model.py

# Copyright (c) OpenMMLab. All rights reserved.
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'tests/test_codebase/test_mmseg/data'
crop_size = (128, 128)

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=crop_size, keep_ratio=False),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='PackSegInputs')
]
val_dataloader = dict(
    batch_size=1,
    num_workers=1,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        lazy_init=True,
        serialize_data=False,
        data_prefix=dict(img_path='', seg_map_path=''),
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator

# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
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)

model = dict(
    type='EncoderDecoder',
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type='FastSCNN',
        downsample_dw_channels=(32, 48),
        global_in_channels=64,
        global_block_channels=(64, 96, 128),
        global_block_strides=(2, 2, 1),
        global_out_channels=128,
        higher_in_channels=64,
        lower_in_channels=128,
        fusion_out_channels=128,
        out_indices=(0, 1, 2),
        norm_cfg=norm_cfg,
        align_corners=False),
    decode_head=dict(
        type='DepthwiseSeparableFCNHead',
        in_channels=128,
        channels=128,
        concat_input=False,
        num_classes=19,
        in_index=-1,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)),

    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))

# from default_runtime
default_scope = 'mmseg'
env_cfg = dict(
    cudnn_benchmark=True,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'),
)
log_level = 'INFO'
load_from = None
resume = False

vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')

# from schedules
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=50),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
    sampler_seed=dict(type='DistSamplerSeedHook'),
)