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T4
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import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
class slam(nn.Module):
def __init__(self, spatial_dim):
super(slam,self).__init__()
self.spatial_dim = spatial_dim
self.linear = nn.Sequential(
nn.Linear(spatial_dim**2,512),
nn.ReLU(),
nn.Linear(512,1),
nn.Sigmoid()
)
def forward(self, feature):
n,c,h,w = feature.shape
if (h != self.spatial_dim):
x = F.interpolate(feature,size=(self.spatial_dim,self.spatial_dim),mode= "bilinear", align_corners=True)
else:
x = feature
x = x.view(n,c,-1)
x = self.linear(x)
x = x.unsqueeze(dim =3)
out = x.expand_as(feature)*feature
return out
class to_map(nn.Module):
def __init__(self,channels):
super(to_map,self).__init__()
self.to_map = nn.Sequential(
nn.Conv2d(in_channels=channels,out_channels=1, kernel_size=1,stride=1),
nn.Sigmoid()
)
def forward(self,feature):
return self.to_map(feature)
class conv_bn_relu(nn.Module):
def __init__(self,in_channels, out_channels, kernel_size = 3, padding = 1, stride = 1):
super(conv_bn_relu,self).__init__()
self.conv = nn.Conv2d(in_channels= in_channels, out_channels= out_channels, kernel_size= kernel_size, padding= padding, stride = stride)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self,x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class up_conv_bn_relu(nn.Module):
def __init__(self,up_size, in_channels, out_channels = 64, kernal_size = 1, padding =0, stride = 1):
super(up_conv_bn_relu,self).__init__()
self.upSample = nn.Upsample(size = (up_size,up_size),mode="bilinear",align_corners=True)
self.conv = nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size = kernal_size, stride = stride, padding= padding)
self.bn = nn.BatchNorm2d(num_features=out_channels)
self.act = nn.ReLU()
def forward(self,x):
x = self.upSample(x)
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
return x
class ICNet(nn.Module):
def __init__(self, is_pretrain = True, size1 = 512, size2 = 256):
super(ICNet,self).__init__()
resnet18Pretrained1 = torchvision.models.resnet18(pretrained= is_pretrain)
resnet18Pretrained2 = torchvision.models.resnet18(pretrained= is_pretrain)
self.size1 = size1
self.size2 = size2
## detail branch
self.b1_1 = nn.Sequential(*list(resnet18Pretrained1.children())[:5])
self.b1_1_slam = slam(32)
self.b1_2 = list(resnet18Pretrained1.children())[5]
self.b1_2_slam = slam(32)
## context branch
self.b2_1 = nn.Sequential(*list(resnet18Pretrained2.children())[:5])
self.b2_1_slam = slam(32)
self.b2_2 = list(resnet18Pretrained2.children())[5]
self.b2_2_slam = slam(32)
self.b2_3 = list(resnet18Pretrained2.children())[6]
self.b2_3_slam = slam(16)
self.b2_4 = list(resnet18Pretrained2.children())[7]
self.b2_4_slam = slam(8)
## upsample
self.upsize = size1 // 8
self.up1 = up_conv_bn_relu(up_size = self.upsize, in_channels = 128, out_channels = 256)
self.up2 = up_conv_bn_relu(up_size = self.upsize, in_channels = 512, out_channels = 256)
## map prediction head
self.to_map_f = conv_bn_relu(256*2,256*2)
self.to_map_f_slam = slam(32)
self.to_map = to_map(256*2)
## score prediction head
self.to_score_f = conv_bn_relu(256*2,256*2)
self.to_score_f_slam = slam(32)
self.head = nn.Sequential(
nn.Linear(256*2,512),
nn.ReLU(),
nn.Linear(512,1),
nn.Sigmoid()
)
self.avgpool = nn.AdaptiveAvgPool2d((1,1))
def forward(self,x1):
assert(x1.shape[2] == x1.shape[3] == self.size1)
x2 = F.interpolate(x1, size= (self.size2,self.size2), mode = "bilinear", align_corners= True)
x1 = self.b1_2_slam(self.b1_2(self.b1_1_slam(self.b1_1(x1))))
x2 = self.b2_2_slam(self.b2_2(self.b2_1_slam(self.b2_1(x2))))
x2 = self.b2_4_slam(self.b2_4(self.b2_3_slam(self.b2_3(x2))))
x1 = self.up1(x1)
x2 = self.up2(x2)
x_cat = torch.cat((x1,x2),dim = 1)
cly_map = self.to_map(self.to_map_f_slam(self.to_map_f(x_cat)))
score_feature = self.to_score_f_slam(self.to_score_f(x_cat))
score_feature = self.avgpool(score_feature)
score_feature = score_feature.squeeze()
score = self.head(score_feature)
score = score.squeeze()
return score,cly_map
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