解释nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True),
时间: 2024-05-21 14:15:19 浏览: 18
nn.BatchNorm2d(mid_channels)是用于二维卷积的批量标准化层。它可以对输入的每个通道的数据进行标准化处理,使它们的均值为0,方差为1,从而加速神经网络的收敛速度并提高模型的准确率。mid_channels是指输入的特征图的通道数。
而nn.ReLU(inplace=True)是整流线性单元(Rectified Linear Unit)。它是一个非线性激活函数,可以将输入的负值设为0,使得神经网络能够更好地处理非线性问题。inplace=True表示执行该操作时可以覆盖输入的内存,从而减少内存占用。
相关问题
设计一个扩张卷积模块,具体过程为首先使用1×1深度可分离卷积压缩通道,然后使用不同大小的深度可分离卷积分别为5*5、7*7、9*9和由它们构造的不同膨胀速率的扩张卷积形成多个平行分支,膨胀率分别为5、7和9。此外,还有一个残差连接分支。最后,将这些并行分支的输出拼接在一起,再输出一个1×1深度可分离卷积,形成特征图y,请按照上述要求编写出可以应用到yolov5上的代码
以下是基于PyTorch实现的扩张卷积模块代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class ExpandConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(ExpandConv, self).__init__()
mid_channels = in_channels // 2
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True)
)
self.conv2_5 = nn.Sequential(
nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=2, dilation=2, groups=mid_channels),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.conv2_7 = nn.Sequential(
nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=3, dilation=3, groups=mid_channels),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.conv2_9 = nn.Sequential(
nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=4, dilation=4, groups=mid_channels),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
self.residual = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
x1 = self.conv1(x)
x2_5 = self.conv2_5(x1)
x2_7 = self.conv2_7(x1)
x2_9 = self.conv2_9(x1)
res = self.residual(x)
out = torch.cat([x2_5, x2_7, x2_9, res], dim=1)
out = nn.Conv2d(out.size(1), out.size(1), kernel_size=1)(out)
return out
```
在Yolov5的网络结构中,可以将这个扩张卷积模块嵌入到主干网络中,例如:
```python
import torch
import torch.nn as nn
from models.common import Conv, BottleneckCSP
from models.expand_conv import ExpandConv
class YoloV5(nn.Module):
def __init__(self, num_classes=80):
super(YoloV5, self).__init__()
self.num_classes = num_classes
self.backbone = nn.Sequential(
# C1
Conv(3, 32, 3, 1, activation='hardswish'),
# C2
BottleneckCSP(32, 64, n=2, shortcut=True),
# C3
BottleneckCSP(64, 128, n=3, shortcut=True),
# C4
BottleneckCSP(128, 256, n=9, shortcut=True),
# C5
BottleneckCSP(256, 512, n=9, shortcut=True),
# C6
BottleneckCSP(512, 1024, n=3, shortcut=False),
# SPP
Conv(1024, 1024, 1, 1, activation='hardswish'),
ExpandConv(1024, 1024),
Conv(1024, 512, 1, 1, activation='hardswish')
)
self.head = nn.Sequential(
# Final bottleneck
BottleneckCSP(512, 1024, shortcut=False),
# Detect
nn.Conv2d(1024, 255, kernel_size=1, stride=1, padding=0)
)
def forward(self, x):
x = self.backbone(x)
x = self.head(x)
x = x.view(x.size(0), -1, self.num_classes + 5)
return x
```
在SPP层之后,我们添加了一个ExpandConv模块,然后再接上一个1x1卷积层。注意,这里的512是SPP输出的通道数,1024是ExpandConv的输出通道数。
pytorch实现用shufflenetv2代替CSPdarknet53的代码
以下是将ShuffleNetV2替换为CSPDarknet53的代码示例:
首先,我们需要导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
```
然后,我们定义ShuffleNetV2模块:
```python
class ShuffleNetV2Block(nn.Module):
def __init__(self, input_channels, output_channels, mid_channels=None, ksize=3, stride=1, use_bn=True):
super(ShuffleNetV2Block, self).__init__()
if mid_channels is None:
mid_channels = output_channels // 2
self.stride = stride
self.use_bn = use_bn
if stride == 1:
self.branch1 = nn.Sequential()
else:
self.branch1 = nn.Sequential(
nn.AvgPool2d(kernel_size=3, stride=stride, padding=1),
nn.Conv2d(input_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(input_channels),
nn.ReLU(inplace=True),
)
self.branch2 = nn.Sequential(
nn.Conv2d(input_channels if stride > 1 else mid_channels, mid_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride, padding=ksize // 2, groups=mid_channels, bias=False),
nn.BatchNorm2d(mid_channels),
nn.Conv2d(mid_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(output_channels),
)
def forward(self, x):
x1 = self.branch1(x)
x2 = self.branch2(x if self.stride > 1 else F.relu(x1))
out = torch.cat((x1, x2), dim=1)
out = F.shuffle(out, 2) if self.stride == 2 else out
return out
```
接下来,我们定义CSPDarknet53模块:
```python
class CSPDarknet53(nn.Module):
def __init__(self):
super(CSPDarknet53, self).__init__()
self.stem = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
)
self.layer1 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
ShuffleNetV2Block(64, 64, ksize=3, stride=1),
nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
ShuffleNetV2Block(64, 64, ksize=3, stride=1),
nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
ShuffleNetV2Block(64, 64, ksize=3, stride=1),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
ShuffleNetV2Block(128, 128, ksize=3, stride=1),
nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
ShuffleNetV2Block(128, 128, ksize=3, stride=1),
nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
ShuffleNetV2Block(128, 128, ksize=3, stride=1),
nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
ShuffleNetV2Block(128, 128, ksize=3, stride=1),
nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
ShuffleNetV2Block(128, 128, ksize=3, stride=1),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
ShuffleNetV2Block(256, 256, ksize=3, stride=1),
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
ShuffleNetV2Block(512, 512, ksize=3, stride=1),
nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
ShuffleNetV2Block(512, 512, ksize=3, stride=1),
nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
ShuffleNetV2Block(512, 512, ksize=3, stride=1),
)
def forward(self, x):
x = self.stem(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
```
最后,我们可以使用CSPDarknet53替换任何需要使用Darknet53的模型。
例如,下面是一个RetinaNet模型,其中使用CSPDarknet53代替了标准的Darknet53:
```python
class RetinaNet(nn.Module):
def __init__(self, num_classes):
super(RetinaNet, self).__init__()
self.backbone = CSPDarknet53()
self.fpn = FPN([512, 1024, 2048], 256)
self.head = RetinaNetHead(num_classes)
def forward(self, x):
x = self.backbone(x)
features = self.fpn(x)
out = self.head(features)
return out
```
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