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- _base_ = '../_base_/default_runtime.py'
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- # Example to use different file client
- # Method 1: simply set the data root and let the file I/O module
- # automatically infer from prefix (not support LMDB and Memcache yet)
- # data_root = 's3://openmmlab/datasets/detection/coco/'
- # Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
- # backend_args = dict(
- # backend='petrel',
- # path_mapping=dict({
- # './data/': 's3://openmmlab/datasets/detection/',
- # 'data/': 's3://openmmlab/datasets/detection/'
- # }))
- backend_args = None
- # Align with Detectron2
- backend = 'pillow'
- train_pipeline = [
- dict(
- type='LoadImageFromFile',
- backend_args=backend_args,
- imdecode_backend=backend),
- dict(
- type='LoadAnnotations',
- with_bbox=True,
- with_mask=True,
- poly2mask=False),
- dict(
- type='RandomChoiceResize',
- scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- keep_ratio=True,
- backend=backend),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(
- type='LoadImageFromFile',
- backend_args=backend_args,
- imdecode_backend=backend),
- dict(type='Resize', scale=(1333, 800), keep_ratio=True, backend=backend),
- dict(
- type='LoadAnnotations',
- with_bbox=True,
- with_mask=True,
- poly2mask=False),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(
- batch_size=2,
- num_workers=2,
- persistent_workers=True,
- pin_memory=True,
- sampler=dict(type='InfiniteSampler', shuffle=True),
- batch_sampler=dict(type='AspectRatioBatchSampler'),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
- filter_cfg=dict(filter_empty_gt=True, min_size=32),
- pipeline=train_pipeline,
- backend_args=backend_args))
- val_dataloader = dict(
- batch_size=1,
- num_workers=2,
- persistent_workers=True,
- drop_last=False,
- pin_memory=True,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file='annotations/instances_val2017.json',
- data_prefix=dict(img='val2017/'),
- test_mode=True,
- pipeline=test_pipeline,
- backend_args=backend_args))
- test_dataloader = val_dataloader
- val_evaluator = dict(
- type='CocoMetric',
- ann_file=data_root + 'annotations/instances_val2017.json',
- metric=['bbox', 'segm'],
- format_only=False,
- backend_args=backend_args)
- test_evaluator = val_evaluator
- # training schedule for 90k
- max_iter = 90000
- train_cfg = dict(
- type='IterBasedTrainLoop', max_iters=max_iter, val_interval=10000)
- val_cfg = dict(type='ValLoop')
- test_cfg = dict(type='TestLoop')
- # learning rate
- param_scheduler = [
- dict(
- type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
- end=1000),
- dict(
- type='MultiStepLR',
- begin=0,
- end=max_iter,
- by_epoch=False,
- milestones=[60000, 80000],
- gamma=0.1)
- ]
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
- # Default setting for scaling LR automatically
- # - `enable` means enable scaling LR automatically
- # or not by default.
- # - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
- auto_scale_lr = dict(enable=False, base_batch_size=16)
- default_hooks = dict(checkpoint=dict(by_epoch=False, interval=10000))
- log_processor = dict(by_epoch=False)
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