用tensorflow2写一个RepVGG块
时间: 2023-05-16 16:06:34 浏览: 72
可以使用以下代码来实现一个RepVGG块:
```python
import tensorflow as tf
class RepVGGBlock(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, strides=1, padding='same', dilation_rate=1, groups=1, use_bias=True, activation='relu', deploy=False):
super(RepVGGBlock, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.padding = padding
self.dilation_rate = dilation_rate
self.groups = groups
self.use_bias = use_bias
self.activation = activation
self.deploy = deploy
if self.groups > 1:
self.conv = tf.keras.layers.DepthwiseConv2D(kernel_size=self.kernel_size, strides=self.strides, padding=self.padding, dilation_rate=self.dilation_rate, use_bias=self.use_bias)
else:
self.conv = tf.keras.layers.Conv2D(filters=self.filters, kernel_size=self.kernel_size, strides=self.strides, padding=self.padding, dilation_rate=self.dilation_rate, use_bias=self.use_bias)
self.bn = tf.keras.layers.BatchNormalization()
if not self.deploy:
self.relu = tf.keras.layers.Activation(self.activation)
def call(self, inputs):
if self.groups > 1:
x = tf.keras.layers.Lambda(lambda x: tf.split(x, self.groups, axis=-1))(inputs)
x = tf.keras.layers.Concatenate(axis=-1)([self.conv(x_i) for x_i in x])
else:
x = self.conv(inputs)
x = self.bn(x)
if not self.deploy:
x = self.relu(x)
return x
```
这个RepVGG块可以用于构建RepVGG网络。
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