Deeplabv3+中ASPP模块用深度可分离卷积代替3×3卷积完整pytorch代码
时间: 2024-03-16 07:47:00 浏览: 26
以下是使用深度可分离卷积替代3x3卷积的ASPP模块的完整PyTorch代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels=256, rates=[6, 12, 18]):
super(ASPP, self).__init__()
# 1x1 convolution layer
self.conv_1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
# Atrous Spatial Pyramid Pooling (ASPP) layers
self.conv_3x3_1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[0], dilation=rates[0], groups=out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.conv_3x3_2 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[1], dilation=rates[1], groups=out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.conv_3x3_3 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=rates[2], dilation=rates[2], groups=out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.conv_1x1_out = nn.Sequential(
nn.Conv2d(out_channels * 4, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
# Apply 1x1 convolution
x_1x1 = self.conv_1x1(x)
# Apply Atrous Spatial Pyramid Pooling (ASPP)
x_3x3_1 = self.conv_3x3_1(x)
x_3x3_2 = self.conv_3x3_2(x)
x_3x3_3 = self.conv_3x3_3(x)
x_avg_pool = F.adaptive_avg_pool2d(x, (1, 1))
x_avg_pool = self.conv_1x1(x_avg_pool)
x_avg_pool = F.interpolate(x_avg_pool, size=x_3x3_3.size()[2:], mode='bilinear', align_corners=True)
# Concatenate the outputs of all ASPP layers
out = torch.cat([x_1x1, x_3x3_1, x_3x3_2, x_3x3_3, x_avg_pool], dim=1)
# Apply 1x1 convolution to the concatenated output
out = self.conv_1x1_out(out)
return out
```
其中,`in_channels`为输入特征图的通道数,`out_channels`为ASPP模块输出的通道数,`rates`为ASPP模块中各个卷积层的采样率。可以根据具体的任务和网络结构对这些参数进行调整。