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。