musepose / pose /config /yolox_l_8xb8-300e_coco.py
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img_scale = (640, 640) # width, height
# model settings
model = dict(
type='YOLOX',
data_preprocessor=dict(
type='DetDataPreprocessor',
pad_size_divisor=32,
batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(480, 800),
size_divisor=32,
interval=10)
]),
backbone=dict(
type='CSPDarknet',
deepen_factor=1.0,
widen_factor=1.0,
out_indices=(2, 3, 4),
use_depthwise=False,
spp_kernal_sizes=(5, 9, 13),
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish'),
),
neck=dict(
type='YOLOXPAFPN',
in_channels=[256, 512, 1024],
out_channels=256,
num_csp_blocks=3,
use_depthwise=False,
upsample_cfg=dict(scale_factor=2, mode='nearest'),
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')),
bbox_head=dict(
type='YOLOXHead',
num_classes=80,
in_channels=256,
feat_channels=256,
stacked_convs=2,
strides=(8, 16, 32),
use_depthwise=False,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish'),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_bbox=dict(
type='IoULoss',
mode='square',
eps=1e-16,
reduction='sum',
loss_weight=5.0),
loss_obj=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0),
loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
# In order to align the source code, the threshold of the val phase is
# 0.01, and the threshold of the test phase is 0.001.
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
# dataset settings
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MixUp',
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
# According to the official implementation, multi-scale
# training is not considered here but in the
# 'mmdet/models/detectors/yolox.py'.
# Resize and Pad are for the last 15 epochs when Mosaic,
# RandomAffine, and MixUp are closed by YOLOXModeSwitchHook.
dict(type='Resize', scale=img_scale, keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
# If the image is three-channel, the pad value needs
# to be set separately for each channel.
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='PackDetInputs')
]
train_dataset = dict(
# use MultiImageMixDataset wrapper to support mosaic and mixup
type='MultiImageMixDataset',
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
pipeline=[
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_cfg=dict(filter_empty_gt=False, min_size=32),
backend_args=backend_args),
pipeline=train_pipeline)
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=img_scale, keep_ratio=True),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=train_dataset)
val_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
# training settings
max_epochs = 300
num_last_epochs = 15
interval = 10
train_cfg = dict(max_epochs=max_epochs, val_interval=interval)
# optimizer
# default 8 gpu
base_lr = 0.01
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(
type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4,
nesterov=True),
paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
# learning rate
param_scheduler = [
dict(
# use quadratic formula to warm up 5 epochs
# and lr is updated by iteration
# TODO: fix default scope in get function
type='mmdet.QuadraticWarmupLR',
by_epoch=True,
begin=0,
end=5,
convert_to_iter_based=True),
dict(
# use cosine lr from 5 to 285 epoch
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=5,
T_max=max_epochs - num_last_epochs,
end=max_epochs - num_last_epochs,
by_epoch=True,
convert_to_iter_based=True),
dict(
# use fixed lr during last 15 epochs
type='ConstantLR',
by_epoch=True,
factor=1,
begin=max_epochs - num_last_epochs,
end=max_epochs,
)
]
default_hooks = dict(
checkpoint=dict(
interval=interval,
max_keep_ckpts=3 # only keep latest 3 checkpoints
))
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
num_last_epochs=num_last_epochs,
priority=48),
dict(type='SyncNormHook', priority=48),
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0001,
update_buffers=True,
priority=49)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)