_base_ = [
    '../_base_/default_runtime.py', '../_base_/datasets/cityscapes.py'
]

crop_size = (896, 896)
data_preprocessor = dict(
    type='SegDataPreProcessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    pad_val=0,
    seg_pad_val=255,
    size=crop_size)
num_classes = 19

model = dict(
    type='EncoderDecoder',
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type='DINOv2',
        version='large',
        freeze=False,
        load_from='../checkpoints/depth_anything_vitl14.pth'),
    neck=dict(type='Feature2Pyramid', embed_dim=1024, rescales=[4, 2, 1, 0.5]),
    decode_head=dict(
        type='Mask2FormerHead',
        in_channels=[1024, 1024, 1024, 1024],
        # strides=[4, 8, 16, 32],
        feat_channels=1024,
        out_channels=1024,
        num_classes=num_classes,
        num_queries=200,
        num_transformer_feat_level=3,
        align_corners=False,
        pixel_decoder=dict(
            type='mmdet.MSDeformAttnPixelDecoder',
            num_outs=3,
            norm_cfg=dict(type='GN', num_groups=32),
            act_cfg=dict(type='ReLU'),
            encoder=dict(  # DeformableDetrTransformerEncoder
                num_layers=6,
                layer_cfg=dict(  # DeformableDetrTransformerEncoderLayer
                    self_attn_cfg=dict(  # MultiScaleDeformableAttention
                        embed_dims=1024,
                        num_heads=32,
                        num_levels=3,
                        num_points=4,
                        im2col_step=64,
                        dropout=0.0,
                        batch_first=True,
                        norm_cfg=None,
                        init_cfg=None),
                    ffn_cfg=dict(
                        embed_dims=1024,
                        feedforward_channels=4096,
                        num_fcs=2,
                        ffn_drop=0.0,
                        act_cfg=dict(type='ReLU', inplace=True))),
                init_cfg=None),
            positional_encoding=dict(  # SinePositionalEncoding
                num_feats=512, normalize=True),
            init_cfg=None),
        enforce_decoder_input_project=False,
        positional_encoding=dict(  # SinePositionalEncoding
            num_feats=512, normalize=True),
        transformer_decoder=dict(  # Mask2FormerTransformerDecoder
            return_intermediate=True,
            num_layers=9,
            layer_cfg=dict(  # Mask2FormerTransformerDecoderLayer
                self_attn_cfg=dict(  # MultiheadAttention
                    embed_dims=1024,
                    num_heads=32,
                    attn_drop=0.0,
                    proj_drop=0.0,
                    dropout_layer=None,
                    batch_first=True),
                cross_attn_cfg=dict(  # MultiheadAttention
                    embed_dims=1024,
                    num_heads=32,
                    attn_drop=0.0,
                    proj_drop=0.0,
                    dropout_layer=None,
                    batch_first=True),
                ffn_cfg=dict(
                    embed_dims=1024,
                    feedforward_channels=4096,
                    num_fcs=2,
                    act_cfg=dict(type='ReLU', inplace=True),
                    ffn_drop=0.0,
                    dropout_layer=None,
                    add_identity=True)),
            init_cfg=None),
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=False,
            loss_weight=2.0,
            reduction='mean',
            class_weight=[1.0] * num_classes + [0.1]),
        loss_mask=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='mean',
            loss_weight=5.0),
        loss_dice=dict(
            type='mmdet.DiceLoss',
            use_sigmoid=True,
            activate=True,
            reduction='mean',
            naive_dice=True,
            eps=1.0,
            loss_weight=5.0),
        train_cfg=dict(
            num_points=12544,
            oversample_ratio=3.0,
            importance_sample_ratio=0.75,
            assigner=dict(
                type='mmdet.HungarianAssigner',
                match_costs=[
                    dict(type='mmdet.ClassificationCost', weight=2.0),
                    dict(
                        type='mmdet.CrossEntropyLossCost',
                        weight=5.0,
                        use_sigmoid=True),
                    dict(
                        type='mmdet.DiceCost',
                        weight=5.0,
                        pred_act=True,
                        eps=1.0)
                ]),
            sampler=dict(type='mmdet.MaskPseudoSampler'))),
    train_cfg=dict(),
    test_cfg=dict(mode='slide', crop_size=crop_size, stride=(518, 518)))

# dataset config
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        type='RandomChoiceResize',
        scales=[int(x * 0.1 * 896) for x in range(5, 21)],
        resize_type='ResizeShortestEdge',
        max_size=896 * 4),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='PackSegInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(896 * 4, 896), keep_ratio=True),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations'),
    dict(type='PackSegInputs')
]
train_dataloader = dict(batch_size=1, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

# set all layers in backbone to lr_mult=0.1
# set all norm layers, position_embeding,
# query_embeding, level_embeding to decay_multi=0.0
backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
custom_keys = {
    'backbone.dinov2': dict(lr_mult=0.1, decay_mult=1.0),
    'backbone.dinov2.norm': backbone_norm_multi,
    'pos_embed': backbone_embed_multi,
    'query_embed': embed_multi,
    'query_feat': embed_multi,
    'level_embed': embed_multi
}
custom_keys.update({
    f'backbone.dinov2.blocks.{block_id}.norm': backbone_norm_multi
    for block_id in range(24)
})
# optimizer
optimizer = dict(
    type='AdamW', lr=0.00003, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=optimizer,
    clip_grad=dict(max_norm=0.01, norm_type=2),
    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))

find_unused_parameters=True

param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
    dict(
        type='PolyLR',
        power=1.0,
        begin=1500,
        end=80000,
        eta_min=0.0,
        by_epoch=False,
    )
]

# training schedule for 160k
train_cfg = dict(
    type='IterBasedTrainLoop', max_iters=80000, val_interval=5000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(
        type='CheckpointHook', by_epoch=False, interval=5000, save_best='mIoU', max_keep_ckpts=1),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    visualization=dict(type='SegVisualizationHook'))

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)

work_dir = './work_dirs/depth_anything_large_mask2former_16xb1_80k_cityscapes_896x896'
