_base_ = [
    '../_base_/models/htc_without_semantic_swin_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]

model = dict(
    backbone=dict(
        type='CBSwinTransformer',
        embed_dim=128,
        depths=[2, 2, 18, 2],
        num_heads=[4, 8, 16, 32],
        window_size=7,
        ape=False,
        drop_path_rate=0.3,
        patch_norm=True,
        use_checkpoint=False),
    neck=dict(type='CBFPN', in_channels=[128, 256, 512, 1024]),
    roi_head=dict(
        semantic_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[8]),
        semantic_head=dict(
            type='FusedSemanticHead',
            num_ins=5,
            fusion_level=1,
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=183,
            ignore_label=255,
            loss_weight=0.2)),
    test_cfg=dict(rcnn=dict(score_thr=0.001, nms=dict(type='soft_nms'))))

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)

# augmentation strategy originates from HTC
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
    dict(
        type='Resize',
        img_scale=[(1600, 400), (1600, 1400)],
        multiscale_mode='range',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='SegRescale', scale_factor=1 / 8),
    dict(type='DefaultFormatBundle'),
    dict(
        type='Collect',
        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1600, 1400),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

samples_per_gpu = 1
data = dict(
    samples_per_gpu=samples_per_gpu,
    train=dict(
        seg_prefix=data_root + 'stuffthingmaps/train2017/',
        pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))
optimizer = dict(
    _delete_=True,
    type='AdamW',
    lr=0.0001 / 2,
    betas=(0.9, 0.999),
    weight_decay=0.05,
    paramwise_cfg=dict(
        custom_keys={
            'absolute_pos_embed': dict(decay_mult=0.),
            'relative_position_bias_table': dict(decay_mult=0.),
            'norm': dict(decay_mult=0.)
        }))

fp16 = dict(loss_scale=dict(init_scale=512))

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
