_base_ = [
    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
    '../_base_/default_runtime.py'
]
# https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster-rcnn_r50-caffe-c4_ms-18k_voc0712.py

norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    backbone=dict(
        frozen_stages=-1,
        norm_cfg=norm_cfg,
        norm_eval=False,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    roi_head=dict(
        shared_head=dict(
            type='ResLayerExtraNorm',
            norm_cfg=norm_cfg,
            norm_eval=False,
            style='pytorch'),
        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=[18000, 22000])

# Runner type
runner = dict(type='IterBasedRunner', max_iters=24000)

checkpoint_config = dict(interval=3000, max_keep_ckpts=1)
evaluation = dict(interval=3000, metric='mAP')

custom_imports = dict(
    imports=['openmixup.models.utils.layers.res_layer_extra_norm'],
    allow_failed_imports=False)
