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import gradio as gr |
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import torch |
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import torchvision.transforms as T |
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from model import DocuGAN |
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chk_path = "best_model.ckpt" |
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model = DocuGAN.load_from_checkpoint(chk_path, strict=False) |
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model.eval() |
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transform = T.ToPILImage() |
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def fn(seed: int = 42): |
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torch.manual_seed(seed) |
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noise = torch.randn(1, 128, 1, 1) |
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with torch.no_grad(): |
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pred = model(noise) |
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pred = pred.mul(0.5).add(0.5) |
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img = transform(pred.squeeze(1)) |
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return img |
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gr.Interface( |
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fn, |
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inputs=[ |
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gr.inputs.Slider(minimum=0, maximum=999999999, step=1, default=298422436, label='Random Seed') |
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], |
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outputs='image', |
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examples=[], |
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enable_queue=True, |
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title="π DocuGAN - This document doesn't exist", |
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description="Select your seed and click on `Submit` to generate a new document", |
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article="<p>The SN-GAN model has been trained on the `invoice` part of RVL-CDIP dataset, available <a href='https://huggingface.co/datasets/ChainYo/rvl-cdip-invoice' target='_blank'>here</a>.<br> You can see the full implementation on the dedicated <a href='https://colab.research.google.com/drive/1u6Ct3KnNl7rcgla0268cp-XGTMmVUuJL?usp=sharing' target='_blank'>Colab notebook</a>. <br> Made with β€οΈ by <a href='https://huggingface.co/ChainYo' target='_blank'>@ChainYo</a></p>", |
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css=".panel { padding: 5px } .moflo-link { color: #999 }" |
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).launch() |
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