_base_ = [
    '../_base_/models/cascade-rcnn_r50_fpn.py',
    '../_base_/datasets/panicle_detection.py',
    '../_base_/schedules/schedule_2x.py', '../_base_/custom_runtime.py'
]

custom_imports = dict(
    imports=['mmcls.models', 'mmyolo.datasets'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa

model = dict(
    backbone=dict(
        _delete_=True,
        type='mmcls.ConvNeXt',
        arch='tiny',
        out_indices=[0, 1, 2, 3],
        drop_path_rate=0.4,
        layer_scale_init_value=1.0,
        gap_before_final_norm=False,
        init_cfg=dict(
            type='Pretrained', checkpoint=checkpoint_file,
            prefix='backbone.')),
    neck=dict(in_channels=[96, 192, 384, 768]),
    roi_head=dict(bbox_head=[
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.05, 0.05, 0.1, 0.1]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
        dict(
            type='ConvFCBBoxHead',
            num_shared_convs=4,
            num_shared_fcs=1,
            in_channels=256,
            conv_out_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=1,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.033, 0.033, 0.067, 0.067]),
            reg_class_agnostic=False,
            reg_decoded_bbox=True,
            norm_cfg=dict(type='SyncBN', requires_grad=True),
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
    ]))

env_cfg = dict(cudnn_benchmark=True, )

max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)

# learning rate
param_scheduler = [
    dict(
        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
        end=1000),
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[27, 33],
        gamma=0.1)
]

# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(
    type='OptimWrapper',
    constructor='LearningRateDecayOptimizerConstructor',
    paramwise_cfg={
        'decay_rate': 0.7,
        'decay_type': 'layer_wise',
        'num_layers': 6
    },
    optimizer=dict(
        _delete_=True,
        type='AdamW',
        lr=0.0002,
        betas=(0.9, 0.999),
        weight_decay=0.05))

load_from = 'https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth'  # noqa
