def __init__(self, maxdisp=192):
super(DispAgg, self).__init__()
self.maxdisp = maxdisp
self.LGA3 = LGA3(radius=2)
self.LGA2 = LGA2(radius=2)
self.LGA = LGA(radius=2)
self.softmax = nn.Softmin(dim=1)
self.disparity = DisparityRegression(maxdisp=int(self.maxdisp))
# self.conv32x1 = BasicConv(32, 1, kernel_size=3)
self.conv32x1 = nn.Conv3d(32, 1, (3, 3, 3), (1, 1, 1), (1, 1, 1), bias=False)
def __init__(self, maxdisp, channel):
super(ResidualPredition2, self).__init__()
self.maxdisp = maxdisp + 1
self.conv1 = nn.Sequential(
BasicConv(1, channel, kernel_size=3, padding=1, is_3d=True),
BasicConv(channel, channel, kernel_size=3, padding=1, is_3d=True))
self.conv2 = BasicConv(channel,
channel,
kernel_size=3,
padding=1,
is_3d=True)
self.conv3 = nn.Sequential(
BasicConv(channel, channel, kernel_size=3, padding=1, is_3d=True),
BasicConv(channel, 1, kernel_size=3, padding=1, is_3d=True))
self.softmax = nn.Softmin(dim=1)
self.sga11 = SGABlock(channels=channel, refine=True)
self.sga12 = SGABlock(channels=channel, refine=True)
self.LGA2 = LGA2(radius=2)
