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add mobilenet hand #377

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add mobilenet hand
liuxin9608 Dec 21, 2020
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liuxin9608 Dec 21, 2020
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28 changes: 28 additions & 0 deletions configs/hand/mobilenet_v2/README.md
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# Simple baselines for human pose estimation and tracking

## Introduction
```
@inproceedings{sandler2018mobilenetv2,
title={Mobilenetv2: Inverted residuals and linear bottlenecks},
author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4510--4520},
year={2018}
}
```

## Results and models

### 2d Hand Pose Estimation

#### Results on OneHand10K val set.

| Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log |
| :--- | :--------: | :------: | :------: | :------: |:------: |:------: |
| [pose_mobilenet_v2](/configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py) | 256x256 | 0.984 | 0.526 | 29.52 | [ckpt](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_onehand10k_256x256-55d34d7d_20201218.pth) | [log](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_onehand10k_256x256_20201218.log.json) |

#### Results on CMU Panoptic (MPII+NZSL val set).

| Arch | Input Size | PCKh@0.7 | AUC | EPE | ckpt | log |
| :--- | :--------: | :------: | :------: | :------: |:------: |:------: |
| [pose_mobilenet_v2](/configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py) | 256x256 | 0.998 | 0.684 | 10.09 | [ckpt](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_panoptic_256x256-b9ec9b68_20201218.pth) | [log](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_panoptic_256x256_20201218.log.json) |
130 changes: 130 additions & 0 deletions configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py
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log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(
interval=10, metric=['PCK', 'AUC', 'EPE'], key_indicator='AUC')

optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=10,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])

channel_cfg = dict(
num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20
])

# model settings
model = dict(
type='TopDown',
pretrained='mmcls://mobilenet_v2',
backbone=dict(type='MobileNetV2', widen_factor=1., out_indices=(7, )),
keypoint_head=dict(
type='TopDownSimpleHead',
in_channels=1280,
out_channels=channel_cfg['num_output_channels'],
),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11),
loss_pose=dict(type='JointsMSELoss', use_target_weight=True))

data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
)

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=20, scale_factor=0.3),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'flip_pairs'
]),
]

val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
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', 'flip_pairs']),
]

test_pipeline = val_pipeline

data_root = 'data/onehand10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
train=dict(
type='OneHand10KDataset',
ann_file=f'{data_root}/annotations/onehand10k_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline),
val=dict(
type='OneHand10KDataset',
ann_file=f'{data_root}/annotations/onehand10k_test.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline),
test=dict(
type='OneHand10KDataset',
ann_file=f'{data_root}/annotations/onehand10k_test.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline),
)
130 changes: 130 additions & 0 deletions configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py
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log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
checkpoint_config = dict(interval=10)
evaluation = dict(
interval=10, metric=['PCKh', 'AUC', 'EPE'], key_indicator='AUC')

optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=10,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])

channel_cfg = dict(
num_output_channels=21,
dataset_joints=21,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20
])

# model settings
model = dict(
type='TopDown',
pretrained='mmcls://mobilenet_v2',
backbone=dict(type='MobileNetV2', widen_factor=1., out_indices=(7, )),
keypoint_head=dict(
type='TopDownSimpleHead',
in_channels=1280,
out_channels=channel_cfg['num_output_channels'],
),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11),
loss_pose=dict(type='JointsMSELoss', use_target_weight=True))

data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
)

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=20, scale_factor=0.3),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'flip_pairs'
]),
]

val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
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', 'flip_pairs']),
]

test_pipeline = val_pipeline

data_root = 'data/panoptic'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
train=dict(
type='PanopticDataset',
ann_file=f'{data_root}/annotations/panoptic_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline),
val=dict(
type='PanopticDataset',
ann_file=f'{data_root}/annotations/panoptic_test.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline),
test=dict(
type='PanopticDataset',
ann_file=f'{data_root}/annotations/panoptic_test.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline),
)