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_base_ = [
'../_base_/models/fpn_poolformer_s12.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_40k.py'
]
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
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),
dict(type='ResizeToMultiple', size_divisor=32),
# 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')
]
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='InfiniteSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=50,
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
img_path='images/training',
seg_map_path='annotations/training'),
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='images/validation',
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator
# model settings
model = dict(
data_preprocessor=data_preprocessor,
neck=dict(in_channels=[64, 128, 320, 512]),
decode_head=dict(num_classes=150))
# optimizer
# optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
# optimizer_config = dict()
# # learning policy
# lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)
optim_wrapper = dict(
_delete_=True,
type='AmpOptimWrapper',
optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001))
param_scheduler = [
dict(
type='PolyLR',
power=0.9,
begin=0,
end=40000,
eta_min=0.0,
by_epoch=False,
)
]
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