MMDetection and MMEngine provide users with various useful hooks including log hooks,
NumClassCheckHook, etc. This tutorial introduces the functionalities and usages of hooks implemented in MMDetection. For using hooks in MMEngine, please read the API documentation in MMEngine.
Memory profiler hook records memory information including virtual memory, swap memory, and the memory of the current process. This hook helps grasp the memory usage of the system and discover potential memory leak bugs. To use this hook, users should install
pip install memory_profiler psutil first.
To use this hook, users should add the following code to the config file.
custom_hooks = [ dict(type='MemoryProfilerHook', interval=50) ]
During training, you can see the messages in the log recorded by
MemoryProfilerHook as below.
The system has 250 GB (246360 MB + 9407 MB) of memory and 8 GB (5740 MB + 2452 MB) of swap memory in total. Currently 9407 MB (4.4%) of memory and 5740 MB (29.9%) of swap memory were consumed. And the current training process consumed 5434 MB of memory.
2022-04-21 08:49:56,881 - mmengine - INFO - Memory information available_memory: 246360 MB, used_memory: 9407 MB, memory_utilization: 4.4 %, available_swap_memory: 5740 MB, used_swap_memory: 2452 MB, swap_memory_utilization: 29.9 %, current_process_memory: 5434 MB
How to implement a custom hook¶
In general, there are 20 points where hooks can be inserted from the beginning to the end of model training. The users can implement custom hooks and insert them at different points in the process of training to do what they want.
points in training:
points in validation:
points at testing:
For example, users can implement a hook to check loss and terminate training when loss goes NaN. To achieve that, there are three steps to go:
Implement a new hook that inherits the
Hookclass in MMEngine, and implement
after_train_itermethod which checks whether loss goes NaN after every
The implemented hook should be registered in
@HOOKS.register_module()as shown in the code below.
custom_hooks = [dict(type='MemoryProfilerHook', interval=50)]in the config file.
from typing import Optional import torch from mmengine.hooks import Hook from mmengine.runner import Runner from mmdet.registry import HOOKS @HOOKS.register_module() class CheckInvalidLossHook(Hook): """Check invalid loss hook. This hook will regularly check whether the loss is valid during training. Args: interval (int): Checking interval (every k iterations). Default: 50. """ def __init__(self, interval: int = 50) -> None: self.interval = interval def after_train_iter(self, runner: Runner, batch_idx: int, data_batch: Optional[dict] = None, outputs: Optional[dict] = None) -> None: """Regularly check whether the loss is valid every n iterations. Args: runner (:obj:`Runner`): The runner of the training process. batch_idx (int): The index of the current batch in the train loop. data_batch (dict, Optional): Data from dataloader. Defaults to None. outputs (dict, Optional): Outputs from model. Defaults to None. """ if self.every_n_train_iters(runner, self.interval): assert torch.isfinite(outputs['loss']), \ runner.logger.info('loss become infinite or NaN!')
Please read customize_runtime for more about implementing a custom hook.