写一下pytorch实现maskrcnn模型的实例分割的代码

用Pytorch实现Mask RCNN模型的实例分割代码如下：import torch
import torch.nn as nn
import torch.nn.functional as Fclass MaskRCNN(nn.Module):
def __init__(self):
super(MaskRCNN, self).__init__()
# define the convolutional layers
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
# define the prediction layers
self.pred1 = nn.Conv2d(512, 6, kernel_size=1, stride=1, padding=0)
self.pred2 = nn.Conv2d(256, 6, kernel_size=1, stride=1, padding=0)
self.pred3 = nn.Conv2d(128, 6, kernel_size=1, stride=1, padding=0)
# define the mask layers
self.mask1 = nn.Conv2d(512, 6, kernel_size=1, stride=1, padding=0)
self.mask2 = nn.Conv2d(256, 6, kernel_size=1, stride=1, padding=0)
self.mask3 = nn.Conv2d(128, 6, kernel_size=1, stride=1, padding=0)

def forward(self, x):
# compute the forward pass of the network
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
# compute the prediction maps
pred1 = self.pred1(x)
pred2 = self.pred2(x)
pred3 = self.pred3(x)
# compute the mask maps
mask1 = self.mask1(x)
mask2 = self.mask2(x)
mask3 = self.mask3(x)
# return the prediction maps and mask maps
return pred1, pred2, pred3, mask1, mask2, mask3