# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'

# 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='Resize', scale=(750, 1101), 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=(750, 1101), 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='RepeatDataset',
        times=2,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            ann_file='Anno/segmentation/DeepFashion_segmentation_train.json',
            data_prefix=dict(img='Img/'),
            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='Anno/segmentation/DeepFashion_segmentation_query.json',
        data_prefix=dict(img='Img/'),
        test_mode=True,
        pipeline=test_pipeline))
test_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='Anno/segmentation/DeepFashion_segmentation_gallery.json',
        data_prefix=dict(img='Img/'),
        test_mode=True,
        pipeline=test_pipeline))

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root +
    'Anno/segmentation/DeepFashion_segmentation_query.json',
    metric=['bbox', 'segm'],
    format_only=False)
test_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root +
    'Anno/segmentation/DeepFashion_segmentation_gallery.json',
    metric=['bbox', 'segm'],
    format_only=False)

# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', img_scale=(750, 1101), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(750, 1101),
        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']),
        ])
]
data = dict(
    imgs_per_gpu=2,
    workers_per_gpu=1,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
        img_prefix=data_root + 'Img/',
        pipeline=train_pipeline,
        data_root=data_root),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
        img_prefix=data_root + 'Img/',
        pipeline=test_pipeline,
        data_root=data_root),
    test=dict(
        type=dataset_type,
        ann_file=data_root +
        'annotations/DeepFashion_segmentation_gallery.json',
        img_prefix=data_root + 'Img/',
        pipeline=test_pipeline,
        data_root=data_root))
evaluation = dict(interval=5, metric=['bbox', 'segm'])
