mirror of
https://github.com/open-mmlab/mmsegmentation.git
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befdc93dc3
* upload original backbone and configs * ConvNext Refactor * ConvNext Refactor * convnext customization refactor with mmseg style * convnext customization refactor with mmseg style * add ade20k_640x640.py * upload files for training * delete dist_optimizer_hook and remove layer_decay_optimizer_constructor * check max(out_indices) < num_stages * add unittest * fix lint error * use MMClassification backbone * fix bugs in base_1k * add mmcls in requirements/mminstall.txt * add mmcls in requirements/mminstall.txt * fix drop_path_rate and layer_scale_init_value * use logger.info instead of print * add mmcls in runtime.txt * fix f string && delete * add doctring in LearningRateDecayOptimizerConstructor and fix mmcls version in requirements * fix typo in LearningRateDecayOptimizerConstructor * use ConvNext models in unit test for LearningRateDecayOptimizerConstructor * add unit test * fix typo * fix typo * add layer_wise and fix redundant backbone.downsample_norm in it * fix unit test * give a ground truth lr_scale and weight_decay * upload models and readme * delete 'backbone.stem_norm' and 'backbone.downsample_norm' in get_num_layer() * fix unit test and use mmcls url * update md2yml.py and metafile * fix typo
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
norm_cfg = dict(type='SyncBN', requires_grad=True)
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custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
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checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' # noqa
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model = dict(
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type='EncoderDecoder',
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pretrained=None,
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backbone=dict(
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type='mmcls.ConvNeXt',
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arch='base',
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out_indices=[0, 1, 2, 3],
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drop_path_rate=0.4,
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layer_scale_init_value=1.0,
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gap_before_final_norm=False,
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init_cfg=dict(
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type='Pretrained', checkpoint=checkpoint_file,
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prefix='backbone.')),
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decode_head=dict(
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type='UPerHead',
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in_channels=[128, 256, 512, 1024],
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in_index=[0, 1, 2, 3],
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pool_scales=(1, 2, 3, 6),
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channels=512,
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dropout_ratio=0.1,
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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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auxiliary_head=dict(
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type='FCNHead',
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in_channels=384,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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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=0.4)),
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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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