fcn网络中的padding 100
时间: 2024-04-05 14:32:42 浏览: 14
FCN(Fully Convolutional Networks)网络中的padding指的是在输入图片的边缘周围添加一圈固定数值的像素值。padding的目的是为了让卷积核在边缘处也能够完整地卷积,从而避免边缘信息的丢失。在FCN中,padding通常设置为100,即在每个边缘周围添加100个像素值。这个数值可以根据具体的应用场景进行调整。
相关问题
tensorflow定义fcn网络结构
下面是一个简单的FCN网络结构的TensorFlow实现示例:
```python
import tensorflow as tf
def fcn(images, num_classes):
# 编码器部分
conv1 = tf.layers.conv2d(images, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv2 = tf.layers.conv2d(conv1, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(conv2, pool_size=2, strides=2, padding='same')
conv3 = tf.layers.conv2d(pool1, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv4 = tf.layers.conv2d(conv3, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(conv4, pool_size=2, strides=2, padding='same')
conv5 = tf.layers.conv2d(pool2, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv6 = tf.layers.conv2d(conv5, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv7 = tf.layers.conv2d(conv6, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(conv7, pool_size=2, strides=2, padding='same')
conv8 = tf.layers.conv2d(pool3, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv9 = tf.layers.conv2d(conv8, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv10 = tf.layers.conv2d(conv9, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool4 = tf.layers.max_pooling2d(conv10, pool_size=2, strides=2, padding='same')
conv11 = tf.layers.conv2d(pool4, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv12 = tf.layers.conv2d(conv11, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv13 = tf.layers.conv2d(conv12, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
pool5 = tf.layers.max_pooling2d(conv13, pool_size=2, strides=2, padding='same')
# 解码器部分
conv14 = tf.layers.conv2d(pool5, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv15 = tf.layers.conv2d(conv14, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv16 = tf.layers.conv2d(conv15, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
upconv1 = tf.layers.conv2d_transpose(conv16, filters=512, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
concat1 = tf.concat([conv13, upconv1], axis=3)
conv17 = tf.layers.conv2d(concat1, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv18 = tf.layers.conv2d(conv17, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv19 = tf.layers.conv2d(conv18, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
upconv2 = tf.layers.conv2d_transpose(conv19, filters=256, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
concat2 = tf.concat([conv10, upconv2], axis=3)
conv20 = tf.layers.conv2d(concat2, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv21 = tf.layers.conv2d(conv20, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv22 = tf.layers.conv2d(conv21, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
upconv3 = tf.layers.conv2d_transpose(conv22, filters=128, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
concat3 = tf.concat([conv4, upconv3], axis=3)
conv23 = tf.layers.conv2d(concat3, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv24 = tf.layers.conv2d(conv23, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
upconv4 = tf.layers.conv2d_transpose(conv24, filters=64, kernel_size=3, strides=2, padding='same', activation=tf.nn.relu)
concat4 = tf.concat([conv2, upconv4], axis=3)
conv25 = tf.layers.conv2d(concat4, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
conv26 = tf.layers.conv2d(conv25, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
# 最后一层卷积层输出预测结果
output = tf.layers.conv2d(conv26, filters=num_classes, kernel_size=1, strides=1, padding='same', activation=None)
return output
```
此代码中,我们定义了一个名为`fcn`的函数,它接受两个参数:`images`表示输入的图像,`num_classes`表示分类的类别数。函数中的代码定义了一个标准的FCN网络结构,其中包括编码器和解码器部分。编码器部分包括多个卷积层和池化层,用于提取输入图像的特征。解码器部分包括多个反卷积层和卷积层,用于将特征图还原为原始大小,并输出分类结果。最后一层卷积层输出预测结果,其通道数为分类的类别数。
用pytorch写一个FCN网络
以下是一个简单的FCN网络的PyTorch实现:
```python
import torch
import torch.nn as nn
class FCN(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(512)
self.relu4 = nn.ReLU(inplace=True)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.bn5 = nn.BatchNorm2d(512)
self.relu5 = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc6 = nn.Conv2d(in_channels=512, out_channels=4096, kernel_size=7)
self.drop6 = nn.Dropout2d()
self.fc7 = nn.Conv2d(in_channels=4096, out_channels=4096, kernel_size=1)
self.drop7 = nn.Dropout2d()
self.score_fr = nn.Conv2d(in_channels=4096, out_channels=num_classes, kernel_size=1)
self.upscore = nn.ConvTranspose2d(in_channels=num_classes, out_channels=num_classes, kernel_size=64, stride=32, bias=False)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.conv5(x)
x = self.bn5(x)
x = self.relu5(x)
x = self.pool(x)
x = self.fc6(x)
x = self.drop6(x)
x = self.fc7(x)
x = self.drop7(x)
x = self.score_fr(x)
x = self.upscore(x)
return x
```
该网络由一些卷积层、池化层和全连接层组成,其输出通过反卷积层进行上采样,最终生成与输入图像大小相同的语义分割图像。
相关推荐
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![py](https://img-home.csdnimg.cn/images/20210720083646.png)