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)