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| import numpy as np | |
| import random | |
| import torch | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| from models.tag2text import tag2text_caption | |
| import gradio as gr | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| image_size = 384 | |
| normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]) | |
| transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize]) | |
| #######Swin Version | |
| pretrained = 'tag2text_swin_14m.pth' | |
| model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' ) | |
| model.eval() | |
| model = model.to(device) | |
| def inference(raw_image, input_tag): | |
| raw_image = raw_image.resize((image_size, image_size)) | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| model.threshold = 0.68 | |
| if input_tag == '' or input_tag == 'none' or input_tag == 'None': | |
| input_tag_list = None | |
| else: | |
| input_tag_list = [] | |
| input_tag_list.append(input_tag.replace(',',' | ')) | |
| with torch.no_grad(): | |
| caption, tag_predict = model.generate(image,tag_input = input_tag_list,max_length = 50, return_tag_predict = True) | |
| if input_tag_list == None: | |
| tag_1 = tag_predict | |
| tag_2 = ['none'] | |
| else: | |
| _, tag_1 = model.generate(image,tag_input = None, max_length = 50, return_tag_predict = True) | |
| tag_2 = tag_predict | |
| return tag_1[0],tag_2[0],caption[0] | |
| inputs = [gr.inputs.Image(type='pil'),gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional, Enter with commas)")] | |
| outputs = [gr.outputs.Textbox(label="Model Identified Tags"),gr.outputs.Textbox(label="User Specified Tags"), gr.outputs.Textbox(label="Image Caption") ] | |
| title = "Tag2Text" | |
| description = "Welcome to Tag2Text demo! (Supported by Fudan University, OPPO Research Institute, International Digital Economy Academy) <br/> Upload your image to get the <b>tags</b> and <b>caption</b> of the image. Optional: You can also input specified tags to get the corresponding caption." | |
| article = "<p style='text-align: center'>Tag2text training on open-source datasets, and we are persisting in refining and iterating upon it.<br/><a href='https://arxiv.org/abs/2303.05657' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='https://github.com/xinyu1205/Tag2Text' target='_blank'>Github Repo</a></p>" | |
| demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000483108.jpg',"none"], | |
| ['images/COCO_val2014_000000483108.jpg',"power line"], | |
| ['images/COCO_val2014_000000483108.jpg',"track, train"] , | |
| ['images/bdf391a6f4b1840a.jpg',"none"], | |
| ['images/64891_194270823.jpg',"none"], | |
| ['images/2800737_834897251.jpg',"none"], | |
| ['images/1641173_2291260800.jpg',"none"], | |
| ]) | |
| demo.launch(enable_queue=True) | |