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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(hidden_size=64, num_channel=1, latent_size=128, batch_size=128) |
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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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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="DocuGAN", |
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description="Select random seed and click on Submit to generate a new Document", |
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article="DocuGAN, Document Generator by ChainYo", |
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css=".panel { padding: 5px } .moflo-link { color: #999 }" |
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).launch() |
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