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- import torch
- import torch.nn as nn
- import torchvision
-
- def Conv3x3ReLU(in_channels,out_channels):
- return nn.Sequential(
- nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=1,padding=1),
- nn.ReLU6(inplace=True)
- )
-
- def locLayer(in_channels,out_channels):
- return nn.Sequential(
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1),
- )
-
- def conf_centernessLayer(in_channels,out_channels):
- return nn.Sequential(
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- Conv3x3ReLU(in_channels=in_channels, out_channels=in_channels),
- nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1),
- )
-
- class PolarMask(nn.Module):
- def __init__(self, num_classes=21):
- super(PolarMask, self).__init__()
- self.num_classes = num_classes
- resnet = torchvision.models.resnet50()
- layers = list(resnet.children())
-
- self.layer1 = nn.Sequential(*layers[:5])
- self.layer2 = nn.Sequential(*layers[5])
- self.layer3 = nn.Sequential(*layers[6])
- self.layer4 = nn.Sequential(*layers[7])
-
- self.lateral5 = nn.Conv2d(in_channels=2048, out_channels=256, kernel_size=1)
- self.lateral4 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1)
- self.lateral3 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1)
-
- self.upsample4 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=4, stride=2, padding=1)
- self.upsample3 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=4, stride=2, padding=1)
-
- self.downsample6 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1)
- self.downsample5 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1)
-
- self.loc_layer3 = locLayer(in_channels=256,out_channels=36)
- self.conf_centerness_layer3 = conf_centernessLayer(in_channels=256,out_channels=self.num_classes)
-
- self.loc_layer4 = locLayer(in_channels=256, out_channels=36)
- self.conf_centerness_layer4 = conf_centernessLayer(in_channels=256, out_channels=self.num_classes)
-
- self.loc_layer5 = locLayer(in_channels=256, out_channels=36)
- self.conf_centerness_layer5 = conf_centernessLayer(in_channels=256, out_channels=self.num_classes)
-
- self.loc_layer6 = locLayer(in_channels=256, out_channels=36)
- self.conf_centerness_layer6 = conf_centernessLayer(in_channels=256, out_channels=self.num_classes)
-
- self.loc_layer7 = locLayer(in_channels=256, out_channels=36)
- self.conf_centerness_layer7 = conf_centernessLayer(in_channels=256, out_channels=self.num_classes)
-
- self.init_params()
-
- def init_params(self):
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.BatchNorm2d):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- x = self.layer1(x)
- c3 =x = self.layer2(x)
- c4 =x = self.layer3(x)
- c5 = x = self.layer4(x)
-
- p5 = self.lateral5(c5)
- p4 = self.upsample4(p5) + self.lateral4(c4)
- p3 = self.upsample3(p4) + self.lateral3(c3)
-
- p6 = self.downsample5(p5)
- p7 = self.downsample6(p6)
-
- loc3 = self.loc_layer3(p3)
- conf_centerness3 = self.conf_centerness_layer3(p3)
- conf3, centerness3 = conf_centerness3.split([self.num_classes, 1], dim=1)
-
- loc4 = self.loc_layer4(p4)
- conf_centerness4 = self.conf_centerness_layer4(p4)
- conf4, centerness4 = conf_centerness4.split([self.num_classes, 1], dim=1)
-
- loc5 = self.loc_layer5(p5)
- conf_centerness5 = self.conf_centerness_layer5(p5)
- conf5, centerness5 = conf_centerness5.split([self.num_classes, 1], dim=1)
-
- loc6 = self.loc_layer6(p6)
- conf_centerness6 = self.conf_centerness_layer6(p6)
- conf6, centerness6 = conf_centerness6.split([self.num_classes, 1], dim=1)
-
- loc7 = self.loc_layer7(p7)
- conf_centerness7 = self.conf_centerness_layer7(p7)
- conf7, centerness7 = conf_centerness7.split([self.num_classes, 1], dim=1)
-
- locs = torch.cat([loc3.permute(0, 2, 3, 1).contiguous().view(loc3.size(0), -1),
- loc4.permute(0, 2, 3, 1).contiguous().view(loc4.size(0), -1),
- loc5.permute(0, 2, 3, 1).contiguous().view(loc5.size(0), -1),
- loc6.permute(0, 2, 3, 1).contiguous().view(loc6.size(0), -1),
- loc7.permute(0, 2, 3, 1).contiguous().view(loc7.size(0), -1)],dim=1)
-
- confs = torch.cat([conf3.permute(0, 2, 3, 1).contiguous().view(conf3.size(0), -1),
- conf4.permute(0, 2, 3, 1).contiguous().view(conf4.size(0), -1),
- conf5.permute(0, 2, 3, 1).contiguous().view(conf5.size(0), -1),
- conf6.permute(0, 2, 3, 1).contiguous().view(conf6.size(0), -1),
- conf7.permute(0, 2, 3, 1).contiguous().view(conf7.size(0), -1),], dim=1)
-
- centernesses = torch.cat([centerness3.permute(0, 2, 3, 1).contiguous().view(centerness3.size(0), -1),
- centerness4.permute(0, 2, 3, 1).contiguous().view(centerness4.size(0), -1),
- centerness5.permute(0, 2, 3, 1).contiguous().view(centerness5.size(0), -1),
- centerness6.permute(0, 2, 3, 1).contiguous().view(centerness6.size(0), -1),
- centerness7.permute(0, 2, 3, 1).contiguous().view(centerness7.size(0), -1), ], dim=1)
-
- out = (locs, confs, centernesses)
- return out
-
- if __name__ == '__main__':
- model = PolarMask()
- print(model)
-
- input = torch.randn(1, 3, 800, 1024)
- out = model(input)
- print(out[0].shape)
- print(out[1].shape)
- print(out[2].shape)
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