How can I implement deconvolution layer for a CNN in numpy?

As discussed in this question, a deconvolution is just a convolutional layer, but with a particular choice of padding, stride and filter size.

For example, if your current image size is 55x55, you can apply a convolution with padding=20, stride=1 and filter=[21x21] to obtain a 75x75 image, then 95x95 and so on. (I'm not saying this choice of numbers gives the desired quality of the output image, just the size. Actually, I think downsampling from 227x227 to 55x55 and then upsampling back to 227x227 is too aggressive, but you are free to try any architecture).

Here's the implementation of a forward pass for any stride and padding. It does im2col transformation, but using stride_tricks from numpy. It's not as optimized as modern GPU implementations, but definitely faster than 4 inner loops:

import numpy as npdef conv_forward(x, w, b, stride, pad):  N, C, H, W = x.shape  F, _, HH, WW = w.shape  # Check dimensions  assert (W + 2 * pad - WW) % stride == 0, 'width does not work'  assert (H + 2 * pad - HH) % stride == 0, 'height does not work'  # Pad the input  p = pad  x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant')  # Figure out output dimensions  H += 2 * pad  W += 2 * pad  out_h = (H - HH) / stride + 1  out_w = (W - WW) / stride + 1  # Perform an im2col operation by picking clever strides  shape = (C, HH, WW, N, out_h, out_w)  strides = (H * W, W, 1, C * H * W, stride * W, stride)  strides = x.itemsize * np.array(strides)  x_stride = np.lib.stride_tricks.as_strided(x_padded,                                             shape=shape, strides=strides)  x_cols = np.ascontiguousarray(x_stride)  x_cols.shape = (C * HH * WW, N * out_h * out_w)  # Now all our convolutions are a big matrix multiply  res = w.reshape(F, -1).dot(x_cols) + b.reshape(-1, 1)  # Reshape the output  res.shape = (F, N, out_h, out_w)  out = res.transpose(1, 0, 2, 3)  out = np.ascontiguousarray(out)  return out