_base_ = [
    '../_base_/models/deeplabv3plus_r50-d8.py',
    '../_base_/datasets/cityscapes.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_40k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnet101_v1c',
    backbone=dict(
        type='ResNetV1c',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 2, 4),
        strides=(1, 2, 1, 1),
        norm_cfg=norm_cfg,
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    decode_head=dict(
        type='DepthwiseSeparableASPPHead',
        in_channels=2048,
        in_index=3,
        channels=512,
        dilations=(1, 12, 24, 36),
        c1_in_channels=256,
        c1_channels=48,
        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=19,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
# model training and testing settings
train_cfg = dict() # set the weight for the consistency loss
test_cfg = dict(mode='whole')

optimizer = dict(lr=0.02)
lr_config = dict(min_lr=1e-4)

data_root='../../../../dataset/cityscapes/'
dataset_type = 'CityscapesDataset'
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir=['train/image'],
        ann_dir=['train/label'],
        split = ['train.txt']),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='val/image',
        ann_dir='val/label'),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='val/image',
        ann_dir='val/label'))

find_unused_parameters=True