app.py

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
        with torch.no_grad():
            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]


def build_gui():

    description = """
        <center><strong><font size='10'>Recognize Anything Model</font></strong></center>
        <br>
        Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo! <br><br>
        <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>
    """  # noqa

    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>
    """  # noqa

    def inference_with_ram(img):
        res = inference(img, "Recognize Anything Model", None)
        return res[0], res[1]

    def inference_with_t2t(img, input_tags):
        res = inference(img, "Tag2Text Model", input_tags)
        return res[0], res[2]

    with gr.Blocks(title="Recognize Anything Model") as demo:
        ###############
        # components
        ###############
        gr.HTML(description)

        with gr.Tab(label="Recognize Anything Model"):
            with gr.Row():
                with gr.Column():
                    ram_in_img = gr.Image(type="pil")
                    with gr.Row():
                        ram_btn_run = gr.Button(value="Run")
                        ram_btn_clear = gr.Button(value="Clear")
                with gr.Column():
                    ram_out_tag = gr.Textbox(label="Tags")
                    ram_out_biaoqian = gr.Textbox(label="标签")
            gr.Examples(
                examples=[
                    ["images/demo1.jpg"],
                    ["images/demo2.jpg"],
                    ["images/demo4.jpg"],
                ],
                fn=inference_with_ram,
                inputs=[ram_in_img],
                outputs=[ram_out_tag, ram_out_biaoqian],
                cache_examples=True
            )

        with gr.Tab(label="Tag2Text Model"):
            with gr.Row():
                with gr.Column():
                    t2t_in_img = gr.Image(type="pil")
                    t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)")
                    with gr.Row():
                        t2t_btn_run = gr.Button(value="Run")
                        t2t_btn_clear = gr.Button(value="Clear")
                with gr.Column():
                    t2t_out_tag = gr.Textbox(label="Tags")
                    t2t_out_cap = gr.Textbox(label="Caption")
            gr.Examples(
                examples=[
                    ["images/demo4.jpg", ""],
                    ["images/demo4.jpg", "power line"],
                    ["images/demo4.jpg", "track, train"],
                ],
                fn=inference_with_t2t,
                inputs=[t2t_in_img, t2t_in_tag],
                outputs=[t2t_out_tag, t2t_out_cap],
                cache_examples=True
            )

        gr.HTML(article)

        ###############
        # events
        ###############
        # run inference
        ram_btn_run.click(
            fn=inference_with_ram,
            inputs=[ram_in_img],
            outputs=[ram_out_tag, ram_out_biaoqian]
        )
        t2t_btn_run.click(
            fn=inference_with_t2t,
            inputs=[t2t_in_img, t2t_in_tag],
            outputs=[t2t_out_tag, t2t_out_cap]
        )

        # # images of two image panels should keep the same
        # # and clear old outputs when image changes
        # # slow due to internet latency when deployed on huggingface, comment out
        # def sync_img(v):
        #     return [gr.update(value=v)] + [gr.update(value="")] * 4

        # ram_in_img.upload(fn=sync_img, inputs=[ram_in_img], outputs=[
        #     t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
        # ])
        # ram_in_img.clear(fn=sync_img, inputs=[ram_in_img], outputs=[
        #     t2t_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
        # ])
        # t2t_in_img.clear(fn=sync_img, inputs=[t2t_in_img], outputs=[
        #     ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
        # ])
        # t2t_in_img.upload(fn=sync_img, inputs=[t2t_in_img], outputs=[
        #     ram_in_img, ram_out_tag, ram_out_biaoqian, t2t_out_tag, t2t_out_cap
        # ])

        # clear all
        def clear_all():
            return [gr.update(value=None)] * 2 + [gr.update(value="")] * 5

        ram_btn_clear.click(fn=clear_all, inputs=[], outputs=[
            ram_in_img, t2t_in_img,
            ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap
        ])
        t2t_btn_clear.click(fn=clear_all, inputs=[], outputs=[
            ram_in_img, t2t_in_img,
            ram_out_tag, ram_out_biaoqian, t2t_in_tag, t2t_out_tag, t2t_out_cap
        ])

    return demo


if __name__ == "__main__":
    demo = build_gui()
    demo.launch(enable_queue=True)