resnest50解释

ResNeSt-50 是一种深度神经网络模型，它使用了一种新型的网络结构设计，称为“ResNeSt”（ResNet的集成）。

ResNeSt-50 是基于 ResNet-50 构建的模型，它在保留 ResNet 的基本结构和特点的同时，引入了新的模块化设计，使网络更加高效和精确。

ResNeSt-50 还使用了一种新的特征聚合方法，称为“Split-Attention”，它能够更好地捕捉不同特征之间的相互依赖关系，从而提高模型的性能。

总的来说，ResNeSt-50 是一种创新性的深度学习模型，旨在提高计算机视觉任务的准确性和效率。

相关问题

ResNeSt-50

ResNeSt-50是一个用于图像分类的深度神经网络模型。它是在ResNet的基础上进行了改进和优化。ResNeSt-50的网络结构由多个层组成，其中的layer1是其中的一层。具体的结构可以通过查看resnest50的源码来获取。你可以使用resnest.torch库中的resnest50模块来导入并使用ResNeSt-50模型。通过设置pretrained参数为True，可以加载预训练的权重文件来使用模型。对于预训练权重文件的加载过程，程序会根据文件名查找对应的url地址进行在线下载和加载。在预训练权重文件的字典中，每个模型都有一个相应的hash值，用于验证文件的完整性。ResNeSt-50的预训练权重文件可以通过"https://s3.us-west-1.wasabisys.com/resnest/torch/resnest50-528c19ca.pth"地址进行下载。

请帮我写 1通道resnest50的对比学习模型，要求不借助resnest.torch

好的，这里是一个使用PyTorch实现的1通道resnest50的对比学习模型：

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

class BasicBlock(nn.Module):
    expansion = 4

    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.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, stride=1, padding=0, 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 = self.bn2(self.conv2(out))
        out = self.bn3(self.conv3(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(1, 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(num_classes=10):
    return ResNet(Bottleneck, [3,4,6,3], num_classes)

def ResNet101(num_classes=10):
    return ResNet(Bottleneck, [3,4,23,3], num_classes)

def ResNet152(num_classes=10):
    return ResNet(Bottleneck, [3,8,36,3], num_classes)

这个模型使用了ResNet的基本结构，并在其中使用了1通道的输入，同时没有使用预训练的resnest.torch模型。你可以通过调用`ResNet50()`函数来创建一个ResNet50模型，其中num_classes参数指定了输出类别数。