def conv_backward_im2col(dout, cache):
"""
A fast implementation of the backward pass for a convolutional layer
based on im2col and col2im.
"""
x, w, b, conv_param, x_cols = cache
stride, pad = conv_param["stride"], conv_param["pad"]
db = np.sum(dout, axis=(0, 2, 3))
num_filters, _, filter_height, filter_width = w.shape
dout_reshaped = dout.transpose(1, 2, 3, 0).reshape(num_filters, -1)
dw = dout_reshaped.dot(x_cols.T).reshape(w.shape)
dx_cols = w.reshape(num_filters, -1).T.dot(dout_reshaped)
# dx = col2im_indices(dx_cols, x.shape, filter_height, filter_width, pad, stride)
dx = col2im_cython(
dx_cols,
x.shape[0],
x.shape[1],
x.shape[2],
x.shape[3],
filter_height,
filter_width,
pad,
stride,
)
return dx, dw, db
def conv_backward_im2col(dout, cache):
"""
A fast implementation of the backward pass for a convolutional layer
based on im2col and col2im.
"""
x, w, conv_param, x_cols = cache
stride, pad = conv_param['stride'], conv_param['pad']
num_filters, _, filter_height, filter_width = w.shape
dout_reshaped = dout.transpose(1, 2, 3, 0).reshape(num_filters, -1)
dw = dout_reshaped.dot(x_cols.T).reshape(w.shape)
dx_cols = w.reshape(num_filters, -1).T.dot(dout_reshaped)
# dx = col2im_indices(dx_cols, x.shape, filter_height, filter_width, pad, stride)
dx = col2im_cython(dx_cols, x.shape[0], x.shape[1], x.shape[2], x.shape[3],
filter_height, filter_width, pad, stride)
return dx, dw
