91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
_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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image_size = (1024, 1024)
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file_client_args = dict(backend='disk')
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# comment out the code below to use different file client
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# file_client_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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train_pipeline = [
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dict(type='LoadImageFromFile', file_client_args=file_client_args),
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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=image_size,
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ratio_range=(0.1, 2.0),
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multiscale_mode='range',
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keep_ratio=True),
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dict(
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type='RandomCrop',
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crop_type='absolute_range',
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crop_size=image_size,
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recompute_bbox=True,
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allow_negative_crop=True),
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dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
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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=image_size), # padding to image_size leads 0.5+ mAP
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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', file_client_args=file_client_args),
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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=4, # simply change this from 2 to 16 for 50e - 400e training.
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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=5, metric=['bbox', 'segm'])
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# optimizer assumes bs=64
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optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
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optimizer_config = dict(grad_clip=None)
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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=0.067,
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step=[22, 24])
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runner = dict(type='EpochBasedRunner', max_epochs=25)
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