# Copyright (c) OpenMMLab. All rights reserved.

# ---------------- 模型設定 ---------------- #
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='STDCContextPathNet',
        backbone_cfg=dict(
            type='STDCNet',
            stdc_type='STDCNet1',
            in_channels=3,
            channels=(32, 64, 256, 512, 1024),
            bottleneck_type='cat',
            num_convs=4,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='ReLU'),
            with_final_conv=False,
            init_cfg=dict(
                type='Pretrained',
                checkpoint='https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth'
            )
        ),
        last_in_channels=(1024, 512),
        out_channels=128,
        ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4)
    ),
    decode_head=dict(
        type='FCNHead',
        in_channels=256,
        channels=256,
        num_convs=1,
        num_classes=4,  # ✅ 四類
        in_index=3,
        concat_input=False,
        dropout_ratio=0.1,
        norm_cfg=norm_cfg,
        align_corners=True,
        sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
    ),
    auxiliary_head=[
        dict(
            type='FCNHead',
            in_channels=128,
            channels=64,
            num_convs=1,
            num_classes=4,  # ✅
            in_index=2,
            norm_cfg=norm_cfg,
            concat_input=False,
            align_corners=False,
            sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
        ),
        dict(
            type='FCNHead',
            in_channels=128,
            channels=64,
            num_convs=1,
            num_classes=4,  # ✅
            in_index=1,
            norm_cfg=norm_cfg,
            concat_input=False,
            align_corners=False,
            sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
            loss_decode=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
        ),
        dict(
            type='STDCHead',
            in_channels=256,
            channels=64,
            num_convs=1,
            num_classes=4,  # ✅ 最重要
            boundary_threshold=0.1,
            in_index=0,
            norm_cfg=norm_cfg,
            concat_input=False,
            align_corners=True,
            loss_decode=[
                dict(
                    type='CrossEntropyLoss',
                    loss_name='loss_ce',
                    use_sigmoid=True,
                    loss_weight=1.0),
                dict(
                    type='DiceLoss',
                    loss_name='loss_dice',
                    loss_weight=1.0)
            ]
        )
    ],
    train_cfg=dict(),
    test_cfg=dict(mode='whole')
)

# ---------------- 資料集設定 ---------------- #
dataset_type = 'GolfDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
    mean=[128., 128., 128.], std=[256., 256., 256.], to_rgb=True)
crop_size = (512, 1024)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1024, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/train',
        ann_dir='gtFine/train',
        pipeline=train_pipeline
    ),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/val',
        ann_dir='gtFine/val',
        pipeline=test_pipeline
    ),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/test',
        ann_dir='gtFine/test',
        pipeline=test_pipeline
    )
)

# ---------------- 額外設定 ---------------- #
log_config = dict(
    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
checkpoint_config = dict(by_epoch=False, interval=1000)
evaluation = dict(interval=2000, metric='mIoU', pre_eval=True)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
lr_config = dict(
    policy='poly',
    power=0.9,
    min_lr=0.0001,
    by_epoch=False,
    warmup='linear',
    warmup_iters=1000)
runner = dict(type='IterBasedRunner', max_iters=20000)
cudnn_benchmark = True
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
work_dir = './work_dirs/kn_stdc1_golf4class'
gpu_ids = [0]

# ✅ 可選：僅供視覺化或 post-processing 用，不會傳給 dataset
classes = ('car', 'grass', 'people', 'road')
palette = [
    [246, 14, 135],   # car
    [233, 81, 78],    # grass
    [220, 148, 21],   # people
    [207, 215, 220],  # road
]