im2col解析4

之前实现了一个图像和行向量相互转换的函数,逐图像进行局部连接矩阵的转换

其实现原理较下标计算更易理解,通过循环,逐个图像对局部连接矩阵进行切片操作,得到矩阵后拉平为向量,以行向量方式进行保存

反向转换图像可以设置标志位isstinct,是否返回叠加图像还是原图,其实现原理是在指定位置赋值过程中是执行累加还是执行覆盖

实现代码

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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]

时间测试

大小(128x3x32x32) 卷积核(3x3) 步长(1) 零填充(0)大小(128x3x227x227) 卷积核(3x3) 步长(1) 零填充(0)
im2row 图像转行向量0.0870.941
自定义 图像转行向量0.2573.475
im2row 行向量转图像0.2383.519
自定义 行向量转图像0.4117.413

经过测试发现,下标计算方式快过循环计算方式,并且图像转行向量操作比行向量转图像操作快

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