MindSpore resnet101

MindSpore是华为公司开发的深度学习框架，ResNet101是一种深度卷积神经网络，可以用于图像分类和目标检测等任务。在MindSpore中，可以使用ResNet101模型进行图像分类，代码示例如下：

import mindspore.nn as nn
import mindspore.ops.operations as P

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, has_bias=False, pad_mode='pad')
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
        self.layer1 = self._make_layer(64, 3, stride=1)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 23, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(512 * Bottleneck.expansion, num_classes)

    def _make_layer(self, planes, blocks, stride):
        downsample = None
        if stride != 1 or self.inplanes != planes * Bottleneck.expansion:
            downsample = nn.SequentialCell([
                nn.Conv2d(self.inplanes, planes * Bottleneck.expansion, kernel_size=1, stride=stride, has_bias=False),
                nn.BatchNorm2d(planes * Bottleneck.expansion)
            ])
        layers = [Bottleneck(self.inplanes, planes, stride, downsample)]
        self.inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self.inplanes, 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 = self.flatten(x)
        x = self.fc(x)
        return x

class Bottleneck(nn.Cell):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, has_bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, has_bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * Bottleneck.expansion, kernel_size=1, has_bias=False)
        self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
        self.relu = nn.ReLU()
        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)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

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

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

        return out

这段代码定义了一个名为ResNet101的神经网络模型，可以使用MindSpore中的数据集进行训练和测试。