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| from PIL import Image | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| from torchvision.transforms.functional import InterpolationMode | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| import gradio as gr | |
| from models.blip import blip_decoder | |
| image_size = 384 | |
| 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)) | |
| ]) | |
| model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' | |
| model = blip_decoder(pretrained=model_url, image_size=384, vit='large') | |
| model.eval() | |
| model = model.to(device) | |
| from models.blip_vqa import blip_vqa | |
| image_size_vq = 480 | |
| transform_vq = transforms.Compose([ | |
| transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth' | |
| model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base') | |
| model_vq.eval() | |
| model_vq = model_vq.to(device) | |
| def inference(raw_image, model_n, question, strategy): | |
| if model_n == 'Image Captioning': | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| if strategy == "Beam search": | |
| caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) | |
| else: | |
| caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) | |
| return caption[0] | |
| else: | |
| image_vq = transform_vq(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| answer = model_vq(image_vq, question, train=False, inference='generate') | |
| return 'answer: '+answer[0] | |
| inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning',"Visual Question Answering"], type="value", default="Image Captioning", label="Task"),gr.inputs.Textbox(lines=2, label="Question"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")] | |
| outputs = gr.outputs.Textbox(label="Output") | |
| title = "BLIP" | |
| description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>" | |
| gr.Interface(inference, | |
| inputs, | |
| outputs, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]], | |
| allow_flagging='never', | |
| cache_examples=False).queue(concurrency_count=1).launch(show_error=True) | |