The code below receives a variable called tensor and outputs the tensor to which nothing is applied.
class Normalize(object):
"""Normalize an tensor image with mean and standard deviation.
Given mean: (R, G, B) and std: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = (channel - mean) / std
Args:
mean (sequence): Sequence of means for R, G, B channels respecitvely.
std (sequence): Sequence of standard deviations for R, G, B channels
respecitvely.
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
# TODO: make efficient
for t, m, s in zip(tensor, self.mean, self.std):
t.sub_(m).div_(s)
return tensor
def randomize_parameters(self):
pass
The code below receives a variable called tensor and outputs the tensor to which nothing is applied.