Comments (1)
Thank you for your interest in our work! It seems like your code would backprop through the normalization, which it should not. Your code is subject to a division by zero if the image is a solid color. Your code shows the inverse of the normalization but that is not necessary, one can simply apply the adaptive kernels to the non-normalized inputs. The code we use is roughly as follows.
tenSeq = [first_frame, second_frame]
with torch.set_grad_enabled(False):
tenStack = torch.stack(tenSeq, 1)
tenMean = tenStack.view(tenStack.shape[0], -1).mean(1, True).view(tenStack.shape[0], 1, 1, 1)
tenStd = tenStack.view(tenStack.shape[0], -1).std(1, True).view(tenStack.shape[0], 1, 1, 1)
tenSeq = [(tenFrame - tenMean) / (tenStd + 0.0000001) for tenFrame in tenSeq]
tenSeq = [tenFrame.detach() for tenFrame in tenSeq]
# end
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Related Issues (20)
- see_you_again_540.mp4 HOT 1
- vaule of interpolation HOT 1
- Make it work with pytorch installations from wheel packages HOT 1
- YUV HOT 1
- resolution HOT 1
- test HOT 1
- nn.Upsampling is deprecated. Use nn.functional.interpolate instead. HOT 1
- Flow calculation HOT 3
- Perceptual loss HOT 42
- some question about softmax-splatting HOT 1
- some question about code HOT 1
- Need your help to analyze the reason HOT 8
- Assertion Error in Video Frame interpolation HOT 1
- How to train a model HOT 1
- a question about the loss function LF HOT 1
- load_state_dict_from_url() got an unexpected keyword argument 'file_name' HOT 1
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- AttributeError: 'Tensor' object has no attribute 'clip' HOT 2
- Checkpoint Downloading HOT 8
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