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 from transformers import BlipProcessor, BlipForConditionalGeneration model_id = "Salesforce/blip-image-captioning-base" model = BlipForConditionalGeneration.from_pretrained(model_id) 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.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")] inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], 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 = "

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | Github Repo

" gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]]).launch(enable_queue=True)