def __init__(self, evaluation, div=1.0, iterative_refinement=False,
refinement_at_all_levels=False, refinement_at_adaptive_reso=True,
batch_norm=True, pyramid_type='VGG', md=4, upfeat_channels=2, dense_connection=True,
consensus_network=False, cyclic_consistency=True, decoder_inputs='corr_flow_feat'):
"""
input: md --- maximum displacement (for correlation. default: 4), after warpping
"""
super(GLUNet_model, self).__init__()
self.div=div
self.pyramid_type = pyramid_type
self.leakyRELU = nn.LeakyReLU(0.1)
self.l2norm = FeatureL2Norm()
self.iterative_refinement = iterative_refinement #only during evaluation
# where to put the refinement networks
self.refinement_at_all_levels = refinement_at_all_levels
self.refinement_at_adaptive_reso = refinement_at_adaptive_reso
# definition of the inputs to the decoders
self.decoder_inputs = decoder_inputs
self.dense_connection = dense_connection
self.upfeat_channels = upfeat_channels
# improvement of the global correlation
self.cyclic_consistency=cyclic_consistency
self.consensus_network = consensus_network
if self.cyclic_consistency:
self.corr = FeatureCorrelation(shape='4D', normalization=False)
elif consensus_network:
ncons_kernel_sizes = [3, 3, 3]
ncons_channels = [10, 10, 1]
self.corr = FeatureCorrelation(shape='4D', normalization=False)
# normalisation is applied in code here
self.NeighConsensus = NeighConsensus(use_cuda=True,
kernel_sizes=ncons_kernel_sizes,
channels=ncons_channels)
else:
self.corr = CorrelationVolume()
dd = np.cumsum([128,128,96,64,32])
# 16x16
nd = 16*16 # global correlation
od = nd + 2
self.decoder4 = CMDTop(in_channels=od, bn=batch_norm)
self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
# 32x32
nd = (2*md+1)**2 # constrained correlation, 4 pixels on each side
if self.decoder_inputs == 'corr_flow_feat':
od = nd + 2
elif self.decoder_inputs == 'corr':
od = nd
elif self.decoder_inputs == 'corr_flow':
od = nd + 2
if dense_connection:
self.decoder3 = OpticalFlowEstimator(in_channels=od, batch_norm=batch_norm)
input_to_refinement = od + dd[4]
else:
self.decoder3 = OpticalFlowEstimatorNoDenseConnection(in_channels=od, batch_norm=batch_norm)
input_to_refinement = 32
# weights for refinement module
if self.refinement_at_all_levels or self.refinement_at_adaptive_reso:
self.dc_conv1 = conv(input_to_refinement, 128, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2, batch_norm=batch_norm)
self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4, batch_norm=batch_norm)
self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8, batch_norm=batch_norm)
self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16, batch_norm=batch_norm)
self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.dc_conv7 = predict_flow(32)
# 1/8 of original resolution
nd = (2*md+1)**2 # constrained correlation, 4 pixels on each side
if self.decoder_inputs == 'corr_flow_feat':
od = nd + 2
elif self.decoder_inputs == 'corr':
od = nd
elif self.decoder_inputs == 'corr_flow':
od = nd + 2
if dense_connection:
self.decoder2 = OpticalFlowEstimator(in_channels=od, batch_norm=batch_norm)
input_to_refinement = od + dd[4]
else:
self.decoder2 = OpticalFlowEstimatorNoDenseConnection(in_channels=od, batch_norm=batch_norm)
input_to_refinement = 32
if self.decoder_inputs == 'corr_flow_feat':
self.upfeat2 = deconv(input_to_refinement, self.upfeat_channels, kernel_size=4, stride=2, padding=1)
self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
if refinement_at_all_levels:
# weights for refinement module
self.dc_conv1_level2 = conv(input_to_refinement, 128, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.dc_conv2_level2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2, batch_norm=batch_norm)
self.dc_conv3_level2 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4, batch_norm=batch_norm)
self.dc_conv4_level2 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8, batch_norm=batch_norm)
self.dc_conv5_level2 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16, batch_norm=batch_norm)
self.dc_conv6_level2 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.dc_conv7_level2 = predict_flow(32)
# 1/4 of original resolution
nd = (2*md+1)**2 # constrained correlation, 4 pixels on each side
if self.decoder_inputs == 'corr_flow_feat':
od = nd + self.upfeat_channels + 2
elif self.decoder_inputs == 'corr':
od = nd
elif self.decoder_inputs == 'corr_flow':
od = nd + 2
if dense_connection:
self.decoder1 = OpticalFlowEstimator(in_channels=od, batch_norm=batch_norm)
input_to_refinement = od+dd[4]
else:
self.decoder1 = OpticalFlowEstimatorNoDenseConnection(in_channels=od, batch_norm=batch_norm)
input_to_refinement = 32
self.l_dc_conv1 = conv(input_to_refinement, 128, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.l_dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2, batch_norm=batch_norm)
self.l_dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4, batch_norm=batch_norm)
self.l_dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8, batch_norm=batch_norm)
self.l_dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16, batch_norm=batch_norm)
self.l_dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1, batch_norm=batch_norm)
self.l_dc_conv7 = predict_flow(32)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight.data, mode='fan_in')
if m.bias is not None:
m.bias.data.zero_()
if pyramid_type == 'ResNet':
self.pyramid = ResNetPyramid()
else:
self.pyramid = VGGPyramid()
self.evaluation=evaluation
def __init__(self,
evaluation,
div=1.0,
batch_norm=True,
pyramid_type='VGG',
md=4,
cyclic_consistency=False,
consensus_network=True,
iterative_refinement=False):
"""
input: md --- maximum displacement (for correlation. default: 4), after warpping
"""
super(SemanticGLUNet_model, self).__init__()
self.div = div
self.pyramid_type = pyramid_type
self.leakyRELU = nn.LeakyReLU(0.1)
self.iterative_refinement = iterative_refinement
self.cyclic_consistency = cyclic_consistency
self.consensus_network = consensus_network
if self.cyclic_consistency:
self.corr = FeatureCorrelation(shape='4D', normalization=False)
elif consensus_network:
ncons_kernel_sizes = [3, 3, 3]
ncons_channels = [10, 10, 1]
self.corr = FeatureCorrelation(shape='4D', normalization=False)
# normalisation is applied in code here
self.NeighConsensus = NeighConsensus(
use_cuda=True,
kernel_sizes=ncons_kernel_sizes,
channels=ncons_channels)
else:
self.corr = CorrelationVolume()
# L2 feature normalisation
self.l2norm = FeatureL2Norm()
dd = np.cumsum([128, 128, 96, 64, 32])
# weights for decoder at different levels
nd = 16 * 16 # global correlation
od = nd + 2
self.decoder4 = CMDTop(in_channels=od, bn=batch_norm)
self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
nd = (2 * md + 1)**2 # constrained correlation, 4 pixels on each side
od = nd + 2
self.decoder3 = OpticalFlowEstimator(in_channels=od,
batch_norm=batch_norm)
# weights for refinement module
self.dc_conv1 = conv(od + dd[4],
128,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
batch_norm=batch_norm)
self.dc_conv2 = conv(128,
128,
kernel_size=3,
stride=1,
padding=2,
dilation=2,
batch_norm=batch_norm)
self.dc_conv3 = conv(128,
128,
kernel_size=3,
stride=1,
padding=4,
dilation=4,
batch_norm=batch_norm)
self.dc_conv4 = conv(128,
96,
kernel_size=3,
stride=1,
padding=8,
dilation=8,
batch_norm=batch_norm)
self.dc_conv5 = conv(96,
64,
kernel_size=3,
stride=1,
padding=16,
dilation=16,
batch_norm=batch_norm)
self.dc_conv6 = conv(64,
32,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
batch_norm=batch_norm)
self.dc_conv7 = predict_flow(32)
# 1/8 of original resolution
nd = (2 * md + 1)**2 # constrained correlation, 4 pixels on each side
od = nd + 2 # only gets the upsampled flow
self.decoder2 = OpticalFlowEstimator(in_channels=od,
batch_norm=batch_norm)
self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1)
self.upfeat2 = deconv(od + dd[4],
2,
kernel_size=4,
stride=2,
padding=1)
# 1/4 of original resolution
nd = (2 * md + 1)**2 # constrained correlation, 4 pixels on each side
od = nd + 4
self.decoder1 = OpticalFlowEstimator(in_channels=od,
batch_norm=batch_norm)
self.l_dc_conv1 = conv(od + dd[4],
128,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
batch_norm=batch_norm)
self.l_dc_conv2 = conv(128,
128,
kernel_size=3,
stride=1,
padding=2,
dilation=2,
batch_norm=batch_norm)
self.l_dc_conv3 = conv(128,
128,
kernel_size=3,
stride=1,
padding=4,
dilation=4,
batch_norm=batch_norm)
self.l_dc_conv4 = conv(128,
96,
kernel_size=3,
stride=1,
padding=8,
dilation=8,
batch_norm=batch_norm)
self.l_dc_conv5 = conv(96,
64,
kernel_size=3,
stride=1,
padding=16,
dilation=16,
batch_norm=batch_norm)
self.l_dc_conv6 = conv(64,
32,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
batch_norm=batch_norm)
self.l_dc_conv7 = predict_flow(32)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight.data, mode='fan_in')
if m.bias is not None:
m.bias.data.zero_()
if pyramid_type == 'ResNet':
self.pyramid = ResNetPyramid()
else:
self.pyramid = VGGPyramid()
self.evaluation = evaluation