| 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: '+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.Image(type='pil'), |
| gr.Radio(choices=['Image Captioning',"Visual Question Answering"], type="value", value="Image Captioning", label="Task"), |
| gr.Textbox(lines=2, label="Question"), |
| gr.Radio(choices=['Beam search','Nucleus sampling'], type="value", value="Nucleus sampling", label="Caption Decoding Strategy")] |
| outputs = gr.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>" |
|
|
|
|
| demo = gr.Interface(inference, |
| inputs, |
| outputs, |
| title=title, |
| description=description, |
| article=article, |
| examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]], |
| allow_flagging='never', |
| cache_examples="lazy", |
| delete_cache=(4000, 4000)) |
| demo.queue(default_concurrency_limit=1).launch(show_error=True, show_api=True, mcp_server=True) |
|
|
