Skip to content

Commit

Permalink
[Feature] Upload RTMPose Hand (alpha version) (#2353)
Browse files Browse the repository at this point in the history
  • Loading branch information
@Tau-J
Tau-J authored May 14, 2023
1 parent 43b39e6 commit 93119ae
Show file tree
Hide file tree
Showing 8 changed files with 721 additions and 15 deletions.
2 changes: 1 addition & 1 deletion configs/face_2d_keypoint/rtmpose/README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -29,4 +29,4 @@ Results on LaPa dataset

| Model | Input Size | NME | Details and Download |
| :-------: | :--------: | :--: | :---------------------------------------: |
| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./wflw/rtmpose_lapa.md) |
| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./lapa/rtmpose_lapa.md) |
2 changes: 1 addition & 1 deletion configs/face_2d_keypoint/rtmpose/lapa/rtmpose_lapa.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@

</details>

Results on COCO-WholeBody-Face val set
Results on LaPa val set

| Arch | Input Size | NME | ckpt | log |
| :------------------------------------------------------------- | :--------: | :--: | :------------------------------------------------------------: | :------------------------------------------------------------: |
Expand Down
381 changes: 381 additions & 0 deletions configs/hand_2d_keypoint/rtmpose/hand5/rtmpose-m_8xb256-210e_hand5-256x256.py
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,381 @@
_base_ = ['../../../_base_/default_runtime.py']

# coco-hand onehand10k freihand2d rhd2d halpehand

# runtime
max_epochs = 210
stage2_num_epochs = 10
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(
# use cosine lr from 150 to 300 epoch
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=256)

# codec settings
codec = dict(
type='SimCCLabel',
input_size=(256, 256),
sigma=(5.66, 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'),
init_cfg=dict(
type='Pretrained',
prefix='backbone.',
checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa
)),
head=dict(
type='RTMCCHead',
in_channels=768,
out_channels=21,
input_size=codec['input_size'],
in_featuremap_size=(8, 8),
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 = 'CocoWholeBodyHandDataset'
data_mode = 'topdown'
data_root = 'data/'

backend_args = dict(backend='local')

# pipelines
train_pipeline = [
dict(type='LoadImage', backend_args=backend_args),
dict(type='GetBBoxCenterScale'),
# dict(type='RandomHalfBody'),
dict(
type='RandomBBoxTransform', scale_factor=[0.5, 1.5],
rotate_factor=180),
dict(type='RandomFlip', direction='horizontal'),
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='RandomHalfBody'),
dict(
type='RandomBBoxTransform',
shift_factor=0.,
scale_factor=[0.75, 1.25],
rotate_factor=180),
dict(type='RandomFlip', direction='horizontal'),
dict(type='TopdownAffine', input_size=codec['input_size']),
dict(type='mmdet.YOLOXHSVRandomAug'),
dict(
type='Albumentation',
transforms=[
dict(type='Blur', p=0.2),
dict(type='MedianBlur', p=0.2),
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')
]

# train datasets
dataset_coco = dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
data_prefix=dict(img='detection/coco/train2017/'),
pipeline=[],
)

dataset_onehand10k = dict(
type='OneHand10KDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='onehand10k/annotations/onehand10k_train.json',
data_prefix=dict(img='pose/OneHand10K/'),
pipeline=[],
)

dataset_freihand = dict(
type='FreiHandDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='freihand/annotations/freihand_train.json',
data_prefix=dict(img='pose/FreiHand/'),
pipeline=[],
)

dataset_rhd = dict(
type='Rhd2DDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='rhd/annotations/rhd_train.json',
data_prefix=dict(img='pose/RHD/'),
pipeline=[
dict(
type='KeypointConverter',
num_keypoints=21,
mapping=[
(0, 0),
(1, 4),
(2, 3),
(3, 2),
(4, 1),
(5, 8),
(6, 7),
(7, 6),
(8, 5),
(9, 12),
(10, 11),
(11, 10),
(12, 9),
(13, 16),
(14, 15),
(15, 14),
(16, 13),
(17, 20),
(18, 19),
(19, 18),
(20, 17),
])
],
)

dataset_halpehand = dict(
type='HalpeHandDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='halpe/annotations/halpe_train_v1.json',
data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015/'),
pipeline=[],
)

# data loaders
train_dataloader = dict(
batch_size=256,
num_workers=10,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='CombinedDataset',
metainfo=dict(
from_file='configs/_base_/datasets/coco_wholebody_hand.py'),
datasets=[
dataset_coco, dataset_onehand10k, dataset_freihand, dataset_rhd,
dataset_halpehand
],
pipeline=train_pipeline,
test_mode=False,
))

# test datasets
val_coco = dict(
type=dataset_type,
data_root=data_root,
data_mode=data_mode,
ann_file='coco/annotations/coco_wholebody_val_v1.0.json',
data_prefix=dict(img='detection/coco/val2017/'),
pipeline=[],
)

val_onehand10k = dict(
type='OneHand10KDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='onehand10k/annotations/onehand10k_test.json',
data_prefix=dict(img='pose/OneHand10K/'),
pipeline=[],
)

val_freihand = dict(
type='FreiHandDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='freihand/annotations/freihand_test.json',
data_prefix=dict(img='pose/FreiHand/'),
pipeline=[],
)

val_rhd = dict(
type='Rhd2DDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='rhd/annotations/rhd_test.json',
data_prefix=dict(img='pose/RHD/'),
pipeline=[
dict(
type='KeypointConverter',
num_keypoints=21,
mapping=[
(0, 0),
(1, 4),
(2, 3),
(3, 2),
(4, 1),
(5, 8),
(6, 7),
(7, 6),
(8, 5),
(9, 12),
(10, 11),
(11, 10),
(12, 9),
(13, 16),
(14, 15),
(15, 14),
(16, 13),
(17, 20),
(18, 19),
(19, 18),
(20, 17),
])
],
)

val_halpehand = dict(
type='HalpeHandDataset',
data_root=data_root,
data_mode=data_mode,
ann_file='halpe/annotations/halpe_val_v1.json',
data_prefix=dict(img='detection/coco/val2017/'),
pipeline=[],
)

test_dataloader = dict(
batch_size=32,
num_workers=10,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
dataset=dict(
type='CombinedDataset',
metainfo=dict(
from_file='configs/_base_/datasets/coco_wholebody_hand.py'),
datasets=[
val_coco, val_onehand10k, val_freihand, val_rhd, val_halpehand
],
pipeline=val_pipeline,
test_mode=True,
))

val_dataloader = test_dataloader

# hooks
default_hooks = dict(
checkpoint=dict(save_best='AUC', 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='PCKAccuracy', thr=0.2),
dict(type='AUC'),
dict(type='EPE')
]
test_evaluator = val_evaluator
Loading

0 comments on commit 93119ae

Please sign in to comment.