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Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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liyang
2026-09-15 14:13:49 +08:00
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third_party/hamer/third-party/ViTPose/mmpose/models/backbones/hrnet.py
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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
normal_init)
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import BasicBlock, Bottleneck, get_expansion
from .utils import load_checkpoint
class HRModule(nn.Module):
"""High-Resolution Module for HRNet.
In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
is in this module.
"""
def __init__(self,
num_branches,
blocks,
num_blocks,
in_channels,
num_channels,
multiscale_output=False,
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
upsample_cfg=dict(mode='nearest', align_corners=None)):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self._check_branches(num_branches, num_blocks, in_channels,
num_channels)
self.in_channels = in_channels
self.num_branches = num_branches
self.multiscale_output = multiscale_output
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.upsample_cfg = upsample_cfg
self.with_cp = with_cp
self.branches = self._make_branches(num_branches, blocks, num_blocks,
num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=True)
@staticmethod
def _check_branches(num_branches, num_blocks, in_channels, num_channels):
"""Check input to avoid ValueError."""
if num_branches != len(num_blocks):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_BLOCKS({len(num_blocks)})'
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_CHANNELS({len(num_channels)})'
raise ValueError(error_msg)
if num_branches != len(in_channels):
error_msg = f'NUM_BRANCHES({num_branches}) ' \
f'!= NUM_INCHANNELS({len(in_channels)})'
raise ValueError(error_msg)
def _make_one_branch(self,
branch_index,
block,
num_blocks,
num_channels,
stride=1):
"""Make one branch."""
downsample = None
if stride != 1 or \
self.in_channels[branch_index] != \
num_channels[branch_index] * get_expansion(block):
downsample = nn.Sequential(
build_conv_layer(
self.conv_cfg,
self.in_channels[branch_index],
num_channels[branch_index] * get_expansion(block),
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(
self.norm_cfg,
num_channels[branch_index] * get_expansion(block))[1])
layers = []
layers.append(
block(
self.in_channels[branch_index],
num_channels[branch_index] * get_expansion(block),
stride=stride,
downsample=downsample,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
self.in_channels[branch_index] = \
num_channels[branch_index] * get_expansion(block)
for _ in range(1, num_blocks[branch_index]):
layers.append(
block(
self.in_channels[branch_index],
num_channels[branch_index] * get_expansion(block),
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
"""Make branches."""
branches = []
for i in range(num_branches):
branches.append(
self._make_one_branch(i, block, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
"""Make fuse layer."""
if self.num_branches == 1:
return None
num_branches = self.num_branches
in_channels = self.in_channels
fuse_layers = []
num_out_branches = num_branches if self.multiscale_output else 1
for i in range(num_out_branches):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=1,
stride=1,
padding=0,
bias=False),
build_norm_layer(self.norm_cfg, in_channels[i])[1],
nn.Upsample(
scale_factor=2**(j - i),
mode=self.upsample_cfg['mode'],
align_corners=self.
upsample_cfg['align_corners'])))
elif j == i:
fuse_layer.append(None)
else:
conv_downsamples = []
for k in range(i - j):
if k == i - j - 1:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[i],
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[i])[1]))
else:
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels[j],
in_channels[j],
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
in_channels[j])[1],
nn.ReLU(inplace=True)))
fuse_layer.append(nn.Sequential(*conv_downsamples))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def forward(self, x):
"""Forward function."""
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = 0
for j in range(self.num_branches):
if i == j:
y += x[j]
else:
y += self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
@BACKBONES.register_module()
class HRNet(nn.Module):
"""HRNet backbone.
`High-Resolution Representations for Labeling Pixels and Regions
<https://arxiv.org/abs/1904.04514>`__
Args:
extra (dict): detailed configuration for each stage of HRNet.
in_channels (int): Number of input image channels. Default: 3.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Default: -1.
Example:
>>> from mmpose.models import HRNet
>>> import torch
>>> extra = dict(
>>> stage1=dict(
>>> num_modules=1,
>>> num_branches=1,
>>> block='BOTTLENECK',
>>> num_blocks=(4, ),
>>> num_channels=(64, )),
>>> stage2=dict(
>>> num_modules=1,
>>> num_branches=2,
>>> block='BASIC',
>>> num_blocks=(4, 4),
>>> num_channels=(32, 64)),
>>> stage3=dict(
>>> num_modules=4,
>>> num_branches=3,
>>> block='BASIC',
>>> num_blocks=(4, 4, 4),
>>> num_channels=(32, 64, 128)),
>>> stage4=dict(
>>> num_modules=3,
>>> num_branches=4,
>>> block='BASIC',
>>> num_blocks=(4, 4, 4, 4),
>>> num_channels=(32, 64, 128, 256)))
>>> self = HRNet(extra, in_channels=1)
>>> self.eval()
>>> inputs = torch.rand(1, 1, 32, 32)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 32, 8, 8)
"""
blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
def __init__(self,
extra,
in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=False,
with_cp=False,
zero_init_residual=False,
frozen_stages=-1):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
self.frozen_stages = frozen_stages
# stem net
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
self.conv1 = build_conv_layer(
self.conv_cfg,
in_channels,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm1_name, norm1)
self.conv2 = build_conv_layer(
self.conv_cfg,
64,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.add_module(self.norm2_name, norm2)
self.relu = nn.ReLU(inplace=True)
self.upsample_cfg = self.extra.get('upsample', {
'mode': 'nearest',
'align_corners': None
})
# stage 1
self.stage1_cfg = self.extra['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = self.stage1_cfg['block']
num_blocks = self.stage1_cfg['num_blocks'][0]
block = self.blocks_dict[block_type]
stage1_out_channels = num_channels * get_expansion(block)
self.layer1 = self._make_layer(block, 64, stage1_out_channels,
num_blocks)
# stage 2
self.stage2_cfg = self.extra['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = self.stage2_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition1 = self._make_transition_layer([stage1_out_channels],
num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
# stage 3
self.stage3_cfg = self.extra['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = self.stage3_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition2 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
# stage 4
self.stage4_cfg = self.extra['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = self.stage4_cfg['block']
block = self.blocks_dict[block_type]
num_channels = [
channel * get_expansion(block) for channel in num_channels
]
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg,
num_channels,
multiscale_output=self.stage4_cfg.get('multiscale_output', False))
self._freeze_stages()
@property
def norm1(self):
"""nn.Module: the normalization layer named "norm1" """
return getattr(self, self.norm1_name)
@property
def norm2(self):
"""nn.Module: the normalization layer named "norm2" """
return getattr(self, self.norm2_name)
def _make_transition_layer(self, num_channels_pre_layer,
num_channels_cur_layer):
"""Make transition layer."""
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
num_channels_pre_layer[i],
num_channels_cur_layer[i],
kernel_size=3,
stride=1,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg,
num_channels_cur_layer[i])[1],
nn.ReLU(inplace=True)))
else:
transition_layers.append(None)
else:
conv_downsamples = []
for j in range(i + 1 - num_branches_pre):
in_channels = num_channels_pre_layer[-1]
out_channels = num_channels_cur_layer[i] \
if j == i - num_branches_pre else in_channels
conv_downsamples.append(
nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels,
out_channels,
kernel_size=3,
stride=2,
padding=1,
bias=False),
build_norm_layer(self.norm_cfg, out_channels)[1],
nn.ReLU(inplace=True)))
transition_layers.append(nn.Sequential(*conv_downsamples))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, in_channels, out_channels, blocks, stride=1):
"""Make layer."""
downsample = None
if stride != 1 or in_channels != out_channels:
downsample = nn.Sequential(
build_conv_layer(
self.conv_cfg,
in_channels,
out_channels,
kernel_size=1,
stride=stride,
bias=False),
build_norm_layer(self.norm_cfg, out_channels)[1])
layers = []
layers.append(
block(
in_channels,
out_channels,
stride=stride,
downsample=downsample,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
for _ in range(1, blocks):
layers.append(
block(
out_channels,
out_channels,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
"""Make stage."""
num_modules = layer_config['num_modules']
num_branches = layer_config['num_branches']
num_blocks = layer_config['num_blocks']
num_channels = layer_config['num_channels']
block = self.blocks_dict[layer_config['block']]
hr_modules = []
for i in range(num_modules):
# multi_scale_output is only used for the last module
if not multiscale_output and i == num_modules - 1:
reset_multiscale_output = False
else:
reset_multiscale_output = True
hr_modules.append(
HRModule(
num_branches,
block,
num_blocks,
in_channels,
num_channels,
reset_multiscale_output,
with_cp=self.with_cp,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
upsample_cfg=self.upsample_cfg))
in_channels = hr_modules[-1].in_channels
return nn.Sequential(*hr_modules), in_channels
def _freeze_stages(self):
"""Freeze parameters."""
if self.frozen_stages >= 0:
self.norm1.eval()
self.norm2.eval()
for m in [self.conv1, self.norm1, self.conv2, self.norm2]:
for param in m.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
if i == 1:
m = getattr(self, 'layer1')
else:
m = getattr(self, f'stage{i}')
m.eval()
for param in m.parameters():
param.requires_grad = False
if i < 4:
m = getattr(self, f'transition{i}')
m.eval()
for param in m.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
constant_init(m.norm3, 0)
elif isinstance(m, BasicBlock):
constant_init(m.norm2, 0)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
"""Forward function."""
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.norm2(x)
x = self.relu(x)
x = self.layer1(x)
x_list = []
for i in range(self.stage2_cfg['num_branches']):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
x_list.append(x)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.stage3_cfg['num_branches']):
if self.transition2[i] is not None:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.stage4_cfg['num_branches']):
if self.transition3[i] is not None:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage4(x_list)
return y_list
def train(self, mode=True):
"""Convert the model into training mode."""
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()