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import torch
from tqdm import tqdm
def get_mean_and_std(dataset):
'''Compute the mean and std value of dataset.'''
'''dataset: 0-1 range (ToTensor())'''
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
mean = torch.zeros(3)
std = torch.zeros(3)
print('==> Computing mean and std..')
for inputs, targets in tqdm(dataloader):
for i in range(3):
mean[i] += inputs[:, i, :, :].mean()
std[i] += inputs[:, i, :, :].std()
mean.div_(len(dataset))
std.div_(len(dataset))
return mean, std
class UnNormalize(object):
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.
"""
for t, m, s in zip(tensor, self.mean, self.std):
t.mul_(s).add_(m)
# The normalize code -> t.sub_(m).div_(s)
return tensor
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