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_base_ = [
'../_base_/models/deeplabv3plus_r50-d8.py',
'../_base_/datasets/mapillary_v1_65.py',
'../_base_/default_runtime.py',
]
crop_size = (1280, 1280)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(depth=50),
decode_head=dict(num_classes=65),
auxiliary_head=dict(num_classes=65))
iters = 300000
# optimizer
optimizer = dict(
type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001)
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
param_scheduler = [
dict(
type='PolyLR',
eta_min=0,
power=0.9,
begin=0,
end=iters,
by_epoch=False)
]
# training schedule for 300k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=iters // 10),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
train_dataloader = dict(batch_size=2)
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (4 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=8)
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