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

For each individual image, it may be either the first dimension (before HW) or the
last dimension (after HW).
There are endless discussions about which format is better, faster, or whatever.
We’re not getting into that here. Nonetheless, we need to address this difference
because it is a common source of confusion and error since each package or
framework uses a different one:
• PyTorch uses NCHW.
• TensorFlow uses NHWC.
• PIL images are HWC.
Annoying, right? I think so, too. But it is only a matter of paying close attention to
which format is coming in, and which format is going out after an operation. Let’s
work our way through it.
Our dataset generates images following the PyTorch format; that is, NCHW.
What’s the shape of our dataset, then?
images.shape
Output
(300, 1, 5, 5)
As expected, 300 images, single-channel, five pixels wide, five pixels high. Let’s take
a closer look at one image, say, image #7:
example = images[7]
example
Output
array([[[ 0, 255, 0, 0, 0],
[ 0, 0, 255, 0, 0],
[ 0, 0, 0, 255, 0],
[ 0, 0, 0, 0, 255],
[ 0, 0, 0, 0, 0]]], dtype=uint8)
272 | Chapter 4: Classifying Images