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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, ram | |
| 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]) | |
| #######Tag2Text Model | |
| pretrained = 'tag2text_swin_14m.pth' | |
| model_tag2text = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' ) | |
| model_tag2text.eval() | |
| model_tag2text = model_tag2text.to(device) | |
| #######RAM Model | |
| pretrained = 'ram_swin_large_14m.pth' | |
| model_ram = ram(pretrained=pretrained, image_size=image_size, vit='swin_l' ) | |
| model_ram.eval() | |
| model_ram = model_ram.to(device) | |
| def inference(raw_image, model_n , input_tag): | |
| raw_image = raw_image.resize((image_size, image_size)) | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| if model_n == 'Recognize Anything Model': | |
| model = model_ram | |
| tags, tags_chinese = model.generate_tag(image) | |
| return tags[0],tags_chinese[0], 'none' | |
| else: | |
| model = model_tag2text | |
| 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],'none',caption[0] | |
| inputs = [ | |
| gr.inputs.Image(type='pil'), | |
| gr.inputs.Radio(choices=['Recognize Anything Model',"Tag2Text Model"], | |
| type="value", | |
| default="Recognize Anything Model", | |
| label="Select Model" ), | |
| gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional and Currently only Tag2Text is Supported, Enter with commas)") | |
| ] | |
| outputs = [gr.outputs.Textbox(label="Tags"),gr.outputs.Textbox(label="标签"), gr.outputs.Textbox(label="Caption (currently only Tag2Text is supported)")] | |
| # title = "Recognize Anything Model" | |
| title = "<font size='10'> Recognize Anything Model</font>" | |
| description = "Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo! <li><b>Recognize Anything Model:</b> Upload your image to get the <b>English and Chinese outputs of the image tags</b>!</li><li><b>Tag2Text Model:</b> 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.</li> " | |
| article = "<p style='text-align: center'>RAM and Tag2Text is training on open-source datasets, and we are persisting in refining and iterating upon it.<br/><a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything: A Strong Image Tagging Model</a> | <a href='https://https://tag2text.github.io/' 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/demo1.jpg',"Recognize Anything Model","none"], | |
| ['images/demo2.jpg',"Recognize Anything Model","none"], | |
| ['images/demo4.jpg',"Recognize Anything Model","none"], | |
| ['images/demo4.jpg',"Tag2Text Model","power line"], | |
| ['images/demo4.jpg',"Tag2Text Model","track, train"] , | |
| ]) | |
| demo.launch(enable_queue=True) |