帮我写一段yolov7的pytroch代码
时间: 2023-03-16 10:44:10 浏览: 78
您好,我可以为您提供一段示例的YOLOv7 PyTorch代码:import torch
from torch import nnclass YOLOv7(nn.Module):
def __init__(self):
super(YOLOv7, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.conv4 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
self.conv5 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
self.conv6 = nn.Conv2d(512, 1024, 3, stride=1, padding=1)
self.conv7 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.conv8 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.conv9 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.conv10 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.conv11 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.conv12 = nn.Conv2d(1024, 1024, 3, stride=1, padding=1)
self.fc1 = nn.Linear(7*7*1024, 4096)
self.fc2 = nn.Linear(4096, 1470) def forward(self, x):
# Convolutional Layer 1
x = self.conv1(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 2
x = self.conv2(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 3
x = self.conv3(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 4
x = self.conv4(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 5
x = self.conv5(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 6
x = self.conv6(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 7
x = self.conv7(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 8
x = self.conv8(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 9
x = self.conv9(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 10
x = self.conv10(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 11
x = self.conv11(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Convolutional Layer 12
x = self.conv12(x)
x = nn.functional.leaky_relu(x)
x = nn.functional.max_pool2d(x, 2, 2)
# Fully Connected Layer
x = x.view(-1, 7*7*1024)
x = self.fc1(x)
x = self.fc2(x)
return x
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