用tensorflow2写一个RepVGG
时间: 2023-05-16 08:06:44 浏览: 77
非常感谢您的提问。以下是使用 TensorFlow 2 编写 RepVGG 的示例代码:
```python
import tensorflow as tf
from tensorflow.keras import layers
class RepVGGBlock(layers.Layer):
def __init__(self, filters, kernel_size, strides=1, padding='same'):
super(RepVGGBlock, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.padding = padding
self.conv1 = layers.Conv2D(filters, kernel_size, strides=strides, padding=padding)
self.bn1 = layers.BatchNormalization()
self.relu = layers.ReLU()
self.conv2 = layers.Conv2D(filters, kernel_size, strides=1, padding=padding)
self.bn2 = layers.BatchNormalization()
if strides != 1:
self.shortcut = layers.Conv2D(filters, 1, strides=strides, padding=padding)
else:
self.shortcut = layers.Lambda(lambda x: x)
def call(self, inputs):
x = self.conv1(inputs)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
shortcut = self.shortcut(inputs)
x = layers.add([x, shortcut])
x = self.relu(x)
return x
class RepVGG(tf.keras.Model):
def __init__(self, num_blocks, num_classes=1000):
super(RepVGG, self).__init__()
self.num_blocks = num_blocks
self.num_classes = num_classes
self.stem = layers.Conv2D(64, 3, strides=2, padding='same')
self.bn = layers.BatchNormalization()
self.relu = layers.ReLU()
self.blocks = []
for i in range(num_blocks):
filters = 64 if i == 0 else 128
strides = 2 if i == 0 else 1
self.blocks.append(RepVGGBlock(filters, 3, strides=strides))
self.blocks = tf.keras.Sequential(self.blocks)
self.avgpool = layers.GlobalAveragePooling2D()
self.fc = layers.Dense(num_classes)
def call(self, inputs):
x = self.stem(inputs)
x = self.bn(x)
x = self.relu(x)
x = self.blocks(x)
x = self.avgpool(x)
x = self.fc(x)
return x
```
希望这个示例代码能够帮助您。如果您有任何问题,请随时问我。