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

dataset_type = 'DIORDataset'
data_root = '../Dataset/dior/'
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='LoadOBBAnnotations', with_bbox=True,
         with_label=True, obb_as_mask=True),
    dict(type='Resize', img_scale=(800, 800), keep_ratio=True),
    dict(type='OBBRandomFlip', h_flip_ratio=0.5, v_flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomOBBRotate', rotate_after_flip=True,
         angles=(0, 0), vert_rate=1.),
    dict(type='Pad', size_divisor=800),
    dict(type='Mask2OBB', obb_type='obb'),
    dict(type='OBBDefaultFormatBundle'),
    dict(type='OBBCollect', keys=['img', 'gt_bboxes', 'gt_obboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipRotateAug',
        img_scale=[(800, 800)],
        h_flip=False,
        v_flip=False,
        rotate=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='OBBRandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='RandomOBBRotate', rotate_after_flip=True),
            dict(type='Pad', size_divisor=800),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='OBBCollect', keys=['img']),
        ])
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        xmltype='obb',
        imgset=data_root + 'ImageSets/Main/trainval.txt',
        ann_file=data_root + 'Annotations/Oriented Bounding Boxes',
        img_prefix=data_root + 'JPEGImages-trainval/',
        pipeline=train_pipeline),
    test=dict(
        type=dataset_type,
        xmltype='obb',
        imgset=data_root + 'ImageSets/Main/test.txt',
        ann_file=data_root + 'Annotations/Oriented Bounding Boxes',
        img_prefix=data_root + 'JPEGImages-test/',
        pipeline=test_pipeline))
evaluation = None

optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
                 constructor='LayerDecayOptimizerConstructor', 
                 paramwise_cfg=dict(
                        num_layers=12, 
                        layer_decay_rate=0.75,
                        custom_keys={
                            'bias': dict(decay_multi=0.),
                            'pos_embed': dict(decay_mult=0.),
                            'relative_position_bias_table': dict(decay_mult=0.),
                            'norm': dict(decay_mult=0.),
                            "rel_pos_h": dict(decay_mult=0.),
                            "rel_pos_w": dict(decay_mult=0.),
                            }
                            )
                 )
lr_config = dict(step=[8, 11])

#optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
# RetinaNet nms is slow in early stage, disable every epoch evaluation
evaluation = None

norm_cfg = dict(type='LN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full after  https://github.com/open-mmlab/mmcv/pull/1205.
head_norm_cfg = dict(type='LN', requires_grad=True)

model = dict(
    type='OrientedRCNN',
    pretrained='../mae-main/output/millionAID_224/1600_0.75_0.00015_0.05_2048/checkpoint-1599.pth',
    backbone=dict(
        type='ViT_Win_RVSA_V3_KVDIFF_WSZ7',
        img_size=800,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.,
        attn_drop_rate=0.,
        drop_path_rate=0.15,
        use_abs_pos_emb=True,
        ),
    neck=dict(
        type='FPN',
        in_channels=[768, 768, 768, 768],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='OrientedRPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='MidpointOffsetCoder',
            target_means=[.0, .0, .0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    roi_head=dict(
        type='OBBStandardRoIHead',
        bbox_roi_extractor=dict(
            type='OBBSingleRoIExtractor',
            roi_layer=dict(type='RoIAlignRotated', out_size=7, sample_num=2),
            out_channels=256,
            extend_factor=(1.4, 1.2),
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='OBBShared2FCBBoxHead',
            start_bbox_type='obb',
            end_bbox_type='obb',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=20,
            bbox_coder=dict(
                type='OBB2OBBDeltaXYWHTCoder',
                target_means=[0., 0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2, 0.1]),
            reg_class_agnostic=True,
            loss_cls=dict(
                type='CrossEntropyLoss',
                use_sigmoid=False,
                loss_weight=1.0),
            loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                           loss_weight=1.0))))
# model training and testing settings
train_cfg = dict(
    rpn=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            match_low_quality=True,
            gpu_assign_thr=200,
            ignore_iof_thr=-1),
        sampler=dict(
            type='RandomSampler',
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False),
        allowed_border=0,
        pos_weight=-1,
        debug=False),
    rpn_proposal=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=2000,
        max_num=2000,
        nms_thr=0.8,
        min_bbox_size=0),
    rcnn=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.5,
            match_low_quality=False,
            ignore_iof_thr=-1,
            iou_calculator=dict(type='OBBOverlaps')),
        sampler=dict(
            type='OBBRandomSampler',
            num=512,
            pos_fraction=0.25,
            neg_pos_ub=-1,
            add_gt_as_proposals=True),
        pos_weight=-1,
        debug=False))
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=2000,
        max_num=2000,
        nms_thr=0.8,
        min_bbox_size=0),
    rcnn=dict(
        score_thr=0.05, nms=dict(type='obb_nms', iou_thr=0.1), max_per_img=2000))