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Migration of Runtime Settings

We update runtime settings in MMEdit 1.x. Important modifications are as following.

  • The checkpoint_config is moved to default_hooks.checkpoint and the log_config is moved to default_hooks.logger. And we move many hooks settings from the script code to the default_hooks field in the runtime configuration.

  • The resume_from is removed. And we use resume to replace it.

    • If resume=True and load_from is not None, resume training from the checkpoint in load_from.

    • If resume=True and load_from is None, try to resume from the latest checkpoint in the work directory.

    • If resume=False and load_from is not None, only load the checkpoint, not resume training.

    • If resume=False and load_from is None, do not load nor resume.

  • The dist_params field is a sub field of env_cfg now. And there are some new configurations in the env_cfg.

  • The workflow related functionalities are removed.

  • New field visualizer: The visualizer is a new design. We use a visualizer instance in the runner to handle results & log visualization and save to different backends, like Local, TensorBoard and Wandb.

  • New field default_scope: The start point to search module for all registries.

Original New
checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
    interval=5000,  # The save interval is 5000 iterations
    save_optimizer=True,  # Also save optimizers
    by_epoch=False)  # Count by iterations
log_config = dict(  # Config to register logger hook
    interval=100,  # Interval to print the log
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),  # The logger used to record the training process
        dict(type='TensorboardLoggerHook'),  # The Tensorboard logger is also supported
    ])
visual_config = None  # Visual config, we do not use it.
# runtime settings
dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set
log_level = 'INFO'  # The level of logging
load_from = None # load models as a pre-trained model from a given path. This will not resume training
resume_from = None # Resume checkpoints from a given path, the training will be resumed from the iteration when the checkpoint's is saved
workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. Keep this unchanged when training current matting models
default_hooks = dict(  # Used to build default hooks
    checkpoint=dict(  # Config to set the checkpoint hook
        type='CheckpointHook',
        interval=5000,  # The save interval is 5000 iterations
        save_optimizer=True,
        by_epoch=False,  # Count by iterations
        out_dir=save_dir,
    ),
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),  # Config to register logger hook
    param_scheduler=dict(type='ParamSchedulerHook'),
    sampler_seed=dict(type='DistSamplerSeedHook'),
)
default_scope = 'mmedit' # Used to set registries location
env_cfg = dict(  # Parameters to setup distributed training, the port can also be set
    cudnn_benchmark=False,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=4),
    dist_cfg=dict(backend='nccl'),
)
log_level = 'INFO'  # The level of logging
log_processor = dict(type='LogProcessor', window_size=100, by_epoch=False)  # Used to build log processor
load_from = None  # load models as a pre-trained model from a given path. This will not resume training.
resume = False  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
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