YOLOv5+LSTM

抱歉，但是根据提供的引用内容，没有找到与YOLOv5和LSTM相关的信息。YOLOv5是一种目标检测模型，而LSTM是一种用于处理序列数据的循环神经网络。它们在不同领域有不同的应用。如果您有其他关于YOLOv5或LSTM的问题，请提供更多的上下文信息，我将尽力回答您的问题。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [YOLOV5网络结构设计的思考](https://blog.csdn.net/a699669/article/details/122071671)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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相关问题

将convlstm加到YOLOv7骨干网络中，写pytorch代码

好的，以下是将ConvLSTM加到YOLOv7骨干网络中的PyTorch代码。请注意，这仅是一个示例代码，您需要根据自己的需求进行修改和调整。

首先，我们需要导入所需的库和模块：

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

接下来，我们定义ConvLSTM模块：

class ConvLSTM(nn.Module):
    def __init__(self, in_channels, hidden_channels, kernel_size, batch_size, bias=True):
        super(ConvLSTM, self).__init__()

        self.input_dim = in_channels
        self.hidden_dim = hidden_channels
        self.kernel_size = kernel_size
        self.bias = bias
        self.batch_size = batch_size
        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
                              out_channels=4 * self.hidden_dim,
                              kernel_size=self.kernel_size,
                              padding=self.padding,
                              bias=self.bias)

    def forward(self, input_tensor, cur_state):
        h_cur, c_cur = cur_state

        combined = torch.cat([input_tensor, h_cur], dim=1)
        combined_conv = self.conv(combined)
        cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)

        i = torch.sigmoid(cc_i)
        f = torch.sigmoid(cc_f)
        o = torch.sigmoid(cc_o)
        g = torch.tanh(cc_g)

        c_next = f * c_cur + i * g
        h_next = o * torch.tanh(c_next)

        return h_next, c_next

    def init_hidden(self):
        return (Variable(torch.zeros(self.batch_size, self.hidden_dim, 7, 7)).cuda(),
                Variable(torch.zeros(self.batch_size, self.hidden_dim, 7, 7)).cuda())

然后，我们定义包含ConvLSTM的YOLOv7骨干网络：

class YOLOv7_ConvLSTM(nn.Module):
    def __init__(self):
        super(YOLOv7_ConvLSTM, self).__init__()

        self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 32, 1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm2d(32)
        self.conv4 = nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=False)
        self.bn4 = nn.BatchNorm2d(64)
        self.conv5 = nn.Conv2d(64, 32, 1, stride=1, padding=0, bias=False)
        self.bn5 = nn.BatchNorm2d(32)
        
        self.conv_lstm = ConvLSTM(in_channels=32, hidden_channels=32, kernel_size=3, batch_size=1)
        
    def forward(self, x):
        h, w = x.size()[2:]

        x = F.leaky_relu(self.bn1(self.conv1(x)), 0.1, inplace=True)
        x = F.leaky_relu(self.bn2(self.conv2(x)), 0.1, inplace=True)
        x = F.leaky_relu(self.bn3(self.conv3(x)), 0.1, inplace=True)
        x = F.leaky_relu(self.bn4(self.conv4(x)), 0.1, inplace=True)
        x = F.leaky_relu(self.bn5(self.conv5(x)), 0.1, inplace=True)
        
        x = x.view(-1, 32, h // 16, w // 16)
        
        h, c = self.conv_lstm(x, self.conv_lstm.init_hidden())
        
        return h

在这里，我们使用了YOLOv7的骨干网络，同时还添加了一个包含ConvLSTM的模块。在前向传递中，我们首先对输入图像进行标准卷积操作，然后将输出的张量重塑成适合ConvLSTM模块的形状，并将其输入到ConvLSTM模块中。最后，我们仅返回ConvLSTM的输出。

这是一个简单的示例代码，您可以根据自己的需要进行修改和扩展。