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| def convert_conv_to_fc(input, filter_height=3, filter_width=3, stride=1, padding=0):
input_padded = np.pad(input, ((0, 0), (0, 0), (padding, padding), (padding, padding)),
'constant', constant_values=(0, 0))
# [N, C, H, W]
num, depth, height, width = input_padded.shape[:4]
res = []
for k in range(num):
i = 0
while i < height:
j = 0
while j < width:
arr = input_padded[k, :, i:i + filter_height, j:j + filter_width]
res.append(arr.flatten())
j += stride
if (j + filter_width) > width:
break
i += stride
if (i + filter_height) > height:
break
return np.array(res)
def deconvert_fc_to_conv(input, output_shape, filter_height=3, filter_width=3, stride=2, padding=0, isstinct=False):
output = np.zeros(output_shape)
output_padded = np.pad(output, ((0, 0), (0, 0), (padding, padding), (padding, padding)),
'constant', constant_values=(0, 0))
# [N, C, H, W]
num, depth, height, width = output_padded.shape[:4]
number = 0
for k in range(num):
i = 0
while i < height:
j = 0
while j < width:
if isstinct:
output_padded[k, :, i:i + filter_height, j:j + filter_width] = \
input[number].reshape(depth, filter_height, filter_width)
else:
output_padded[k, :, i:i + filter_height, j:j + filter_width] += \
input[number].reshape(depth, filter_height, filter_width)
j += stride
number += 1
if (j + filter_width) > width:
break
i += stride
if (i + filter_height) > height:
break
if padding == 0:
return output_padded
return output_padded[:, :, padding:-padding, padding:-padding]
|