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="Model" ), gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional, Enter with commas, Currently only Tag2Text is supported)") ] 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 = " Recognize Anything Model" description = "Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo!
  • Recognize Anything Model: Upload your image to get the English and Chinese outputs of the image tags!
  • Tag2Text Model: Upload your image to get the tags and caption of the image. Optional: You can also input specified tags to get the corresponding caption.
  • " article = "

    RAM and Tag2Text is training on open-source datasets, and we are persisting in refining and iterating upon it.
    Recognize Anything: A Strong Image Tagging Model | Tag2Text: Guiding Language-Image Model via Image Tagging | Github Repo

    " 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)