Function for visual grounding | |
| def visual_grounding(Image, Text): | |
| 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(): | |
| result, scores = eval_step(task, generator, models, sample) | |
| img = np.asarray(Image) | |
| cv2.rectangle( | |
| img, | |
| (int(result[0]["box"][0]), int(result[0]["box"][1])), | |
| (int(result[0]["box"][2]), int(result[0]["box"][3])), | |
| (0, 255, 0), | |
| 3 | |
| ) | |
| return img | |
| title = "OFA-Visual_Grounding" | |
| description = "Gradio Demo for OFA-Visual_Grounding. 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 result of grounding. " | |
| article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \ | |
| "Repo</a></p> " | |
| examples = [['pokemons.jpg', 'a blue turtle-like pokemon with round head'], | |
| ['one_piece.jpeg', 'a man in a straw hat and a red dress'], | |
| ['flowers.jpg', 'a white vase and pink flowers']] | |
| io = gr.Interface(fn=visual_grounding, inputs=[gr.inputs.Image(type='pil'), "textbox"], | |
| outputs=gr.outputs.Image(type='numpy'), | |
| title=title, description=description, article=article, examples=examples, | |
| allow_flagging=False, allow_screenshot=False) | |
| io.launch() | |