/
shadownet.py
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/
shadownet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torchvision
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.block1 = nn.Sequential(
nn.Conv2d(in_channels, 64, 1, bias=False), nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d(64, 64, 3, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d(64, in_channels, 1, bias=False), nn.BatchNorm2d(in_channels)
)
self.block2 = nn.Sequential(
nn.Conv2d(in_channels, 32, 1, bias=False), nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d(32, 32, 3, padding=1, groups=32, bias=False), nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d(32, out_channels, 1, bias=False), nn.BatchNorm2d(out_channels)
)
def forward(self, x):
block1 = F.relu(self.block1(x) + x, True)
block2 = self.block2(block1)
return block2
class SoftPred(nn.Module):
def __init__(self, in_channels):
super(SoftPred, self).__init__()
self.conv_1x1 = nn.Sequential(
nn.Conv2d(in_channels, 32, 1, bias=False), nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d(32, 1, 1, bias=False)
)
def forward(self, x):
return self.conv_1x1(x)
class LocalShadowDetector(nn.Module):
def __init__(self, size=(320,320)):
super(LocalShadowDetector, self).__init__()
self.block = nn.Sequential(
nn.Conv2d( 64, 32, kernel_size=(1,1), bias=False, groups=32 ),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d( 32, 32, kernel_size=(7,7), padding=(3, 3), bias=False, groups=8 ),
nn.BatchNorm2d(32),
nn.ReLU()
)
self.conv = nn.Sequential(
nn.Conv2d(64, 32, 1, bias=False, groups=32)
)
self.pred = nn.Conv2d(32, 1, 1, bias=False)
self.size = size
def forward(self, x):
return self.pred( F.interpolate(self.block(x), size=self.size, mode="bilinear") + \
F.interpolate(self.conv(x), size=self.size, mode="bilinear"))
class ShadowNet(nn.Module):
def __init__(self):
super().__init__()
backbone = models.resnext101_32x8d(pretrained=True)
mean = torch.tensor([0.485, 0.456, 0.406])
std = torch.tensor([0.229, 0.224, 0.225])
trans_img = torchvision.transforms.Normalize(mean, std)
trans_back = torchvision.transforms.Normalize(-mean/std, 1.0/std)
self.trans_imgs = lambda x: torch.cat([trans_img(x[_]).unsqueeze(0) for _ in range(x.shape[0])], dim=0)
self.trans_backs = lambda x: torch.cat([trans_back(x[_]).unsqueeze(0) for _ in range(x.shape[0])], dim=0)
self.layer0 = nn.Sequential(
backbone.conv1,
backbone.bn1,
backbone.relu
)
self.layer1 = nn.Sequential(
backbone.maxpool,
backbone.layer1
)
self.layer2 = backbone.layer2
self.layer3 = backbone.layer3
self.layer4 = backbone.layer4
self.reduction4 = nn.Sequential(
nn.Conv2d( 2048, 512, 3, padding=1, bias=False ), nn.BatchNorm2d(512), nn.ReLU(),
nn.Conv2d( 512, 32, 1, bias = False), nn.BatchNorm2d(32), nn.ReLU()
)
self.reduction3 = nn.Sequential(
nn.Conv2d( 1024, 512, 3, padding=1, bias=False ), nn.BatchNorm2d(512), nn.ReLU(),
nn.Conv2d( 512, 32, 1, bias = False), nn.BatchNorm2d(32), nn.ReLU()
)
self.reduction2 = nn.Sequential(
nn.Conv2d( 512, 256, 3, padding=1, bias=False ), nn.BatchNorm2d(256), nn.ReLU(),
nn.Conv2d( 256, 32, 1, bias = False), nn.BatchNorm2d(32), nn.ReLU()
)
self.reduction1 = nn.Sequential(
nn.Conv2d( 256, 128, 3, padding=1, bias=False ), nn.BatchNorm2d(128), nn.ReLU(),
nn.Conv2d( 128, 32, 1, bias = False), nn.BatchNorm2d(32), nn.ReLU()
)
self.reduction0 = nn.Sequential(
nn.Conv2d( 64, 64, 3, padding=1, bias=False ), nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d( 64, 32, 1, bias = False), nn.BatchNorm2d(32), nn.ReLU()
)
self.DRR = nn.Sequential(
nn.Conv2d( 3, 64, kernel_size=(7,7), padding=(3,3), bias=False ), nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d( 64, 32, kernel_size=(3,3), padding=(1,1), bias=False ), nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d( 32, 1, 1, bias=False )
)
self.fusion3 = ConvBlock(64, 32)
self.fusion2 = ConvBlock(64, 32)
self.fusion1 = ConvBlock(96, 32)
self.fusion0 = ConvBlock(128, 32)
self.pred3 = SoftPred(32)
self.pred2 = SoftPred(32)
self.pred1 = SoftPred(32)
self.pred0 = SoftPred(32)
self.LSDForIntersectionBranch0 = LocalShadowDetector()
self.LSDForIntersectionBranch1 = LocalShadowDetector()
self.LSDForIntersectionBranch2 = LocalShadowDetector()
self.LSDForIntersectionBranch3 = LocalShadowDetector()
self.LSDForDivergenceBranch0 = LocalShadowDetector()
self.LSDForDivergenceBranch1 = LocalShadowDetector()
self.LSDForDivergenceBranch2 = LocalShadowDetector()
self.LSDForDivergenceBranch3 = LocalShadowDetector()
self.fuseIntersection = nn.Conv2d(4, 1, 1, bias=False)
self.fuseDivergence = nn.Conv2d(4, 1, 1, bias=False)
self.fuseNonDark = nn.Conv2d(4, 1, 1, bias=False)
def forward(self, x):
layer0 = self.layer0(x)
layer1 = self.layer1(layer0)
layer2 = self.layer2(layer1)
layer3 = self.layer3(layer2)
layer4 = self.layer4(layer3)
df0 = self.reduction0(layer0)
df1 = self.reduction1(layer1)
df2 = self.reduction2(layer2)
df3 = self.reduction3(layer3)
df4 = self.reduction4(layer4)
ff3 = self.fusion3( torch.cat( (
F.interpolate(df4, size=df3.shape[2:4], mode="bilinear"),
df3
), dim = 1 ) )
ff2 = self.fusion2( torch.cat( (
F.interpolate(ff3, size=df2.shape[2:4], mode="bilinear"),
df2
), dim = 1 ) )
ff1 = self.fusion1( torch.cat( (
F.interpolate(ff3, size=df1.shape[2:4], mode="bilinear"),
F.interpolate(ff2, size=df1.shape[2:4], mode="bilinear"),
df1
), dim = 1 ) )
ff0 = self.fusion0( torch.cat( (
F.interpolate(ff3, size=df0.shape[2:4], mode="bilinear"),
F.interpolate(ff2, size=df0.shape[2:4], mode="bilinear"),
F.interpolate(ff1, size=df0.shape[2:4], mode="bilinear"),
df0
), dim = 1 ) )
scoreMap3 = self.pred3( F.interpolate(ff3, size=x.shape[2:4], mode="bilinear") )
scoreMap2 = self.pred2( F.interpolate(ff2, size=x.shape[2:4], mode="bilinear") )
scoreMap1 = self.pred1( F.interpolate(ff1, size=x.shape[2:4], mode="bilinear") )
scoreMap0 = self.pred0( F.interpolate(ff0, size=x.shape[2:4], mode="bilinear") )
darkRegion = self.DRR( x )
DarkRegion = torch.sign( torch.relu(darkRegion) )
ScoreMap = torch.sign( torch.relu(scoreMap0) )
Divergence = torch.relu(DarkRegion - ScoreMap)
NonDarkMap = 1.0 - torch.sign(ScoreMap + DarkRegion)
globalContextCue0 = torch.cat((df0,ff0),dim=1)
globalContextCue1 = torch.cat((df1,ff1),dim=1)
globalContextCue2 = torch.cat((df2,ff2),dim=1)
globalContextCue3 = torch.cat((df3,ff3),dim=1)
dive0 = self.LSDForDivergenceBranch0(globalContextCue0)
dive1 = self.LSDForDivergenceBranch1(globalContextCue1)
dive2 = self.LSDForDivergenceBranch2(globalContextCue2)
dive3 = self.LSDForDivergenceBranch3(globalContextCue3)
isec0 = self.LSDForIntersectionBranch0(globalContextCue0)
isec1 = self.LSDForIntersectionBranch1(globalContextCue1)
isec2 = self.LSDForIntersectionBranch2(globalContextCue2)
isec3 = self.LSDForIntersectionBranch3(globalContextCue3)
divergence = self.fuseDivergence( torch.cat( (
dive0, dive1, dive2, dive3
), dim = 1 ) ) * Divergence
intersection = self.fuseIntersection( torch.cat( (
isec0, isec1, isec2, isec3
), dim = 1) ) * ScoreMap ## can be filter to be intersection
nondark = self.fuseNonDark( torch.cat((
scoreMap0,scoreMap1,scoreMap2,scoreMap3
),dim=1) ) * NonDarkMap
final = divergence + intersection + nondark
return {
"prediction":final,
"darkRegion":darkRegion
}