text: import numpy as np
  
  
  def get_im2col_indices(x_shape, field_height, field_width, padding=1, stride=1):
      # First figure out what the size of the output should be
      N, C, H, W = x_shape
      assert (H + 2 * padding - field_height) % stride == 0
      assert (W + 2 * padding - field_height) % stride == 0
      out_height = (H + 2 * padding - field_height) / stride + 1
      out_width = (W + 2 * padding - field_width) / stride + 1
  
      i0 = np.repeat(np.arange(field_height), field_width)
      i0 = np.tile(i0, C)
      i1 = stride * np.repeat(np.arange(out_height), out_width)
      j0 = np.tile(np.arange(field_width), field_height * C)
      j1 = stride * np.tile(np.arange(out_width), out_height)
      i = i0.reshape(-1, 1) + i1.reshape(1, -1)
      j = j0.reshape(-1, 1) + j1.reshape(1, -1)
  
      k = np.repeat(np.arange(C), field_height * field_width).reshape(-1, 1)
  
      return (k, i, j)
  
  
  def im2col_indices(x, field_height, field_width, padding=1, stride=1):
      """ An implementation of im2col based on some fancy indexing """
      # Zero-pad the input
      p = padding
      x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant')
  
      k, i, j = get_im2col_indices(x.shape, field_height, field_width, padding,
                                   stride)
  
      cols = x_padded[:, k, i, j]
      C = x.shape[1]
      cols = cols.transpose(1, 2, 0).reshape(field_height * field_width * C, -1)
      return cols
  
  
  def col2im_indices(cols, x_shape, field_height=3, field_width=3, padding=1,
                     stride=1):
      """ An implementation of col2im based on fancy indexing and np.add.at """
      N, C, H, W = x_shape
      H_padded, W_padded = H + 2 * padding, W + 2 * padding
      x_padded = np.zeros((N, C, H_padded, W_padded), dtype=cols.dtype)
      k, i, j = get_im2col_indices(x_shape, field_height, field_width, padding,
                                   stride)
      cols_reshaped = cols.reshape(C * field_height * field_width, -1, N)
      cols_reshaped = cols_reshaped.transpose(2, 0, 1)
      np.add.at(x_padded, (slice(None), k, i, j), cols_reshaped)
      if padding == 0:
          return x_padded
      return x_padded[:, :, padding:-padding, padding:-padding]
  
  
  pass
  