_base_ = [
    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
    '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))

# dataset settings
train_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args={{_base_.file_client_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='RandomChoiceResize',
        scales=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                (1333, 608), (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={{_base_.file_client_args}}),
    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
    # avoid bboxes being resized
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]

train_dataloader = dict(
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        _delete_=True,
        type='ConcatDataset',
        datasets=[
            dict(
                type='VOCDataset',
                data_root={{_base_.data_root}},
                ann_file='VOC2007/ImageSets/Main/trainval.txt',
                data_prefix=dict(sub_data_root='VOC2007/'),
                filter_cfg=dict(filter_empty_gt=True, min_size=32),
                pipeline=train_pipeline),
            dict(
                type='VOCDataset',
                data_root={{_base_.data_root}},
                ann_file='VOC2012/ImageSets/Main/trainval.txt',
                data_prefix=dict(sub_data_root='VOC2012/'),
                filter_cfg=dict(filter_empty_gt=True, min_size=32),
                pipeline=train_pipeline)
        ]))

val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# training schedule for 18k
max_iter = 18000
train_cfg = dict(
    _delete_=True,
    type='IterBasedTrainLoop',
    max_iters=max_iter,
    val_interval=3000)

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=100),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_iter,
        by_epoch=False,
        milestones=[12000, 16000],
        gamma=0.1)
]

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))

default_hooks = dict(checkpoint=dict(by_epoch=False, interval=3000))
log_processor = dict(by_epoch=False)

_base_ = [
    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
    '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))

# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
                   (1333, 736), (1333, 768), (1333, 800)],
        multiscale_mode='value',
        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']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    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']),
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file=[
            data_root + 'VOC2007/ImageSets/Main/trainval.txt',
            data_root + 'VOC2012/ImageSets/Main/trainval.txt'
        ],
        img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
        img_prefix=data_root + 'VOC2007/',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
        img_prefix=data_root + 'VOC2007/',
        pipeline=test_pipeline))

# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)

# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=100,
    warmup_ratio=0.001,
    step=[12000, 16000])

# Runner type
runner = dict(type='IterBasedRunner', max_iters=18000)

checkpoint_config = dict(interval=3000)
evaluation = dict(interval=3000, metric='mAP')
