_base_ = '../../base.py'

# model settings s m l x
model = dict(
    type='YOLOX',
    num_classes=80,
    model_type='s',  # s m l x tiny nano
    test_conf=0.01,
    nms_thre=0.65)

# s m l x
img_scale = (640, 640)
random_size = (14, 26)
scale_ratio = (0.1, 2)

# tiny nano without mixup
# img_scale = (416, 416)
# random_size = (10, 20)
# scale_ratio = (0.5, 1.5)

CLASSES = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
    'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
    'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
    'hair drier', 'toothbrush'
]

# dataset settings
data_root = 'data/coco/'
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='MMMosaic', img_scale=img_scale, pad_val=114.0),
    dict(
        type='MMRandomAffine',
        scaling_ratio_range=scale_ratio,
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='MMMixUp',  # s m x l; tiny nano will detele
        img_scale=img_scale,
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(
        type='MMPhotoMetricDistortion',
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18),
    dict(type='MMRandomFlip', flip_ratio=0.5),
    dict(type='MMResize', keep_ratio=True),
    dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
    dict(type='MMNormalize', **img_norm_cfg),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
    dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
    dict(type='MMNormalize', **img_norm_cfg),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img'])
]

train_dataset = dict(
    type='DetImagesMixDataset',
    data_source=dict(
        type='DetSourceCoco',
        ann_file=data_root + 'annotations/instances_train2017.json',
        img_prefix=data_root + 'train2017/',
        pipeline=[
            dict(type='LoadImageFromFile', to_float32=True),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        classes=CLASSES,
        filter_empty_gt=False,
        iscrowd=False),
    pipeline=train_pipeline,
    dynamic_scale=img_scale)

val_dataset = dict(
    type='DetImagesMixDataset',
    imgs_per_gpu=2,
    data_source=dict(
        type='DetSourceCoco',
        ann_file=data_root + 'annotations/instances_val2017.json',
        img_prefix=data_root + 'val2017/',
        pipeline=[
            dict(type='LoadImageFromFile', to_float32=True),
            dict(type='LoadAnnotations', with_bbox=True)
        ],
        classes=CLASSES,
        filter_empty_gt=False,
        iscrowd=True),
    pipeline=test_pipeline,
    dynamic_scale=None,
    label_padding=False)

data = dict(
    imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)

# additional hooks
interval = 10
custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        no_aug_epochs=15,
        skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
        priority=48),
    dict(
        type='SyncRandomSizeHook',
        ratio_range=random_size,
        img_scale=img_scale,
        interval=interval,
        priority=48),
    dict(
        type='SyncNormHook',
        num_last_epochs=15,
        interval=interval,
        priority=48)
]

# evaluation
eval_config = dict(interval=10, gpu_collect=False)
eval_pipelines = [
    dict(
        mode='test',
        data=data['val'],
        evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
    )
]

checkpoint_config = dict(interval=interval)

# optimizer
optimizer = dict(
    type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}

# learning policy
lr_config = dict(
    policy='YOLOX',
    warmup='exp',
    by_epoch=False,
    warmup_by_epoch=True,
    warmup_ratio=1,
    warmup_iters=5,  # 5 epoch
    num_last_epochs=15,
    min_lr_ratio=0.05)

# exponetial model average
ema = dict(decay=0.9998)

# runtime settings
total_epochs = 300

# yapf:disable
log_config = dict(
    interval=100,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(type='TensorboardLoggerHook')
    ])

export = dict(use_jit=False)
