Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

StepByStep Method
@staticmethod
def make_normalizer(loader):
total_samples, total_means, total_stds = \
StepByStep.loader_apply(
loader,
StepByStep.statistics_per_channel
)
norm_mean = total_means / total_samples
norm_std = total_stds / total_samples
return Normalize(mean=norm_mean, std=norm_std)
setattr(StepByStep, 'make_normalizer', make_normalizer)
IMPORTANT: Always use the training set to compute statistics
for standardization!
Now, we can use this method to create a transformation that standardizes our
dataset:
Creating Normalizer Transform
1 normalizer = StepByStep.make_normalizer(temp_loader)
2 normalizer
Output
Normalize(mean=tensor([0.8502, 0.8215, 0.8116]),
std=tensor([0.2089, 0.2512, 0.2659]))
Remember that PyTorch converts the pixel values into the [0, 1] range. The
average mean value of a pixel for the red (first) channel is 0.8502, while its average
standard deviation is 0.2089.
In the next chapter, we’ll use pre-computed statistics to
standardize the inputs when using a pre-trained model.
Data Preparation | 423