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# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import HEADS
from .deconv_head import DeconvHead
@HEADS.register_module()
class AESimpleHead(DeconvHead):
"""Associative embedding simple head.
paper ref: Alejandro Newell et al. "Associative
Embedding: End-to-end Learning for Joint Detection
and Grouping"
Args:
in_channels (int): Number of input channels.
num_joints (int): Number of joints.
num_deconv_layers (int): Number of deconv layers.
num_deconv_layers should >= 0. Note that 0 means
no deconv layers.
num_deconv_filters (list|tuple): Number of filters.
If num_deconv_layers > 0, the length of
num_deconv_kernels (list|tuple): Kernel sizes.
tag_per_joint (bool): If tag_per_joint is True,
the dimension of tags equals to num_joints,
else the dimension of tags is 1. Default: True
with_ae_loss (list[bool]): Option to use ae loss or not.
loss_keypoint (dict): Config for loss. Default: None.
"""
def __init__(self,
in_channels,
num_joints,
num_deconv_layers=3,
num_deconv_filters=(256, 256, 256),
num_deconv_kernels=(4, 4, 4),
tag_per_joint=True,
with_ae_loss=None,
extra=None,
loss_keypoint=None):
dim_tag = num_joints if tag_per_joint else 1
if with_ae_loss[0]:
out_channels = num_joints + dim_tag
else:
out_channels = num_joints
super().__init__(
in_channels,
out_channels,
num_deconv_layers=num_deconv_layers,
num_deconv_filters=num_deconv_filters,
num_deconv_kernels=num_deconv_kernels,
extra=extra,
loss_keypoint=loss_keypoint)
def get_loss(self, outputs, targets, masks, joints):
"""Calculate bottom-up keypoint loss.
Note:
- batch_size: N
- num_keypoints: K
- num_outputs: O
- heatmaps height: H
- heatmaps weight: W
Args:
outputs (list(torch.Tensor[N,K,H,W])): Multi-scale output heatmaps.
targets (List(torch.Tensor[N,K,H,W])): Multi-scale target heatmaps.
masks (List(torch.Tensor[N,H,W])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[N,M,K,2])): Joints of multi-scale target
heatmaps for ae loss
"""
losses = dict()
heatmaps_losses, push_losses, pull_losses = self.loss(
outputs, targets, masks, joints)
for idx in range(len(targets)):
if heatmaps_losses[idx] is not None:
heatmaps_loss = heatmaps_losses[idx].mean(dim=0)
if 'heatmap_loss' not in losses:
losses['heatmap_loss'] = heatmaps_loss
else:
losses['heatmap_loss'] += heatmaps_loss
if push_losses[idx] is not None:
push_loss = push_losses[idx].mean(dim=0)
if 'push_loss' not in losses:
losses['push_loss'] = push_loss
else:
losses['push_loss'] += push_loss
if pull_losses[idx] is not None:
pull_loss = pull_losses[idx].mean(dim=0)
if 'pull_loss' not in losses:
losses['pull_loss'] = pull_loss
else:
losses['pull_loss'] += pull_loss
return losses
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