57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='CascadeEncoderDecoder',
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num_stages=2,
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pretrained='open-mmlab://resnet50_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 1, 1),
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strides=(1, 2, 2, 2),
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norm_cfg=norm_cfg,
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=4),
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decode_head=[
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dict(
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type='FPNHead',
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in_channels=[256, 256, 256, 256],
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in_index=[0, 1, 2, 3],
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feature_strides=[4, 8, 16, 32],
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channels=128,
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dropout_ratio=-1,
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num_classes=19,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
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dict(
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type='PointHead',
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in_channels=[256],
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in_index=[0],
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channels=256,
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num_fcs=3,
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coarse_pred_each_layer=True,
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dropout_ratio=-1,
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num_classes=19,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
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])
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# model training and testing settings
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train_cfg = dict(
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num_points=2048, oversample_ratio=3, importance_sample_ratio=0.75)
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test_cfg = dict(
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mode='whole',
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subdivision_steps=2,
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subdivision_num_points=8196,
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scale_factor=2)
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