# dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'

# file_client_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
file_client_args = dict(backend='disk')

train_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                (1333, 768), (1333, 800)],
        keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=file_client_args),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    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,
    sampler=dict(type='DefaultSampler', shuffle=True),
    batch_sampler=dict(type='AspectRatioBatchSampler'),
    dataset=dict(
        type='ClassBalancedDataset',
        oversample_thr=1e-3,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file='annotations/lvis_v0.5_train.json',
            data_prefix=dict(img='train2017/'),
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            pipeline=train_pipeline)))
val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='annotations/lvis_v0.5_val.json',
        data_prefix=dict(img='val2017/'),
        test_mode=True,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='LVISMetric',
    ann_file=data_root + 'annotations/lvis_v0.5_val.json',
    metric=['bbox', 'segm'])
test_evaluator = val_evaluator

# dataset settings
_base_ = 'coco_instance.py'
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        _delete_=True,
        type='ClassBalancedDataset',
        oversample_thr=1e-3,
        dataset=dict(
            type=dataset_type,
            ann_file=data_root + 'annotations/lvis_v0.5_train.json',
            img_prefix=data_root + 'train2017/')),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/lvis_v0.5_val.json',
        img_prefix=data_root + 'val2017/'),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/lvis_v0.5_val.json',
        img_prefix=data_root + 'val2017/'))
evaluation = dict(metric=['bbox', 'segm'])
