pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py

_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],
    )
]