mirror of https://github.com/open-mmlab/mmpose
251 lines
7.5 KiB
Python
251 lines
7.5 KiB
Python
_base_ = [
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'mmdet::_base_/schedules/schedule_1x.py',
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'mmdet::_base_/default_runtime.py', 'mmdet::yolox/yolox_tta.py'
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]
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img_scale = (640, 640) # width, height
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# model settings
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model = dict(
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type='YOLOX',
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data_preprocessor=dict(
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type='DetDataPreprocessor',
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pad_size_divisor=32,
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batch_augments=[
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dict(
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type='BatchSyncRandomResize',
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random_size_range=(480, 800),
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size_divisor=32,
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interval=10)
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]),
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backbone=dict(
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type='CSPDarknet',
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deepen_factor=0.33,
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widen_factor=0.5,
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out_indices=(2, 3, 4),
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use_depthwise=False,
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spp_kernal_sizes=(5, 9, 13),
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish'),
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),
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neck=dict(
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type='YOLOXPAFPN',
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in_channels=[128, 256, 512],
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out_channels=128,
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num_csp_blocks=1,
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use_depthwise=False,
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upsample_cfg=dict(scale_factor=2, mode='nearest'),
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish')),
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bbox_head=dict(
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type='YOLOXHead',
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num_classes=80,
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in_channels=128,
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feat_channels=128,
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stacked_convs=2,
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strides=(8, 16, 32),
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use_depthwise=False,
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='Swish'),
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loss_cls=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='sum',
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loss_weight=1.0),
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loss_bbox=dict(
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type='IoULoss',
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mode='square',
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eps=1e-16,
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reduction='sum',
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loss_weight=5.0),
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loss_obj=dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='sum',
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loss_weight=1.0),
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loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
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train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
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# In order to align the source code, the threshold of the val phase is
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# 0.01, and the threshold of the test phase is 0.001.
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test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
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# dataset settings
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data_root = 'data/'
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dataset_type = 'CocoDataset'
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# Example to use different file client
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# Method 1: simply set the data root and let the file I/O module
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# automatically infer from prefix (not support LMDB and Memcache yet)
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# data_root = 's3://openmmlab/datasets/detection/coco/'
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# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
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# backend_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# './data/': 's3://openmmlab/datasets/detection/',
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# 'data/': 's3://openmmlab/datasets/detection/'
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# }))
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backend_args = None
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train_pipeline = [
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dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
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dict(
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type='RandomAffine',
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scaling_ratio_range=(0.1, 2),
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# img_scale is (width, height)
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border=(-img_scale[0] // 2, -img_scale[1] // 2)),
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dict(
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type='MixUp',
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img_scale=img_scale,
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ratio_range=(0.8, 1.6),
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pad_val=114.0),
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dict(type='YOLOXHSVRandomAug'),
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dict(type='RandomFlip', prob=0.5),
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# According to the official implementation, multi-scale
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# training is not considered here but in the
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# 'mmdet/models/detectors/yolox.py'.
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# Resize and Pad are for the last 15 epochs when Mosaic,
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# RandomAffine, and MixUp are closed by YOLOXModeSwitchHook.
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dict(type='Resize', scale=img_scale, keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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# If the image is three-channel, the pad value needs
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# to be set separately for each channel.
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
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dict(type='PackDetInputs')
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]
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train_dataset = dict(
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# use MultiImageMixDataset wrapper to support mosaic and mixup
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type='MultiImageMixDataset',
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='HumanArt/annotations/training_humanart_coco.json',
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data_prefix=dict(img=''),
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pipeline=[
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dict(type='LoadImageFromFile', backend_args=backend_args),
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dict(type='LoadAnnotations', with_bbox=True)
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],
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filter_cfg=dict(filter_empty_gt=False, min_size=32),
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backend_args=backend_args),
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pipeline=train_pipeline)
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test_pipeline = [
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dict(type='LoadImageFromFile', backend_args=backend_args),
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dict(type='Resize', scale=img_scale, keep_ratio=True),
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dict(
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type='Pad',
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pad_to_square=True,
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pad_val=dict(img=(114.0, 114.0, 114.0))),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(
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type='PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor'))
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]
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train_dataloader = dict(
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batch_size=8,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=train_dataset)
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val_dataloader = dict(
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batch_size=8,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='HumanArt/annotations/validation_humanart_coco.json',
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data_prefix=dict(img=''),
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test_mode=True,
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pipeline=test_pipeline,
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backend_args=backend_args))
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test_dataloader = val_dataloader
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val_evaluator = dict(
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type='CocoMetric',
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ann_file=data_root + 'HumanArt/annotations/validation_humanart_coco.json',
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metric='bbox',
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backend_args=backend_args)
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test_evaluator = val_evaluator
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# training settings
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max_epochs = 300
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num_last_epochs = 15
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interval = 10
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train_cfg = dict(max_epochs=max_epochs, val_interval=interval)
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# optimizer
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# default 8 gpu
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base_lr = 0.01
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(
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type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4,
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nesterov=True),
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paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
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# learning rate
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param_scheduler = [
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dict(
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# use quadratic formula to warm up 5 epochs
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# and lr is updated by iteration
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# TODO: fix default scope in get function
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type='mmdet.QuadraticWarmupLR',
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by_epoch=True,
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begin=0,
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end=5,
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convert_to_iter_based=True),
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dict(
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# use cosine lr from 5 to 285 epoch
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type='CosineAnnealingLR',
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eta_min=base_lr * 0.05,
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begin=5,
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T_max=max_epochs - num_last_epochs,
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end=max_epochs - num_last_epochs,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(
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# use fixed lr during last 15 epochs
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type='ConstantLR',
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by_epoch=True,
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factor=1,
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begin=max_epochs - num_last_epochs,
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end=max_epochs,
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)
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]
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default_hooks = dict(
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checkpoint=dict(
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interval=interval,
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max_keep_ckpts=3 # only keep latest 3 checkpoints
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))
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custom_hooks = [
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dict(
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type='YOLOXModeSwitchHook',
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num_last_epochs=num_last_epochs,
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priority=48),
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dict(type='SyncNormHook', priority=48),
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dict(
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type='EMAHook',
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ema_type='ExpMomentumEMA',
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momentum=0.0001,
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update_buffers=True,
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priority=49)
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]
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# NOTE: `auto_scale_lr` is for automatically scaling LR,
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# USER SHOULD NOT CHANGE ITS VALUES.
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# base_batch_size = (8 GPUs) x (8 samples per GPU)
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auto_scale_lr = dict(base_batch_size=64)
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