基于tensorflow,使用winograd算法编写一个可以直接插入神经网络使用的优化的深度可分离卷积模块并示例如何使用

以下是基于tensorflow实现的深度可分离卷积模块，使用winograd算法进行优化：

import tensorflow as tf

def depthwise_conv2d(inputs, kernel_size, strides, padding='SAME', name=None):
    with tf.variable_scope(name):
        in_channels = inputs.get_shape().as_list()[-1]
        channel_multiplier = 1
        filter_shape = [kernel_size, kernel_size, in_channels, channel_multiplier]
        strides = [1, strides, strides, 1]
        filter = tf.get_variable('filter', shape=filter_shape, dtype=tf.float32,
                                 initializer=tf.truncated_normal_initializer(stddev=0.1))
        outputs = tf.nn.depthwise_conv2d(inputs, filter, strides, padding=padding)
        return outputs

def pointwise_conv2d(inputs, output_channels, name=None):
    with tf.variable_scope(name):
        in_channels = inputs.get_shape().as_list()[-1]
        filter_shape = [1, 1, in_channels, output_channels]
        filter = tf.get_variable('filter', shape=filter_shape, dtype=tf.float32,
                                 initializer=tf.truncated_normal_initializer(stddev=0.1))
        outputs = tf.nn.conv2d(inputs, filter, [1, 1, 1, 1], padding='SAME')
        return outputs

def winograd_conv2d(inputs, kernel_size, output_channels, strides, padding='SAME', name=None):
    with tf.variable_scope(name):
        batch_size, input_height, input_width, input_channels = inputs.get_shape().as_list()
        filter_shape = [kernel_size, kernel_size, input_channels, output_channels]
        strides = [1, strides, strides, 1]
        filter = tf.get_variable('filter', shape=filter_shape, dtype=tf.float32,
                                 initializer=tf.truncated_normal_initializer(stddev=0.1))
        M = [[1, 0, 0], [-1/6, 1/6, 1/6], [-1/6, -1/6, 1/6]]
        Mt = [[1, -1/6, -1/6], [0, 1/6, -1/6], [0, 1/6, 1/6]]
        G = tf.constant(M, dtype=tf.float32)
        Gt = tf.constant(Mt, dtype=tf.float32)
        inputs = tf.pad(inputs, [[0, 0], [1, 1], [1, 1], [0, 0]], 'CONSTANT')
        inputs_shape = inputs.get_shape().as_list()
        N = inputs_shape[1] - kernel_size + 1
        outputs_shape = [batch_size, N, N, output_channels]
        outputs = tf.zeros(outputs_shape, dtype=tf.float32)
        for i in range(N):
            for j in range(N):
                patch = inputs[:, i:i+kernel_size+2, j:j+kernel_size+2, :]
                patch = tf.reshape(patch, [batch_size, kernel_size+2, input_channels])
                U = tf.matmul(patch, G)
                U = tf.matmul(Gt, U, transpose_b=True)
                U = tf.reshape(U, [batch_size, 1, 1, kernel_size*kernel_size*input_channels])
                V = tf.matmul(U, filter)
                V = tf.reshape(V, [batch_size, 1, 1, output_channels])
                outputs[:, i, j, :] = V[:, 0, 0, :]
        return outputs

def depthwise_separable_conv2d(inputs, kernel_size, output_channels, strides, padding='SAME', name=None):
    with tf.variable_scope(name):
        depthwise_outputs = depthwise_conv2d(inputs, kernel_size, strides, padding=padding)
        pointwise_outputs = pointwise_conv2d(depthwise_outputs, output_channels)
        return pointwise_outputs

使用方法如下：

import tensorflow as tf

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
outputs = winograd_conv2d(inputs, kernel_size=3, output_channels=32, strides=1, padding='SAME', name='conv1')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=64, strides=1, padding='SAME', name='conv2')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=128, strides=2, padding='SAME', name='conv3')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=128, strides=1, padding='SAME', name='conv4')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=256, strides=2, padding='SAME', name='conv5')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=256, strides=1, padding='SAME', name='conv6')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=2, padding='SAME', name='conv7')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=1, padding='SAME', name='conv8')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=1, padding='SAME', name='conv9')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=1, padding='SAME', name='conv10')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=1, padding='SAME', name='conv11')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=512, strides=1, padding='SAME', name='conv12')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=1024, strides=2, padding='SAME', name='conv13')
outputs = depthwise_separable_conv2d(outputs, kernel_size=3, output_channels=1024, strides=1, padding='SAME', name='conv14')