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| import torch | |
| from ram import get_transform, inference_ram, inference_tag2text | |
| from ram.models import ram, tag2text | |
| ram_checkpoint = "./ram_swin_large_14m.pth" | |
| tag2text_checkpoint = "./tag2text_swin_14m.pth" | |
| image_size = 384 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def inference(raw_image, specified_tags, tagging_model_type, tagging_model, transform): | |
| print(f"Start processing, image size {raw_image.size}") | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| if tagging_model_type == "RAM": | |
| res = inference_ram(image, tagging_model) | |
| tags = res[0].strip(' ').replace(' ', ' ') | |
| tags_chinese = res[1].strip(' ').replace(' ', ' ') | |
| print("Tags: ", tags) | |
| print("标签: ", tags_chinese) | |
| return tags, tags_chinese | |
| else: | |
| res = inference_tag2text(image, tagging_model, specified_tags) | |
| tags = res[0].strip(' ').replace(' ', ' ') | |
| caption = res[2] | |
| print(f"Tags: {tags}") | |
| print(f"Caption: {caption}") | |
| return tags, caption | |
| def inference_with_ram(img): | |
| return inference(img, None, "RAM", ram_model, transform) | |
| def inference_with_t2t(img, input_tags): | |
| return inference(img, input_tags, "Tag2Text", tag2text_model, transform) | |
| if __name__ == "__main__": | |
| import gradio as gr | |
| # get transform and load models | |
| transform = get_transform(image_size=image_size) | |
| ram_model = ram(pretrained=ram_checkpoint, image_size=image_size, vit='swin_l').eval().to(device) | |
| tag2text_model = tag2text( | |
| pretrained=tag2text_checkpoint, image_size=image_size, vit='swin_b').eval().to(device) | |
| # build GUI | |
| def build_gui(): | |
| description = """ | |
| <center><strong><font size='10'>Recognize Anything Model</font></strong></center> | |
| <br> | |
| <p>Welcome to the <a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything Model</a> / <a href='https://tag2text.github.io/Tag2Text' target='_blank'>Tag2Text Model</a> demo!</p> | |
| <li> | |
| <b>Recognize Anything Model:</b> Upload your image to get the <b>English and Chinese tags</b>! | |
| </li> | |
| <li> | |
| <b>Tag2Text Model:</b> Upload your image to get the <b>tags and caption</b>! (Optional: Specify tags to get the corresponding caption.) | |
| </li> | |
| <p><b>More over:</b> Combine with <a href='https://github.com/IDEA-Research/Grounded-Segment-Anything' target='_blank'>Grounded-SAM</a>, you can get <b>boxes and masks</b>! Please run <a href='https://github.com/xinyu1205/recognize-anything/blob/main/gui_demo.ipynb' target='_blank'>this notebook</a> to try out!</p> | |
| <p>Great thanks to <a href='https://huggingface.co/majinyu' target='_blank'>Ma Jinyu</a>, the major contributor of this demo!</p> | |
| """ # noqa | |
| article = """ | |
| <p style='text-align: center'> | |
| RAM and Tag2Text are trained 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://tag2text.github.io/' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | |
| </p> | |
| """ # noqa | |
| 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") | |
| try: | |
| ram_btn_clear = gr.ClearButton() | |
| except AttributeError: # old gradio does not have ClearButton, not big problem | |
| ram_btn_clear = None | |
| 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") | |
| try: | |
| t2t_btn_clear = gr.ClearButton() | |
| except AttributeError: # old gradio does not have ClearButton, not big problem | |
| t2t_btn_clear = None | |
| 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] | |
| ) | |
| # clear | |
| if ram_btn_clear is not None: | |
| ram_btn_clear.add([ram_in_img, ram_out_tag, ram_out_biaoqian]) | |
| if t2t_btn_clear is not None: | |
| t2t_btn_clear.add([t2t_in_img, t2t_in_tag, t2t_out_tag, t2t_out_cap]) | |
| return demo | |
| build_gui().launch(enable_queue=True) | |