Source code for mmpose.models.heads.heatmap_heads.cpm_head
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Sequence, Union
import torch
from mmcv.cnn import build_conv_layer, build_upsample_layer
from mmengine.structures import PixelData
from torch import Tensor, nn
from mmpose.evaluation.functional import pose_pck_accuracy
from mmpose.models.utils.tta import flip_heatmaps
from mmpose.registry import KEYPOINT_CODECS, MODELS
from mmpose.utils.tensor_utils import to_numpy
from mmpose.utils.typing import (Features, MultiConfig, OptConfigType,
OptSampleList, Predictions)
from ..base_head import BaseHead
OptIntSeq = Optional[Sequence[int]]
[docs]@MODELS.register_module()
class CPMHead(BaseHead):
"""Multi-stage heatmap head introduced in `Convolutional Pose Machines`_ by
Wei et al (2016) and used by `Stacked Hourglass Networks`_ by Newell et al
(2016). The head consists of multiple branches, each of which has some
deconv layers and a simple conv2d layer.
Args:
in_channels (int | Sequence[int]): Number of channels in the input
feature maps.
out_channels (int): Number of channels in the output heatmaps.
num_stages (int): Number of stages.
deconv_out_channels (Sequence[int], optional): The output channel
number of each deconv layer. Defaults to ``(256, 256, 256)``
deconv_kernel_sizes (Sequence[int | tuple], optional): The kernel size
of each deconv layer. Each element should be either an integer for
both height and width dimensions, or a tuple of two integers for
the height and the width dimension respectively.
Defaults to ``(4, 4, 4)``
has_final_layer (bool): Whether have the final 1x1 Conv2d layer.
Defaults to ``True``
loss (Config | List[Config]): Config of the keypoint loss of different
stages. Defaults to use :class:`KeypointMSELoss`.
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
.. _`Convolutional Pose Machines`: https://arxiv.org/abs/1602.00134
.. _`Stacked Hourglass Networks`: https://arxiv.org/abs/1603.06937
"""
_version = 2
def __init__(self,
in_channels: Union[int, Sequence[int]],
out_channels: int,
num_stages: int,
deconv_out_channels: OptIntSeq = None,
deconv_kernel_sizes: OptIntSeq = None,
has_final_layer: bool = True,
loss: MultiConfig = dict(
type='KeypointMSELoss', 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.num_stages = num_stages
self.in_channels = in_channels
self.out_channels = out_channels
if isinstance(loss, list):
if len(loss) != num_stages:
raise ValueError(
f'The length of loss_module({len(loss)}) did not match '
f'`num_stages`({num_stages})')
self.loss_module = nn.ModuleList(
MODELS.build(_loss) for _loss in loss)
else:
self.loss_module = MODELS.build(loss)
if decoder is not None:
self.decoder = KEYPOINT_CODECS.build(decoder)
else:
self.decoder = None
# build multi-stage deconv layers
self.multi_deconv_layers = nn.ModuleList([])
if deconv_out_channels:
if deconv_kernel_sizes is None or len(deconv_out_channels) != len(
deconv_kernel_sizes):
raise ValueError(
'"deconv_out_channels" and "deconv_kernel_sizes" should '
'be integer sequences with the same length. Got '
f'mismatched lengths {deconv_out_channels} and '
f'{deconv_kernel_sizes}')
for _ in range(self.num_stages):
deconv_layers = self._make_deconv_layers(
in_channels=in_channels,
layer_out_channels=deconv_out_channels,
layer_kernel_sizes=deconv_kernel_sizes,
)
self.multi_deconv_layers.append(deconv_layers)
in_channels = deconv_out_channels[-1]
else:
for _ in range(self.num_stages):
self.multi_deconv_layers.append(nn.Identity())
# build multi-stage final layers
self.multi_final_layers = nn.ModuleList([])
if has_final_layer:
cfg = dict(
type='Conv2d',
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1)
for _ in range(self.num_stages):
self.multi_final_layers.append(build_conv_layer(cfg))
else:
for _ in range(self.num_stages):
self.multi_final_layers.append(nn.Identity())
@property
def default_init_cfg(self):
init_cfg = [
dict(
type='Normal', layer=['Conv2d', 'ConvTranspose2d'], std=0.001),
dict(type='Constant', layer='BatchNorm2d', val=1)
]
return init_cfg
def _make_deconv_layers(self, in_channels: int,
layer_out_channels: Sequence[int],
layer_kernel_sizes: Sequence[int]) -> nn.Module:
"""Create deconvolutional layers by given parameters."""
layers = []
for out_channels, kernel_size in zip(layer_out_channels,
layer_kernel_sizes):
if kernel_size == 4:
padding = 1
output_padding = 0
elif kernel_size == 3:
padding = 1
output_padding = 1
elif kernel_size == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Unsupported kernel size {kernel_size} for'
'deconvlutional layers in '
f'{self.__class__.__name__}')
cfg = dict(
type='deconv',
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False)
layers.append(build_upsample_layer(cfg))
layers.append(nn.BatchNorm2d(num_features=out_channels))
layers.append(nn.ReLU(inplace=True))
in_channels = out_channels
return nn.Sequential(*layers)
[docs] def forward(self, feats: Sequence[Tensor]) -> List[Tensor]:
"""Forward the network. The input is multi-stage feature maps and the
output is a list of heatmaps from multiple stages.
Args:
feats (Sequence[Tensor]): Multi-stage feature maps.
Returns:
List[Tensor]: A list of output heatmaps from multiple stages.
"""
out = []
assert len(feats) == self.num_stages, (
f'The length of feature maps did not match the '
f'`num_stages` in {self.__class__.__name__}')
for i in range(self.num_stages):
y = self.multi_deconv_layers[i](feats[i])
y = self.multi_final_layers[i](y)
out.append(y)
return out
[docs] def predict(self,
feats: Features,
batch_data_samples: OptSampleList,
test_cfg: OptConfigType = {}) -> Predictions:
"""Predict results from multi-stage feature maps.
Args:
feats (Tuple[Tensor] | List[Tuple[Tensor]]): The multi-stage
features (or multiple multi-stage features in TTA)
batch_data_samples (List[:obj:`PoseDataSample`]): The batch
data samples
test_cfg (dict): The runtime config for testing process. Defaults
to {}
Returns:
Union[InstanceList | Tuple[InstanceList | PixelDataList]]: If
``test_cfg['output_heatmap']==True``, return both pose and heatmap
prediction; otherwise only return the pose prediction.
The pose prediction is a list of ``InstanceData``, each contains
the following fields:
- keypoints (np.ndarray): predicted keypoint coordinates in
shape (num_instances, K, D) where K is the keypoint number
and D is the keypoint dimension
- keypoint_scores (np.ndarray): predicted keypoint scores in
shape (num_instances, K)
The heatmap prediction is a list of ``PixelData``, each contains
the following fields:
- heatmaps (Tensor): The predicted heatmaps in shape (K, h, w)
"""
if test_cfg.get('flip_test', False):
# TTA: flip test
assert isinstance(feats, list) and len(feats) == 2
flip_indices = batch_data_samples[0].metainfo['flip_indices']
_feats, _feats_flip = feats
_batch_heatmaps = self.forward(_feats)[-1]
_batch_heatmaps_flip = flip_heatmaps(
self.forward(_feats_flip)[-1],
flip_mode=test_cfg.get('flip_mode', 'heatmap'),
flip_indices=flip_indices,
shift_heatmap=test_cfg.get('shift_heatmap', False))
batch_heatmaps = (_batch_heatmaps + _batch_heatmaps_flip) * 0.5
else:
multi_stage_heatmaps = self.forward(feats)
batch_heatmaps = multi_stage_heatmaps[-1]
preds = self.decode(batch_heatmaps)
if test_cfg.get('output_heatmaps', False):
pred_fields = [
PixelData(heatmaps=hm) for hm in batch_heatmaps.detach()
]
return preds, pred_fields
else:
return preds
[docs] def loss(self,
feats: Sequence[Tensor],
batch_data_samples: OptSampleList,
train_cfg: OptConfigType = {}) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
feats (Sequence[Tensor]): Multi-stage feature maps.
batch_data_samples (List[:obj:`PoseDataSample`]): The Data
Samples. It usually includes information such as
`gt_instances`.
train_cfg (Config, optional): The training config.
Returns:
dict: A dictionary of loss components.
"""
multi_stage_pred_heatmaps = self.forward(feats)
gt_heatmaps = torch.stack(
[d.gt_fields.heatmaps for d in batch_data_samples])
keypoint_weights = torch.cat([
d.gt_instance_labels.keypoint_weights for d in batch_data_samples
])
# calculate losses over multiple stages
losses = dict()
for i in range(self.num_stages):
if isinstance(self.loss_module, nn.ModuleList):
# use different loss_module over different stages
loss_func = self.loss_module[i]
else:
# use the same loss_module over different stages
loss_func = self.loss_module
# the `gt_heatmaps` and `keypoint_weights` used to calculate loss
# for different stages are the same
loss_i = loss_func(multi_stage_pred_heatmaps[i], gt_heatmaps,
keypoint_weights)
if 'loss_kpt' not in losses:
losses['loss_kpt'] = loss_i
else:
losses['loss_kpt'] += loss_i
# calculate accuracy
_, avg_acc, _ = pose_pck_accuracy(
output=to_numpy(multi_stage_pred_heatmaps[-1]),
target=to_numpy(gt_heatmaps),
mask=to_numpy(keypoint_weights) > 0)
acc_pose = torch.tensor(avg_acc, device=gt_heatmaps.device)
losses.update(acc_pose=acc_pose)
return losses