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Source code for ignite.contrib.handlers.visdom_logger

import numbers
import os
import warnings

import torch

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

__all__ = [
    "VisdomLogger",
    "OptimizerParamsHandler",
    "OutputHandler",
    "WeightsScalarHandler",
    "GradsScalarHandler",
    "global_step_from_engine",
]


class _BaseVisDrawer:
    def __init__(self, show_legend=False):
        self.windows = {}
        self.show_legend = show_legend

    def add_scalar(self, logger, k, v, event_name, global_step):
        """
        Helper method to log a scalar with VisdomLogger.

        Args:
            logger (VisdomLogger): visdom logger
            k (str): scalar name which is used to set window title and y-axis label
            v (int or float): scalar value, y-axis value
            event_name: Event name which is used to setup x-axis label. Valid events are from
                :class:`~ignite.engine.Events` or any `event_name` added by
                :meth:`~ignite.engine.Engine.register_events`.
            global_step (int): global step, x-axis value

        """
        if k not in self.windows:
            self.windows[k] = {
                "win": None,
                "opts": {"title": k, "xlabel": str(event_name), "ylabel": k, "showlegend": self.show_legend},
            }

        update = None if self.windows[k]["win"] is None else "append"

        kwargs = {
            "X": [global_step,],
            "Y": [v,],
            "env": logger.vis.env,
            "win": self.windows[k]["win"],
            "update": update,
            "opts": self.windows[k]["opts"],
            "name": k,
        }

        future = logger.executor.submit(logger.vis.line, **kwargs)
        if self.windows[k]["win"] is None:
            self.windows[k]["win"] = future.result()


[docs]class OutputHandler(BaseOutputHandler, _BaseVisDrawer): """Helper handler to log engine's output and/or metrics Examples: .. code-block:: python from ignite.contrib.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # 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`: vd_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 vd_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.visdom_logger import * @trainer.on(Events.ITERATION_COMPLETED(every=500)) def evaluate(engine): evaluator.run(validation_set, max_epochs=1) vd_logger = VisdomLogger() 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 Visdom. vd_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.visdom_logger.global_step_from_engine`. show_legend (bool, optional): flag to show legend in the window 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, show_legend=False, ): super(OutputHandler, self).__init__(tag, metric_names, output_transform, global_step_transform) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine, logger, event_name): if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'OutputHandler' works only with VisdomLogger") 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(): values = [] keys = [] if isinstance(value, numbers.Number) or isinstance(value, torch.Tensor) and value.ndimension() == 0: values.append(value) keys.append(key) elif isinstance(value, torch.Tensor) and value.ndimension() == 1: values = value keys = ["{}/{}".format(key, i) for i in range(len(value))] else: warnings.warn("VisdomLogger output_handler can not log " "metrics value type {}".format(type(value))) for k, v in zip(keys, values): k = "{}/{}".format(self.tag, k) self.add_scalar(logger, k, v, event_name, global_step) logger._save()
[docs]class OptimizerParamsHandler(BaseOptimizerParamsHandler, _BaseVisDrawer): """Helper handler to log optimizer parameters Examples: .. code-block:: python from ignite.contrib.handlers.visdom_logger import * # Create a logger vb_logger = VisdomLogger() # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration vd_logger.attach( trainer, log_handler=OptimizerParamsHandler(optimizer), event_name=Events.ITERATION_STARTED ) # or equivalently vd_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" show_legend (bool, optional): flag to show legend in the window """ def __init__(self, optimizer, param_name="lr", tag=None, show_legend=False): super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine, logger, event_name): if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler OptimizerParamsHandler works only with VisdomLogger") 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(): self.add_scalar(logger, k, v, event_name, global_step) logger._save()
[docs]class WeightsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer): """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.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log model's weights norm after each iteration vd_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" show_legend (bool, optional): flag to show legend in the window """ def __init__(self, model, reduction=torch.norm, tag=None, show_legend=False): super(WeightsScalarHandler, self).__init__(model, reduction, tag=tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine, logger, event_name): if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'WeightsScalarHandler' works only with VisdomLogger") 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(): name = name.replace(".", "/") k = "{}weights_{}/{}".format(tag_prefix, self.reduction.__name__, name) v = float(self.reduction(p.data)) self.add_scalar(logger, k, v, event_name, global_step) logger._save()
[docs]class GradsScalarHandler(BaseWeightsScalarHandler, _BaseVisDrawer): """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.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log model's weights norm after each iteration vd_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" show_legend (bool, optional): flag to show legend in the window """ def __init__(self, model, reduction=torch.norm, tag=None, show_legend=False): super(GradsScalarHandler, self).__init__(model, reduction, tag) _BaseVisDrawer.__init__(self, show_legend=show_legend) def __call__(self, engine, logger, event_name): if not isinstance(logger, VisdomLogger): raise RuntimeError("Handler 'GradsScalarHandler' works only with VisdomLogger") 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(): name = name.replace(".", "/") k = "{}grads_{}/{}".format(tag_prefix, self.reduction.__name__, name) v = float(self.reduction(p.grad)) self.add_scalar(logger, k, v, event_name, global_step) logger._save()
[docs]class VisdomLogger(BaseLogger): """ VisdomLogger handler to log metrics, model/optimizer parameters, gradients during the training and validation. This class requires `visdom <https://github.com/facebookresearch/visdom/>`_ package to be installed: .. code-block:: bash pip install git+https://github.com/facebookresearch/visdom.git Args: server (str, optional): visdom server URL. It can be also specified by environment variable `VISDOM_SERVER_URL` port (int, optional): visdom server's port. It can be also specified by environment variable `VISDOM_PORT` num_workers (int, optional): number of workers to use in `concurrent.futures.ThreadPoolExecutor` to post data to visdom server. Default, `num_workers=1`. If `num_workers=0` and logger uses the main thread. If using Python 2.7 and `num_workers>0` the package `futures` should be installed: `pip install futures` **kwargs: kwargs to pass into `visdom.Visdom <https://github.com/facebookresearch/visdom#visdom-arguments-python-only>`_. Note: We can also specify username/password using environment variables: VISDOM_USERNAME, VISDOM_PASSWORD .. warning:: Frequent logging, e.g. when logger is attached to `Events.ITERATION_COMPLETED`, can slow down the run if the main thread is used to send the data to visdom server (`num_workers=0`). To avoid this situation we can either log less frequently or set `num_workers=1`. Examples: .. code-block:: python from ignite.contrib.handlers.visdom_logger import * # Create a logger vd_logger = VisdomLogger() # Attach the logger to the trainer to log training loss at each iteration vd_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`. vd_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`. vd_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 vd_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 vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=WeightsScalarHandler(model) ) # Attach the logger to the trainer to log model's gradients norm after each iteration vd_logger.attach( trainer, event_name=Events.ITERATION_COMPLETED, log_handler=GradsScalarHandler(model) ) # We need to close the logger with we are done vd_logger.close() It is also possible to use the logger as context manager: .. code-block:: python from ignite.contrib.handlers.visdom_logger import * with VisdomLogger() as vd_logger: trainer = Engine(update_fn) # Attach the logger to the trainer to log training loss at each iteration vd_logger.attach_output_handler( trainer, event_name=Events.ITERATION_COMPLETED, tag="training", output_transform=lambda loss: {"loss": loss} ) """ def __init__(self, server=None, port=None, num_workers=1, **kwargs): try: import visdom except ImportError: raise RuntimeError( "This contrib module requires visdom package. " "Please install it with command:\n" "pip install git+https://github.com/facebookresearch/visdom.git" ) if num_workers > 0: # If visdom is installed, one of its dependencies `tornado` # requires also `futures` to be installed. # Let's check anyway if we can import it. try: import concurrent.futures except ImportError: raise RuntimeError( "This contrib module requires concurrent.futures module" "Please install it with command:\n" "pip install futures" ) if server is None: server = os.environ.get("VISDOM_SERVER_URL", "localhost") if port is None: port = int(os.environ.get("VISDOM_PORT", 8097)) if "username" not in kwargs: username = os.environ.get("VISDOM_USERNAME", None) kwargs["username"] = username if "password" not in kwargs: password = os.environ.get("VISDOM_PASSWORD", None) kwargs["password"] = password self.vis = visdom.Visdom(server=server, port=port, **kwargs) if not self.vis.check_connection(): raise RuntimeError( "Failed to connect to Visdom server at {}. " "Did you run python -m visdom.server ?".format(server) ) self.executor = _DummyExecutor() if num_workers > 0: from concurrent.futures import ThreadPoolExecutor self.executor = ThreadPoolExecutor(max_workers=num_workers) def _save(self): self.vis.save([self.vis.env]) def close(self): self.vis = None self.executor.shutdown() def _create_output_handler(self, *args, **kwargs): return OutputHandler(*args, **kwargs) def _create_opt_params_handler(self, *args, **kwargs): return OptimizerParamsHandler(*args, **kwargs)
class _DummyExecutor: class _DummyFuture: def __init__(self, result): self._output = result def result(self): return self._output def __init__(self, *args, **kwargs): pass def submit(self, fn, **kwargs): return _DummyExecutor._DummyFuture(fn(**kwargs)) def shutdown(self, *args, **kwargs): pass

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