OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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77 lines
2.4 KiB
77 lines
2.4 KiB
3 years ago
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_base_ = '../_base_/default_runtime.py'
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# dataset settings
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dataset_type = 'CocoDataset'
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data_root = 'data/coco/'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
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# multiscale_mode='range'
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(
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type='Resize',
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img_scale=[(1333, 640), (1333, 800)],
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multiscale_mode='range',
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keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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# Use RepeatDataset to speed up training
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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type='RepeatDataset',
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times=3,
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dataset=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_train2017.json',
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img_prefix=data_root + 'train2017/',
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pipeline=train_pipeline)),
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val=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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ann_file=data_root + 'annotations/instances_val2017.json',
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img_prefix=data_root + 'val2017/',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric=['bbox', 'segm'])
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# optimizer
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optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
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optimizer_config = dict(grad_clip=None)
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# learning policy
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# Experiments show that using step=[9, 11] has higher performance
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lr_config = dict(
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policy='step',
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warmup='linear',
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warmup_iters=500,
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warmup_ratio=0.001,
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step=[9, 11])
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runner = dict(type='EpochBasedRunner', max_epochs=12)
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