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README.md
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sdk: gradio
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app_file:
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sdk: gradio
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app_file: app.py
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pinned: false
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app.py
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from fastai.vision.all import *
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from fastai.basics import *
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from upit.models.cyclegan import *
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from upit.train.cyclegan import *
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from upit.data.unpaired import *
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import torchvision
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import gradio as gr
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dls = get_dls_from_hf("huggan/horse2zebra", load_size=286)
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cycle_gan = CycleGAN.from_pretrained('tmabraham/horse2zebra_cyclegan')
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totensor = torchvision.transforms.ToTensor()
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normalize_fn = torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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topilimage = torchvision.transforms.ToPILImage()
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model = cycle_gan.G_B.cpu().eval()
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def predict(input):
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im = normalize_fn(totensor(input))
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print(im.shape)
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preds = model(im.unsqueeze(0))/2 + 0.5
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print(preds.shape)
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return topilimage(preds.squeeze(0).detach())
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gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), outputs="image", title='Horse-to-Zebra CycleGAN with UPIT', description='[This](https://huggingface.co/tmabraham/horse2zebra_cyclegan) CycleGAN model trained on [this dataset](https://huggingface.co/datasets/huggan/horse2zebra), using the [UPIT package](https://github.com/tmabraham/UPIT)', examples=['horse.jpg'])
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gr_interface.launch(inline=False,share=False)
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horse.jpg
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requirements.txt
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torchvision
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upit
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gradio
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torchvision
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