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- 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 = int((H + 2 * padding - field_height) / stride + 1)
- out_width = int((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.astype(int), i.astype(int), j.astype(int))
- 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]
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