62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# This is a BETA new format config file, and the usage may change recently.
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from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
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LoggerHook, ParamSchedulerHook)
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from mmengine.visualization import LocalVisBackend
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from mmpretrain.engine.hooks import VisualizationHook
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from mmpretrain.visualization import UniversalVisualizer
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# configure default hooks
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default_hooks = dict(
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# record the time of every iteration.
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timer=dict(type=IterTimerHook),
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# print log every 100 iterations.
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logger=dict(type=LoggerHook, interval=100),
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# enable the parameter scheduler.
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param_scheduler=dict(type=ParamSchedulerHook),
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# save checkpoint per epoch.
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checkpoint=dict(type=CheckpointHook, interval=1),
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# set sampler seed in distributed evrionment.
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sampler_seed=dict(type=DistSamplerSeedHook),
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# validation results visualization, set True to enable it.
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visualization=dict(type=VisualizationHook, enable=False),
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)
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# configure environment
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env_cfg = dict(
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# whether to enable cudnn benchmark
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cudnn_benchmark=False,
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# set multi process parameters
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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# set distributed parameters
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dist_cfg=dict(backend='nccl'),
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)
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# set visualizer
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vis_backends = [dict(type=LocalVisBackend)]
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visualizer = dict(type=UniversalVisualizer, vis_backends=vis_backends)
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# set log level
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log_level = 'INFO'
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# load from which checkpoint
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load_from = None
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# whether to resume training from the loaded checkpoint
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resume = False
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# Defaults to use random seed and disable `deterministic`
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randomness = dict(seed=None, deterministic=False)
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# Do not need to specify default_scope with new config. Therefore set it to
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# None to avoid BC-breaking.
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default_scope = None
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