_base_ = [
    '../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
    '../_base_/default_runtime.py'
]
crop_size = (512, 1024)
cudnn_benchmark = True
# model training and testing settings
train_cfg = dict()
test_cfg = dict(mode='whole')

# Here: What is parameter 'with_seg'?
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', to_float32=True),
    dict(type='LoadAnnotations'),   # with_seg=True
    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomCrop', crop_size=crop_size),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 1024),
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        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=8,
    workers_per_gpu=4,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline))

# optimizer
optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=4e-5)
optimizer_config = dict()
# learning policy
lr_config = dict(
    policy='poly',
    power=0.9,
    by_epoch=False,
)
# runtime settings
# total_epochs = 1000
total_iters = 100000
evaluation = dict(interval=2000, metric='mIoU')
checkpoint_config = dict(interval=2000)

# log config: log by iter.
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])