实验环境:tensorflow版本1.2.0,python2.7
depthwise_conv2d来源于深度可分离卷积:
Xception: Deep Learning with Depthwise Separable Convolutions
tf.nn.depthwise_conv2d(input,filter,strides,padding,rate=None,name=None,data_format=None)
除去name参数用以指定该操作的name,data_format指定数据格式,与方法有关的一共五个参数:
结果返回一个Tensor,shape为[batch, out_height, out_width, in_channels * channel_multiplier],注意这里输出通道变成了in_channels * channel_multiplier
为了形象的展示depthwise_conv2d,我们必须要建立自定义的输入图像和卷积核
img1 = tf.constant(value=[[[[1],[2],[3],[4]],[[1],[2],[3],[4]],[[1],[2],[3],[4]],[[1],[2],[3],[4]]]],dtype=tf.float32)
img2 = tf.constant(value=[[[[1],[1],[1],[1]],[[1],[1],[1],[1]],[[1],[1],[1],[1]],[[1],[1],[1],[1]]]],dtype=tf.float32)
img = tf.concat(values=[img1,img2],axis=3)
filter1 = tf.constant(value=0, shape=[3,3,1,1],dtype=tf.float32)
filter2 = tf.constant(value=1, shape=[3,3,1,1],dtype=tf.float32)
filter3 = tf.constant(value=2, shape=[3,3,1,1],dtype=tf.float32)
filter4 = tf.constant(value=3, shape=[3,3,1,1],dtype=tf.float32)
filter_out1 = tf.concat(values=[filter1,filter2],axis=2)
filter_out2 = tf.concat(values=[filter3,filter4],axis=2)
filter = tf.concat(values=[filter_out1,filter_out2],axis=3)
建立好了img和filter,就可以做卷积了
out_img = tf.nn.conv2d(input=img, filter=filter, strides=[1,1,1,1], padding='VALID')
好了,用一张图来详细展示这个过程
这是普通的卷积过程,我们再来看深度卷积。
out_img = tf.nn.depthwise_conv2d(input=img, filter=filter, strides=[1,1,1,1], rate=[1,1], padding='VALID')
现在我们可以形象的解释一下depthwise_conv2d卷积了。看普通的卷积,我们对卷积核每一个out_channel的两个通道分别和输入的两个通道做卷积相加,得到feature map的一个channel,而depthwise_conv2d卷积,我们对每一个对应的in_channel,分别卷积生成两个out_channel,所以获得的feature map的通道数量可以用in_channel* channel_multiplier来表达,这个channel_multiplier,就可以理解为卷积核的第四维。
import tensorflow as tf
img1 = tf.constant(value=[[[[1],[2],[3],[4]],[[1],[2],[3],[4]],[[1],[2],[3],[4]],[[1],[2],[3],[4]]]],dtype=tf.float32)
img2 = tf.constant(value=[[[[1],[1],[1],[1]],[[1],[1],[1],[1]],[[1],[1],[1],[1]],[[1],[1],[1],[1]]]],dtype=tf.float32)
img = tf.concat(values=[img1,img2],axis=3)
filter1 = tf.constant(value=0, shape=[3,3,1,1],dtype=tf.float32)
filter2 = tf.constant(value=1, shape=[3,3,1,1],dtype=tf.float32)
filter3 = tf.constant(value=2, shape=[3,3,1,1],dtype=tf.float32)
filter4 = tf.constant(value=3, shape=[3,3,1,1],dtype=tf.float32)
filter_out1 = tf.concat(values=[filter1,filter2],axis=2)
filter_out2 = tf.concat(values=[filter3,filter4],axis=2)
filter = tf.concat(values=[filter_out1,filter_out2],axis=3)
out_img = tf.nn.depthwise_conv2d(input=img, filter=filter, strides=[1,1,1,1], rate=[1,1], padding='VALID')
输出:
rate=1, VALID mode result:
[[[[ 0. 36. 9. 27.]
[ 0. 54. 9. 27.]]
[[ 0. 36. 9. 27.]
[ 0. 54. 9. 27.]]]]
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作者:xf__mao
来源:https://blog.csdn.net/mao_xiao_feng/article/details/78003476
版权声明:本文为博主xf__mao原创文章,转载请附上博文链接!