# _base_ = ["../_base_/schedules/schedule_1x.py", "../_base_/default_runtime.py"]
# model settings
data_preprocessor = dict(
    type="DetDataPreprocessor",
    mean=[0, 0, 0],
    std=[255.0, 255.0, 255.0],
    bgr_to_rgb=True,
    pad_size_divisor=32,
)
model = dict(
    type="YOLOV3",
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type="Darknet",
        depth=53,
        out_indices=(3, 4, 5),
        init_cfg=dict(type="Pretrained", checkpoint="open-mmlab://darknet53"),
    ),
    neck=dict(
        type="YOLOV3Neck",
        num_scales=3,
        in_channels=[1024, 512, 256],
        out_channels=[512, 256, 128],
    ),
    bbox_head=dict(
        type="YOLOV3Head",
        num_classes=1,
        in_channels=[512, 256, 128],
        out_channels=[1024, 512, 256],
        anchor_generator=dict(
            type="YOLOAnchorGenerator",
            base_sizes=[
                [(116, 90), (156, 198), (373, 326)],
                [(30, 61), (62, 45), (59, 119)],
                [(10, 13), (16, 30), (33, 23)],
            ],
            strides=[32, 16, 8],
        ),
        bbox_coder=dict(type="YOLOBBoxCoder"),
        featmap_strides=[32, 16, 8],
        loss_cls=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum"
        ),
        loss_conf=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum"
        ),
        loss_xy=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=2.0, reduction="sum"
        ),
        loss_wh=dict(type="MSELoss", loss_weight=2.0, reduction="sum"),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type="GridAssigner", pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0
        )
    ),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        conf_thr=0.005,
        nms=dict(type="nms", iou_threshold=0.45),
        max_per_img=100,
    ),
)
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"

# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection/coco/'

# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection/',
#         'data/': 's3://openmmlab/datasets/detection/'
#     }))
backend_args = None

train_pipeline = [
    dict(type="LoadImageFromFile", backend_args=backend_args),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Expand",
        mean=data_preprocessor["mean"],
        to_rgb=data_preprocessor["bgr_to_rgb"],
        ratio_range=(1, 2),
    ),
    dict(
        type="MinIoURandomCrop",
        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
        min_crop_size=0.3,
    ),
    dict(type="RandomResize", scale=[(320, 320), (608, 608)], keep_ratio=True),
    dict(type="RandomFlip", prob=0.5),
    dict(type="PhotoMetricDistortion"),
    dict(type="PackDetInputs"),
]
test_pipeline = [
    dict(type="LoadImageFromFile", backend_args=backend_args),
    dict(type="Resize", scale=(608, 608), keep_ratio=True),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="PackDetInputs",
        meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
    ),
]

train_dataloader = dict(
    batch_size=8,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type="DefaultSampler", shuffle=True),
    batch_sampler=dict(type="AspectRatioBatchSampler"),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file="annotations/instances_train2017.json",
        data_prefix=dict(img="train2017/"),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline,
        backend_args=backend_args,
    ),
)
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="annotations/instances_val2017.json",
        data_prefix=dict(img="val2017/"),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args,
    ),
)
test_dataloader = val_dataloader

val_evaluator = dict(
    type="CocoMetric",
    ann_file=data_root + "annotations/instances_val2017.json",
    metric="bbox",
    backend_args=backend_args,
)
test_evaluator = val_evaluator

train_cfg = dict(max_epochs=273, val_interval=7)

# optimizer
optim_wrapper = dict(
    type="OptimWrapper",
    optimizer=dict(type="SGD", lr=0.001, momentum=0.9, weight_decay=0.0005),
    clip_grad=dict(max_norm=35, norm_type=2),
)

# learning policy
param_scheduler = [
    dict(type="LinearLR", start_factor=0.1, by_epoch=False, begin=0, end=2000),
    dict(type="MultiStepLR", by_epoch=True, milestones=[218, 246], gamma=0.1),
]

default_hooks = dict(checkpoint=dict(type="CheckpointHook", interval=7))

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)