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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch.nn as nn
from mmcv.cnn import build_conv_layer, constant_init, kaiming_init
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmpose.core import (WeightNormClipHook, compute_similarity_transform,
fliplr_regression)
from mmpose.models.builder import HEADS, build_loss
@HEADS.register_module()
class TemporalRegressionHead(nn.Module):
"""Regression head of VideoPose3D.
"3D human pose estimation in video with temporal convolutions and
semi-supervised training", CVPR'2019.
Args:
in_channels (int): Number of input channels
num_joints (int): Number of joints
loss_keypoint (dict): Config for keypoint loss. Default: None.
max_norm (float|None): if not None, the weight of convolution layers
will be clipped to have a maximum norm of max_norm.
is_trajectory (bool): If the model only predicts root joint
position, then this arg should be set to True. In this case,
traj_loss will be calculated. Otherwise, it should be set to
False. Default: False.
"""
def __init__(self,
in_channels,
num_joints,
max_norm=None,
loss_keypoint=None,
is_trajectory=False,
train_cfg=None,
test_cfg=None):
super().__init__()
self.in_channels = in_channels
self.num_joints = num_joints
self.max_norm = max_norm
self.loss = build_loss(loss_keypoint)
self.is_trajectory = is_trajectory
if self.is_trajectory:
assert self.num_joints == 1
self.train_cfg = {} if train_cfg is None else train_cfg
self.test_cfg = {} if test_cfg is None else test_cfg
self.conv = build_conv_layer(
dict(type='Conv1d'), in_channels, num_joints * 3, 1)
if self.max_norm is not None:
# Apply weight norm clip to conv layers
weight_clip = WeightNormClipHook(self.max_norm)
for module in self.modules():
if isinstance(module, nn.modules.conv._ConvNd):
weight_clip.register(module)
@staticmethod
def _transform_inputs(x):
"""Transform inputs for decoder.
Args:
inputs (tuple or list of Tensor | Tensor): multi-level features.
Returns:
Tensor: The transformed inputs
"""
if not isinstance(x, (list, tuple)):
return x
assert len(x) > 0
# return the top-level feature of the 1D feature pyramid
return x[-1]
def forward(self, x):
"""Forward function."""
x = self._transform_inputs(x)
assert x.ndim == 3 and x.shape[2] == 1, f'Invalid shape {x.shape}'
output = self.conv(x)
N = output.shape[0]
return output.reshape(N, self.num_joints, 3)
def get_loss(self, output, target, target_weight):
"""Calculate keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
Args:
output (torch.Tensor[N, K, 3]): Output keypoints.
target (torch.Tensor[N, K, 3]): Target keypoints.
target_weight (torch.Tensor[N, K, 3]):
Weights across different joint types.
If self.is_trajectory is True and target_weight is None,
target_weight will be set inversely proportional to joint
depth.
"""
losses = dict()
assert not isinstance(self.loss, nn.Sequential)
# trajectory model
if self.is_trajectory:
if target.dim() == 2:
target.unsqueeze_(1)
if target_weight is None:
target_weight = (1 / target[:, :, 2:]).expand(target.shape)
assert target.dim() == 3 and target_weight.dim() == 3
losses['traj_loss'] = self.loss(output, target, target_weight)
# pose model
else:
if target_weight is None:
target_weight = target.new_ones(target.shape)
assert target.dim() == 3 and target_weight.dim() == 3
losses['reg_loss'] = self.loss(output, target, target_weight)
return losses
def get_accuracy(self, output, target, target_weight, metas):
"""Calculate accuracy for keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
Args:
output (torch.Tensor[N, K, 3]): Output keypoints.
target (torch.Tensor[N, K, 3]): Target keypoints.
target_weight (torch.Tensor[N, K, 3]):
Weights across different joint types.
metas (list(dict)): Information about data augmentation including:
- target_image_path (str): Optional, path to the image file
- target_mean (float): Optional, normalization parameter of
the target pose.
- target_std (float): Optional, normalization parameter of the
target pose.
- root_position (np.ndarray[3,1]): Optional, global
position of the root joint.
- root_index (torch.ndarray[1,]): Optional, original index of
the root joint before root-centering.
"""
accuracy = dict()
N = output.shape[0]
output_ = output.detach().cpu().numpy()
target_ = target.detach().cpu().numpy()
# Denormalize the predicted pose
if 'target_mean' in metas[0] and 'target_std' in metas[0]:
target_mean = np.stack([m['target_mean'] for m in metas])
target_std = np.stack([m['target_std'] for m in metas])
output_ = self._denormalize_joints(output_, target_mean,
target_std)
target_ = self._denormalize_joints(target_, target_mean,
target_std)
# Restore global position
if self.test_cfg.get('restore_global_position', False):
root_pos = np.stack([m['root_position'] for m in metas])
root_idx = metas[0].get('root_position_index', None)
output_ = self._restore_global_position(output_, root_pos,
root_idx)
target_ = self._restore_global_position(target_, root_pos,
root_idx)
# Get target weight
if target_weight is None:
target_weight_ = np.ones_like(target_)
else:
target_weight_ = target_weight.detach().cpu().numpy()
if self.test_cfg.get('restore_global_position', False):
root_idx = metas[0].get('root_position_index', None)
root_weight = metas[0].get('root_joint_weight', 1.0)
target_weight_ = self._restore_root_target_weight(
target_weight_, root_weight, root_idx)
mpjpe = np.mean(
np.linalg.norm((output_ - target_) * target_weight_, axis=-1))
transformed_output = np.zeros_like(output_)
for i in range(N):
transformed_output[i, :, :] = compute_similarity_transform(
output_[i, :, :], target_[i, :, :])
p_mpjpe = np.mean(
np.linalg.norm(
(transformed_output - target_) * target_weight_, axis=-1))
accuracy['mpjpe'] = output.new_tensor(mpjpe)
accuracy['p_mpjpe'] = output.new_tensor(p_mpjpe)
return accuracy
def inference_model(self, x, flip_pairs=None):
"""Inference function.
Returns:
output_regression (np.ndarray): Output regression.
Args:
x (torch.Tensor[N, K, 2]): Input features.
flip_pairs (None | list[tuple()):
Pairs of keypoints which are mirrored.
"""
output = self.forward(x)
if flip_pairs is not None:
output_regression = fliplr_regression(
output.detach().cpu().numpy(),
flip_pairs,
center_mode='static',
center_x=0)
else:
output_regression = output.detach().cpu().numpy()
return output_regression
def decode(self, metas, output):
"""Decode the keypoints from output regression.
Args:
metas (list(dict)): Information about data augmentation.
By default this includes:
- "target_image_path": path to the image file
output (np.ndarray[N, K, 3]): predicted regression vector.
metas (list(dict)): Information about data augmentation including:
- target_image_path (str): Optional, path to the image file
- target_mean (float): Optional, normalization parameter of
the target pose.
- target_std (float): Optional, normalization parameter of the
target pose.
- root_position (np.ndarray[3,1]): Optional, global
position of the root joint.
- root_index (torch.ndarray[1,]): Optional, original index of
the root joint before root-centering.
"""
# Denormalize the predicted pose
if 'target_mean' in metas[0] and 'target_std' in metas[0]:
target_mean = np.stack([m['target_mean'] for m in metas])
target_std = np.stack([m['target_std'] for m in metas])
output = self._denormalize_joints(output, target_mean, target_std)
# Restore global position
if self.test_cfg.get('restore_global_position', False):
root_pos = np.stack([m['root_position'] for m in metas])
root_idx = metas[0].get('root_position_index', None)
output = self._restore_global_position(output, root_pos, root_idx)
target_image_paths = [m.get('target_image_path', None) for m in metas]
result = {'preds': output, 'target_image_paths': target_image_paths}
return result
@staticmethod
def _denormalize_joints(x, mean, std):
"""Denormalize joint coordinates with given statistics mean and std.
Args:
x (np.ndarray[N, K, 3]): Normalized joint coordinates.
mean (np.ndarray[K, 3]): Mean value.
std (np.ndarray[K, 3]): Std value.
"""
assert x.ndim == 3
assert x.shape == mean.shape == std.shape
return x * std + mean
@staticmethod
def _restore_global_position(x, root_pos, root_idx=None):
"""Restore global position of the root-centered joints.
Args:
x (np.ndarray[N, K, 3]): root-centered joint coordinates
root_pos (np.ndarray[N,1,3]): The global position of the
root joint.
root_idx (int|None): If not none, the root joint will be inserted
back to the pose at the given index.
"""
x = x + root_pos
if root_idx is not None:
x = np.insert(x, root_idx, root_pos.squeeze(1), axis=1)
return x
@staticmethod
def _restore_root_target_weight(target_weight, root_weight, root_idx=None):
"""Restore the target weight of the root joint after the restoration of
the global position.
Args:
target_weight (np.ndarray[N, K, 1]): Target weight of relativized
joints.
root_weight (float): The target weight value of the root joint.
root_idx (int|None): If not none, the root joint weight will be
inserted back to the target weight at the given index.
"""
if root_idx is not None:
root_weight = np.full(
target_weight.shape[0], root_weight, dtype=target_weight.dtype)
target_weight = np.insert(
target_weight, root_idx, root_weight[:, None], axis=1)
return target_weight
def init_weights(self):
"""Initialize the weights."""
for m in self.modules():
if isinstance(m, nn.modules.conv._ConvNd):
kaiming_init(m, mode='fan_in', nonlinearity='relu')
elif isinstance(m, _BatchNorm):
constant_init(m, 1)
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