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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union
import torch
from torch import Tensor, nn
from mmpose.evaluation.functional import keypoint_mpjpe
from mmpose.registry import KEYPOINT_CODECS, MODELS
from mmpose.utils.tensor_utils import to_numpy
from mmpose.utils.typing import (ConfigType, OptConfigType, OptSampleList,
Predictions)
from ..base_head import BaseHead
OptIntSeq = Optional[Sequence[int]]
@MODELS.register_module()
class TrajectoryRegressionHead(BaseHead):
"""Trajectory Regression head of `VideoPose3D`_ by Dario et al (CVPR'2019).
Args:
in_channels (int | sequence[int]): Number of input channels
num_joints (int): Number of joints
loss (Config): Config for trajectory loss. Defaults to use
:class:`MPJPELoss`
decoder (Config, optional): The decoder config that controls decoding
keypoint coordinates from the network output. Defaults to ``None``
init_cfg (Config, optional): Config to control the initialization. See
:attr:`default_init_cfg` for default settings
.. _`VideoPose3D`: https://arxiv.org/abs/1811.11742
"""
_version = 2
def __init__(self,
in_channels: Union[int, Sequence[int]],
num_joints: int,
loss: ConfigType = dict(
type='MPJPELoss', use_target_weight=True),
decoder: OptConfigType = None,
init_cfg: OptConfigType = None):
if init_cfg is None:
init_cfg = self.default_init_cfg
super().__init__(init_cfg)
self.in_channels = in_channels
self.num_joints = num_joints
self.loss_module = MODELS.build(loss)
if decoder is not None:
self.decoder = KEYPOINT_CODECS.build(decoder)
else:
self.decoder = None
# Define fully-connected layers
self.conv = nn.Conv1d(in_channels, self.num_joints * 3, 1)
def forward(self, feats: Tuple[Tensor]) -> Tensor:
"""Forward the network. The input is multi scale feature maps and the
output is the coordinates.
Args:
feats (Tuple[Tensor]): Multi scale feature maps.
Returns:
Tensor: output coordinates(and sigmas[optional]).
"""
x = feats[-1]
x = self.conv(x)
return x.reshape(-1, self.num_joints, 3)
def predict(self,
feats: Tuple[Tensor],
batch_data_samples: OptSampleList,
test_cfg: ConfigType = {}) -> Predictions:
"""Predict results from outputs.
Returns:
preds (sequence[InstanceData]): Prediction results.
Each contains the following fields:
- keypoints: Predicted keypoints of shape (B, N, K, D).
- keypoint_scores: Scores of predicted keypoints of shape
(B, N, K).
"""
batch_coords = self.forward(feats) # (B, K, D)
# Restore global position with target_root
target_root = batch_data_samples[0].metainfo.get('target_root', None)
if target_root is not None:
target_root = torch.stack([
torch.from_numpy(b.metainfo['target_root'])
for b in batch_data_samples
])
else:
target_root = torch.stack([
torch.empty((0), dtype=torch.float32)
for _ in batch_data_samples
])
preds = self.decode((batch_coords, target_root))
return preds
def loss(self,
inputs: Union[Tensor, Tuple[Tensor]],
batch_data_samples: OptSampleList,
train_cfg: ConfigType = {}) -> dict:
"""Calculate losses from a batch of inputs and data samples."""
pred_outputs = self.forward(inputs)
lifting_target_label = torch.cat([
d.gt_instance_labels.lifting_target_label
for d in batch_data_samples
])
trajectory_weights = torch.cat([
d.gt_instance_labels.trajectory_weights for d in batch_data_samples
])
# calculate losses
losses = dict()
loss = self.loss_module(pred_outputs, lifting_target_label,
trajectory_weights.unsqueeze(-1))
losses.update(loss_traj=loss)
# calculate accuracy
mpjpe_err = keypoint_mpjpe(
pred=to_numpy(pred_outputs),
gt=to_numpy(lifting_target_label),
mask=to_numpy(trajectory_weights) > 0)
mpjpe_traj = torch.tensor(
mpjpe_err, device=lifting_target_label.device)
losses.update(mpjpe_traj=mpjpe_traj)
return losses
@property
def default_init_cfg(self):
init_cfg = [dict(type='Normal', layer=['Linear'], std=0.01, bias=0)]
return init_cfg
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