mirror of https://github.com/open-mmlab/mmyolo.git
279 lines
7.9 KiB
Python
279 lines
7.9 KiB
Python
_base_ = '../_base_/default_runtime.py'
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data_root = 'data/coco/'
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dataset_type = 'YOLOv5CocoDataset'
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img_scale = (640, 640) # height, width
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deepen_factor = 0.33
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widen_factor = 0.5
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save_epoch_intervals = 10
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train_batch_size_per_gpu = 8
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train_num_workers = 8
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val_batch_size_per_gpu = 1
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val_num_workers = 2
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max_epochs = 300
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num_last_epochs = 15
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# model settings
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model = dict(
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type='YOLODetector',
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init_cfg=dict(
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type='Kaiming',
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layer='Conv2d',
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a=2.23606797749979, # math.sqrt(5)
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distribution='uniform',
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mode='fan_in',
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nonlinearity='leaky_relu'),
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# TODO: Waiting for mmengine support
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use_syncbn=False,
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data_preprocessor=dict(
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type='mmdet.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='mmdet.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='YOLOXCSPDarknet',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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out_indices=(2, 3, 4),
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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='SiLU', inplace=True),
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),
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neck=dict(
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type='YOLOXPAFPN',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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in_channels=[256, 512, 1024],
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out_channels=256,
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norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
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act_cfg=dict(type='SiLU', inplace=True)),
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bbox_head=dict(
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type='YOLOXHead',
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head_module=dict(
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type='YOLOXHeadModule',
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num_classes=80,
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in_channels=256,
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feat_channels=256,
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widen_factor=widen_factor,
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stacked_convs=2,
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featmap_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='SiLU', inplace=True),
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),
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loss_cls=dict(
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type='mmdet.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='mmdet.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='mmdet.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_aux=dict(
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type='mmdet.L1Loss', reduction='sum', loss_weight=1.0)),
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train_cfg=dict(
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assigner=dict(
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type='mmdet.SimOTAAssigner',
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center_radius=2.5,
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iou_calculator=dict(type='mmdet.BboxOverlaps2D'))),
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test_cfg=dict(
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yolox_style=True, # better
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multi_label=True, # 40.5 -> 40.7
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score_thr=0.001,
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max_per_img=300,
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nms=dict(type='nms', iou_threshold=0.65)))
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pre_transform = [
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dict(
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type='LoadImageFromFile',
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file_client_args={{_base_.file_client_args}}),
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dict(type='LoadAnnotations', with_bbox=True)
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]
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train_pipeline_stage1 = [
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*pre_transform,
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dict(
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type='Mosaic',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(
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type='mmdet.RandomAffine',
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scaling_ratio_range=(0.1, 2),
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border=(-img_scale[0] // 2, -img_scale[1] // 2)),
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dict(
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type='YOLOXMixUp',
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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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pre_transform=pre_transform),
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dict(type='mmdet.YOLOXHSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='mmdet.FilterAnnotations',
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min_gt_bbox_wh=(1, 1),
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keep_empty=False),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
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'flip_direction'))
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]
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train_pipeline_stage2 = [
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*pre_transform,
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dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True),
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dict(
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type='mmdet.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='mmdet.YOLOXHSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='mmdet.FilterAnnotations',
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min_gt_bbox_wh=(1, 1),
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keep_empty=False),
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dict(type='mmdet.PackDetInputs')
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]
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train_dataloader = dict(
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batch_size=train_batch_size_per_gpu,
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num_workers=train_num_workers,
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persistent_workers=True,
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pin_memory=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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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='annotations/instances_train2017.json',
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data_prefix=dict(img='train2017/'),
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filter_cfg=dict(filter_empty_gt=False, min_size=32),
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pipeline=train_pipeline_stage1))
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test_pipeline = [
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dict(
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type='LoadImageFromFile',
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file_client_args={{_base_.file_client_args}}),
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dict(type='mmdet.Resize', scale=img_scale, keep_ratio=True),
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dict(
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type='mmdet.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, _scope_='mmdet'),
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dict(
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type='mmdet.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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val_dataloader = dict(
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batch_size=val_batch_size_per_gpu,
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num_workers=val_num_workers,
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persistent_workers=True,
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pin_memory=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='annotations/instances_val2017.json',
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data_prefix=dict(img='val2017/'),
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test_mode=True,
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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# Reduce evaluation time
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val_evaluator = dict(
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type='mmdet.CocoMetric',
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proposal_nums=(100, 1, 10),
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ann_file=data_root + 'annotations/instances_val2017.json',
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metric='bbox')
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test_evaluator = val_evaluator
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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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type='CheckpointHook', interval=1, max_keep_ckpts=3, save_best='auto'))
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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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new_train_pipeline=train_pipeline_stage2,
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priority=48),
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dict(type='mmdet.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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train_cfg = dict(
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type='EpochBasedTrainLoop',
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max_epochs=max_epochs,
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val_interval=save_epoch_intervals,
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dynamic_intervals=[(max_epochs - num_last_epochs, 1)])
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auto_scale_lr = dict(base_batch_size=64)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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