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_base_ = [
'../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
]
crop_size = (512, 1024)
data_preprocessor = dict(size=crop_size)
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
data_preprocessor=data_preprocessor,
pretrained=None,
backbone=dict(
type='ResNet',
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint),
dilations=(1, 1, 2, 4),
strides=(1, 2, 2, 2)))
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05),
clip_grad=dict(max_norm=1, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=1000,
end=80000,
by_epoch=False,
milestones=[60000, 72000],
)
]
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