import os os.system('cd fairseq;' 'pip install --use-feature=in-tree-build ./; cd ..') os.system('ls -l') import torch import numpy as np import gradio as gr import cv2 from PIL import Image from torchvision import transforms from fairseq import utils, tasks, options from fairseq import checkpoint_utils from fairseq.dataclass.utils import convert_namespace_to_omegaconf from tasks.mm_tasks.caption import CaptionTask from tasks.mm_tasks.refcoco import RefcocoTask from tasks.mm_tasks.vqa_gen import VqaGenTask def move2gpu(models, cfg): 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) def construct_transform(patch_image_size): 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((patch_image_size, patch_image_size), interpolation=Image.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=mean, std=std), ]) return patch_resize_transform # Register tasks tasks.register_task('caption', CaptionTask) tasks.register_task('refcoco', RefcocoTask) tasks.register_task('vqa_gen', VqaGenTask) # turn on cuda if GPU is available use_cuda = torch.cuda.is_available() # use fp16 only when GPU is available use_fp16 = False # download checkpoints os.system('wget https://ofa-silicon.oss-us-west-1.aliyuncs.com/checkpoints/caption_demo.pt; ' 'mkdir -p checkpoints; mv caption_demo.pt checkpoints/caption_demo.pt') os.system('wget https://ofa-silicon.oss-us-west-1.aliyuncs.com/checkpoints/refcoco_demo.pt; ' 'mkdir -p checkpoints; mv refcoco_demo.pt checkpoints/refcoco_demo.pt') os.system('wget https://ofa-silicon.oss-us-west-1.aliyuncs.com/checkpoints/general_demo.pt; ' 'mkdir -p checkpoints; mv general_demo.pt checkpoints/general_demo.pt') # Load ckpt & config for Image Captioning caption_overrides = {"bpe_dir": "utils/BPE", "eval_cider": False, "beam": 5, "max_len_b": 16, "no_repeat_ngram_size": 3, "seed": 7} caption_models, caption_cfg, caption_task = checkpoint_utils.load_model_ensemble_and_task( utils.split_paths('checkpoints/caption_demo.pt'), arg_overrides=caption_overrides ) # Load ckpt & config for Refcoco refcoco_overrides = {"bpe_dir": "utils/BPE", "eval_cider": False, "beam": 5, "max_len_b": 16, "no_repeat_ngram_size": 3, "seed": 7} refcoco_models, refcoco_cfg, refcoco_task = checkpoint_utils.load_model_ensemble_and_task( utils.split_paths('checkpoints/refcoco_demo.pt'), arg_overrides=refcoco_overrides ) refcoco_cfg.common.seed = 7 refcoco_cfg.generation.beam = 5 refcoco_cfg.generation.min_len = 4 refcoco_cfg.generation.max_len_a = 0 refcoco_cfg.generation.max_len_b = 4 refcoco_cfg.generation.no_repeat_ngram_size = 3 # Load pretrained ckpt & config for VQA parser = options.get_generation_parser() input_args = ["", "--task=vqa_gen", "--beam=100", "--unnormalized", "--path=checkpoints/general_demo.pt", "--bpe-dir=utils/BPE"] args = options.parse_args_and_arch(parser, input_args) vqa_cfg = convert_namespace_to_omegaconf(args) vqa_task = tasks.setup_task(vqa_cfg.task) vqa_models, vqa_cfg = checkpoint_utils.load_model_ensemble( utils.split_paths(vqa_cfg.common_eval.path), task=vqa_task ) # Load pretrained ckpt & config for Generic Interface parser = options.get_generation_parser() input_args = ["", "--task=refcoco", "--beam=10", "--path=checkpoints/general_demo.pt", "--bpe-dir=utils/BPE", "--no-repeat-ngram-size=3", "--patch-image-size=384"] args = options.parse_args_and_arch(parser, input_args) general_cfg = convert_namespace_to_omegaconf(args) general_task = tasks.setup_task(general_cfg.task) general_models, general_cfg = checkpoint_utils.load_model_ensemble( utils.split_paths(general_cfg.common_eval.path), task=general_task ) # move models to gpu move2gpu(caption_models, caption_cfg) move2gpu(refcoco_models, refcoco_cfg) move2gpu(vqa_models, vqa_cfg) move2gpu(general_models, general_cfg) # Initialize generator caption_generator = caption_task.build_generator(caption_models, caption_cfg.generation) refcoco_generator = refcoco_task.build_generator(refcoco_models, refcoco_cfg.generation) vqa_generator = vqa_task.build_generator(vqa_models, vqa_cfg.generation) vqa_generator.zero_shot = True vqa_generator.constraint_trie = None general_generator = general_task.build_generator(general_models, general_cfg.generation) # Construct image transforms caption_transform = construct_transform(caption_cfg.task.patch_image_size) refcoco_transform = construct_transform(refcoco_cfg.task.patch_image_size) vqa_transform = construct_transform(vqa_cfg.task.patch_image_size) general_transform = construct_transform(general_cfg.task.patch_image_size) # Text preprocess bos_item = torch.LongTensor([caption_task.src_dict.bos()]) eos_item = torch.LongTensor([caption_task.src_dict.eos()]) pad_idx = caption_task.src_dict.pad() def get_symbols_to_strip_from_output(generator): if hasattr(generator, "symbols_to_strip_from_output"): return generator.symbols_to_strip_from_output else: return {generator.bos, generator.eos} def decode_fn(x, tgt_dict, bpe, generator, tokenizer=None): x = tgt_dict.string(x.int().cpu(), extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator)) token_result = [] bin_result = [] img_result = [] for token in x.strip().split(): if token.startswith('<bin_'): bin_result.append(token) elif token.startswith('<code_'): img_result.append(token) else: if bpe is not None: token = bpe.decode('{}'.format(token)) if tokenizer is not None: token = tokenizer.decode(token) if token.startswith(' ') or len(token_result) == 0: token_result.append(token.strip()) else: token_result[-1] += token return ' '.join(token_result), ' '.join(bin_result), ' '.join(img_result) def bin2coord(bins, w_resize_ratio, h_resize_ratio, cfg): bin_list = [int(bin[5:-1]) for bin in bins.strip().split()] coord_list = [] coord_list += [bin_list[0] / (cfg.task.num_bins - 1) * cfg.task.max_image_size / w_resize_ratio] coord_list += [bin_list[1] / (cfg.task.num_bins - 1) * cfg.task.max_image_size / h_resize_ratio] coord_list += [bin_list[2] / (cfg.task.num_bins - 1) * cfg.task.max_image_size / w_resize_ratio] coord_list += [bin_list[3] / (cfg.task.num_bins - 1) * cfg.task.max_image_size / h_resize_ratio] return coord_list def encode_text(text, length=None, append_bos=False, append_eos=False): line = [ caption_task.bpe.encode(' {}'.format(word.strip())) if not word.startswith('<code_') and not word.startswith('<bin_') else word for word in text.strip().split() ] line = ' '.join(line) s = caption_task.tgt_dict.encode_line( line=line, 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 def construct_sample(image: Image, instruction: str, transform): patch_image = transform(image).unsqueeze(0) patch_mask = torch.tensor([True]) instruction = encode_text(' {}'.format(instruction.lower().strip()), append_bos=True, append_eos=True).unsqueeze(0) instruction_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in instruction]) sample = { "id": np.array(['42']), "net_input": { "src_tokens": instruction, "src_lengths": instruction_length, "patch_images": patch_image, "patch_masks": patch_mask, } } 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 def inference(image, task_type, instruction): if task_type == 'Image Captioning': task = caption_task models = caption_models generator = caption_generator instruction = 'what does the image describe?' transform = caption_transform cfg = caption_cfg elif task_type == 'Visual Question Answering': task = vqa_task models = vqa_models generator = vqa_generator transform = vqa_transform cfg = vqa_cfg elif task_type == 'Visual Grounding': task = refcoco_task models = refcoco_models generator = refcoco_generator instruction = 'which region does the text " {} " describe?'.format(instruction) transform = refcoco_transform cfg = refcoco_cfg elif task_type == 'General': task = general_task models = general_models generator = general_generator transform = general_transform cfg = general_cfg else: raise NotImplementedError # Construct input sample & preprocess for GPU if cuda available sample = construct_sample(image, instruction, transform) sample = utils.move_to_cuda(sample) if use_cuda else sample sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample # Generate result with torch.no_grad(): hypos = task.inference_step(generator, models, sample) tokens, bins, imgs = decode_fn(hypos[0][0]["tokens"], task.tgt_dict, task.bpe, generator) if bins.strip() != '': w, h = image.size w_resize_ratio = task.cfg.patch_image_size / w h_resize_ratio = task.cfg.patch_image_size / h img = np.asarray(image) coord_list = bin2coord(bins, w_resize_ratio, h_resize_ratio, cfg) cv2.rectangle( img, (int(coord_list[0]), int(coord_list[1])), (int(coord_list[2]), int(coord_list[3])), (0, 255, 0), 3 ) return img, None else: return None, tokens inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=['Image Captioning',"Visual Question Answering", "Visual Grounding", "General"], type="value", default="Image Captioning", label="Task"), gr.inputs.Textbox(lines=1, label="Instruction")] outputs = [gr.outputs.Image(type='pil'), 'text'] examples = [ ['examples/pokemons.jpeg', 'Image Captioning', None], ['examples/cats.jpeg', 'Visual Question Answering', 'where are the cats?'], ['examples/one_piece.jpeg', 'Visual Grounding', 'a man in a straw hat and a red dress'], ['examples/three_houses.jpeg', 'General', 'which region does the text " a grey car " describe?'], ['examples/three_houses.jpeg', 'General', 'what color is the left car?'] ] title = "OFA" description = "Gradio Demo for OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework" article = "<p style='text-align: center'><a href='http://arxiv.org/abs/2202.03052' target='_blank'>Paper</a> | <a href='https://github.com/OFA-Sys/OFA' target='_blank'>Github Repo</a></p>" io = gr.Interface(fn=inference, inputs=inputs, outputs=outputs, title=title, description=description, article=article, examples=examples, cache_examples=False) io.launch()