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99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
"""
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Download the weights in ./checkpoints beforehand for fast inference
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wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth
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wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth
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wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth
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"""
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from pathlib import Path
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from PIL import Image
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import torch
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from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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import cog
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from models.blip import blip_decoder
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from models.blip_vqa import blip_vqa
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from models.blip_itm import blip_itm
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class Predictor(cog.Predictor):
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def setup(self):
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self.device = "cuda:0"
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self.models = {
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'image_captioning': blip_decoder(pretrained='checkpoints/model*_base_caption.pth',
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image_size=384, vit='base'),
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'visual_question_answering': blip_vqa(pretrained='checkpoints/model*_vqa.pth',
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image_size=480, vit='base'),
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'image_text_matching': blip_itm(pretrained='checkpoints/model_base_retrieval_coco.pth',
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image_size=384, vit='base')
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}
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@cog.input(
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"image",
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type=Path,
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help="input image",
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)
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@cog.input(
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"task",
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type=str,
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default='image_captioning',
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options=['image_captioning', 'visual_question_answering', 'image_text_matching'],
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help="Choose a task.",
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)
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@cog.input(
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"question",
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type=str,
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default=None,
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help="Type question for the input image for visual question answering task.",
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)
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@cog.input(
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"caption",
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type=str,
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default=None,
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help="Type caption for the input image for image text matching task.",
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)
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def predict(self, image, task, question, caption):
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if task == 'visual_question_answering':
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assert question is not None, 'Please type a question for visual question answering task.'
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if task == 'image_text_matching':
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assert caption is not None, 'Please type a caption for mage text matching task.'
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im = load_image(image, image_size=480 if task == 'visual_question_answering' else 384, device=self.device)
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model = self.models[task]
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model.eval()
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model = model.to(self.device)
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if task == 'image_captioning':
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with torch.no_grad():
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caption = model.generate(im, sample=False, num_beams=3, max_length=20, min_length=5)
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return 'Caption: ' + caption[0]
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if task == 'visual_question_answering':
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with torch.no_grad():
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answer = model(im, question, train=False, inference='generate')
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return 'Answer: ' + answer[0]
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# image_text_matching
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itm_output = model(im, caption, match_head='itm')
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itm_score = torch.nn.functional.softmax(itm_output, dim=1)[:, 1]
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itc_score = model(im, caption, match_head='itc')
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return f'The image and text is matched with a probability of {itm_score.item():.4f}.\n' \
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f'The image feature and text feature has a cosine similarity of {itc_score.item():.4f}.'
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def load_image(image, image_size, device):
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raw_image = Image.open(str(image)).convert('RGB')
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w, h = raw_image.size
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transform = transforms.Compose([
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transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC),
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transforms.ToTensor(),
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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])
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image = transform(raw_image).unsqueeze(0).to(device)
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return image
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