def __init__(self, maxdisp=192):
super(GANet, self).__init__()
self.maxdisp = maxdisp
self.conv_start = nn.Sequential(BasicConv(3, 16, kernel_size=3, stride=2, padding=1),
BasicConv(16, 32, kernel_size=3, stride=2, padding=1),
BasicConv(32, 32, kernel_size=3, stride=2, padding=1),
BasicConv(32, 32, kernel_size=3, padding=1))
self.conv_x = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_y = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_x11 = BasicConv(16, 16, kernel_size=3, padding=1)
self.conv_y11 = BasicConv(16, 16, kernel_size=3, padding=1)
self.conv_x22 = BasicConv(8, 8, kernel_size=3, padding=1)
self.conv_y22 = BasicConv(8, 8, kernel_size=3, padding=1)
self.conv_refine = nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), bias=False)
self.bn_relu = nn.Sequential(apex.parallel.SyncBatchNorm(32),
nn.ReLU(inplace=True))
self.feature = Feature()
self.guidance = Guidance()
self.cost_agg = CostAggregation(int(self.maxdisp / 8))
self.cv = GetCostVolume(int(self.maxdisp / 8))
self.Res_cv1 = RebuildCostVolume(2 * 8)
self.Res_Pred11 = ResidualPredition(2 * 8, 32)
self.Res_cv2 = RebuildCostVolume(2 * 4)
self.Res_Pred22 = ResidualPredition(2 * 4, 16)
self.edge_refine = EdgeRefine()
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (apex.parallel.SyncBatchNorm, apex.parallel.SyncBatchNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self, maxdisp=192):
super(GANet, self).__init__()
self.maxdisp = maxdisp
self.conv_start = nn.Sequential(
BasicConv(3, 16, kernel_size=3, padding=1),
BasicConv(16, 32, kernel_size=3, padding=1))
self.conv_x = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_y = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_refine = nn.Conv2d(32,
32, (3, 3), (1, 1), (1, 1),
bias=False)
self.bn_relu = nn.Sequential(BatchNorm2d(32), nn.ReLU(inplace=True))
self.feature = Feature()
self.guidance = Guidance()
self.cost_agg = CostAggregation(self.maxdisp)
self.cv = GetCostVolume(int(self.maxdisp / 3))
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (BatchNorm2d, BatchNorm3d)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self, maxdisp=192):
super(GANet, self).__init__()
self.maxdisp = maxdisp
self.conv_start = nn.Sequential(
BasicConv(3, 16, kernel_size=3, padding=1),
BasicConv(16, 32, kernel_size=3, padding=1))
self.conv_x = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_y = BasicConv(32, 32, kernel_size=3, padding=1)
self.conv_refine = nn.Conv2d(32,
32, (3, 3), (1, 1), (1, 1),
bias=False)
self.bn_relu = nn.Sequential(apex.parallel.SyncBatchNorm(32),
nn.ReLU(inplace=True))
self.feature = Feature()
self.guidance = Guidance()
self.cost_agg = CostAggregation(self.maxdisp)
self.cv = GetCostVolume(int(self.maxdisp / 3))
# TODO freeze above
for m in self.children():
m.eval()
for p in self.parameters():
p.requires_grad = False
self.edge_refine = EdgeRefine()
for m in self.modules():
if isinstance(m, (nn.Conv2d, nn.Conv3d)):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(
m,
(apex.parallel.SyncBatchNorm, apex.parallel.SyncBatchNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)