def __init__(self, max_disp, in_planes=64, batch_norm=True):
super(AcfAggregator, self).__init__()
self.max_disp = max_disp
self.in_planes = in_planes
self.batch_norm = batch_norm
self.dres0 = nn.Sequential(
conv3d_bn_relu(batch_norm, self.in_planes, 32, 3, 1, 1),
conv3d_bn_relu(batch_norm, 32, 32, 3, 1, 1),
)
self.dres1 = nn.Sequential(conv3d_bn_relu(batch_norm, 32, 32, 3, 1, 1),
conv3d_bn(batch_norm, 32, 32, 3, 1, 1))
self.dres2 = Hourglass(in_planes=32, batch_norm=batch_norm)
self.dres3 = Hourglass(in_planes=32, batch_norm=batch_norm)
self.dres4 = Hourglass(in_planes=32, batch_norm=batch_norm)
self.classif1 = nn.Sequential(
conv3d_bn_relu(batch_norm, 32, 32, 3, 1, 1),
nn.Conv3d(32, 1, kernel_size=3, stride=1, padding=1, bias=False),
)
self.classif2 = nn.Sequential(
conv3d_bn_relu(batch_norm, 32, 32, 3, 1, 1),
nn.Conv3d(32, 1, kernel_size=3, stride=1, padding=1, bias=False),
)
self.classif3 = nn.Sequential(
conv3d_bn_relu(batch_norm, 32, 32, 3, 1, 1),
nn.Conv3d(32, 1, kernel_size=3, stride=1, padding=1, bias=False))
self.deconv1 = nn.ConvTranspose3d(1, 1, 8, 4, 2, bias=False)
self.deconv2 = nn.ConvTranspose3d(1, 1, 8, 4, 2, bias=False)
self.deconv3 = nn.ConvTranspose3d(1, 1, 8, 4, 2, bias=False)
def __init__(self, in_planes, batchNorm=True):
super(Hourglass, self).__init__()
self.batchNorm = batchNorm
self.conv1 = conv3d_bn_relu(self.batchNorm,
in_planes,
in_planes * 2,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.conv2 = conv3d_bn(self.batchNorm,
in_planes * 2,
in_planes * 2,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.conv3 = conv3d_bn_relu(self.batchNorm,
in_planes * 2,
in_planes * 2,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.conv4 = conv3d_bn_relu(self.batchNorm,
in_planes * 2,
in_planes * 2,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.conv5 = deconv3d_bn(self.batchNorm,
in_planes * 2,
in_planes * 2,
kernel_size=3,
padding=1,
output_padding=1,
stride=2,
bias=False)
self.conv6 = deconv3d_bn(self.batchNorm,
in_planes * 2,
in_planes,
kernel_size=3,
padding=1,
output_padding=1,
stride=2,
bias=False)