CNN: Why is the image matrix transformed to (channel, width, height)?

Question

I am going through some CNN articles. I see that they transform the input image to (channel, width, height).

A code example taken from MXNET CNN Tutorial.

def transform(data, label):
  # 2,0,1 means channels,width, height
  return nd.transpose(data.astype(np.float32), (2,0,1))/255, label.astype(np.float32)

Can any one explain why do we do this transformation?

Answer 1

There are several image formats for 2-dimensional convolution, the main ones are:

• Channel-first or NCHW format, i.e., (batch, channels, height, width).
• Channel-last or NHWC format, i.e., (batch, height, width, channels).

They are basically equivalent and can be easily converted from one to another, though there is evidence that certain low-level implementations perform more efficiently when a particular data format is used (see this question).

Computational engines usually accept both formats, but have different defaults, e.g.,

• Tensorflow accepts both and uses NHWC by default.
• Theano accepts only NCHW format.
• Keras also works with both and has a dedicated setting for it. The latest version also uses NHWC by default.

• MXNet accepts both formats too, but the default is NCHW:

  The default data layout is NCHW, namely (batch_size, channel, height, width). We can choose other layouts such as NHWC.

This default is pretty much the only reason to reshape the tensors, simply to avoid layout argument in the network.