torchbearer metric

Posted at November 7, 2020

torchbearer.metrics.aggregators; Source code for torchbearer.metrics.aggregators """ Aggregators are a special kind of :class:`.Metric` which takes as input, the output from a previous metric or metrics. This is useful for using DataParallel which pass the main state dict directly. Added callback for generating confusion matrices with PyCM, Added a mixup callback with associated loss, Added Label Smoothing Regularisation (LSR) callback, Added default metric from paper for when Mixup loss is used, Changed history to now just be a list of records, Categorical Accuracy metric now also accepts tensors of size (B, C) and gets the max over C for the taget class, Removed the variational sub-package, this will now be packaged separately, Fixed a bug where list or dictionary metrics would cause the tensorboard callback to error, Fixed a bug where running a trial without training steps would error, Fixed a bug where the caching imaging callback didn't reset data so couldn't be run in multiple trials, Fixed a bug where the state given to predict was not a State object, Fixed a bug where MakeGrid callback wasn't passing all arguments correctly, Fixed a bug where the verbose argument would not work unless given as a keyword argument, Fixed a bug where the data_key argument would sometimes not work as expected, Fixed a bug where cutmix wasn't sendign the beta distribution sample to the device, Fixed a bug where for_steps would sometimes not work as expected if called in the wrong order, Fixed a bug where torchbearer installed via pip would crash on import, Added on_init callback hook to run at the end of trial init, Added callbacks for weight initialisation in, Added Layer-sequential unit-variance (LSUV) initialization, Added ClassAppearanceModel callback and example page for visualising CNNs, Removed the fluent decorator, just use return self, Fixed bug where replay errored when train or val steps were None, Fixed a bug where mock optimser wouldn't call it's closure, Fixed a bug where the notebook check raised ModuleNotFoundError when IPython not installed, Fixed a memory leak with metrics that causes issues with very long epochs, Fixed a bug with the once and once_per_epoch decorators, Fixed a bug where the test criterion wouldn't accept a function of state, Fixed a bug where type inference would not work correctly when chaining, Fixed a bug where checkpointers would error when they couldn't find the old checkpoint to overwrite, Fixed a bug where the 'test' label would sometimes not populate correctly in the default accuracy metric, Added torchbearer.variational, a sub-package for implementations of state of the art variational auto-encoders, Added SimpleUniform and SimpleExponential distributions, Added a decorator which can be used to cite a research article as part of a doc string, Added an optional dimension argument to the mean, std and running_mean metric aggregators, Added a var metric and decorator which can be used to calculate the variance of a metric, Added an unbiased flag to the std and var metrics to optionally not apply Bessel's correction (consistent with torch.std / torch.var), Added support for rounding 1D lists to the Tqdm callback, Added SimpleExponentialSimpleExponentialKL.

The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Any strings in the list will be retrieved from metrics.DEFAULT_METRICS. © Copyright Torchbearer Contributors Metric wrappers are classes which wrap instances of :class:`.Metric` or, in the case of :class:`EpochLambda` and.

Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition. Revision d2b21b8f. Torchbearers Bible school programs provide foundational teaching to equip men and women to live, with integrity and vitality, the authentic Christian life that “turned the world upside down” in … State keys are also metrics which, State dictionary that behaves like a python dict but accepts StateKeys, #: The PyTorch module / model that will be trained, #: The criterion to use when model fitting, #: The optimizer to use when model fitting, #: The device currently in use by the :class:`.Trial` and PyTorch model, #: The data type of tensors in use by the model, match this to avoid type issues, #: The list of metrics in use by the :class:`.Trial`, #: The metric dict from the current batch of data. For example, in Listing 2 producing the standard deviation and running mean of an accuracy metric. class torchbearer.metrics.metrics.AdvancedMetric(name)[source] ¶. As a result, via a :class:`.MetricTree`, a series of aggregators can collect statistics such as Mean or Standard Deviation without needing to compute the underlying metric multiple times. We could change this and just have everything return a dictionary but then we would be unable to tell the difference between metrics we wish to display / log … """Reset the 'y_true' and 'y_pred' caches. Added a Mock Model which is set when None is passed as the model to a Trial. The extensive documentation includes an example library for deep learning and dynamic programming problems and can be found at http://torchbearer.readthedocs.io. """Process this node and then pass the output to each child. At the root level, torchbearer expects metrics to output a dictionary which maps the metric name to the value. Added a callback to unpack state into torchbearer.X at sample time for specified keys and update state after the forward pass based on model outputs. """Process the given state and return the metric value for a training iteration. Learn more, This commit was created on GitHub.com and signed with a. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. dict[str,any]: A dictionary which maps metric names to values. The following bindings are in place for both nn, - cross entropy loss -> :class:`.CategoricalAccuracy` [DEFAULT], - nll loss -> :class:`.CategoricalAccuracy`, - bce loss with logits -> :class:`.BinaryAccuracy`, Using DistributedDataParallel with Torchbearer on CPU. We introduce torchbearer, a model fitting library for pytorchaimed at researchers work-ing on deep learning or differentiable programming. If the node output is already a dict (i.e. Torchbearer is a PyTorch model fitting library designed for use by researchers (or anyone really) working in deep learning or differentiable programming. A dict containing all results from the children, """The :class:`MetricList` class is a wrapper for a list of metrics which acts as a single metric and produces a, metric_list (list): The list of metrics to be wrapped. Can be used as a running metric which computes the function for batches of outputs with a given step size during. Since we had the same bug in a couple callbacks, I just made a generic get_metric function in bases.py which handles both checking for presence in the metrics dictionary and throwing the warning if it fails. state: The current state dict of the :class:`.Trial`. but via the :mod:`decorator API<.metrics.decorators>`. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. torchbearer.state.METRICS = metrics.

The default_for_key(…) decorator enables the metric to be referenced with a string in the Trial definition. unwrapped. Contribute to utkuozbulak/torchbearer development by creating an account on GitHub. The list of metrics in use by the Trial. #: The current data generator (DataLoader), #: The train data generator in the Trial object, #: Flag for refreshing of training iterator when finished instead of each epoch, #: The validation data generator in the Trial object, #: The number of validation steps to take, #: The test data generator in the Trial object, #: A flag that can be set to true to stop the current fit call, #: The current batch of ground truth data, #: The sampler which loads data from the generator onto the correct device, #: The batch loader which handles formatting data from each batch, #: The key which maps to the predictions over the dataset when calling predict, #: The timings keys used by the timer callback, #: The :class:`.CallbackList` object which is called by the Trial, #: The history list of the Trial instance, #: The optional arguments which should be passed to the backward call, #: The lambda coefficient of the linear combination of inputs, #: The permutation of input indices for input mixup, Using DistributedDataParallel with Torchbearer on CPU.

The lambda coefficient of the linear combination of inputs. they're used to log you in. data_key (StateKey): The torchbearer data_key, if used, Using DistributedDataParallel with Torchbearer on CPU. """Reset each metric with the given state. The metric value for a training iteration. args: The :class:`.torchbearer.Trial` state. """Return the output of the wrapped function. When in `eval` mode, 'val\_' will be prepended to the metric, >>> metric = metrics.ToDict(my_metric().build()), >>> metric.process({'y_pred': 4, 'y_true': 5}).

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