trw.transforms.normalize¶
Module Contents¶
Functions¶
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Normalize a tensor image with mean and standard deviation. |
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Normalize a tensor image with mean and standard deviation. |
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Normalize a tensor image with mean and standard deviation. |
- trw.transforms.normalize.normalize_numpy(array, mean, std)¶
Normalize a tensor image with mean and standard deviation.
Given mean: (M1,…,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input torch.Tensor, input[channel] = (input[channel] - mean[channel]) / std[channel]
- Parameters
array – the numpy array to normalize. Expected layout is (sample, filter, d0, … dN)
mean – a N-dimensional sequence
std – a N-dimensional sequence
- Returns
A normalized tensor such that the mean is 0 and std is 1
- trw.transforms.normalize.normalize_torch(array, mean, std)¶
Normalize a tensor image with mean and standard deviation.
Given mean: (M1,…,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input torch.Tensor, input[channel] = (input[channel] - mean[channel]) / std[channel]
- Parameters
array – the torch array to normalize. Expected layout is (sample, filter, d0, … dN)
mean – a N-dimensional sequence
std – a N-dimensional sequence
- Returns
A normalized tensor such that the mean is 0 and std is 1
- trw.transforms.normalize.normalize(array, mean, std)¶
Normalize a tensor image with mean and standard deviation.
Given mean: (M1,…,Mn) and std: (S1,..,Sn) for n channels, this transform will normalize each channel of the input torch.Tensor, input[channel] = (input[channel] - mean[channel]) / std[channel]
- Parameters
array – the torch array to normalize. Expected layout is (sample, filter, d0, … dN)
mean – a N-dimensional sequence
std – a N-dimensional sequence
- Returns
A normalized tensor such that the mean is 0 and std is 1