我现在要求使用yolov4-tiny,环境为pytorch,请提供给我相关的算法
时间: 2024-05-15 20:17:37 浏览: 10
确定,以下是yolov4-tiny算法的代码示例(使用pytorch框架):
```
import torch
import torch.nn as nn
import torch.nn.functional as F
class YOLOv4Tiny(nn.Module):
def __init__(self):
super(YOLOv4Tiny, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(num_features=32)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(num_features=64)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(num_features=128)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2d(num_features=256)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2d(num_features=512)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv6 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1)
self.bn6 = nn.BatchNorm2d(num_features=1024)
self.conv7 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1, stride=1, padding=0)
self.bn7 = nn.BatchNorm2d(num_features=256)
self.conv8 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)
self.bn8 = nn.BatchNorm2d(num_features=512)
self.conv9 = nn.Conv2d(in_channels=512, out_channels=255, kernel_size=1, stride=1, padding=0)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool2(x)
x = F.relu(self.bn3(self.conv3(x)))
x = self.pool3(x)
x = F.relu(self.bn4(self.conv4(x)))
x = self.pool4(x)
x = F.relu(self.bn5(self.conv5(x)))
x = self.pool5(x)
x = F.relu(self.bn6(self.conv6(x)))
x = F.relu(self.bn7(self.conv7(x)))
x = F.relu(self.bn8(self.conv8(x)))
x = self.conv9(x)
x = torch.sigmoid(x)
return x
```
希望能解决你的问题!