以VGG16为基础网络的锚框细化模块代码
时间: 2023-07-15 22:15:59 浏览: 47
这里提供一份以VGG16为基础网络的锚框细化模块代码,供参考:
```python
import tensorflow as tf
def anchor_refinement_module(input_tensor, num_anchors=9):
"""
Anchor Refinement Module
:param input_tensor: input tensor
:param num_anchors: number of anchors
:return: output tensor
"""
# Convolutional Layer 1
x = tf.layers.conv2d(input_tensor, filters=256, kernel_size=(3, 3), padding='same', activation=tf.nn.relu)
x = tf.layers.batch_normalization(x)
x = tf.layers.max_pooling2d(x, pool_size=(2, 2), strides=(2, 2), padding='same')
# Convolutional Layer 2
x = tf.layers.conv2d(x, filters=256, kernel_size=(3, 3), padding='same', activation=tf.nn.relu)
x = tf.layers.batch_normalization(x)
x = tf.layers.max_pooling2d(x, pool_size=(2, 2), strides=(2, 2), padding='same')
# Convolutional Layer 3
x = tf.layers.conv2d(x, filters=256, kernel_size=(3, 3), padding='same', activation=tf.nn.relu)
x = tf.layers.batch_normalization(x)
x = tf.layers.max_pooling2d(x, pool_size=(2, 2), strides=(2, 2), padding='same')
# Convolutional Layer 4
x = tf.layers.conv2d(x, filters=256, kernel_size=(3, 3), padding='same', activation=tf.nn.relu)
x = tf.layers.batch_normalization(x)
# 1x1 Convolution for Classification
cls = tf.layers.conv2d(x, filters=num_anchors * 2, kernel_size=(1, 1), padding='same', activation=None)
# 1x1 Convolution for Regression
reg = tf.layers.conv2d(x, filters=num_anchors * 4, kernel_size=(1, 1), padding='same', activation=None)
return cls, reg
```
以上代码中,我们定义了一个名为anchor_refinement_module的函数,它接受一个输入张量和一个锚框数量作为参数,并返回两个输出张量,一个用于分类,一个用于回归。
该函数的实现基于VGG16网络结构,并在其基础上添加了4个卷积层和2个1x1卷积层来生成分类和回归输出。分类输出有num_anchors * 2个通道,用于预测每个锚框的正/负标签,回归输出有num_anchors * 4个通道,用于预测每个锚框的坐标偏移量。