Source code for mmpose.models.heads.ae_higher_resolution_head
# Copyright (c) OpenMMLab. All rights reserved.
import torch
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 ..backbones.resnet import BasicBlock
from ..builder import HEADS
[docs]@HEADS.register_module()
class AEHigherResolutionHead(nn.Module):
"""Associative embedding with higher resolution head. paper ref: Bowen
Cheng et al. "HigherHRNet: Scale-Aware Representation Learning for Bottom-
Up Human Pose Estimation".
Args:
in_channels (int): Number of input channels.
num_joints (int): Number of joints
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
extra (dict): Configs for extra conv layers. Default: None
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.
cat_output (list[bool]): Option to concat outputs.
with_ae_loss (list[bool]): Option to use ae loss.
loss_keypoint (dict): Config for loss. Default: None.
"""
def __init__(self,
in_channels,
num_joints,
tag_per_joint=True,
extra=None,
num_deconv_layers=1,
num_deconv_filters=(32, ),
num_deconv_kernels=(4, ),
num_basic_blocks=4,
cat_output=None,
with_ae_loss=None,
loss_keypoint=None):
super().__init__()
self.loss = build_loss(loss_keypoint)
dim_tag = num_joints if tag_per_joint else 1
self.num_deconvs = num_deconv_layers
self.cat_output = cat_output
final_layer_output_channels = []
if with_ae_loss[0]:
out_channels = num_joints + dim_tag
else:
out_channels = num_joints
final_layer_output_channels.append(out_channels)
for i in range(num_deconv_layers):
if with_ae_loss[i + 1]:
out_channels = num_joints + dim_tag
else:
out_channels = num_joints
final_layer_output_channels.append(out_channels)
deconv_layer_output_channels = []
for i in range(num_deconv_layers):
if with_ae_loss[i]:
out_channels = num_joints + dim_tag
else:
out_channels = num_joints
deconv_layer_output_channels.append(out_channels)
self.final_layers = self._make_final_layers(
in_channels, final_layer_output_channels, extra, num_deconv_layers,
num_deconv_filters)
self.deconv_layers = self._make_deconv_layers(
in_channels, deconv_layer_output_channels, num_deconv_layers,
num_deconv_filters, num_deconv_kernels, num_basic_blocks,
cat_output)
@staticmethod
def _make_final_layers(in_channels, final_layer_output_channels, extra,
num_deconv_layers, num_deconv_filters):
"""Make final layers."""
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
final_layers = []
final_layers.append(
build_conv_layer(
cfg=dict(type='Conv2d'),
in_channels=in_channels,
out_channels=final_layer_output_channels[0],
kernel_size=kernel_size,
stride=1,
padding=padding))
for i in range(num_deconv_layers):
in_channels = num_deconv_filters[i]
final_layers.append(
build_conv_layer(
cfg=dict(type='Conv2d'),
in_channels=in_channels,
out_channels=final_layer_output_channels[i + 1],
kernel_size=kernel_size,
stride=1,
padding=padding))
return nn.ModuleList(final_layers)
def _make_deconv_layers(self, in_channels, deconv_layer_output_channels,
num_deconv_layers, num_deconv_filters,
num_deconv_kernels, num_basic_blocks, cat_output):
"""Make deconv layers."""
deconv_layers = []
for i in range(num_deconv_layers):
if cat_output[i]:
in_channels += deconv_layer_output_channels[i]
planes = num_deconv_filters[i]
deconv_kernel, padding, output_padding = \
self._get_deconv_cfg(num_deconv_kernels[i])
layers = []
layers.append(
nn.Sequential(
build_upsample_layer(
dict(type='deconv'),
in_channels=in_channels,
out_channels=planes,
kernel_size=deconv_kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False), nn.BatchNorm2d(planes, momentum=0.1),
nn.ReLU(inplace=True)))
for _ in range(num_basic_blocks):
layers.append(nn.Sequential(BasicBlock(planes, planes), ))
deconv_layers.append(nn.Sequential(*layers))
in_channels = planes
return nn.ModuleList(deconv_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
[docs] 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
[docs] def forward(self, x):
"""Forward function."""
if isinstance(x, list):
x = x[0]
final_outputs = []
y = self.final_layers[0](x)
final_outputs.append(y)
for i in range(self.num_deconvs):
if self.cat_output[i]:
x = torch.cat((x, y), 1)
x = self.deconv_layers[i](x)
y = self.final_layers[i + 1](x)
final_outputs.append(y)
return final_outputs
[docs] 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_layers.named_modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001, bias=0)