| """ |
| 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') |
| } |
|
|
| @cog.input( |
| "image", |
| type=Path, |
| help="input image", |
| ) |
| @cog.input( |
| "task", |
| type=str, |
| default='image_captioning', |
| options=['image_captioning', 'visual_question_answering', 'image_text_matching'], |
| help="Choose a task.", |
| ) |
| @cog.input( |
| "question", |
| type=str, |
| default=None, |
| help="Type question for the input image for visual question answering task.", |
| ) |
| @cog.input( |
| "caption", |
| type=str, |
| default=None, |
| help="Type caption for the input image for image text matching task.", |
| ) |
| 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] |
|
|
| |
| 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 |
|
|
