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Source code for ignite.contrib.handlers.tensorboard_logger

"""TensorBoard logger and its helper handlers."""
import numbers
import warnings
from typing import Any, Callable, List, Optional, Union

import torch
import torch.nn as nn
from torch.optim import Optimizer

from ignite.contrib.handlers.base_logger import (
    BaseLogger,
    BaseOptimizerParamsHandler,
    BaseOutputHandler,
    BaseWeightsHistHandler,
    BaseWeightsScalarHandler,
)
from ignite.engine import Engine, EventEnum, Events
from ignite.handlers import global_step_from_engine

__all__ = [
    "TensorboardLogger",
    "OptimizerParamsHandler",
    "OutputHandler",
    "WeightsScalarHandler",
    "WeightsHistHandler",
    "GradsScalarHandler",
    "GradsHistHandler",
    "global_step_from_engine",
]


[docs]class TensorboardLogger(BaseLogger): """ TensorBoard handler to log metrics, model/optimizer parameters, gradients during the training and validation. By default, this class favors `tensorboardX <https://github.com/lanpa/tensorboardX>`_ package if installed: .. code-block:: bash pip install tensorboardX otherwise, it falls back to using `PyTorch's SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html>`_ (>=v1.2.0). Args: args: Positional arguments accepted from `SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html>`_. kwargs: Keyword arguments accepted from `SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html>`_. For example, `log_dir` to setup path to the directory where to log. Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log training loss at each iteration tb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer` instead of `train_evaluator`. tb_logger.attach_output_handler( train_evaluator, event_name=Events.EPOCH_COMPLETED, tag="training", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer), ) # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the # `trainer` instead of `evaluator`. tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer)), ) # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration tb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer, param_name='lr' # optional ) # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model) ) # Attach the logger to the trainer to log model's weights as a histogram after each epoch tb_logger.attach( trainer, event_name=Events.EPOCH_COMPLETED, log_handler=WeightsHistHandler(model) ) # Attach the logger to the trainer to log model's gradients norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model) ) # Attach the logger to the trainer to log model's gradients as a histogram after each epoch tb_logger.attach( trainer, event_name=Events.EPOCH_COMPLETED, log_handler=GradsHistHandler(model) ) # We need to close the logger when we are done tb_logger.close() It is also possible to use the logger as context manager: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * with TensorboardLogger(log_dir="experiments/tb_logs") as tb_logger: trainer = Engine(update_fn) # Attach the logger to the trainer to log training loss at each iteration tb_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) """ def __init__(self, *args: Any, **kwargs: Any): try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter # type: ignore[no-redef] except ImportError: raise RuntimeError( "This contrib module requires either tensorboardX or torch >= 1.2.0. " "You may install tensorboardX with command: \n pip install tensorboardX \n" "or upgrade PyTorch using your package manager of choice (pip or conda)." ) self.writer = SummaryWriter(*args, **kwargs) def close(self) -> None: self.writer.close() def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler": return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler": return OptimizerParamsHandler(*args, **kwargs)
[docs]class OutputHandler(BaseOutputHandler): """Helper handler to log engine's output and/or metrics Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch # of the `trainer`: tb_logger.attach( evaluator, log_handler=OutputHandler( tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ), event_name=Events.EPOCH_COMPLETED ) # or equivalently tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metric_names=["nll", "accuracy"], global_step_transform=global_step_from_engine(trainer) ) Another example, where model is evaluated every 500 iterations: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") def global_step_transform(*args, **kwargs): return trainer.state.iteration # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after # every 500 iterations. Since evaluator engine does not have access to the training iteration, we # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time # evaluator metrics are plotted on Tensorboard. tb_logger.attach_output_handler( evaluator, event_name=Events.EPOCH_COMPLETED, tag="validation", metrics=["nll", "accuracy"], global_step_transform=global_step_transform ) Args: tag: common title for all produced plots. For example, "training" metric_names: list of metric names to plot or a string "all" to plot all available metrics. output_transform: output transform function to prepare `engine.state.output` as a number. For example, `output_transform = lambda output: output` This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot with corresponding keys. global_step_transform: global step transform function to output a desired global step. Input of the function is `(engine, event_name)`. Output of function should be an integer. Default is None, global_step based on attached engine. If provided, uses function output as global_step. To setup global step from another engine, please use :meth:`~ignite.contrib.handlers.tensorboard_logger.global_step_from_engine`. Note: Example of `global_step_transform`: .. code-block:: python def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) """ def __init__( self, tag: str, metric_names: Optional[List[str]] = None, output_transform: Optional[Callable] = None, global_step_transform: Optional[Callable] = None, ): super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, EventEnum]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'OutputHandler' works only with TensorboardLogger") metrics = self._setup_output_metrics(engine) global_step = self.global_step_transform(engine, event_name) # type: ignore[misc] if not isinstance(global_step, int): raise TypeError( f"global_step must be int, got {type(global_step)}." " Please check the output of global_step_transform." ) for key, value in metrics.items(): if isinstance(value, numbers.Number): logger.writer.add_scalar(f"{self.tag}/{key}", value, global_step) elif isinstance(value, torch.Tensor) and value.ndimension() == 0: logger.writer.add_scalar(f"{self.tag}/{key}", value.item(), global_step) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): logger.writer.add_scalar(f"{self.tag}/{key}/{i}", v.item(), global_step) else: warnings.warn(f"TensorboardLogger output_handler can not log metrics value type {type(value)}")
[docs]class OptimizerParamsHandler(BaseOptimizerParamsHandler): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration tb_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently tb_logger.attach_opt_params_handler( trainer, event_name=Events.ITERATION_STARTED, optimizer=optimizer ) Args: optimizer: torch optimizer or any object with attribute ``param_groups`` as a sequence. param_name: parameter name tag: common title for all produced plots. For example, "generator" """ def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" params = { f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name]) for i, param_group in enumerate(self.optimizer.param_groups) } for k, v in params.items(): logger.writer.add_scalar(k, v, global_step)
[docs]class WeightsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's weights as scalars. Handler iterates over named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model, reduction=torch.norm) ) Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None): super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'WeightsScalarHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_scalar( f"{tag_prefix}weights_{self.reduction.__name__}/{name}", self.reduction(p.data), global_step )
[docs]class WeightsHistHandler(BaseWeightsHistHandler): """Helper handler to log model's weights as histograms. Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsHistHandler(model) ) Args: model: model to log weights tag: common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, tag: Optional[str] = None): super(WeightsHistHandler, self).__init__(model, tag=tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'WeightsHistHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}weights/{name}", values=p.data.detach().cpu().numpy(), global_step=global_step, )
[docs]class GradsScalarHandler(BaseWeightsScalarHandler): """Helper handler to log model's gradients as scalars. Handler iterates over the gradients of named parameters of the model, applies reduction function to each parameter produce a scalar and then logs the scalar. Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model, reduction=torch.norm) ) Args: model: model to log weights reduction: function to reduce parameters into scalar tag: common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None): super(GradsScalarHandler, self).__init__(model, reduction, tag=tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'GradsScalarHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_scalar( f"{tag_prefix}grads_{self.reduction.__name__}/{name}", self.reduction(p.grad), global_step )
[docs]class GradsHistHandler(BaseWeightsHistHandler): """Helper handler to log model's gradients as histograms. Examples: .. code-block:: python from ignite.contrib.handlers.tensorboard_logger import * # Create a logger tb_logger = TensorboardLogger(log_dir="experiments/tb_logs") # Attach the logger to the trainer to log model's weights norm after each iteration tb_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsHistHandler(model) ) Args: model: model to log weights tag: common title for all produced plots. For example, "generator" """ def __init__(self, model: nn.Module, tag: Optional[str] = None): super(GradsHistHandler, self).__init__(model, tag=tag) def __call__(self, engine: Engine, logger: TensorboardLogger, event_name: Union[str, Events]) -> None: if not isinstance(logger, TensorboardLogger): raise RuntimeError("Handler 'GradsHistHandler' works only with TensorboardLogger") global_step = engine.state.get_event_attrib_value(event_name) tag_prefix = f"{self.tag}/" if self.tag else "" for name, p in self.model.named_parameters(): if p.grad is None: continue name = name.replace(".", "/") logger.writer.add_histogram( tag=f"{tag_prefix}grads/{name}", values=p.grad.detach().cpu().numpy(), global_step=global_step )

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