def __init__(self, intLevel): super(Decoder, self).__init__() intPrevious = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 1] intCurrent = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 0] if intLevel < 6: self.moduleUpflow = torch.nn.ConvTranspose2d(in_channels=2, out_channels=2, kernel_size=4, stride=2, padding=1) if intLevel < 6: self.moduleUpfeat = torch.nn.ConvTranspose2d(in_channels=intPrevious + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=4, stride=2, padding=1) if intLevel < 6: self.dblBackward = [ None, None, None, 5.0, 2.5, 1.25, 0.625, None ][intLevel + 1] if intLevel < 6: self.moduleBackward = Backward() self.moduleCorrelation = correlation.ModuleCorrelation() self.moduleCorreleaky = torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) self.moduleOne = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent, out_channels=128, kernel_size=3, stride=1, padding=1), torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) ) self.moduleTwo = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent + 128, out_channels=128, kernel_size=3, stride=1, padding=1), torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) ) self.moduleThr = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent + 128 + 128, out_channels=96, kernel_size=3, stride=1, padding=1), torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) ) self.moduleFou = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96, out_channels=64, kernel_size=3, stride=1, padding=1), torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) ) self.moduleFiv = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64, out_channels=32, kernel_size=3, stride=1, padding=1), torch.nn.LeakyReLU(inplace=False, negative_slope=0.1) ) self.moduleSix = torch.nn.Sequential( torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=3, stride=1, padding=1) )
def __init__(self):
super(Complex, self).__init__()
self.moduleOne = torch.nn.Sequential(
torch.nn.ZeroPad2d([2, 4, 2, 4]),
torch.nn.Conv2d(in_channels=3,
out_channels=64,
kernel_size=7,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleTwo = torch.nn.Sequential(
torch.nn.ZeroPad2d([1, 3, 1, 3]),
torch.nn.Conv2d(in_channels=64,
out_channels=128,
kernel_size=5,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleThr = torch.nn.Sequential(
torch.nn.ZeroPad2d([1, 3, 1, 3]),
torch.nn.Conv2d(in_channels=128,
out_channels=256,
kernel_size=5,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleRedir = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=256,
out_channels=32,
kernel_size=1,
stride=1,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleCorrelation = correlation.ModuleCorrelation()
self.moduleCombined = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=473,
out_channels=256,
kernel_size=3,
stride=1,
padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleFou = torch.nn.Sequential(
torch.nn.ZeroPad2d([0, 2, 0, 2]),
torch.nn.Conv2d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleFiv = torch.nn.Sequential(
torch.nn.ZeroPad2d([0, 2, 0, 2]),
torch.nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=512,
out_channels=512,
kernel_size=3,
stride=1,
padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleSix = torch.nn.Sequential(
torch.nn.ZeroPad2d([0, 2, 0, 2]),
torch.nn.Conv2d(in_channels=512,
out_channels=1024,
kernel_size=3,
stride=2,
padding=0),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=1024,
out_channels=1024,
kernel_size=3,
stride=1,
padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
)
self.moduleUpconv = Upconv()