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| import pathlib | |
| import gradio as gr | |
| import open_clip | |
| import torch | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model, _, transform = open_clip.create_model_and_transforms( | |
| "coca_ViT-L-14", | |
| pretrained="mscoco_finetuned_laion2B-s13B-b90k" | |
| ) | |
| model.to(device) | |
| title="""<h1 align="center">CoCa: Contrastive Captioners</h1>""" | |
| description=( | |
| """<br> An open source implementation of <strong>CoCa: Contrastive Captioners are Image-Text Foundation Models</strong> <a href=https://arxiv.org/abs/2205.01917>https://arxiv.org/abs/2205.01917.</a> | |
| <br> Built using <a href=https://github.com/mlfoundations/open_clip>open_clip</a> with an effort from <a href=https://laion.ai/>LAION</a>. | |
| <br> For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.<a href="https://huggingface.co/spaces/laion/CoCa?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>""" | |
| ) | |
| def output_generate(image): | |
| im = transform(image).unsqueeze(0).to(device) | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| generated = model.generate(im, seq_len=20) | |
| return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "") | |
| def inference_caption(image, decoding_method="Beam search", rep_penalty=1.2, top_p=0.5, min_seq_len=5, seq_len=20): | |
| im = transform(image).unsqueeze(0).to(device) | |
| generation_type = "beam_search" if decoding_method == "Beam search" else "top_p" | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| generated = model.generate( | |
| im, | |
| generation_type=generation_type, | |
| top_p=float(top_p), | |
| min_seq_len=min_seq_len, | |
| seq_len=seq_len, | |
| repetition_penalty=float(rep_penalty) | |
| ) | |
| return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "") | |
| paths = sorted(pathlib.Path("images").glob("*.jpg")) | |
| with gr.Blocks() as iface: | |
| state = gr.State([]) | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| image_input = gr.Image(type="pil") | |
| # with gr.Row(): | |
| sampling = gr.Radio( | |
| choices=["Beam search", "Nucleus sampling"], | |
| value="Beam search", | |
| label="Text Decoding Method", | |
| interactive=True, | |
| ) | |
| rep_penalty = gr.Slider( | |
| minimum=1.0, | |
| maximum=5.0, | |
| value=1.0, | |
| step=0.5, | |
| interactive=True, | |
| label="Repeat Penalty (larger value prevents repetition)", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.5, | |
| step=0.1, | |
| interactive=True, | |
| label="Top p (used with nucleus sampling)", | |
| ) | |
| min_seq_len = gr.Number( | |
| value=5, label="Minimum Sequence Length", precision=0, interactive=True | |
| ) | |
| seq_len = gr.Number( | |
| value=20, label="Maximum Sequence Length (has to higher than Minimum)", precision=0, interactive=True | |
| ) | |
| with gr.Column(scale=1): | |
| with gr.Column(): | |
| caption_output = gr.Textbox(lines=1, label="Caption Output") | |
| caption_button = gr.Button( | |
| value="Caption it!", interactive=True, variant="primary" | |
| ) | |
| caption_button.click( | |
| inference_caption, | |
| [ | |
| image_input, | |
| sampling, | |
| rep_penalty, | |
| top_p, | |
| min_seq_len, | |
| seq_len | |
| ], | |
| [caption_output], | |
| ) | |
| examples = gr.Examples( | |
| examples=[path.as_posix() for path in paths], | |
| inputs=[image_input], | |
| ) | |
| iface.launch() | |