使用mindspore写出resnet101的代码，包括单独的推理程序，数据集格式采用ImageNet...

偏执的太偏执、 2024-03-26 09:09 12阅读 0赞

MindSpore是一个开源的深度学习框架，可以用来构建、训练和部署深度学习模型。在MindSpore中，可以使用`ResNet`类实现ResNet101模型。

下面是一个示例代码，展示了如何使用MindSpore实现ResNet101模型，并使用ImageNet数据集进行训练和推理：

\`\`\`python import mindspore import mindspore.nn as nn import mindspore.common.dtype as mstype

## 定义ResNet101模型 ##

class ResNet101(nn.Cell): def **init**(self, num\_classes=1000): super(ResNet101, self).**init**() self.conv1 = nn.Conv2d(3, 64, kernel\_size=7, stride=2, padding=3, weight\_init=nn.initializer.TruncatedNormal(0.1)) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU() self.maxpool = nn.MaxPool2d(kernel\_size=3, stride=2, padding=1) self.layer1 = ResNet101\_Bottleneck(64, 256, stride=1, num\_blocks=3) self.layer2 = ResNet101\_Bottleneck(256, 512, stride=2, num\_blocks=4) self.layer3 = ResNet101\_Bottleneck(512, 1024, stride=2, num\_blocks=23) self.layer4 = ResNet101\_Bottleneck(1024, 2048, stride=2, num\_blocks=3) self.avgpool = nn.AvgPool2d(7) self.flatten = nn.Flatten() self.fc = nn.Dense(num\_classes, weight\_init=nn.initializer.TruncatedNormal(0.1))

def construct(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x

## 定义ResNet101的Bottleneck块 ##

class ResNet101\_Bottleneck(nn.Cell): def **init**(self, in\_channels, out