yolo1代码pytorch

以下是一个简单的 YOLOv1 的 PyTorch 实现代码：

import torch
import torch.nn as nn

class YOLOv1(nn.Module):
    def __init__(self, num_classes=20, S=7, B=2, C=2):
        super(YOLOv1, self).__init__()
        self.num_classes = num_classes
        self.S = S
        self.B = B
        self.C = C
        self.conv1 = nn.Conv2d(3, 64, 7, stride=2, padding=3)
        self.pool1 = nn.MaxPool2d(2, stride=2)
        self.conv2 = nn.Conv2d(64, 192, 3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(2, stride=2)
        self.conv3 = nn.Conv2d(192, 128, 1, stride=1, padding=0)
        self.conv4 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
        self.conv5 = nn.Conv2d(256, 256, 1, stride=1, padding=0)
        self.conv6 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.pool3 = nn.MaxPool2d(2, stride=2)
        self.conv7 = nn.Conv2d(512, 256, 1, stride=1, padding=0)
        self.conv8 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.conv9 = nn.Conv2d(512, 256, 1, stride=1, padding=0)
        self.conv10 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.conv11 = nn.Conv2d(512, 256, 1, stride=1, padding=0)
        self.conv12 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.conv13 = nn.Conv2d(512, 256, 1, stride=1, padding=0)
        self.conv14 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
        self.conv15 = nn.Conv2d(512, 512, 1, stride=1, padding=0)
        self.conv16 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
        self.pool4 = nn.MaxPool2d(2, stride=2)
        self.conv17 = nn.Conv2d(1024, 512, 1, stride=1, padding=0)
        self.conv18 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
        self.conv19 = nn.Conv2d(1024, 512, 1, stride=1, padding=0)
        self.conv20 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
        self.conv21 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
        self.conv22 = nn.Conv2d(1024, 1024, 3, stride=2, padding=1)
        self.conv23 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
        self.conv24 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
        self.fc1 = nn.Linear(7 * 7 * 1024, 4096)
        self.fc2 = nn.Linear(4096, S * S * (C + B * 5))

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = torch.relu(self.conv3(x))
        x = torch.relu(self.conv4(x))
        x = torch.relu(self.conv5(x))
        x = self.pool3(torch.relu(self.conv6(x)))
        x = torch.relu(self.conv7(x))
        x = torch.relu(self.conv8(x))
        x = torch.relu(self.conv9(x))
        x = torch.relu(self.conv10(x))
        x = torch.relu(self.conv11(x))
        x = torch.relu(self.conv12(x))
        x = torch.relu(self.conv13(x))
        x = torch.relu(self.conv14(x))
        x = torch.relu(self.conv15(x))
        x = self.pool4(torch.relu(self.conv16(x)))
        x = torch.relu(self.conv17(x))
        x = torch.relu(self.conv18(x))
        x = torch.relu(self.conv19(x))
        x = torch.relu(self.conv20(x))
        x = torch.relu(self.conv21(x))
        x = torch.relu(self.conv22(x))
        x = torch.relu(self.conv23(x))
        x = torch.relu(self.conv24(x))
        x = x.view(-1, 7 * 7 * 1024)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        x = x.view(-1, self.S, self.S, self.C + self.B * 5)
        return x

注意，这只是一个简单的实现，可能与原论文中的细节不完全一致。同时，这个模型没有进行训练，不具备实际应用价值，只是为了展示 YOLOv1 的模型结构。