voc.py

_base_ = [
    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_20k.py'
]

# dataset settings
dataset_type = 'DSDLSegDataset'
data_root = 'data/PASCAL_VOC2012'
img_prefix = 'raw/VOCdevkit/VOC2012'
train_ann = 'dsdl/dsdl_SemSeg_full/set-train/train.yaml'
val_ann = 'dsdl/dsdl_SemSeg_full/set-val/val.yaml'
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(
        type='RandomResize',
        scale=(2048, 512),
        ratio_range=(0.5, 2.0),
        keep_ratio=True),
    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=(2048, 512), 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=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='InfiniteSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(img_path=img_prefix, seg_map_path=img_prefix),
        ann_file=train_ann,
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(img_path=img_prefix, seg_map_path=img_prefix),
        ann_file=val_ann,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

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

data_preprocessor = dict(size=crop_size)
model = dict(
    data_preprocessor=data_preprocessor,
    decode_head=dict(num_classes=21),
    auxiliary_head=dict(num_classes=21))