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20 hours ago •
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open-mmlab
Use_udp error: cannot convert float NaN to integer
Hi folks, I used MMPose to train on CarFusion dataset using HRNet with UDP implemented (hrnet_w32_coco_384x288_udp). The error ValueError: cannot convert float NaN to integer happened as I passed the following code in the training validation pipeline.
dict(type='TopDownAffine', use_udp=True)
Without use_udp=True (False), the training is working fine.
Training config:
joint_weights = [1, 1, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]
sigmas = [0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.01, 0.062, 0.062, 0.107, 0.107, 0.01]
keypoint_info = dict({
# bottom keypoints
0: dict(name='bottom_front_right', id=0, color=[30, 252, 100], type='lower', swap='bottom_front_left'),
1: dict(name='bottom_front_left', id=1, color=[30, 252, 100], type='lower', swap='bottom_front_right'),
2: dict(name='bottom_back_right', id=2, color=[30, 252, 100], type='lower', swap='bottom_back_left'),
3: dict(name='bottom_back_left', id=3, color=[30, 252, 100], type='lower', swap='bottom_back_right'),
# middle keypoints
4: dict(name='middle_front_right', id=4, color=[255, 246, 1], type='upper', swap='middle_front_left'),
5: dict(name='middle_front_left', id=5, color=[255, 246, 1], type='upper', swap='middle_front_right'),
6: dict(name='middle_back_right', id=6, color=[250, 84, 124], type='upper', swap='middle_back_left'),
7: dict(name='middle_back_left', id=7, color=[250, 84, 124], type='upper', swap='middle_back_right'),
8: dict(name='ghost_8', id=8, color=[0, 0, 0], type='', swap=''),
# top keypoints
9: dict(name='top_front_right', id=9, color=[255, 246, 1], type='upper', swap='top_front_left'),
10: dict(name='top_front_left', id=10, color=[255, 246, 1], type='upper', swap='top_front_right'),
11: dict(name='top_back_right', id=11, color=[250, 84, 124], type='upper', swap='top_back_left'),
12: dict(name='top_back_left', id=12, color=[250, 84, 124], type='upper', swap='top_back_right'),
13: dict(name='ghost_13', id=13, color=[0, 0, 0], type='', swap='')
})
skeleton_info = dict({
# bottom edges
0: dict(link=('bottom_front_right', 'bottom_front_left'), id=0, color=[30, 252, 100]),
1: dict(link=('bottom_back_right', 'bottom_back_left'), id=1, color=[30, 252, 100]),
2: dict(link=('bottom_front_right', 'bottom_back_right'), id=2, color=[30, 252, 100]),
3: dict(link=('bottom_front_left', 'bottom_back_left'), id=3, color=[30, 252, 100]),
# front edges
4: dict(link=('middle_front_left', 'middle_front_right'), id=4, color=[255, 246, 1]),
5: dict(link=('bottom_front_left', 'middle_front_left'), id=5, color=[255, 246, 1]),
6: dict(link=('bottom_front_right', 'middle_front_right'), id=6, color=[255, 246, 1]),
7: dict(link=('middle_front_left', 'top_front_left'), id=7, color=[255, 246, 1]),
8: dict(link=('middle_front_right', 'top_front_right'), id=8, color=[255, 246, 1]),
# back edges
9: dict(link=('middle_back_left', 'middle_back_right'), id=9, color=[250, 84, 124]),
10: dict(link=('bottom_back_left', 'middle_back_left'), id=10, color=[250, 84, 124]),
11: dict(link=('bottom_back_right', 'middle_back_right'), id=11, color=[250, 84, 124]),
12: dict(link=('middle_back_left', 'top_back_left'), id=12, color=[250, 84, 124]),
13: dict(link=('middle_back_right', 'top_back_right'), id=13, color=[250, 84, 124])
})
dataset_info = dict(
dataset_name='carfusion',
paper_info=paper_info,
keypoint_info=keypoint_info,
skeleton_info=skeleton_info,
joint_weights = joint_weights,
sigmas=sigmas)
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(type='Adam', lr=0.0005)
optimizer_config = dict(grad_clip=None)
target_type = 'GaussianHeatmap'
work_dir = 'work_dirs/hrnet_w32_coco_tiny_256x192'
data_root = 'data/coco_tiny'
gpu_ids = range(0, 1)
seed = 0
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
# change it to 60.
total_epochs = 60
log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook')])
channel_cfg = dict(
num_output_channels=14,
dataset_joints=14,
dataset_channel=[[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
]],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
])
model = dict(
type='TopDown',
pretrained=
'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
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)))),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=14,
num_deconv_layers=0,
extra=dict(final_conv_kernel=1),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
target_type=target_type,
shift_heatmap=False,
modulate_kernel=17,
use_udp=True))
data_cfg = dict(
image_size=[288, 384], # 192 256
heatmap_size=[72, 96],
num_output_channels=14,
num_joints=14,
dataset_channel=[[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
]],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=False,
det_bbox_thr=0.0,
bbox_file=
'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine', use_udp=True),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget',
sigma=3,
encoding='UDP',
target_type=target_type),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
])
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine', use_udp=True),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
])
]
test_pipeline = val_pipeline
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='TopDownCOCOTinyDataset',
ann_file='data/coco_tiny/train.json',
img_prefix='data/coco_tiny/images/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info=dict(
dataset_name='coco',
paper_info=paper_info,
keypoint_info=keypoint_info,
skeleton_info=skeleton_info,
joint_weights = joint_weights,
sigmas=sigmas)),
val=dict(
type='TopDownCOCOTinyDataset',
ann_file='data/coco_tiny/val.json',
img_prefix='data/coco_tiny/images/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info=dict(
dataset_name='coco',
paper_info=paper_info,
keypoint_info=keypoint_info,
skeleton_info=skeleton_info,
joint_weights = joint_weights,
sigmas=sigmas)),
test=dict(
type='TopDownCOCOTinyDataset',
ann_file='data/coco_tiny/val.json',
img_prefix='data/coco_tiny/images/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info=dict(
dataset_name='coco',
paper_info=paper_info,
keypoint_info=keypoint_info,
skeleton_info=skeleton_info,
joint_weights = joint_weights,
sigmas=sigmas)))
Dataset Config
import json
import os
import os.path as osp
from collections import OrderedDict
import numpy as np
from mmpose.core.evaluation.top_down_eval import (keypoint_nme,
keypoint_pck_accuracy)
from mmpose.datasets.builder import DATASETS
from ..base import Kpt2dSviewRgbImgTopDownDataset
@DATASETS.register_module(name="TopDownCarFusionDataset")
class TopDownCarFusionDataset(Kpt2dSviewRgbImgTopDownDataset):
def __init__(self,
ann_file,
img_prefix,
data_cfg,
pipeline,
dataset_info=None,
test_mode=False):
super().__init__(
ann_file, img_prefix, data_cfg, pipeline, dataset_info, coco_style=False, test_mode=test_mode
)
# flip_pairs, upper_body_ids and lower_body_ids will be used
# in some data augmentations like random flip
self.ann_info['flip_pairs'] = [[0, 1], [2, 3], [4, 5], [6, 7], [9, 10], [11, 12]]
# To be confirmed: suppose the middle-points should be upper
self.ann_info['upper_ids'] = (0, 1, 2, 3)
self.ann_info['lower_ids'] = (4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
# Parameter Tunning
self.ann_info['joint_weights'] = [1, 1, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]
self.ann_info['use_different_joint_weights'] = False
self.dataset_name = 'carfusion'
self.db = self._get_db()
def _get_db(self):
with open(self.ann_file) as f:
anns = json.load(f)
db = []
for idx, ann in enumerate(anns):
# get image path
image_file = osp.join(self.img_prefix, ann['image_file'])
# get bbox
bbox = ann['bbox']
center, scale = self._xywh2cs(*bbox)
# get keypoints
keypoints = np.array(
ann['keypoints'], dtype=np.float32).reshape(-1, 3)
num_joints = keypoints.shape[0]
joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d[:, :2] = keypoints[:, :2]
joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d_visible[:, :2] = np.minimum(1, keypoints[:, 2:3])
sample = {
'image_file': image_file,
'center': center,
'scale': scale,
'bbox': bbox,
'rotation': 0,
'joints_3d': joints_3d,
'joints_3d_visible': joints_3d_visible,
'bbox_score': 1,
'bbox_id': idx,
}
db.append(sample)
return db
def _xywh2cs(self, x, y, w, h):
"""This encodes bbox(x, y, w, h) into (center, scale)
Args:
x, y, w, h
Returns:
tuple: A tuple containing center and scale.
- center (np.ndarray[float32](2,)): center of the bbox (x, y).
- scale (np.ndarray[float32](2,)): scale of the bbox w & h.
"""
aspect_ratio = self.ann_info['image_size'][0] / self.ann_info[
'image_size'][1]
center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)
if w > aspect_ratio * h:
h = w * 1.0 / aspect_ratio
elif w < aspect_ratio * h:
w = h * aspect_ratio
# pixel std is 200.0
scale = np.array([w / 200.0, h / 200.0], dtype=np.float32)
# padding to include proper amount of context
scale = scale * 1.25
return center, scale
def evaluate(self, outputs, res_folder, metric='PCK', **kwargs):
"""Evaluate keypoint detection results. The pose prediction results will
be saved in `${res_folder}/result_keypoints.json`.
Note:
batch_size: N
num_keypoints: K
heatmap height: H
heatmap width: W
Args:
outputs (list(preds, boxes, image_path, output_heatmap))
:preds (np.ndarray[N,K,3]): The first two dimensions are
coordinates, score is the third dimension of the array.
:boxes (np.ndarray[N,6]): [center[0], center[1], scale[0]
, scale[1],area, score]
:image_paths (list[str]): For example, ['Test/source/0.jpg']
:output_heatmap (np.ndarray[N, K, H, W]): model outputs.
res_folder (str): Path of directory to save the results.
metric (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
Returns:
dict: Evaluation results for evaluation metric.
"""
metrics = metric if isinstance(metric, list) else [metric]
allowed_metrics = ['PCK', 'NME']
for metric in metrics:
if metric not in allowed_metrics:
raise KeyError(f'metric {metric} is not supported')
res_file = os.path.join(res_folder, 'result_keypoints.json')
kpts = []
for output in outputs:
preds = output['preds']
boxes = output['boxes']
image_paths = output['image_paths']
bbox_ids = output['bbox_ids']
batch_size = len(image_paths)
for i in range(batch_size):
kpts.append({
'keypoints': preds[i].tolist(),
'center': boxes[i][0:2].tolist(),
'scale': boxes[i][2:4].tolist(),
'area': float(boxes[i][4]),
'score': float(boxes[i][5]),
'bbox_id': bbox_ids[i]
})
kpts = self._sort_and_unique_bboxes(kpts)
self._write_keypoint_results(kpts, res_file)
info_str = self._report_metric(res_file, metrics)
name_value = OrderedDict(info_str)
return name_value
def _report_metric(self, res_file, metrics, pck_thr=0.3):
"""Keypoint evaluation.
Args:
res_file (str): Json file stored prediction results.
metrics (str | list[str]): Metric to be performed.
Options: 'PCK', 'NME'.
pck_thr (float): PCK threshold, default: 0.3.
Returns:
dict: Evaluation results for evaluation metric.
"""
info_str = []
with open(res_file, 'r') as fin:
preds = json.load(fin)
assert len(preds) == len(self.db)
outputs = []
gts = []
masks = []
for pred, item in zip(preds, self.db):
outputs.append(np.array(pred['keypoints'])[:, :-1])
gts.append(np.array(item['joints_3d'])[:, :-1])
masks.append((np.array(item['joints_3d_visible'])[:, 0]) > 0)
outputs = np.array(outputs)
gts = np.array(gts)
masks = np.array(masks)
normalize_factor = self._get_normalize_factor(gts)
if 'PCK' in metrics:
_, pck, _ = keypoint_pck_accuracy(outputs, gts, masks, pck_thr,
normalize_factor)
info_str.append(('PCK', pck))
if 'NME' in metrics:
info_str.append(
('NME', keypoint_nme(outputs, gts, masks, normalize_factor)))
return info_str
@staticmethod
def _write_keypoint_results(keypoints, res_file):
"""Write results into a json file."""
with open(res_file, 'w') as f:
json.dump(keypoints, f, sort_keys=True, indent=4)
@staticmethod
def _sort_and_unique_bboxes(kpts, key='bbox_id'):
"""sort kpts and remove the repeated ones."""
kpts = sorted(kpts, key=lambda x: x[key])
num = len(kpts)
for i in range(num - 1, 0, -1):
if kpts[i][key] == kpts[i - 1][key]:
del kpts[i]
return kpts
@staticmethod
def _get_normalize_factor(gts):
"""Get inter-ocular distance as the normalize factor, measured as the
Euclidean distance between the outer corners of the eyes.
Args:
gts (np.ndarray[N, K, 2]): Groundtruth keypoint location.
Return:
np.ndarray[N, 2]: normalized factor
"""
interocular = np.linalg.norm(
gts[:, 0, :] - gts[:, 1, :], axis=1, keepdims=True)
return np.tile(interocular, [1, 2])
Environment
sys.platform: linux
Python: 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) [GCC 9.4.0]
CUDA available: True
GPU 0,1,2,3: Tesla V100-DGXS-32GB
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.6.r11.6/compiler.30794723_0
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.11.0a0+bfe5ad2
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash N/A)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.6
- NVCC architecture flags: -gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_86,code=compute_86
- CuDNN 8.3.2 (built against CUDA 11.5)
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS=-fno-gnu-unique -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.12.0a0
OpenCV: 4.5.5
MMCV: 1.4.6
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.6
MMPose: 0.23.0+d612605
Error Traceback
ValueError Traceback (most recent call last)
/tmp/ipykernel_3232/3683948197.py in <module>
18
19 # train model
---> 20 train_model(
21 model, datasets, cfg, distributed=False, validate=True, meta=dict())
/workspace/mmpose/mmpose/apis/train.py in train_model(model, dataset, cfg, distributed, validate, timestamp, meta)
189 elif cfg.load_from:
190 runner.load_checkpoint(cfg.load_from)
--> 191 runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
/workspace/mmcv/mmcv/runner/epoch_based_runner.py in run(self, data_loaders, workflow, max_epochs, **kwargs)
125 if mode == 'train' and self.epoch >= self._max_epochs:
126 break
--> 127 epoch_runner(data_loaders[i], **kwargs)
128
129 time.sleep(1) # wait for some hooks like loggers to finish
/workspace/mmcv/mmcv/runner/epoch_based_runner.py in train(self, data_loader, **kwargs)
45 self.call_hook('before_train_epoch')
46 time.sleep(2) # Prevent possible deadlock during epoch transition
---> 47 for i, data_batch in enumerate(self.data_loader):
48 self._inner_iter = i
49 self.call_hook('before_train_iter')
/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in __next__(self)
519 if self._sampler_iter is None:
520 self._reset()
--> 521 data = self._next_data()
522 self._num_yielded += 1
523 if self._dataset_kind == _DatasetKind.Iterable and \
/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in _next_data(self)
1201 else:
1202 del self._task_info[idx]
-> 1203 return self._process_data(data)
1204
1205 def _try_put_index(self):
/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)
1227 self._try_put_index()
1228 if isinstance(data, ExceptionWrapper):
-> 1229 data.reraise()
1230 return data
1231
/opt/conda/lib/python3.8/site-packages/torch/_utils.py in reraise(self)
436 # instantiate since we don't know how to
437 raise RuntimeError(msg) from None
--> 438 raise exception
439
440
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/workspace/mmpose/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py", line 274, in __getitem__
return self.pipeline(results)
File "/workspace/mmpose/mmpose/datasets/pipelines/shared_transform.py", line 99, in __call__
data = t(data)
File "/workspace/mmpose/mmpose/datasets/pipelines/top_down_transform.py", line 629, in __call__
else:
File "/workspace/mmpose/mmpose/datasets/pipelines/top_down_transform.py", line 484, in _udp_generate_target
feat_stride = (image_size - 1.0) / (heatmap_size - 1.0)
ValueError: cannot convert float NaN to integer
Could you please check if image_size and heatmap_size are properly set in the ann_info in the data samples? Please check following references:
- https://github.com/open-mmlab/mmpose/blob/882babe2d051a1b9ef3b847c8d36fb1657585160/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_256x192.py#L79-L80
- https://github.com/open-mmlab/mmpose/blob/882babe2d051a1b9ef3b847c8d36fb1657585160/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py#L55-L56
- https://github.com/open-mmlab/mmpose/blob/882babe2d051a1b9ef3b847c8d36fb1657585160/mmpose/datasets/datasets/base/kpt_2d_sview_rgb_img_top_down_dataset.py#L273
