在pytorch中,用torch自带的卷积模块将[11, 3, 224, 224]变为[1, 3, 224, 224]
时间: 2024-05-16 13:18:23 浏览: 9
可以使用PyTorch中的nn.Conv2d模块来实现。该模块可以对二维图像进行卷积操作,可以使用它对输入数据进行卷积操作,从而将[11, 3, 224, 224]变为[1, 3, 224, 224]。具体实现代码如下:
```python
import torch.nn as nn
# 定义卷积模型
conv = nn.Conv2d(11, 1, kernel_size=1)
# 输入数据
x = torch.randn(11, 3, 224, 224)
# 将输入数据通过卷积模块conv进行卷积操作
out = conv(x)
# 输出数据的shape为[1, 3, 224, 224]
print(out.shape)
```
其中,nn.Conv2d的第一个参数表示输入数据的通道数,这里为11;第二个参数表示输出数据的通道数,这里为1;kernel_size参数表示卷积核的大小,这里为1。
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Deeplabv3+中ASPP模块用1×3卷积和3×1卷积代替3×3卷积完整pytorch代码
以下是使用1x3和3x1卷积代替3x3卷积的Deeplabv3+中ASPP模块的完整PyTorch代码:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels=256, rates=[1, 6, 12, 18]):
super(ASPP, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv3a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv3b = nn.Conv2d(out_channels, out_channels, kernel_size=(1, 3), padding=(0, 1))
self.conv3c = nn.Conv2d(out_channels, out_channels, kernel_size=(3, 1), padding=(1, 0))
self.conv4a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv4b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[0], padding=rates[0])
self.conv5a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv5b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[1], padding=rates[1])
self.conv6a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv6b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[2], padding=rates[2])
self.conv7a = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.conv7b = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=rates[3], padding=rates[3])
self.conv8 = nn.Conv2d(out_channels * 5, out_channels, kernel_size=1)
def forward(self, x):
x1 = self.conv1(x)
x1 = F.relu(x1)
x2 = F.avg_pool2d(x, kernel_size=x.size()[2:])
x2 = self.conv2(x2)
x2 = F.relu(x2)
x3 = self.conv3a(x)
x3 = self.conv3b(x3)
x3 = self.conv3c(x3)
x3 = F.relu(x3)
x4 = self.conv4a(x)
x4 = self.conv4b(x4)
x4 = F.relu(x4)
x5 = self.conv5a(x)
x5 = self.conv5b(x5)
x5 = F.relu(x5)
x6 = self.conv6a(x)
x6 = self.conv6b(x6)
x6 = F.relu(x6)
x7 = self.conv7a(x)
x7 = self.conv7b(x7)
x7 = F.relu(x7)
out = torch.cat((x1, x2, x3, x4, x5, x6, x7), dim=1)
out = self.conv8(out)
out = F.relu(out)
return out
```
Deeplabv3+中ASPP模块用深度可分离卷积代替3×3卷积完整pytorch代码
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。