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https://github.com/open-mmlab/mmselfsup.git
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5e44bf7179
* refactor evaluation folder * update knn script * update ut * update configs * update config names according to mmdet and mmseg * update docs
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
_base_ = 'mmdet::mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py'
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# https://github.com/open-mmlab/mmdetection/blob/dev-3.x/configs/mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py
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data_preprocessor = dict(
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type='DetDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_mask=True,
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pad_size_divisor=32)
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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data_preprocessor=data_preprocessor,
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backbone=dict(
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frozen_stages=-1,
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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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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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roi_head=dict(
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shared_head=dict(
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type='ResLayerExtraNorm',
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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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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24)
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
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end=1000),
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dict(
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type='MultiStepLR',
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begin=0,
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end=24,
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by_epoch=True,
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milestones=[16, 22],
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gamma=0.1)
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]
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(
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type='RandomChoiceResize',
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scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
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(1333, 768), (1333, 800)],
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keep_ratio=True),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PackDetInputs')
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
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custom_imports = dict(
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imports=['mmselfsup.models.utils.res_layer_extra_norm'],
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allow_failed_imports=False)
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