生成代码，用torch写bifpn结构

可以使用以下代码来生成bifpn结构：

import torch.nn as nn

class BiFPN(nn.Module):
def __init__(self, num_channels):
super(BiFPN, self).__init__()
self.num_channels = num_channels
self.conv6_up = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv5_up = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv4_up = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv3_up = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv4_down = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv5_down = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv6_down = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv7_down = nn.Conv2d(num_channels, num_channels, kernel_size=1, stride=1, padding=)
self.conv6_ = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv5_ = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv4_ = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv3_ = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv2_ = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv3_1 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv5_1 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv6_1 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.conv7_1 = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()

def forward(self, inputs):
if len(inputs) == 5:
inputs = [inputs[], None, inputs[1], None, inputs[2], None, inputs[3], None, inputs[4]]
# P6_, P5_, P4_, P3_, P2_
P6_, P5_, P4_, P3_, P2_ = inputs

# P6_1, P5_1, P4_1, P3_1
P6_1 = self.conv6_1(self.relu(self.conv6_(P6_)))
P5_1 = self.conv5_1(self.relu(self.conv5_(P5_)))
P4_1 = self.conv4_1(self.relu(self.conv4_(P4_)))
P3_1 = self.conv3_1(self.relu(self.conv3_(P3_)))

# P4_2, P5_2, P6_2, P7_2
P4_2 = P4_1 + self.upsample_add(P5_1, P4_1)
P5_2 = P5_1 + self.upsample_add(P6_1, P5_1, self.conv5_up(P4_2))
P6_2 = P6_1 + self.upsample_add(self.conv6_up(P5_2), P6_1, self.conv6_down(P7_2))
P7_2 = P7_1 + self.upsample_add(self.conv7_down(P6_2))

return [P2_, P3_1, P4_2, P5_2, P6_2, P7_2]

def upsample_add(self, x, y, c=None):
_, _, H, W = y.size()
if c is not None:
x = nn.functional.interpolate(x, size=(H, W), mode='nearest')
y = c(nn.functional.interpolate(y, size=(H, W), mode='nearest'))
else:
x = nn.functional.interpolate(x, size=(H, W), mode='nearest')
y = nn.functional.interpolate(y, size=(H, W), mode='nearest')
return x + y

# 使用示例
num_channels = 256
inputs = [torch.randn(1, num_channels, 64, 64), torch.randn(1, num_channels, 32, 32), torch.randn(1, num_channels, 16, 16), torch.randn(1, num_channels, 8, 8), torch.randn(1, num_channels, 4, 4)]
bifpn = BiFPN(num_channels)
outputs = bifpn(inputs)
print(outputs)