ResNet 模型代码示例 - MindSpore

以下是一个 ResNet 模型的示例代码，使用 MindSpore 库实现：

import mindspore.nn as nn
import mindspore.ops as ops

class BasicBlock(nn.Cell):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def construct(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

class ResNet(nn.Cell):

    def __init__(self, block, layers, num_classes=7):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Dense(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            downsample = nn.SequentialCell([
                nn.Conv2d(self.in_planes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            ])

        layers = []
        layers.append(block(self.in_planes, planes, stride, downsample))
        self.in_planes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_planes, planes))

        return nn.SequentialCell(layers)

    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 = ops.Reshape()(x, (x.shape[0], -1))
        x = self.fc(x)

        return x

def resnet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

这段代码定义了两个类：BasicBlock 和 ResNet。

BasicBlock 类代表 ResNet 模型中的一个基本块，包含两个卷积层、两个 Batch Normalization 层和一个 ReLU 激活函数。ResNet 类则定义了整个 ResNet 模型，包含多个 BasicBlock 层以及其他层，例如卷积层、池化层和全连接层。

resnet18() 函数则用于构建一个 ResNet18 模型，它使用 BasicBlock 作为基本块，并指定每个层包含的块数量。

这段代码仅供参考，你可以根据自己的需求进行修改和扩展。