strides = [stride] + [1]*(num_blocks-1)

这段代码是用来生成一个长度为 `num_blocks` 的列表，其中第一个元素是 `stride`，后面的元素都是 1。这个列表通常用于指定卷积层或池化层在每个维度上的步幅。举个例子，如果 `stride=2`，`num_blocks=3`，那么生成的列表就是 `[2, 1, 1]`，表示在第一个维度上步幅为 2，在其他维度上步幅为 1。

相关问题

def make_layer(self, block, channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) #strides=[1,1] layers = [] for stride in strides: layers.append(block(self.inchannel, channels, stride)) self.inchannel = channels return nn.Sequential(*layers)

根据你提供的代码，`make_layer`函数接受以下参数：
- `block`: 表示要使用的残差块类型
- `channels`: 表示输出通道数
- `num_blocks`: 表示要堆叠的残差块数量
- `stride`: 表示第一个残差块的步幅

该函数会根据`num_blocks`的值生成一个步幅列表`strides`，其中第一个元素为`stride`，后续元素都为1。然后，使用循环迭代`strides`中的每个步幅，并将对应的残差块添加到`layers`列表中。

在每次迭代中，使用指定的步幅、当前的输入通道数和输出通道数创建一个残差块，并将其添加到`layers`列表中。在添加之前，将输入通道数更新为输出通道数，以便在下一次迭代中使用。

最后，将`layers`列表中的所有层封装在`nn.Sequential`中，并将其作为结果返回。

这个函数的作用是生成一个包含多个残差块的层序列，用于构建深度残差网络。每个残差块的输出通道数相同，但步幅只在第一个残差块中使用。这样可以构建出具有不同层次结构和感受野的网络。

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable class Bottleneck(nn.Module): def init(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer): super(Bottleneck, self).init() self.out_planes = out_planes self.dense_depth = dense_depth self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False) self.bn2 = nn.BatchNorm2d(in_planes) self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(out_planes+dense_depth) self.shortcut = nn.Sequential() if first_layer: self.shortcut = nn.Sequential( nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes+dense_depth) ) 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)) x = self.shortcut(x) d = self.out_planes out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1) out = F.relu(out) return out class DPN(nn.Module): def init(self, cfg): super(DPN, self).init() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(64) self.last_planes = 64 self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1) self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2) self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2) self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2) self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)dense_depth[3], 10) def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride): strides = [stride] + 1 layers = [] for i,stride in (strides): layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0)) self.last_planes = out_planes + (i+2) * dense_depth 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 DPN92(): cfg = { 'in_planes': (96,192,384,768), 'out_planes': (256,512,1024,2048), 'num_blocks': (3,4,20,3), 'dense_depth': (16,32,24,128) } return DPN(cfg)基于这个程序改成对摄像头采集的图像检测与分类输出坐标、大小和种类

这个程序是一个深度残差网络（DPN）用于图像分类，需要进行较大的修改才能用于目标检测和分类。需要添加目标检测和分类的头部网络，以输出目标的坐标、大小和种类。同时需要将输入改为摄像头采集的图像。

以下是基于这个程序的修改建议：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class Bottleneck(nn.Module):
    def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
        super(Bottleneck, self).__init__()
        self.out_planes = out_planes
        self.dense_depth = dense_depth
        self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
        self.bn2 = nn.BatchNorm2d(in_planes)
        self.conv3 = nn.Conv2d(in_planes, out_planes * dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes * dense_depth)
        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv2d(last_planes, out_planes * dense_depth, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_planes * dense_depth)
            )

    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))
        x = self.shortcut(x)
        d = self.out_planes
        out = torch.cat([x[:, :d, :, :], out[:, :d, :, :], x[:, d:, :, :], out[:, d:, :, :]], 1)
        out = F.relu(out)
        return out

class DPN(nn.Module):
    def __init__(self, cfg, num_classes):
        super(DPN, self).__init__()
        in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
        num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.last_planes = 64
        self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
        self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
        self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
        self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(out_planes[3] * dense_depth[3], num_classes * 5)  # 输出目标的坐标、大小和种类

    def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for i, stride in enumerate(strides):
            layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
            self.last_planes = out_planes * dense_depth
        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 = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def DPN92(num_classes=80):
    cfg = {
        'in_planes': (96, 192, 384, 768),
        'out_planes': (256, 512, 1024, 2048),
        'num_blocks': (3, 4, 20, 3),
        'dense_depth': (16, 32, 24, 128)
    }
    return DPN(cfg, num_classes)

在上述修改中，我们添加了一个新的类别数参数 `num_classes`，用于控制输出的目标种类数。同时将最后的全连接层 `linear` 替换为了一个新的全连接层 `fc`，输出目标的坐标、大小和种类。在 `forward` 函数中，我们添加了一个自适应平均池化层 `avgpool`，用于将最后一层特征图的大小缩小为 `1x1`。最后将特征图展平后送入全连接层 `fc` 进行分类和回归。