Row 14281

Row ID: 14281 | Dataset Entry | Axioma AXP Content Repository

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I think we might be misunderstanding each other. I didn't say that upsampling was a *requirement* of this process... I meant that since you are doing it during training you also have to do it during inference.

Here's a simplified version of what my inference code looks like:

def upscale_image(model, image_path, output_path): model.eval() image = Image.open(image_path).convert('YCbCr') y, cb, cr = image.split() transform = transforms.ToTensor() input_tensor = transform(y).unsqueeze(0).unsqueeze(0).float() with torch.no_grad(): output_tensor = model(input_tensor) output_image_y = output_tensor.squeeze().numpy() output_image_y = np.clip(output_image_y, 0, 1) output_image_y = (output_image_y * 255.0).astype(np.uint8) output_image = Image.merge('YCbCr', [ Image.fromarray(output_image_y), cb.resize(output_image_y.shape, Image.BICUBIC), cr.resize(output_image_y.shape, Image.BICUBIC) ]).convert('RGB') output_image.save(output_path) # Usage upscale_image(model, 'data/low_res/some_image.png', 'data/upscaled_image.png')

FieldValue
text I think we might be misunderstanding each other. I didn't say that upsampling was a *requirement* of this process... I meant that since you are doing it during training you also have to do it during inference. Here's a simplified version of what my inference code looks like: def upscale_image(model, image_path, output_path): model.eval() image = Image.open(image_path).convert('YCbCr') y, cb, cr = image.split() transform = transforms.ToTensor() i…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-20
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url_encoded Z0FBQUFBQm5Lak9MdVVDREdIQTdrNjFiY3h3UTNnN19HcnhqS3hqNlMxNEFFSi1DZmdCWFBzVW1IRC14U3A5RDdkWmZCM05HWjdydzEtS0RYa0N1Z0oxMVhXOXpxbDFnMEhfQWhEX2hFZ3M0VkJGdGZkTFh1YXpEY0dEQ2FXSTY4ZnBhbzNiaXBoeWVPMHdHNlA5WV92RnBYdUFSR2dOYndNT1g4MnY0M0lJVzhxa1dJTnMtOEZjNXNpNG5UVk5SdXNZbjVadGpKckNz

Raw Record

{
  "text": "I think we might be misunderstanding each other. I didn't say that upsampling was a *requirement* of this process... I meant that since you are doing it during training you also have to do it during inference.\n\nHere's a simplified version of what my inference code looks like:\n\n    def upscale_image(model, image_path, output_path):\n        model.eval()\n        image = Image.open(image_path).convert('YCbCr')\n        y, cb, cr = image.split()\n    \n        transform = transforms.ToTensor()\n        input_tensor = transform(y).unsqueeze(0).unsqueeze(0).float()\n    \n        with torch.no_grad():\n            output_tensor = model(input_tensor)\n        \n        output_image_y = output_tensor.squeeze().numpy()\n        output_image_y = np.clip(output_image_y, 0, 1)\n        output_image_y = (output_image_y * 255.0).astype(np.uint8)\n    \n        output_image = Image.merge('YCbCr', [\n            Image.fromarray(output_image_y),\n            cb.resize(output_image_y.shape, Image.BICUBIC),\n            cr.resize(output_image_y.shape, Image.BICUBIC)\n        ]).convert('RGB')\n        \n        output_image.save(output_path)\n    \n    # Usage\n    upscale_image(model, 'data/low_res/some_image.png', 'data/upscaled_image.png')",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw4b1hGZzdpZmtDREExWUNrd1VUdjM1dW1iZWE0UXhKaUo2eXJJTFZtV2FEZkxsQjdXbkFhMHNaLWMtRmV6d1ZLTXVDQTQ5LTBuakthTlo3QklrOEJZcWc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9MdVVDREdIQTdrNjFiY3h3UTNnN19HcnhqS3hqNlMxNEFFSi1DZmdCWFBzVW1IRC14U3A5RDdkWmZCM05HWjdydzEtS0RYa0N1Z0oxMVhXOXpxbDFnMEhfQWhEX2hFZ3M0VkJGdGZkTFh1YXpEY0dEQ2FXSTY4ZnBhbzNiaXBoeWVPMHdHNlA5WV92RnBYdUFSR2dOYndNT1g4MnY0M0lJVzhxa1dJTnMtOEZjNXNpNG5UVk5SdXNZbjVadGpKckNz"
}
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