base

Source code: tianshou/utils/logger/base.py

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class BaseLogger(train_interval: int = 1000, test_interval: int = 1, update_interval: int = 1000)

The base class for any logger which is compatible with trainer.

Try to overwrite write() method to use your own writer.

Parameters:

• train_interval – the log interval in log_train_data(). Default to 1000.

• test_interval – the log interval in log_test_data(). Default to 1.

• update_interval – the log interval in log_update_data(). Default to 1000.

log_test_data(collect_result: dict, step: int) → None

Use writer to log statistics generated during evaluating.

Parameters:

• collect_result – a dict containing information of data collected in evaluating stage, i.e., returns of collector.collect().

• step – stands for the timestep the collect_result being logged.

log_train_data(collect_result: dict, step: int) → None

Use writer to log statistics generated during training.

Parameters:

• collect_result – a dict containing information of data collected in training stage, i.e., returns of collector.collect().

• step – stands for the timestep the collect_result being logged.

log_update_data(update_result: dict, step: int) → None

Use writer to log statistics generated during updating.

Parameters:

• update_result – a dict containing information of data collected in updating stage, i.e., returns of policy.update().

• step – stands for the timestep the collect_result being logged.

abstract restore_data() → tuple[int, int, int]

Return the metadata from existing log.

If it finds nothing or an error occurs during the recover process, it will return the default parameters.

Returns:

epoch, env_step, gradient_step.

abstract save_data(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: collections.abc.Callable[[int, int, int], str] | None = None) → None

Use writer to log metadata when calling save_checkpoint_fn in trainer.

Parameters:

• epoch – the epoch in trainer.

• env_step – the env_step in trainer.

• gradient_step – the gradient_step in trainer.

• save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.

abstract write(step_type: str, step: int, data: dict[str, int | numbers.Number | numpy.number | numpy.ndarray]) → None

Specify how the writer is used to log data.

Parameters:

• step_type (str) – namespace which the data dict belongs to.

• step – stands for the ordinate of the data dict.

• data – the data to write with format {key: value}.

class LazyLogger

A logger that does nothing. Used as the placeholder in trainer.

restore_data() → tuple[int, int, int]

Return the metadata from existing log.

If it finds nothing or an error occurs during the recover process, it will return the default parameters.

Returns:

epoch, env_step, gradient_step.

save_data(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: collections.abc.Callable[[int, int, int], str] | None = None) → None

Use writer to log metadata when calling save_checkpoint_fn in trainer.

Parameters:

• epoch – the epoch in trainer.

• env_step – the env_step in trainer.

• gradient_step – the gradient_step in trainer.

• save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.

write(step_type: str, step: int, data: dict[str, int | numbers.Number | numpy.number | numpy.ndarray]) → None

The LazyLogger writes nothing.