基于tensorflow2.5,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进行代码注释,解释其总体结构然后示例如何在神经网络中使用
时间: 2024-05-09 21:17:23 浏览: 6
以下是基于tensorflow2.5的Winograd算法深度可分离卷积模块代码和注释:
```python
import tensorflow as tf
class WinogradDepthwiseConv2D(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, strides=1, padding='same', activation=None):
super(WinogradDepthwiseConv2D, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.padding = padding
self.activation = activation
self.padding = tf.keras.layers.ZeroPadding2D(padding=((0, 1), (0, 1)))
self.f1 = tf.Variable(tf.random.normal((kernel_size, kernel_size, filters)))
self.b1 = tf.Variable(tf.zeros((filters,)))
self.f2 = tf.Variable(tf.random.normal((3, 3, filters, filters)))
self.b2 = tf.Variable(tf.zeros((filters,)))
self.f3 = tf.Variable(tf.random.normal((filters,)))
self.b3 = tf.Variable(tf.zeros((filters,)))
self.winograd_3x3_4x4 = tf.constant([[1.0, 0.0, 0.0], [0.5, 0.5, 0.5], [0.5, -0.5, 0.5], [0.0, 0.0, 1.0]])
self.winograd_3x3_6x6 = tf.constant([[
1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-2.0/9, -2.0/9, -2.0/9, -2.0/9, -2.0/9, -2.0/9],
[-2.0/9, 2.0/9, -2.0/9, 2.0/9, -2.0/9, 2.0/9],
[1.0/90, 1.0/45, 2.0/45, 4.0/45, 8.0/45, 16.0/45],
[1.0/90, -1.0/45, 2.0/45, -4.0/45, 8.0/45, -16.0/45],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])
def call(self, inputs):
x = self.padding(inputs)
# Winograd F(2, 3) transform
x = tf.nn.conv2d(x, self.winograd_3x3_4x4, strides=[1, 1, 1, 1], padding='VALID')
x = tf.nn.conv2d_transpose(x, self.winograd_3x3_6x6, output_shape=[tf.shape(x)[0], tf.shape(x)[1]*2-1, tf.shape(x)[2]*2-1, 16], strides=[1, 2, 2, 1], padding='VALID')
# Depthwise Convolution
x = tf.nn.depthwise_conv2d(x, self.f1, strides=[1, self.strides, self.strides, 1], padding=self.padding)
x = tf.nn.bias_add(x, self.b1)
x = tf.nn.relu(x)
# Winograd F(4, 3) transform
x = tf.nn.conv2d(x, self.winograd_3x3_6x6, strides=[1, 1, 1, 1], padding='VALID')
x = tf.nn.conv2d_transpose(x, self.winograd_3x3_4x4, output_shape=[tf.shape(x)[0], tf.shape(x)[1]*2-1, tf.shape(x)[2]*2-1, 16], strides=[1, 2, 2, 1], padding='VALID')
# Pointwise Convolution
x = tf.nn.conv2d(x, self.f2, strides=[1, 1, 1, 1], padding='SAME')
x = tf.nn.bias_add(x, self.b2)
x = tf.nn.relu(x)
# Depthwise Convolution
x = tf.nn.depthwise_conv2d(x, self.f3, strides=[1, 1, 1, 1], padding=self.padding)
x = tf.nn.bias_add(x, self.b3)
if self.activation is not None:
x = self.activation(x)
return x
```
总体结构:
- 输入经过Winograd F(2, 3)变换得到中间矩阵,再进行深度可分离卷积。
- 卷积后的结果再经过Winograd F(4, 3)变换得到中间矩阵,再进行点卷积。
- 最后再进行一次深度可分离卷积,得到最终输出。
神经网络中使用:
```python
model = tf.keras.models.Sequential([
tf.keras.layers.Input(shape=input_shape),
WinogradDepthwiseConv2D(filters=32, kernel_size=3, strides=2),
WinogradDepthwiseConv2D(filters=64, kernel_size=3, strides=2),
WinogradDepthwiseConv2D(filters=128, kernel_size=3, strides=2),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
```
可以直接将WinogradDepthwiseConv2D层插入到神经网络中,其中`filters`为输出通道数,`kernel_size`为卷积核大小,`strides`为步长,`padding`为填充方式,`activation`为激活函数。在上述示例中,我们使用`WinogradDepthwiseConv2D`层构建了一个简单的卷积神经网络,其中包含了三个Winograd深度可分离卷积层和一个全局平均池化层,最后通过一个全连接层输出分类结果。