_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', 
    '../_base_/default_runtime.py'
]

model = dict(
    type='ATSS',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3), # P2, P3, P4, P5
        frozen_stages=4,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1, #start from P3
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=5,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
# training and testing settings
train_cfg = dict(
    assigner=dict(type='ATSSAssigner', topk=9),
    allowed_border=-1,
    pos_weight=-1,
    debug=False)
test_cfg = dict(
    nms_pre=1000,
    min_bbox_size=0,
    score_thr=0.05,
    nms=dict(type='nms', iou_thr=0.6),
    max_per_img=100)

classes = ('motorbike','car','bus','truck','person',)
DATA_ROOT = 'data/'
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),
    dict(type='Resize', img_scale=(int(1333*2.5), int(800*2.5)), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0),
    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=(int(1333*2.5), int(800*2.5)),
        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=8,
    # workers_per_gpu=4,
    train=dict(
        ann_file=DATA_ROOT + 'annotations/train.json',
        img_prefix=DATA_ROOT + 'images/',
        classes=classes,
        pipeline=train_pipeline
    ),
    val=dict(
        ann_file=DATA_ROOT + 'annotations/test.json',
        img_prefix=DATA_ROOT + 'images/',
        classes=classes,
    ),
    test=dict(
        ann_file=DATA_ROOT + 'annotations/test.json',
        img_prefix=DATA_ROOT + 'images/',
        classes=classes,
        pipeline=test_pipeline,
    ))

# optimizer
optimizer = dict(type='SGD', lr=1e-2, momentum=0.9, weight_decay=0.0001)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
total_epochs = 12