Safran/KPConv-PyTorch
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This commit is contained in:
HuguesTHOMAS 2020-04-28 11:07:27 -04:00
parent 5b7ecb418c
commit a101ed2e92
7 changed files with 21 additions and 34 deletions
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20
models/architectures.py
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@ -27,19 +27,6 @@ def p2p_fitting_regularizer(net):
if isinstance(m, KPConv) and m.deformable:
########################
# divide offset gradient
########################
#
# The offset gradient comes from two different losses. The regularizer loss (fitting deformation to point
# cloud) and the output loss (which should force deformations to help get a better score). The strength of
# the regularizer loss is set with the parameter deform_fitting_power. Therefore, this hook control the
# strength of the output loss. This strength can be set with the parameter deform_loss_power
#
m.offset_features.register_hook(lambda grad: grad * net.deform_loss_power)
# m.offset_features.register_hook(lambda grad: print('GRAD2', grad[10, 5, :]))
##############
# Fitting loss
##############
@ -64,8 +51,7 @@ def p2p_fitting_regularizer(net):
rep_loss = torch.sum(torch.clamp_max(distances - net.repulse_extent, max=0.0) ** 2, dim=1)
repulsive_loss += net.l1(rep_loss, torch.zeros_like(rep_loss)) / net.K
# The hook effectively affect both regularizer and output loss. So here we have to divide by deform_loss_power
return (net.deform_fitting_power / net.deform_loss_power) * (2 * fitting_loss + repulsive_loss)
return net.deform_fitting_power * (2 * fitting_loss + repulsive_loss)
class KPCNN(nn.Module):
@ -139,7 +125,7 @@ class KPCNN(nn.Module):
self.criterion = torch.nn.CrossEntropyLoss()
self.deform_fitting_mode = config.deform_fitting_mode
self.deform_fitting_power = config.deform_fitting_power
self.deform_loss_power = config.deform_loss_power
self.deform_lr_factor = config.deform_lr_factor
self.repulse_extent = config.repulse_extent
self.output_loss = 0
self.reg_loss = 0
@ -325,7 +311,7 @@ class KPFCNN(nn.Module):
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
self.deform_fitting_mode = config.deform_fitting_mode
self.deform_fitting_power = config.deform_fitting_power
self.deform_loss_power = config.deform_loss_power
self.deform_lr_factor = config.deform_lr_factor
self.repulse_extent = config.repulse_extent
self.output_loss = 0
self.reg_loss = 0

7
models/blocks.py
View file

@ -174,8 +174,6 @@ class KPConv(nn.Module):
self.deformable = deformable
self.modulated = modulated
self.in_offset_channels = in_channels
# Running variable containing deformed KP distance to input points. (used in regularization loss)
self.min_d2 = None
self.deformed_KP = None
@ -193,7 +191,7 @@ class KPConv(nn.Module):
self.offset_dim = self.p_dim * self.K
self.offset_conv = KPConv(self.K,
self.p_dim,
self.in_offset_channels,
self.in_channels,
self.offset_dim,
KP_extent,
radius,
@ -245,8 +243,7 @@ class KPConv(nn.Module):
if self.deformable:
# Get offsets with a KPConv that only takes part of the features
x_offsets = x[:, :self.in_offset_channels]
self.offset_features = self.offset_conv(q_pts, s_pts, neighb_inds, x_offsets) + self.offset_bias
self.offset_features = self.offset_conv(q_pts, s_pts, neighb_inds, x) + self.offset_bias
if self.modulated:

4
train_ModelNet40.py
View file

@ -124,8 +124,8 @@ class Modelnet40Config(Config):
# 'point2point' fitting geometry by penalizing distance from deform point to input points
# 'point2plane' fitting geometry by penalizing distance from deform point to input point triplet (not implemented)
deform_fitting_mode = 'point2point'
deform_fitting_power = 0.1 # Multiplier for the fitting/repulsive loss
deform_loss_power = 0.1 # Multiplier for output loss applied to the deformations
deform_fitting_power = 1.0 # Multiplier for the fitting/repulsive loss
deform_lr_factor = 0.1 # Multiplier for learning rate applied to the deformations
repulse_extent = 0.8 # Distance of repulsion for deformed kernel points
#####################

6
train_S3DIS.py
View file

@ -132,9 +132,9 @@ class S3DISConfig(Config):
# 'point2point' fitting geometry by penalizing distance from deform point to input points
# 'point2plane' fitting geometry by penalizing distance from deform point to input point triplet (not implemented)
deform_fitting_mode = 'point2point'
deform_fitting_power = 0.1 # Multiplier for the fitting/repulsive loss
deform_loss_power = 0.1 # Multiplier for output loss applied to the deformations
repulse_extent = 0.8 # Distance of repulsion for deformed kernel points
deform_fitting_power = 1.0 # Multiplier for the fitting/repulsive loss
deform_lr_factor = 0.1 # Multiplier for learning rate applied to the deformations
repulse_extent = 1.2 # Distance of repulsion for deformed kernel points
#####################
# Training parameters

4
train_SemanticKitti.py
View file

@ -146,8 +146,8 @@ class SemanticKittiConfig(Config):
# 'point2point' fitting geometry by penalizing distance from deform point to input points
# 'point2plane' fitting geometry by penalizing distance from deform point to input point triplet (not implemented)
deform_fitting_mode = 'point2point'
deform_fitting_power = 0.1 # Multiplier for the fitting/repulsive loss
deform_loss_power = 0.1 # Multiplier for output loss applied to the deformations
deform_fitting_power = 1.0 # Multiplier for the fitting/repulsive loss
deform_lr_factor = 0.1 # Multiplier for learning rate applied to the deformations
repulse_extent = 0.8 # Distance of repulsion for deformed kernel points
#####################

6
utils/config.py
View file

@ -163,8 +163,8 @@ class Config:
# 'point2point' fitting geometry by penalizing distance from deform point to input points
# 'point2plane' fitting geometry by penalizing distance from deform point to input point triplet (not implemented)
deform_fitting_mode = 'point2point'
deform_fitting_power = 0.1 # Multiplier for the fitting/repulsive loss
deform_loss_power = 0.1 # Multiplier for output loss applied to the deformations
deform_fitting_power = 1.0 # Multiplier for the fitting/repulsive loss
deform_lr_factor = 0.1 # Multiplier for learning rate applied to the deformations
repulse_extent = 1.0 # Distance of repulsion for deformed kernel points
# Number of batch
@ -368,7 +368,7 @@ class Config:
text_file.write('\n')
text_file.write('deform_fitting_mode = {:s}\n'.format(self.deform_fitting_mode))
text_file.write('deform_fitting_power = {:.6f}\n'.format(self.deform_fitting_power))
text_file.write('deform_loss_power = {:.6f}\n'.format(self.deform_loss_power))
text_file.write('deform_lr_factor = {:.6f}\n'.format(self.deform_lr_factor))
text_file.write('repulse_extent = {:.6f}\n'.format(self.repulse_extent))
text_file.write('batch_num = {:d}\n'.format(self.batch_num))
text_file.write('val_batch_num = {:d}\n'.format(self.val_batch_num))

8
utils/trainer.py
View file

@ -74,8 +74,12 @@ class ModelTrainer:
self.epoch = 0
self.step = 0
# Optimizer
self.optimizer = torch.optim.SGD(net.parameters(),
# Optimizer with specific learning rate for deformable KPConv
deform_params = [v for k, v in net.named_parameters() if 'offset' in k]
other_params = [v for k, v in net.named_parameters() if 'offset' not in k]
deform_lr = config.learning_rate * config.deform_lr_factor
self.optimizer = torch.optim.SGD([{'params': other_params},
{'params': deform_params, 'lr': deform_lr}],
lr=config.learning_rate,
momentum=config.momentum,
weight_decay=config.weight_decay)