Deep-Learning-with-PyTorch

350 CHAPTER 12 Improving training with metrics and augmentation
ROTATING
Rotation is the first augmentation technique we’re going to use where we have to carefully
consider our data to ensure that we don’t break our sample with a conversion that
causes it to no longer be representative. Recall that our CT slices have uniform spacing
along the rows and columns (X- and Y-axes), but in the index (or Z) direction, the voxels
are non-cubic. That means we can’t treat those axes as interchangeable.
One option is to resample our data so that our resolution along the index-axis is
the same as along the other two, but that’s not a true solution because the data along
that axis would be very blurry and smeared. Even if we interpolate more voxels, the
fidelity of the data would remain poor. Instead, we’ll treat that axis as special and confine
our rotations to the X-Y plane.
Listing 12.16
dsets.py:181, def getCtAugmentedCandidate
if 'rotate' in augmentation_dict:
angle_rad = random.random() * math.pi * 2
s = math.sin(angle_rad)
c = math.cos(angle_rad)
rotation_t = torch.tensor([
[c, -s, 0, 0],
[s, c, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
])
transform_t @= rotation_t
NOISE
Our final augmentation technique is different from the others in that it is actively
destructive to our sample in a way that flipping or rotating the sample is not. If we add
too much noise to the sample, it will swamp the real data and make it effectively
impossible to classify. While shifting and scaling the sample would do something similar
if we used extreme input values, we’ve chosen values that will only impact the edge
of the sample. Noise will have an impact on the entire image.
Listing 12.17
dsets.py:208, def getCtAugmentedCandidate
if 'noise' in augmentation_dict:
noise_t = torch.randn_like(augmented_chunk)
noise_t *= augmentation_dict['noise']
augmented_chunk += noise_t
The other augmentation types have increased the effective size of our dataset. Noise
makes our model’s job harder. We’ll revisit this once we see some training results.