import numbers
import warnings
import torch
from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, \
BaseWeightsScalarHandler, BaseWeightsHistHandler, 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)
Example with CustomPeriodicEvent, where model is evaluated every 500 iterations:
.. code-block:: python
from ignite.contrib.handlers import CustomPeriodicEvent
cpe = CustomPeriodicEvent(n_iterations=500)
cpe.attach(trainer)
@trainer.on(cpe.Events.ITERATIONS_500_COMPLETED)
def evaluate(engine):
evaluator.run(validation_set, max_epochs=1)
from ignite.contrib.handlers.tensorboard_logger import *
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 CustomPeriodicEvent attached to it, 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(evaluator,
log_handler=OutputHandler(tag="validation",
metrics=["nll", "accuracy"],
global_step_transform=global_step_transform),
event_name=Events.EPOCH_COMPLETED)
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.
another_engine (Engine): Deprecated (see :attr:`global_step_transform`). Another engine to use to provide the
value of event. Typically, user can provide
the trainer if this handler is attached to an evaluator and thus it logs proper trainer's
epoch/iteration value.
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, another_engine=None, global_step_transform=None):
super(OutputHandler, self).__init__(tag, metric_names, output_transform, another_engine, 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)
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,
log_handler=WeightsScalarHandler(model, reduction=torch.norm),
event_name=Events.ITERATION_COMPLETED)
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,
log_handler=WeightsHistHandler(model),
event_name=Events.ITERATION_COMPLETED)
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,
log_handler=GradsScalarHandler(model, reduction=torch.norm),
event_name=Events.ITERATION_COMPLETED)
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,
log_handler=GradsHistHandler(model),
event_name=Events.ITERATION_COMPLETED)
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 (>=v1.2.0).
Args:
*args: Positional arguments accepted from :class:`~tensorboardx.SummaryWriter`.
**kwargs: Keyword arguments accepted from :class:`~tensorboardx.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(trainer,
log_handler=OutputHandler(tag="training", output_transform=lambda loss: {'loss': loss}),
event_name=Events.ITERATION_COMPLETED)
# 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(train_evaluator,
log_handler=OutputHandler(tag="training",
metric_names=["nll", "accuracy"],
global_step_transform=global_step_from_engine(trainer)),
event_name=Events.EPOCH_COMPLETED)
# 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(evaluator,
log_handler=OutputHandler(tag="validation",
metric_names=["nll", "accuracy"],
global_step_transform=global_step_from_engine(trainer)),
event_name=Events.EPOCH_COMPLETED)
# 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)
# Attach the logger to the trainer to log model's weights norm after each iteration
tb_logger.attach(trainer,
log_handler=WeightsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED)
# Attach the logger to the trainer to log model's weights as a histogram after each epoch
tb_logger.attach(trainer,
log_handler=WeightsHistHandler(model),
event_name=Events.EPOCH_COMPLETED)
# Attach the logger to the trainer to log model's gradients norm after each iteration
tb_logger.attach(trainer,
log_handler=GradsScalarHandler(model),
event_name=Events.ITERATION_COMPLETED)
# Attach the logger to the trainer to log model's gradients as a histogram after each epoch
tb_logger.attach(trainer,
log_handler=GradsHistHandler(model),
event_name=Events.EPOCH_COMPLETED)
# 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(trainer,
log_handler=OutputHandler(tag="training",
output_transform=lambda loss: {'loss': loss}),
event_name=Events.ITERATION_COMPLETED)
"""
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()