# Copyright (c) OpenMMLab. All rights reserved.

# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0

from mmengine.config import read_base

with read_base():
    from .rtmdet_s_8xb32_300e_coco import *

from mmcv.transforms.loading import LoadImageFromFile
from mmcv.transforms.processing import RandomResize

from mmdet.datasets.transforms.formatting import PackDetInputs
from mmdet.datasets.transforms.loading import LoadAnnotations
from mmdet.datasets.transforms.transforms import (CachedMixUp, CachedMosaic,
                                                  Pad, RandomCrop, RandomFlip,
                                                  Resize, YOLOXHSVRandomAug)

checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'  # noqa

model.update(
    dict(
        backbone=dict(
            deepen_factor=0.167,
            widen_factor=0.375,
            init_cfg=dict(
                type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
        neck=dict(
            in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1),
        bbox_head=dict(in_channels=96, feat_channels=96, exp_on_reg=False)))

train_pipeline = [
    dict(type=LoadImageFromFile, backend_args=backend_args),
    dict(type=LoadAnnotations, with_bbox=True),
    dict(
        type=CachedMosaic,
        img_scale=(640, 640),
        pad_val=114.0,
        max_cached_images=20,
        random_pop=False),
    dict(
        type=RandomResize,
        scale=(1280, 1280),
        ratio_range=(0.5, 2.0),
        resize_type=Resize,
        keep_ratio=True),
    dict(type=RandomCrop, crop_size=(640, 640)),
    dict(type=YOLOXHSVRandomAug),
    dict(type=RandomFlip, prob=0.5),
    dict(type=Pad, size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type=CachedMixUp,
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=10,
        random_pop=False,
        pad_val=(114, 114, 114),
        prob=0.5),
    dict(type=PackDetInputs)
]

train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
