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from PIL import Image |
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import requests |
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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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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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import gradio as gr |
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from models.blip import blip_decoder |
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image_size = 384 |
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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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model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' |
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model = blip_decoder(pretrained=model_url, image_size=384, vit='large') |
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model.eval() |
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model = model.to(device) |
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from models.blip_vqa import blip_vqa |
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image_size_vq = 480 |
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transform_vq = transforms.Compose([ |
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transforms.Resize((image_size_vq,image_size_vq),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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model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth' |
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model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base') |
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model_vq.eval() |
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model_vq = model_vq.to(device) |
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def inference(raw_image, model_n, question, strategy): |
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if model_n == 'Image Captioning': |
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image = transform(raw_image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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if strategy == "Beam search": |
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caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) |
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else: |
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caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) |
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return 'caption: '+caption[0] |
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else: |
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image_vq = transform_vq(raw_image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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answer = model_vq(image_vq, question, train=False, inference='generate') |
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return 'answer: '+answer[0] |
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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")] |
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outputs = gr.outputs.Textbox(label="Output") |
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title = "BLIP" |
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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." |
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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>" |
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gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]]).launch(enable_queue=True) |