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Source code for ignite.contrib.handlers.tensorboard_logger

import numbers
import warnings

import torch

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

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


[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 (str): common title for all produced plots. For example, "training" metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available metrics. output_transform (callable, optional): 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 (callable, optional): 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, metric_names=None, output_transform=None, global_step_transform=None): super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) def __call__(self, engine, logger, event_name): 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) if not isinstance(global_step, int): raise TypeError( "global_step must be int, got {}." " Please check the output of global_step_transform.".format(type(global_step)) ) for key, value in metrics.items(): if isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: logger.writer.add_scalar("{}/{}".format(self.tag, key), value, global_step) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: for i, v in enumerate(value): logger.writer.add_scalar("{}/{}/{}".format(self.tag, key, i), v.item(), global_step) else: warnings.warn("TensorboardLogger output_handler can not log metrics value type {}".format(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.optim.Optimizer): torch optimizer which parameters to log param_name (str): parameter name tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, optimizer, param_name="lr", tag=None): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) def __call__(self, engine, logger, event_name): 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 = "{}/".format(self.tag) if self.tag else "" params = { "{}{}/group_{}".format(tag_prefix, self.param_name, 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 (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model, reduction=torch.norm, tag=None): super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) def __call__(self, engine, logger, event_name): 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 = "{}/".format(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( "{}weights_{}/{}".format(tag_prefix, 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 (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model, tag=None): super(WeightsHistHandler, self).__init__(model, tag=tag) def __call__(self, engine, logger, event_name): 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 = "{}/".format(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="{}weights/{}".format(tag_prefix, 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 (torch.nn.Module): model to log weights reduction (callable): function to reduce parameters into scalar tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model, reduction=torch.norm, tag=None): super(GradsScalarHandler, self).__init__(model, reduction, tag=tag) def __call__(self, engine, logger, event_name): 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 = "{}/".format(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( "{}grads_{}/{}".format(tag_prefix, 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 (torch.nn.Module): model to log weights tag (str, optional): common title for all produced plots. For example, "generator" """ def __init__(self, model, tag=None): super(GradsHistHandler, self).__init__(model, tag=tag) def __call__(self, engine, logger, event_name): 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 = "{}/".format(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="{}grads/{}".format(tag_prefix, name), values=p.grad.detach().cpu().numpy(), global_step=global_step )
[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#torch.utils.tensorboard.writer.SummaryWriter>`_ (>=v1.2.0). Args: *args: Positional arguments accepted from `SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter>`_. **kwargs: Keyword arguments accepted from `SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter>`_. 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 with 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, **kwargs): try: from tensorboardX import SummaryWriter except ImportError: try: from torch.utils.tensorboard import SummaryWriter 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): self.writer.close() def _create_output_handler(self, *args, **kwargs): return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args, **kwargs): return OptimizerParamsHandler(*args, **kwargs)

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