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