import torch.nn as nn
from mmcv.cnn import (build_conv_layer, build_upsample_layer, constant_init,
normal_init)
from mmpose.models.builder import build_loss
from ..builder import HEADS
[文档]@HEADS.register_module()
class AESimpleHead(nn.Module):
"""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):
super().__init__()
self.loss = build_loss(loss_keypoint)
self.in_channels = in_channels
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
if extra is not None and not isinstance(extra, dict):
raise TypeError('extra should be dict or None.')
if num_deconv_layers > 0:
self.deconv_layers = self._make_deconv_layer(
num_deconv_layers,
num_deconv_filters,
num_deconv_kernels,
)
elif num_deconv_layers == 0:
self.deconv_layers = nn.Identity()
else:
raise ValueError(
f'num_deconv_layers ({num_deconv_layers}) should >= 0.')
if extra is not None and 'final_conv_kernel' in extra:
assert extra['final_conv_kernel'] in [1, 3]
if extra['final_conv_kernel'] == 3:
padding = 1
else:
padding = 0
kernel_size = extra['final_conv_kernel']
else:
kernel_size = 1
padding = 0
self.final_layer = build_conv_layer(
cfg=dict(type='Conv2d'),
in_channels=num_deconv_filters[-1]
if num_deconv_layers > 0 else in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding)
[文档] def get_loss(self, output, 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:
output (torch.Tensor[NxKxHxW]): Output heatmaps.
targets(List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
masks(List(torch.Tensor[NxHxW])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
heatmaps for ae loss
"""
losses = dict()
heatmaps_losses, push_losses, pull_losses = self.loss(
output, 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
[文档] def forward(self, x):
"""Forward function."""
if isinstance(x, list):
x = x[0]
final_outputs = []
x = self.deconv_layers(x)
y = self.final_layer(x)
final_outputs.append(y)
return final_outputs
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
"""Make deconv layers."""
if num_layers != len(num_filters):
error_msg = f'num_layers({num_layers}) ' \
f'!= length of num_filters({len(num_filters)})'
raise ValueError(error_msg)
if num_layers != len(num_kernels):
error_msg = f'num_layers({num_layers}) ' \
f'!= length of num_kernels({len(num_kernels)})'
raise ValueError(error_msg)
layers = []
for i in range(num_layers):
kernel, padding, output_padding = \
self._get_deconv_cfg(num_kernels[i])
planes = num_filters[i]
layers.append(
build_upsample_layer(
dict(type='deconv'),
in_channels=self.in_channels,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False))
layers.append(nn.BatchNorm2d(planes))
layers.append(nn.ReLU(inplace=True))
self.in_channels = planes
return nn.Sequential(*layers)
@staticmethod
def _get_deconv_cfg(deconv_kernel):
"""Get configurations for deconv layers."""
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Not supported num_kernels ({deconv_kernel}).')
return deconv_kernel, padding, output_padding
[文档] def init_weights(self):
"""Initialize model weights."""
for _, m in self.deconv_layers.named_modules():
if isinstance(m, nn.ConvTranspose2d):
normal_init(m, std=0.001)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1)
for m in self.final_layer.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)