Source code for ignite.contrib.handlers.base_logger

from abc import ABCMeta, abstractmethod
import numbers
import warnings

import torch

from ignite.engine import State, Engine
from ignite._six import with_metaclass

class BaseLogger(object):
    Base logger handler. See implementations: TensorboardLogger, VisdomLogger, PolyaxonLogger

    def attach(self, engine, log_handler, event_name):
        """Attach the logger to the engine and execute `log_handler` function at `event_name` events.

            engine (Engine): engine object.
            log_handler (callable): a logging handler to execute
            event_name: event to attach the logging handler to. Valid events are from :class:`~ignite.engine.Events`
                or any `event_name` added by :meth:`~ignite.engine.Engine.register_events`.

        if event_name not in State.event_to_attr:
            raise RuntimeError("Unknown event name '{}'".format(event_name))

        engine.add_event_handler(event_name, log_handler, self, event_name)

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):

    def close(self):

class BaseHandler(with_metaclass(ABCMeta, object)):

    def __call__(self, *args, **kwargs):

class BaseOptimizerParamsHandler(BaseHandler):
    Base handler for logging optimizer parameters

    def __init__(self, optimizer, param_name="lr", tag=None):
        if not isinstance(optimizer, torch.optim.Optimizer):
            raise TypeError("Argument optimizer should be of type torch.optim.Optimizer, "
                            "but given {}".format(type(optimizer)))

        self.optimizer = optimizer
        self.param_name = param_name
        self.tag = tag

[docs]def global_step_from_engine(engine): """Helper method to setup `global_step_transform` function using another engine. This can be helpful for logging trainer epoch/iteration while output handler is attached to an evaluator. Args: engine (Engine): engine which state is used to provide the global step Returns: global step """ def wrapper(_, event_name): return engine.state.get_event_attrib_value(event_name) return wrapper
class BaseOutputHandler(BaseHandler): """ Helper handler to log engine's output and/or metrics """ def __init__(self, tag, metric_names=None, output_transform=None, another_engine=None, global_step_transform=None): if metric_names is not None: if not (isinstance(metric_names, list) or (isinstance(metric_names, str) and metric_names == "all")): raise TypeError("metric_names should be either a list or equal 'all', " "got {} instead.".format(type(metric_names))) if output_transform is not None and not callable(output_transform): raise TypeError("output_transform should be a function, got {} instead." .format(type(output_transform))) if output_transform is None and metric_names is None: raise ValueError("Either metric_names or output_transform should be defined") if another_engine is not None: if not isinstance(another_engine, Engine): raise TypeError("Argument another_engine should be of type Engine, " "but given {}".format(type(another_engine))) warnings.warn("Use of another_engine is deprecated and will be removed in 0.3.0. " "Please use global_step_transform instead.", DeprecationWarning) global_step_transform = global_step_from_engine(another_engine) if global_step_transform is not None and not callable(global_step_transform): raise TypeError("global_step_transform should be a function, got {} instead." .format(type(global_step_transform))) if global_step_transform is None: def global_step_transform(engine, event_name): return engine.state.get_event_attrib_value(event_name) self.tag = tag self.metric_names = metric_names self.output_transform = output_transform self.global_step_transform = global_step_transform def _setup_output_metrics(self, engine): """Helper method to setup metrics to log """ metrics = {} if self.metric_names is not None: if isinstance(self.metric_names, str) and self.metric_names == "all": metrics = engine.state.metrics else: for name in self.metric_names: if name not in engine.state.metrics: warnings.warn("Provided metric name '{}' is missing " "in engine's state metrics: {}".format(name, list(engine.state.metrics.keys()))) continue metrics[name] = engine.state.metrics[name] if self.output_transform is not None: output_dict = self.output_transform(engine.state.output) if not isinstance(output_dict, dict): output_dict = {"output": output_dict} metrics.update({name: value for name, value in output_dict.items()}) return metrics class BaseWeightsScalarHandler(BaseHandler): """ Helper handler to log model's weights as scalars. """ def __init__(self, model, reduction=torch.norm, tag=None): if not isinstance(model, torch.nn.Module): raise TypeError("Argument model should be of type torch.nn.Module, " "but given {}".format(type(model))) if not callable(reduction): raise TypeError("Argument reduction should be callable, " "but given {}".format(type(reduction))) def _is_0D_tensor(t): return isinstance(t, torch.Tensor) and t.ndimension() == 0 # Test reduction function on a tensor o = reduction(torch.ones(4, 2)) if not (isinstance(o, numbers.Number) or _is_0D_tensor(o)): raise ValueError("Output of the reduction function should be a scalar, but got {}".format(type(o))) self.model = model self.reduction = reduction self.tag = tag class BaseWeightsHistHandler(BaseHandler): """ Helper handler to log model's weights as histograms. """ def __init__(self, model, tag=None): if not isinstance(model, torch.nn.Module): raise TypeError("Argument model should be of type torch.nn.Module, " "but given {}".format(type(model))) self.model = model self.tag = tag

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