resnet50的pytorch工程下载

您可以在PyTorch官方GitHub仓库中找到ResNet50的实现代码。以下是下载链接：

https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

您可以通过该链接下载resnet.py文件，该文件包含了ResNet50的实现代码。您可以使用该文件来构建和训练ResNet50模型。如果您需要使用已经训练好的ResNet50模型，可以使用PyTorch提供的torchvision.models模块中的预训练模型，如下所示：

import torchvision.models as models
resnet50 = models.resnet50(pretrained=True)

这将下载已经预训练好的ResNet50模型并加载到resnet50变量中。

相关问题

可以提供pytorch版本的resnet50的工程吗？

当然可以，以下是使用PyTorch实现的ResNet50模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        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.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, 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, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def ResNet50():
    return ResNet(Bottleneck, [3,4,6,3])

这个代码实现了ResNet50模型的结构，可以用于图像分类等任务中。如果需要使用，可以按照如下方式进行调用：

net = ResNet50()
y = net(x)

其中，`x`是输入的数据，`y`是经过模型处理后的结果。需要注意的是，这里的输入数据的通道数应该是3，因为ResNet50模型是针对RGB图像的。如果你要使用其他通道数的数据，需要进行相应的修改。

resnet50工程下载地址

你可以通过以下地址下载resnet50的PyTorch工程：

https://github.com/pytorch/vision/tree/master/torchvision/models

在这个工程中，resnet50的定义位于文件resnet.py中。如果需要使用预训练好的resnet50模型，可以通过以下代码进行下载：

import torch.utils.model_zoo as model_zoo

model_urls = {
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
}

class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        ...
        ...
        if pretrained:
            self.load_state_dict(model_zoo.load_url(model_urls['resnet50']))

其中，model_zoo.load_url函数将自动下载预训练模型并加载到网络中。