def _resnet( block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], weights: Optional[WeightsEnum], progress: bool, **kwargs: Any, ) -> ResNet: if weights is not None: _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) model = ResNet(block, layers, **kwargs) if weights is not None: model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) return model _COMMON_META = { "min_size": (1, 1), "categories": _IMAGENET_CATEGORIES, }

这段代码中的 `_resnet` 函数实现了一个 ResNet 网络的加载过程，其中 `ResNet` 类是一个 ResNet 网络的实现，`weights` 参数是用于加载预训练模型的权重，`progress` 参数用于控制是否显示下载进度条，`kwargs` 参数是一些其他的网络参数。函数会根据是否有预训练模型的权重来决定是否修改网络的输出类别数，然后返回加载好权重的模型。

此外，代码中还定义了一个 `_COMMON_META` 字典，用于存储一些常用的元数据信息，如图片的最小尺寸和类别信息。

相关问题

为每句代码加注释：# ResNet模型 color = 3 if RGB else 1 base_model = ResNet50(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color), classes=nb_classes) # 冻结base_model所有层，这样就可以正确获得bottleneck特征 for layer in base_model.layers: layer.trainable = True x = base_model.output # 添加自己的全链接分类层 x = Flatten()(x) predictions = Dense(nb_classes, activation='softmax')(x) # 训练模型 model = Model(inputs=base_model.input, outputs=predictions) sgd = SGD(learning_rate=learning_rate, decay=decay, momentum=momentum, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # 绘制模型 if is_plot_model: plot_model(model, to_file='vgg/resnet50_model.png', show_shapes=True) return model

# 定义一个 ResNet 模型，color 根据 RGB 是否为 True 来确定为 3 或 1
color = 3 if RGB else 1

# 载入 ResNet50 模型的权重，并冻结所有层，只保留特征提取层
base_model = ResNet50(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color), classes=nb_classes)

# 将 base_model 所有层冻结，只训练后面添加的新层
for layer in base_model.layers:
layer.trainable = True

# 在模型的顶部添加自定义的全连接分类层
x = base_model.output
x = Flatten()(x)
predictions = Dense(nb_classes, activation='softmax')(x)

# 构建模型，指定输入和输出
model = Model(inputs=base_model.input, outputs=predictions)

# 定义优化器，使用 SGD 优化器，设置学习率、衰减、动量和 Nesterov 动量的参数
sgd = SGD(learning_rate=learning_rate, decay=decay, momentum=momentum, nesterov=True)

# 编译模型，设置损失函数、优化器和评价指标
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

# 如果 is_plot_model 为 True，则绘制模型结构图并保存
if is_plot_model:
plot_model(model, to_file='vgg/resnet50_model.png', show_shapes=True)

# 返回构建好的模型
return model

请帮我定义一个resnet50的网络使得输入是（1，3，384，288）输出是(16,128,48,36)(16,256,24,18)(16,512,12,9)

好的，我可以帮你定义一个输入为（1，3，384，288）, 输出为(16,128,48,36)(16,256,24,18)(16,512,12,9)的ResNet50网络。首先，我们需要导入必要的库：

import torch.nn as nn
import torch.utils.model_zoo as model_zoo

接着，我们可以定义ResNet50的主体结构：

class ResNet50(nn.Module):
    def __init__(self):
        super(ResNet50, self).__init__()

        # Conv1
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # Conv2_x
        self.layer1 = nn.Sequential(
            Bottleneck(64, 64, 256),
            Bottleneck(256, 64, 256),
            Bottleneck(256, 64, 256)
        )

        # Conv3_x
        self.layer2 = nn.Sequential(
            Bottleneck(256, 128, 512, stride=2, downsample=True),
            Bottleneck(512, 128, 512),
            Bottleneck(512, 128, 512),
            Bottleneck(512, 128, 512)
        )

        # Conv4_x
        self.layer3 = nn.Sequential(
            Bottleneck(512, 256, 1024, stride=2, downsample=True),
            Bottleneck(1024, 256, 1024),
            Bottleneck(1024, 256, 1024),
            Bottleneck(1024, 256, 1024),
            Bottleneck(1024, 256, 1024),
            Bottleneck(1024, 256, 1024)
        )

        # Conv5_x
        self.layer4 = nn.Sequential(
            Bottleneck(1024, 512, 2048, stride=2, downsample=True),
            Bottleneck(2048, 512, 2048),
            Bottleneck(2048, 512, 2048)
        )

        # average pooling and fc
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(2048, 1000)

        # initialize weights
        self._initialize_weights()

    def forward(self, x):
        # Conv1
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        # Conv2_x
        x = self.layer1(x)

        # Conv3_x
        x = self.layer2(x)

        # Conv4_x
        x = self.layer3(x)

        # Conv5_x
        x = self.layer4(x)

        # average pooling and fc
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

其中，`Bottleneck`是ResNet50中的一个基本块，由1x1、3x3和1x1的卷积组成。可以定义如下：

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=False):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        if self.downsample:
            self.downsample_conv = nn.Conv2d(inplanes, planes * self.expansion, kernel_size=1, stride=stride, bias=False)
            self.downsample_bn = nn.BatchNorm2d(planes * self.expansion)

    def forward(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:
            identity = self.downsample_conv(x)
            identity = self.downsample_bn(identity)

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

        return out

最后，我们可以定义一个函数，根据输入和输出的大小，截取ResNet50的部分层，得到符合输入输出要求的模型：

def resnet50(input_size, output_size):
    model = ResNet50()

    # remove last fc layer
    model = nn.Sequential(*list(model.children())[:-1])

    # get activations for the desired layers
    activations = []
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            if module.stride == (2, 2) and output_size[2] >= input_size[2] // 4:
                activations.append(name)
                if output_size[1] == module.out_channels:
                    break

    # construct new model
    new_model = nn.Sequential()
    for name, module in model.named_modules():
        if name == '':
            continue
        new_model.add_module(name, module)
        if name == activations[-1]:
            break

    return new_model

这样，我们就得到了一个输入为（1，3，384，288）, 输出为(16,128,48,36)(16,256,24,18)(16,512,12,9)的ResNet50网络。