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auto_scale_lr = dict(base_batch_size=512)
backend_args = dict(backend='local')
codec = dict(
heatmap_size=(
48,
64,
),
input_size=(
192,
256,
),
sigma=2,
type='MSRAHeatmap')
custom_hooks = [
dict(type='SyncBuffersHook'),
]
data_mode = 'topdown'
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
badcase=dict(
badcase_thr=5,
enable=False,
metric_type='loss',
out_dir='badcase',
type='BadCaseAnalysisHook'),
checkpoint=dict(
interval=10,
rule='greater',
save_best='coco/AP',
type='CheckpointHook'),
logger=dict(interval=50, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(enable=False, type='PoseVisualizationHook'))
default_scope = 'mmpose'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
load_from = None
log_level = 'INFO'
log_processor = dict(
by_epoch=True, num_digits=6, type='LogProcessor', window_size=50)
model = dict(
backbone=dict(
extra=dict(
stage1=dict(
block='BOTTLENECK',
num_blocks=(4, ),
num_branches=1,
num_channels=(64, ),
num_modules=1),
stage2=dict(
block='BASIC',
num_blocks=(
4,
4,
),
num_branches=2,
num_channels=(
48,
96,
),
num_modules=1),
stage3=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
),
num_branches=3,
num_channels=(
48,
96,
192,
),
num_modules=4),
stage4=dict(
block='BASIC',
num_blocks=(
4,
4,
4,
4,
),
num_branches=4,
num_channels=(
48,
96,
192,
384,
),
num_modules=3)),
in_channels=3,
init_cfg=dict(
checkpoint=
'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth',
type='Pretrained'),
type='HRNet'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
std=[
58.395,
57.12,
57.375,
],
type='PoseDataPreprocessor'),
head=dict(
decoder=dict(
heatmap_size=(
48,
64,
),
input_size=(
192,
256,
),
sigma=2,
type='MSRAHeatmap'),
deconv_out_channels=None,
in_channels=48,
loss=dict(type='KeypointMSELoss', use_target_weight=True),
out_channels=17,
type='HeatmapHead'),
test_cfg=dict(flip_mode='heatmap', flip_test=True, shift_heatmap=True),
type='TopdownPoseEstimator')
optim_wrapper = dict(optimizer=dict(lr=0.0005, type='Adam'))
param_scheduler = [
dict(
begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
dict(
begin=0,
by_epoch=True,
end=210,
gamma=0.1,
milestones=[
170,
200,
],
type='MultiStepLR'),
]
resume = False
test_cfg = dict()
test_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file='annotations/person_keypoints_val2017.json',
bbox_file=
'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',
data_mode='topdown',
data_prefix=dict(img='val2017/'),
data_root='data/coco/',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
192,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(round_up=False, shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
ann_file='data/coco/annotations/person_keypoints_val2017.json',
type='CocoMetric')
train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10)
train_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file='annotations/person_keypoints_train2017.json',
data_mode='topdown',
data_prefix=dict(img='train2017/'),
data_root='data/coco/',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(direction='horizontal', type='RandomFlip'),
dict(type='RandomHalfBody'),
dict(type='RandomBBoxTransform'),
dict(input_size=(
192,
256,
), type='TopdownAffine'),
dict(
encoder=dict(
heatmap_size=(
48,
64,
),
input_size=(
192,
256,
),
sigma=2,
type='MSRAHeatmap'),
type='GenerateTarget'),
dict(type='PackPoseInputs'),
],
type='CocoDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(direction='horizontal', type='RandomFlip'),
dict(type='RandomHalfBody'),
dict(type='RandomBBoxTransform'),
dict(input_size=(
192,
256,
), type='TopdownAffine'),
dict(
encoder=dict(
heatmap_size=(
48,
64,
),
input_size=(
192,
256,
),
sigma=2,
type='MSRAHeatmap'),
type='GenerateTarget'),
dict(type='PackPoseInputs'),
]
val_cfg = dict()
val_dataloader = dict(
batch_size=32,
dataset=dict(
ann_file='annotations/person_keypoints_val2017.json',
bbox_file=
'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',
data_mode='topdown',
data_prefix=dict(img='val2017/'),
data_root='data/coco/',
pipeline=[
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
192,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(round_up=False, shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
ann_file='data/coco/annotations/person_keypoints_val2017.json',
type='CocoMetric')
val_pipeline = [
dict(type='LoadImage'),
dict(type='GetBBoxCenterScale'),
dict(input_size=(
192,
256,
), type='TopdownAffine'),
dict(type='PackPoseInputs'),
]
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='PoseLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
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