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)基于这个程序利用pytorch框架修改成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神经网络模型DPN，用于图像分类任务。要将其修改为图像检测和分类模型，需要进行以下步骤：

1. 修改最后一层的输出。原来最后一层是线性层，输出分类结果。现在需要输出物体的坐标、大小和种类，可以将最后一层修改为三个分别输出这三个信息的线性层，例如：

self.classifier = nn.Sequential(
    nn.Linear(out_planes[3] * (num_blocks[3] + 1) * dense_depth[3], 256),
    nn.Linear(256, num_classes + 4)  # 输出种类+4个坐标
)

2. 修改前向传播函数。原来的前向传播函数将最后一层的输出经过线性层，得到分类结果。现在需要将最后一层的输出分别经过三个线性层，得到坐标、大小和种类信息，例如：

out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.classifier(out)

3. 修改训练过程。原来的训练过程只需要计算交叉熵损失，现在需要计算分类损失和回归损失。分类损失可以使用交叉熵损失，回归损失可以使用均方误差损失，例如：

criterion_cls = nn.CrossEntropyLoss()
criterion_reg = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        cls_loss = criterion_cls(outputs[:, :num_classes], labels[:, 0])
        reg_loss = criterion_reg(outputs[:, num_classes:], labels[:, 1:])
        loss = cls_loss + reg_loss
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

其中，`labels`是一个大小为`(batch_size, 5)`的张量，第一列是物体的种类，后四列是物体的坐标和大小。`num_classes`是物体的种类数目，`criterion_cls`是分类损失函数，`criterion_reg`是回归损失函数。