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- _base_ = '../_base_/default_runtime.py'
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
- image_size = (1024, 1024)
-
- file_client_args = dict(backend='disk')
- # comment out the code below to use different file client
- # file_client_args = dict(
- # backend='petrel',
- # path_mapping=dict({
- # './data/': 's3://openmmlab/datasets/detection/',
- # 'data/': 's3://openmmlab/datasets/detection/'
- # }))
-
- train_pipeline = [
- dict(type='LoadImageFromFile', file_client_args=file_client_args),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='Resize',
- img_scale=image_size,
- ratio_range=(0.1, 2.0),
- multiscale_mode='range',
- keep_ratio=True),
- dict(
- type='RandomCrop',
- crop_type='absolute_range',
- crop_size=image_size,
- recompute_bbox=True,
- allow_negative_crop=True),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=image_size), # padding to image_size leads 0.5+ mAP
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', file_client_args=file_client_args),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
- ]
-
- # Use RepeatDataset to speed up training
- data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type='RepeatDataset',
- times=4, # simply change this from 2 to 16 for 50e - 400e training.
- dataset=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline)),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
- evaluation = dict(interval=5, metric=['bbox', 'segm'])
-
- # optimizer assumes bs=64
- optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
- optimizer_config = dict(grad_clip=None)
-
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.067,
- step=[22, 24])
- runner = dict(type='EpochBasedRunner', max_epochs=25)
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