import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
[文档]@LOSSES.register_module()
class SmoothL1Loss(nn.Module):
"""SmoothL1Loss loss .
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self, use_target_weight=False, loss_weight=1.):
super().__init__()
self.criterion = F.smooth_l1_loss
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
target_weight (torch.Tensor[N, K, D]):
Weights across different joint types.
"""
if self.use_target_weight:
loss = self.criterion(output * target_weight,
target * target_weight)
else:
loss = self.criterion(output, target)
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class WingLoss(nn.Module):
"""Wing Loss 'Wing Loss for Robust Facial Landmark Localisation with
Convolutional Neural Networks' Feng et al. CVPR'2018.
Args:
omega (float), epsilon (float) are hyper-parameters.
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self,
omega=10.0,
epsilon=2.0,
use_target_weight=False,
loss_weight=1.):
super().__init__()
self.omega = omega
self.epsilon = epsilon
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
# constant that smoothly links the piecewise-defined linear
# and nonlinear parts
self.C = self.omega * (1.0 - math.log(1.0 + self.omega / self.epsilon))
[文档] def criterion(self, pred, target):
"""Criterion of wingloss.
Note:
batch_size: N
num_keypoints: K
dimension of keypoints: D (D=2 or D=3)
Args:
pred (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
"""
delta = (target - pred).abs()
losses = torch.where(
delta < self.omega,
self.omega * torch.log(1.0 + delta / self.epsilon), delta - self.C)
return torch.mean(torch.sum(losses, dim=[1, 2]), dim=0)
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
target_weight (torch.Tensor[N, K, D]):
Weights across different joint types.
"""
if self.use_target_weight:
loss = self.criterion(output * target_weight,
target * target_weight)
else:
loss = self.criterion(output, target)
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class MPJPELoss(nn.Module):
"""MPJPE (Mean Per Joint Position Error) loss.
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self, use_target_weight=False, loss_weight=1.):
super().__init__()
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
target_weight (torch.Tensor[N, K, D]):
Weights across different joint types.
"""
if self.use_target_weight:
loss = torch.mean(
torch.norm((output - target) * target_weight, dim=-1))
else:
loss = torch.mean(torch.norm(output - target, dim=-1))
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class L1Loss(nn.Module):
"""L1Loss loss ."""
def __init__(self, use_target_weight=False, loss_weight=1.):
super().__init__()
self.criterion = F.l1_loss
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output regression.
target (torch.Tensor[N, K, 2]): Target regression.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
if self.use_target_weight:
loss = self.criterion(output * target_weight,
target * target_weight)
else:
loss = self.criterion(output, target)
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class MSELoss(nn.Module):
"""MSE loss for coordinate regression."""
def __init__(self, use_target_weight=False, loss_weight=1.):
super().__init__()
self.criterion = F.mse_loss
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
Args:
output (torch.Tensor[N, K, 2]): Output regression.
target (torch.Tensor[N, K, 2]): Target regression.
target_weight (torch.Tensor[N, K, 2]):
Weights across different joint types.
"""
if self.use_target_weight:
loss = self.criterion(output * target_weight,
target * target_weight)
else:
loss = self.criterion(output, target)
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class BoneLoss(nn.Module):
"""Bone length loss.
Args:
joint_parents (list): Indices of each joint's parent joint.
use_target_weight (bool): Option to use weighted bone loss.
Different bone types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self, joint_parents, use_target_weight=False, loss_weight=1.):
super().__init__()
self.joint_parents = joint_parents
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
self.non_root_indices = []
for i in range(len(self.joint_parents)):
if i != self.joint_parents[i]:
self.non_root_indices.append(i)
[文档] def forward(self, output, target, target_weight):
"""Forward function.
Note:
batch_size: N
num_keypoints: K
dimension of keypoints: D (D=2 or D=3)
Args:
output (torch.Tensor[N, K, D]): Output regression.
target (torch.Tensor[N, K, D]): Target regression.
target_weight (torch.Tensor[N, K-1]):
Weights across different bone types.
"""
output_bone = torch.norm(
output - output[:, self.joint_parents, :],
dim=-1)[:, self.non_root_indices]
target_bone = torch.norm(
target - target[:, self.joint_parents, :],
dim=-1)[:, self.non_root_indices]
if self.use_target_weight:
loss = torch.mean(
torch.abs((output_bone * target_weight).mean(dim=0) -
(target_bone * target_weight).mean(dim=0)))
else:
loss = torch.mean(
torch.abs(output_bone.mean(dim=0) - target_bone.mean(dim=0)))
return loss * self.loss_weight
[文档]@LOSSES.register_module()
class SemiSupervisionLoss(nn.Module):
"""Semi-supervision loss for unlabeled data. It is composed of projection
loss and bone loss.
Paper ref: `3D human pose estimation in video with temporal convolutions
and semi-supervised training` Dario Pavllo et al. CVPR'2019.
Args:
joint_parents (list): Indices of each joint's parent joint.
projection_loss_weight (float): Weight for projection loss.
bone_loss_weight (float): Weight for bone loss.
warmup_iterations (int): Number of warmup iterations. In the first
`warmup_iterations` iterations, the model is trained only on
labeled data, and semi-supervision loss will be 0.
This is a workaround since currently we cannot access
epoch number in loss functions. Note that the iteration number in
an epoch can be changed due to different GPU numbers in multi-GPU
settings. So please set this parameter carefully.
warmup_iterations = dataset_size // samples_per_gpu // gpu_num
* warmup_epochs
"""
def __init__(self,
joint_parents,
projection_loss_weight=1.,
bone_loss_weight=1.,
warmup_iterations=0):
super().__init__()
self.criterion_projection = MPJPELoss(
loss_weight=projection_loss_weight)
self.criterion_bone = BoneLoss(
joint_parents, loss_weight=bone_loss_weight)
self.warmup_iterations = warmup_iterations
self.num_iterations = 0
[文档] @staticmethod
def project_joints(x, intrinsics):
"""Project 3D joint coordinates to 2D image plane using camera
intrinsic parameters.
Args:
x (torch.Tensor[N, K, 3]): 3D joint coordinates.
intrinsics (torch.Tensor[N, 4] | torch.Tensor[N, 9]): Camera
intrinsics: f (2), c (2), k (3), p (2).
"""
while intrinsics.dim() < x.dim():
intrinsics.unsqueeze_(1)
f = intrinsics[..., :2]
c = intrinsics[..., 2:4]
_x = torch.clamp(x[:, :, :2] / x[:, :, 2:], -1, 1)
if intrinsics.shape[-1] == 9:
k = intrinsics[..., 4:7]
p = intrinsics[..., 7:9]
r2 = torch.sum(_x[:, :, :2]**2, dim=-1, keepdim=True)
radial = 1 + torch.sum(
k * torch.cat((r2, r2**2, r2**3), dim=-1),
dim=-1,
keepdim=True)
tan = torch.sum(p * _x, dim=-1, keepdim=True)
_x = _x * (radial + tan) + p * r2
_x = f * _x + c
return _x
[文档] def forward(self, output, target):
losses = dict()
self.num_iterations += 1
if self.num_iterations <= self.warmup_iterations:
return losses
labeled_pose = output['labeled_pose']
unlabeled_pose = output['unlabeled_pose']
unlabeled_traj = output['unlabeled_traj']
unlabeled_target_2d = target['unlabeled_target_2d']
intrinsics = target['intrinsics']
# projection loss
unlabeled_output = unlabeled_pose + unlabeled_traj
unlabeled_output_2d = self.project_joints(unlabeled_output, intrinsics)
loss_proj = self.criterion_projection(unlabeled_output_2d,
unlabeled_target_2d, None)
losses['proj_loss'] = loss_proj
# bone loss
loss_bone = self.criterion_bone(unlabeled_pose, labeled_pose, None)
losses['bone_loss'] = loss_bone
return losses