import os os.system('cd fairseq;' 'pip install --use-feature=in-tree-build ./; 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.caption import CaptionTask from models.ofa import OFAModel from PIL import Image from torchvision import transforms import gradio as gr # Register caption task tasks.register_task('caption', CaptionTask) # 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/caption_large_best_clean.pt; ' 'mkdir -p checkpoints; mv caption_large_best_clean.pt checkpoints/caption.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/caption.pt'), arg_overrides=overrides ) # 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 # Construct input for caption task def construct_sample(image: Image): patch_image = patch_resize_transform(image).unsqueeze(0) patch_mask = torch.tensor([True]) src_text = encode_text(" what does the image describe?", 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 } } 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 image captioning def image_caption(Image): sample = construct_sample(Image) 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) return result[0]['caption'] title = "OFA-Image_Caption" description = "Gradio Demo for OFA-Image_Caption. Upload your own image or click any one of the examples, and click " \ "\"Submit\" and then wait for the generated caption. " article = "

OFA Github " \ "Repo

" examples = [['beatles.jpeg'], ['aurora.jpeg'], ['good_luck.png'], ['pokemons.jpg'], ['donuts.jpg']] io = gr.Interface(fn=image_caption, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Textbox(label="Caption"), title=title, description=description, article=article, examples=examples, allow_flagging=False, allow_screenshot=False) io.launch(cache_examples=True)