47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
_base_ = [
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'../_base_/models/bisenetv1_r18-d32.py',
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'../_base_/datasets/cityscapes_1024x1024.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
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]
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='EncoderDecoder',
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backbone=dict(
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type='BiSeNetV1',
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context_channels=(512, 1024, 2048),
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spatial_channels=(256, 256, 256, 512),
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out_channels=1024,
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backbone_cfg=dict(
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init_cfg=dict(
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type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'),
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type='ResNet',
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depth=50)),
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decode_head=dict(
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type='FCNHead', in_channels=1024, in_index=0, channels=1024),
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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=512,
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channels=256,
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num_convs=1,
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num_classes=19,
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in_index=1,
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norm_cfg=norm_cfg,
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concat_input=False),
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dict(
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type='FCNHead',
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in_channels=512,
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channels=256,
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num_convs=1,
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num_classes=19,
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in_index=2,
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norm_cfg=norm_cfg,
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concat_input=False),
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])
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lr_config = dict(warmup='linear', warmup_iters=1000)
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optimizer = dict(lr=0.05)
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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)
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