IndexError: index 44 is out of bounds for axis 1 with size 24

[bhoomikaushik-siemens]

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• The bug has not been fixed in the latest version(https://github.com/open-mmlab/mmpose).

Environment

Package Version Editable project location

---

addict 2.4.0
aiofiles 23.2.1
aioice 0.9.0
aiortc 1.9.0
albucore 0.0.13
albumentations 1.4.14
aliyun-python-sdk-core 2.15.1
aliyun-python-sdk-kms 2.16.3
altair 5.3.0
annotated-types 0.7.0
anyio 4.4.0
attrs 23.2.0
av 12.3.0
blinker 1.8.2
cachetools 5.4.0
certifi 2022.12.7
cffi 1.16.0
charset-normalizer 2.1.1
chumpy 0.70
click 8.1.7
colorama 0.4.6
contourpy 1.2.1
coverage 7.5.4
crcmod 1.7
cryptography 42.0.8
cycler 0.12.1
Cython 3.0.10
dnspython 2.6.1
eval_type_backport 0.2.0
exceptiongroup 1.2.1
fastapi 0.112.0
ffmpeg 1.4
ffmpy 0.4.0
filelock 3.14.0
flake8 7.1.0
fonttools 4.53.0
fsspec 2024.6.1
gitdb 4.0.11
GitPython 3.1.43
google-crc32c 1.5.0
gradio 4.40.0
gradio_client 1.2.0
h11 0.14.0
httpcore 1.0.5
httpx 0.27.0
huggingface-hub 0.24.5
idna 3.4
ifaddr 0.2.0
imageio 2.34.2
importlib_metadata 7.2.1
importlib_resources 6.4.0
iniconfig 2.0.0
interrogate 1.7.0
isort 4.3.21
Jinja2 3.1.4
jmespath 0.10.0
joblib 1.4.2
json-tricks 3.17.3
jsonlint 0.1
jsonschema 4.23.0
jsonschema-specifications 2023.12.1
kiwisolver 1.4.5
lazy_loader 0.4
Markdown 3.6
markdown-it-py 3.0.0
MarkupSafe 2.1.5
matplotlib 3.9.0
mccabe 0.7.0
mdurl 0.1.2
mmcv 2.1.0
mmdet 3.2.0
mmengine 0.10.4
mmpose 1.3.2 /home/bhoomi/mmpose
model-index 0.1.11
munkres 1.1.4
networkx 3.3
numpy 1.26.3
opencv-python 4.10.0.84
opencv-python-headless 4.10.0.84
opendatalab 0.0.10
openmim 0.3.9
openxlab 0.1.0
ordered-set 4.1.0
orjson 3.10.6
oss2 2.17.0
packaging 24.1
pandas 2.2.2
parameterized 0.9.0
pillow 10.2.0
pip 22.0.2
platformdirs 4.2.2
pluggy 1.5.0
protobuf 5.27.3
py 1.11.0
pyarrow 17.0.0
pyav 12.1.0
pycocotools 2.0.8
pycodestyle 2.12.0
pycparser 2.22
pycryptodome 3.20.0
pydantic 2.7.4
pydantic_core 2.18.4
pydeck 0.9.1
pydub 0.25.1
pyee 11.1.0
pyflakes 3.2.0
Pygments 2.18.0
pylibsrtp 0.10.0
pyOpenSSL 24.2.1
pyparsing 3.1.2
pytest 8.2.2
pytest-runner 6.0.1
python-dateutil 2.9.0.post0
python-multipart 0.0.9
pytz 2023.4
PyYAML 6.0.1
referencing 0.35.1
requests 2.32.3
rich 13.4.2
rpds-py 0.19.1
ruff 0.5.6
scikit-image 0.24.0
scikit-learn 1.5.0
scipy 1.13.1
semantic-version 2.10.0
setuptools 60.2.0
shapely 2.0.4
shellingham 1.5.4
six 1.16.0
smmap 5.0.1
sniffio 1.3.1
starlette 0.37.2
streamlit 1.37.0
streamlit-webrtc 0.47.7
tabulate 0.9.0
tenacity 8.5.0
termcolor 2.4.0
terminaltables 3.1.10
threadpoolctl 3.5.0
tifffile 2024.6.18
toml 0.10.2
tomli 2.0.1
tomlkit 0.12.0
toolz 0.12.1
torch 1.11.0+cu115
torchaudio 0.11.0+cu115
torchvision 0.12.0+cu115
tornado 6.4.1
tqdm 4.65.2
typer 0.12.3
typing_extensions 4.9.0
tzdata 2024.1
urllib3 2.2.2
uvicorn 0.30.5
watchdog 4.0.1
websockets 12.0
xdoctest 1.1.5
xtcocotools 1.14.3
yapf 0.40.2
zipp 3.19.2

Reproduces the problem - code sample

base = ['../../../base/default_runtime.py']

runtime

max_epochs = 270
stage2_num_epochs = 30
base_lr = 4e-3

train_cfg = dict(max_epochs=max_epochs, val_interval=10)
randomness = dict(seed=21)

optimizer

optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

learning rate

param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
by_epoch=False,
begin=0,
end=1000),
dict(
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=max_epochs // 2,
end=max_epochs,
T_max=max_epochs // 2,
by_epoch=True,
convert_to_iter_based=True),
]

automatically scaling LR based on the actual training batch size

auto_scale_lr = dict(base_batch_size=512)

codec settings

codec = dict(
type='SimCCLabel',
input_size=(192, 256),
sigma=(4.9, 5.66),
simcc_split_ratio=2.0,
normalize=False,
use_dark=False)

model settings

model = dict(
type='TopdownPoseEstimator',
data_preprocessor=dict(
type='PoseDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True
),
backbone=dict(
scope='mmdet',
type='CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=0.67,
widen_factor=0.75,
out_indices=(4, ),
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU'),
# Remove init_cfg for training from scratch
init_cfg=None # Ensure no pre-trained weights are used
),
head=dict(
type='RTMCCHead',
in_channels=768,
out_channels=64,
input_size=codec['input_size'],
in_featuremap_size=tuple([s // 32 for s in codec['input_size']]),
simcc_split_ratio=codec['simcc_split_ratio'],
final_layer_kernel_size=7,
gau_cfg=dict(
hidden_dims=256,
s=128,
expansion_factor=2,
dropout_rate=0.,
drop_path=0.,
act_fn='SiLU',
use_rel_bias=False,
pos_enc=False
),
loss=dict(
type='KLDiscretLoss',
use_target_weight=True,
beta=10.,
label_softmax=True
),
decoder=codec
),
test_cfg=dict(
flip_test=True
)
)

base dataset settings

dataset_type = 'CocoDataset'
data_mode = 'topdown'
data_root = 'data/coco/'

backend_args = dict(backend='local')

backend_args = dict(

backend='petrel',

path_mapping=dict({

f'{data_root}': 's3:https://openmmlab/datasets/detection/coco/',

f'{data_root}': 's3:https://openmmlab/datasets/detection/coco/'

}))

pipelines

train_pipeline = [
dict(type='LoadImage', backend_args=backend_args),
dict(type='GetBBoxCenterScale'),
dict(type='RandomFlip', direction='horizontal'),
dict(type='RandomHalfBody'),
dict(
type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),
dict(type='TopdownAffine', input_size=codec['input_size']),
dict(type='mmdet.YOLOXHSVRandomAug'),
dict(
type='Albumentation',
transforms=[
dict(type='Blur', p=0.1),
dict(type='MedianBlur', p=0.1),
dict(
type='CoarseDropout',
max_holes=1,
max_height=0.4,
max_width=0.4,
min_holes=1,
min_height=0.2,
min_width=0.2,
p=1.0),
]),
dict(type='GenerateTarget', encoder=codec),
dict(type='PackPoseInputs')
]
val_pipeline = [
dict(type='LoadImage', backend_args=backend_args),
dict(type='GetBBoxCenterScale'),
dict(type='TopdownAffine', input_size=codec['input_size']),
dict(type='PackPoseInputs')
]

train_pipeline_stage2 = [
dict(type='LoadImage', backend_args=backend_args),
dict(type='GetBBoxCenterScale'),
dict(type='RandomFlip', direction='horizontal'),
dict(type='RandomHalfBody'),
dict(
type='RandomBBoxTransform',
shift_factor=0.,
scale_factor=[0.75, 1.25],
rotate_factor=60),
dict(type='TopdownAffine', input_size=codec['input_size']),
dict(type='mmdet.YOLOXHSVRandomAug'),
dict(
type='Albumentation',
transforms=[
dict(type='Blur', p=0.1),
dict(type='MedianBlur', p=0.1),
dict(
type='CoarseDropout',
max_holes=1,
max_height=0.4,
max_width=0.4,
min_holes=1,
min_height=0.2,
min_width=0.2,
p=0.5),
]),
dict(type='GenerateTarget', encoder=codec),
dict(type='PackPoseInputs')
]

data loaders

train_dataloader = dict(
batch_size=64,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='annotations/coco_wholebody_train_v1.0.json',

    data_prefix=dict(img='train2017/'),
    metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
    pipeline=train_pipeline,
))

val_dataloader = dict(
batch_size=32,
num_workers= 8,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='annotations/coco_wholebody_val_v1.0.json',
data_prefix=dict(img='val2017/'),
metainfo=dict(from_file='configs/base/datasets/custom.py'),
test_mode=True,
bbox_file='data/coco/person_detection_results/'
'COCO_val2017_detections_AP_H_56_person.json',
pipeline=val_pipeline,
))
test_dataloader = val_dataloader

hooks

default_hooks = dict(
checkpoint=dict(
save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))

custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='mmdet.PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_pipeline=train_pipeline_stage2)
]

evaluators

val_evaluator = dict(
type='CocoWholeBodyMetric',
ann_file=data_root + 'annotations/coco_wholebody_val_v1.0.json')
test_evaluator = val_evaluator

Reproduces the problem - command or script

python3 tools/train.py configs/wholebody_2d_keypoint/rtmpose/coco-wholebody/custom.py

Reproduces the problem - error message

loading annotations into memory...
Done (t=0.80s)
creating index...
index created!
09/04 14:30:45 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
09/04 14:30:45 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
09/04 14:30:45 - mmengine - INFO - Checkpoints will be saved to /home/bhoomi/mmpose/work_dirs/custom.
Traceback (most recent call last):
File "/home/bhoomi/mmpose/tools/train.py", line 162, in
main()
File "/home/bhoomi/mmpose/tools/train.py", line 158, in main
runner.train()
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/loops.py", line 96, in run
self.run_epoch()
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/runner/loops.py", line 112, in run_epoch
for idx, data_batch in enumerate(self.dataloader):
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 530, in next
data = self._next_data()
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
return self._process_data(data)
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
data.reraise()
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/_utils.py", line 457, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/bhoomi/pose/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 410, in getitem
data = self.prepare_data(idx)
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 115, in wrapper
return old_func(obj, *args, **kwargs)
File "/home/bhoomi/mmpose/mmpose/datasets/datasets/base/base_coco_style_dataset.py", line 170, in prepare_data
return self.pipeline(data_info)
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py", line 60, in call
data = t(data)
File "/home/bhoomi/pose/lib/python3.10/site-packages/mmcv/transforms/base.py", line 12, in call
return self.transform(results)
File "/home/bhoomi/mmpose/mmpose/datasets/transforms/common_transforms.py", line 239, in transform
keypoints, keypoints_visible = flip_keypoints(
File "/home/bhoomi/mmpose/mmpose/structures/keypoint/transforms.py", line 52, in flip_keypoints
keypoints = keypoints.take(flip_indices, axis=ndim - 2)
IndexError: index 44 is out of bounds for axis 1 with size 24

Additional information

No response

[bhoomikaushik-siemens]

This is the dataset confiuation:
dataset_info = dict(
dataset_name='coco',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={

0: dict(name='hips', id=0, color=[51, 153, 255], type='lower', swap=''),
1: dict(name='left_upper_leg', id=1, color=[51, 153, 255], type='lower', swap='right_upper_leg'),
2: dict(name='right_upper_leg', id=2, color=[51, 153, 255], type='lower', swap='left_upper_leg'),
3: dict(name='left_lower_leg', id=3, color=[51, 153, 255], type='lower', swap='right_lower_leg'),
4: dict(name='right_lower_leg', id=4, color=[51, 153, 255], type='lower', swap='left_lower_leg'),
5: dict(name='left_foot', id=5, color=[51, 153, 255], type='lower', swap='right_foot'),
6: dict(name='right_foot', id=6, color=[51, 153, 255], type='lower', swap='left_foot'),
7: dict(name='spine', id=7, color=[51, 153, 255], type='upper', swap=''),
8: dict(name='chest', id=8, color=[51, 153, 255], type='upper', swap=''),
9: dict(name='upper_chest', id=9, color=[51, 153, 255], type='upper', swap=''),
10: dict(name='neck', id=10, color=[51, 153, 255], type='upper', swap=''),
11: dict(name='head', id=11, color=[51, 153, 255], type='upper', swap=''),
12: dict(name='left_shoulder', id=12, color=[51, 153, 255], type='upper', swap='right_shoulder'),
13: dict(name='right_shoulder', id=13, color=[51, 153, 255], type='upper', swap='left_shoulder'),
14: dict(name='left_upper_arm', id=14, color=[51, 153, 255], type='upper', swap='right_upper_arm'),
15: dict(name='right_upper_arm', id=15, color=[51, 153, 255], type='upper', swap='left_upper_arm'),
16: dict(name='left_lower_arm', id=16, color=[51, 153, 255], type='upper', swap='right_lower_arm'),
17: dict(name='right_lower_arm', id=17, color=[51, 153, 255], type='upper', swap='left_lower_arm'),
18: dict(name='left_hand', id=18, color=[51, 153, 255], type='upper', swap='right_hand'),
19: dict(name='right_hand', id=19, color=[51, 153, 255], type='upper', swap='left_hand'),
20: dict(name='left_toes', id=20, color=[51, 153, 255], type='lower', swap='right_toes'),
21: dict(name='right_toes', id=21, color=[51, 153, 255], type='lower', swap='left_toes'),
22: dict(name='left_eye', id=22, color=[51, 153, 255], type='upper', swap='right_eye'),
23: dict(name='right_eye', id=23, color=[51, 153, 255], type='upper', swap='left_eye'),
24: dict(name='left_thumb_proximal', id=24, color=[51, 153, 255], type='upper', swap='right_thumb_proximal'),
25: dict(name='left_thumb_intermediate', id=25, color=[51, 153, 255], type='upper', swap='right_thumb_intermediate'),
26: dict(name='left_thumb_distal', id=26, color=[51, 153, 255], type='upper', swap='right_thumb_distal'),
27: dict(name='left_thumb_tip', id=27, color=[51, 153, 255], type='upper', swap='right_thumb_tip'),
28: dict(name='left_index_proximal', id=28, color=[51, 153, 255], type='upper', swap='right_index_proximal'),
29: dict(name='left_index_intermediate', id=29, color=[51, 153, 255], type='upper', swap='right_index_intermediate'),
30: dict(name='left_index_distal', id=30, color=[51, 153, 255], type='upper', swap='right_index_distal'),
31: dict(name='left_index_tip', id=31, color=[51, 153, 255], type='upper', swap='right_index_tip'),
32: dict(name='left_middle_proximal', id=32, color=[51, 153, 255], type='upper', swap='right_middle_proximal'),
33: dict(name='left_middle_intermediate', id=33, color=[51, 153, 255], type='upper', swap='right_middle_intermediate'),
34: dict(name='left_middle_distal', id=34, color=[51, 153, 255], type='upper', swap='right_middle_distal'),
35: dict(name='left_middle_tip', id=35, color=[51, 153, 255], type='upper', swap='right_middle_tip'),
36: dict(name='left_ring_proximal', id=36, color=[51, 153, 255], type='upper', swap='right_ring_proximal'),
37: dict(name='left_ring_intermediate', id=37, color=[51, 153, 255], type='upper', swap='right_ring_intermediate'),
38: dict(name='left_ring_distal', id=38, color=[51, 153, 255], type='upper', swap='right_ring_distal'),
39: dict(name='left_ring_tip', id=39, color=[51, 153, 255], type='upper', swap='right_ring_tip'),
40: dict(name='left_little_proximal', id=40, color=[51, 153, 255], type='upper', swap='right_little_proximal'),
41: dict(name='left_little_intermediate', id=41, color=[51, 153, 255], type='upper', swap='right_little_intermediate'),
42: dict(name='left_little_distal', id=42, color=[51, 153, 255], type='upper', swap='right_little_distal'),
43: dict(name='left_little_tip', id=43, color=[51, 153, 255], type='upper', swap='right_little_tip'),
44: dict(name='right_thumb_proximal', id=44, color=[51, 153, 255], type='upper', swap='left_thumb_proximal'),
45: dict(name='right_thumb_intermediate', id=45, color=[51, 153, 255], type='upper', swap='left_thumb_intermediate'),
46: dict(name='right_thumb_distal', id=46, color=[51, 153, 255], type='upper', swap='left_thumb_distal'),
47: dict(name='right_thumb_tip', id=47, color=[51, 153, 255], type='upper', swap='left_thumb_tip'),
48: dict(name='right_index_proximal', id=48, color=[51, 153, 255], type='upper', swap='left_index_proximal'),
49: dict(name='right_index_intermediate', id=49, color=[51, 153, 255], type='upper', swap='left_index_intermediate'),
50: dict(name='right_index_distal', id=50, color=[51, 153, 255], type='upper', swap='left_index_distal'),
51: dict(name='right_index_tip', id=51, color=[51, 153, 255], type='upper', swap='left_index_tip'),
52: dict(name='right_middle_proximal', id=52, color=[51, 153, 255], type='upper', swap='left_middle_proximal'),
53: dict(name='right_middle_intermediate', id=53, color=[51, 153, 255], type='upper', swap='left_middle_intermediate'),
54: dict(name='right_middle_distal', id=54, color=[51, 153, 255], type='upper', swap='left_middle_distal'),
55: dict(name='right_middle_tip', id=55, color=[51, 153, 255], type='upper', swap='left_middle_tip'),
56: dict(name='right_ring_proximal', id=56, color=[51, 153, 255], type='upper', swap='left_ring_proximal'),
57: dict(name='right_ring_intermediate', id=57, color=[51, 153, 255], type='upper', swap='left_ring_intermediate'),
58: dict(name='right_ring_distal', id=58, color=[51, 153, 255], type='upper', swap='left_ring_distal'),
59: dict(name='right_ring_tip', id=59, color=[51, 153, 255], type='upper', swap='left_ring_tip'),
60: dict(name='right_little_proximal', id=60, color=[51, 153, 255], type='upper', swap='left_little_proximal'),
61: dict(name='right_little_intermediate', id=61, color=[51, 153, 255], type='upper', swap='left_little_intermediate'),
62: dict(name='right_little_distal', id=62, color=[51, 153, 255], type='upper', swap='left_little_distal'),
63: dict(name='right_little_tip', id=63, color=[51, 153, 255], type='upper', swap='left_little_tip'),


},
skeleton_info={
    
0: dict(link=('hips', 'left_upper_leg'), id=0, color=[0, 255, 0]),
1: dict(link=('left_upper_leg', 'left_lower_leg'), id=1, color=[0, 255, 0]),
2: dict(link=('left_lower_leg', 'left_foot'), id=2, color=[0, 255, 0]),
3: dict(link=('hips', 'right_upper_leg'), id=3, color=[255, 128, 0]),
4: dict(link=('right_upper_leg', 'right_lower_leg'), id=4, color=[255, 128, 0]),
5: dict(link=('right_lower_leg', 'right_foot'), id=5, color=[255, 128, 0]),
6: dict(link=('hips', 'spine'), id=6, color=[51, 153, 255]),
7: dict(link=('spine', 'chest'), id=7, color=[51, 153, 255]),
8: dict(link=('chest', 'upper_chest'), id=8, color=[51, 153, 255]),
9: dict(link=('upper_chest', 'neck'), id=9, color=[51, 153, 255]),
10: dict(link=('neck', 'head'), id=10, color=[51, 153, 255]),
11: dict(link=('upper_chest', 'left_shoulder'), id=11, color=[51, 153, 255]),
12: dict(link=('upper_chest', 'right_shoulder'), id=12, color=[51, 153, 255]),
13: dict(link=('left_shoulder', 'left_upper_arm'), id=13, color=[0, 255, 0]),
14: dict(link=('left_upper_arm', 'left_lower_arm'), id=14, color=[0, 255, 0]),
15: dict(link=('left_lower_arm', 'left_hand'), id=15, color=[0, 255, 0]),
16: dict(link=('right_shoulder', 'right_upper_arm'), id=16, color=[255, 128, 0]),
17: dict(link=('right_upper_arm', 'right_lower_arm'), id=17, color=[255, 128, 0]),
18: dict(link=('right_lower_arm', 'right_hand'), id=18, color=[255, 128, 0]),

# Right hand connections
19: dict(link=('right_hand', 'right_thumb_proximal'), id=19, color=[0, 255, 0]),
20: dict(link=('right_thumb_proximal', 'right_thumb_intermediate'), id=20, color=[0, 255, 0]),
21: dict(link=('right_thumb_intermediate', 'right_thumb_distal'), id=21, color=[0, 255, 0]),
22: dict(link=('right_thumb_distal', 'right_thumb_tip'), id=22, color=[0, 255, 0]),
23: dict(link=('right_hand', 'right_index_proximal'), id=23, color=[0, 255, 0]),
24: dict(link=('right_index_proximal', 'right_index_intermediate'), id=24, color=[0, 255, 0]),
25: dict(link=('right_index_intermediate', 'right_index_distal'), id=25, color=[0, 255, 0]),
26: dict(link=('right_index_distal', 'right_index_tip'), id=26, color=[0, 255, 0]),
27: dict(link=('right_hand', 'right_middle_proximal'), id=27, color=[0, 255, 0]),
28: dict(link=('right_middle_proximal', 'right_middle_intermediate'), id=28, color=[0, 255, 0]),
29: dict(link=('right_middle_intermediate', 'right_middle_distal'), id=29, color=[0, 255, 0]),
30: dict(link=('right_middle_distal', 'right_middle_tip'), id=30, color=[0, 255, 0]),
31: dict(link=('right_hand', 'right_ring_proximal'), id=31, color=[0, 255, 0]),
32: dict(link=('right_ring_proximal', 'right_ring_intermediate'), id=32, color=[0, 255, 0]),
33: dict(link=('right_ring_intermediate', 'right_ring_distal'), id=33, color=[0, 255, 0]),
34: dict(link=('right_ring_distal', 'right_ring_tip'), id=34, color=[0, 255, 0]),
35: dict(link=('right_hand', 'right_little_proximal'), id=35, color=[0, 255, 0]),
36: dict(link=('right_little_proximal', 'right_little_intermediate'), id=36, color=[0, 255, 0]),
37: dict(link=('right_little_intermediate', 'right_little_distal'), id=37, color=[0, 255, 0]),
38: dict(link=('right_little_distal', 'right_little_tip'), id=38, color=[0, 255, 0]),

# Left hand connections
39: dict(link=('left_hand', 'left_thumb_proximal'), id=39, color=[255, 128, 0]),
40: dict(link=('left_thumb_proximal', 'left_thumb_intermediate'), id=40, color=[255, 128, 0]),
41: dict(link=('left_thumb_intermediate', 'left_thumb_distal'), id=41, color=[255, 128, 0]),
42: dict(link=('left_thumb_distal', 'left_thumb_tip'), id=42, color=[255, 128, 0]),
43: dict(link=('left_hand', 'left_index_proximal'), id=43, color=[255, 128, 0]),
44: dict(link=('left_index_proximal', 'left_index_intermediate'), id=44, color=[255, 128, 0]),
45: dict(link=('left_index_intermediate', 'left_index_distal'), id=45, color=[255, 128, 0]),
46: dict(link=('left_index_distal', 'left_index_tip'), id=46, color=[255, 128, 0]),
47: dict(link=('left_hand', 'left_middle_proximal'), id=47, color=[255, 128, 0]),
48: dict(link=('left_middle_proximal', 'left_middle_intermediate'), id=48, color=[255, 128, 0]),
49: dict(link=('left_middle_intermediate', 'left_middle_distal'), id=49, color=[255, 128, 0]),
50: dict(link=('left_middle_distal', 'left_middle_tip'), id=50, color=[255, 128, 0]),
51: dict(link=('left_hand', 'left_ring_proximal'), id=51, color=[255, 128, 0]),
52: dict(link=('left_ring_proximal', 'left_ring_intermediate'), id=52, color=[255, 128, 0]),
53: dict(link=('left_ring_intermediate', 'left_ring_distal'), id=53, color=[255, 128, 0]),
54: dict(link=('left_ring_distal', 'left_ring_tip'), id=54, color=[255, 128, 0]),
55: dict(link=('left_hand', 'left_little_proximal'), id=55, color=[255, 128, 0]),
56: dict(link=('left_little_proximal', 'left_little_intermediate'), id=56, color=[255, 128, 0]),
57: dict(link=('left_little_intermediate', 'left_little_distal'), id=57, color=[255, 128, 0]),
58: dict(link=('left_little_distal', 'left_little_tip'), id=58, color=[255, 128, 0]),

}

,
joint_weights=[1.] * 64,
# 'https://github.com/jin-s13/COCO-WholeBody/blob/master/'
# 'evaluation/myeval_wholebody.py#L175'
# no of sigma values=64
sigmas=[
    0.026,  # hips
0.025,  # left_upper_leg
0.025,  # left_lower_leg
0.035,  # left_foot
0.035,  # right_upper_leg
0.079,  # right_lower_leg
0.079,  # right_foot
0.072,  # spine
0.072,  # chest
0.062,  # upper_chest
0.062,  # neck
0.107,  # head
0.107,  # left_shoulder
0.087,  # right_shoulder
0.087,  # left_upper_arm
0.089,  # left_lower_arm
0.089,  # left_hand
0.068,  # right_upper_arm
0.066,  # right_lower_arm
0.066,  # right_hand
0.066,
0.066,
0.066,
0.066,

# Right hand connections
0.092,  # right_thumb_proximal
0.094,  # right_thumb_intermediate
0.094,  # right_thumb_distal
0.042,  # right_thumb_tip
0.043,  # right_index_proximal
0.044,  # right_index_intermediate
0.043,  # right_index_distal
0.040,  # right_index_tip
0.035,  # right_middle_proximal
0.031,  # right_middle_intermediate
0.025,  # right_middle_distal
0.020,  # right_middle_tip
0.023,  # right_ring_proximal
0.029,  # right_ring_intermediate
0.032,  # right_ring_distal
0.037,  # right_ring_tip
0.038,  # right_little_proximal
0.043,  # right_little_intermediate
0.041,  # right_little_distal
0.045,  # right_little_tip

# Left hand connections
0.013,  # left_thumb_proximal
0.012,  # left_thumb_intermediate
0.011,  # left_thumb_distal
0.011,  # left_thumb_tip
0.012,  # left_index_proximal
0.012,  # left_index_intermediate
0.011,  # left_index_distal
0.011,  # left_index_tip
0.013,  # left_middle_proximal
0.015,  # left_middle_intermediate
0.009,  # left_middle_distal
0.007,  # left_middle_tip
0.007,  # left_ring_proximal
0.007,  # left_ring_intermediate
0.007,  # left_ring_distal
0.012,  # left_ring_tip
0.009,  # left_little_proximal
0.008,  # left_little_intermediate
0.016,  # left_little_distal
0.010,  # left_little_tip   
])