def __init__(self, maxdisp=192):
super(Disp, self).__init__()
self.maxdisp = maxdisp
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=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=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=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.conv64x1 = nn.Conv3d(64,
1, (3, 3, 3), (1, 1, 1), (1, 1, 1),
bias=False)
def __init__(self, maxdisp=192, InChannel=32):
super(Disp, self).__init__()
self.maxdisp = maxdisp
self.softmax = nn.Softmin(dim=1)
self.disparity = DisparityRegression(maxdisp=self.maxdisp)
# self.conv32x1 = BasicConv(32, 1, kernel_size=3)
if InChannel == 64:
self.conv3d_2d = nn.Conv3d(InChannel,
1, (1, 1, 1), (1, 1, 1), (1, 1, 1),
bias=False)
else:
self.conv3d_2d = nn.Conv3d(InChannel,
1, (3, 3, 3), (1, 1, 1), (1, 1, 1),
bias=False)
