shufflenetv2 pytorch

ShuffleNet V2是一种用于计算机视觉任务的深度学习模型，PyTorch是一个开源的深度学习框架。您可以在PyTorch中使用ShuffleNet V2来训练和评估模型。有很多关于如何使用PyTorch实现ShuffleNet V2的教程和代码示例可以在网上找到。

相关问题

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：

import torch
import torch.nn as nn
import torch.nn.functional as F

然后，我们定义ShuffleNetV2模块：

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：

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：

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

AttributeError: module 'pytorch_classifier.ShuffleNet.ShuffleNetV2.ShuffleNetv2' has no attribute 'eval'

AttributeError: module 'pytorch_classifier.ShuffleNet.ShuffleNetV2.ShuffleNetv2' has no attribute 'eval' 是一个错误提示，意味着在 pytorch_classifier.ShuffleNet.ShuffleNetV2.ShuffleNetv2 模块中没有名为 'eval' 的属性。

这个错误通常发生在尝试调用一个不存在的方法或属性时。在这种情况下，可能是因为你正在尝试对 ShuffleNetv2 模型进行评估（使用 eval 方法），但该模型并没有定义 eval 方法。

要解决这个问题，你可以检查以下几点：
1. 确保你导入了正确的模块和类。检查导入语句是否正确，并确认模块和类的名称是否拼写正确。
2. 查看模型的文档或源代码，确认该模型是否具有 eval 方法。如果没有 eval 方法，你可能需要使用其他方法来评估模型。
3. 如果你是从其他地方获取的代码或示例，请确保代码和模型版本的兼容性。不同版本的库可能具有不同的方法和属性。

如果你能提供更多的上下文信息，比如你正在尝试做什么、使用的代码片段等，我可以给出更具体的帮助。