ignite-simple 0.0.10

Easily train pytorch models with automatic LR and BS tuning

Ignite Simple

This module provides the necessary functionality for rapidly prototyping machine learning models on conventional datasets.

This also contains ignite_simple.gen_sweep for sweeping across other parameters. See corresponding readme at ignite_simple/gen_sweep/README.md

Usage

You must explicitly specify the model, training set, validation set, and the loss. Then a preset is applied for how to tune hyperparameters and the amount of information to gather and export.

Although not required, it is recommended use torchluent to simplify the model creation process. This package accepts a single model with tensor outputs or a tuple of two models where the first is the stripped model (returns only a single tensor) and the second model has the same underlying parameters but return values are of the form (tensor, list of tensors), where the list of tensors contains relevants snapshots of the data as it was transformed by the network.

This package supports repeating trials with the final selected hyperparameters, either by specifying the number of repeats with trials or by simply setting is_continuation to True and calling train with the same output directory, or both.

API Reference

import torchluent
import ignite_simple
import torch
import torchvision
import logging.config

def model():
    return (
        torchluent.FluentModule((1, 28, 28))
        .wrap(True)
        .conv2d(32, 5)
        .maxpool2d(3)
        .operator('LeakyReLU')
        .save_state()
        .flatten()
        .dense(64)
        .operator('Tanh')
        .save_state()
        .dense(10)
        .save_state()
        .build()
    )

def dataset():
    transform = torchvision.transforms.ToTensor()
    train_set = torchvision.datasets.MNIST(
        'datasets/mnist', download=True, transform=transform)
    val_set = torchvision.datasets.MNIST(
        'datasets/mnist', train=False, download=True, transform=transform)
    return train_set, val_set

loss = torch.nn.CrossEntropyLoss
accuracy_style = 'classification'

def main():
    # a reasonable logging.conf is in this repository to get you started.
    # you won't see any stdout without a logging config!
    logging.config.fileConfig('logging.conf')
    ignite_simple.train((__name__, 'model', tuple(), dict()),
                        (__name__, 'dataset', tuple(), dict()),
                        (__name__, 'loss', tuple(), dict()),
                        folder='out', hyperparameters='fast',
                        analysis='images', allow_later_analysis_up_to='video',
                        accuracy_style=accuracy_style,
                        trials=1, is_continuation=False,
                        history_folder='history', cores='all')

if __name__ == '__main__':
    main()

This involves some boilerplate, especially when you want to include optionally reanalyzing under different settings and configuring the output folder via command line arguments. The ignite_simple.helper module does this for you, reducing the amount of repeated code and allowing one to train models quickly and robustly. In the previous example, everything after 'accuracy_style' can be replaced with

if __name__ == '__main__':
    ignite_simple.helper.handle(__name__)

which will result in the following command-line arguments:

usage: helper.py [-h] [--folder FOLDER] [--hparams HPARAMS]
                 [--analysis ANALYSIS] [--analysis_up_to ANALYSIS_UP_TO]
                 [--trials TRIALS] [--not_continuation] [--cores CORES]
                 [--reanalyze] [--module MODULE] [--loggercfg LOGGERCFG]

Use python -m ignite_simple.helper --help and the module documentation for details.

Continuations and trials

In the above example, by changing is_continuation to True, the file may be invoked multiple times. With is_continuation=True, the hyperparameters will be reused from the first run and the model and statistics will be saved alongside (not overwriting) the existing ones. Furthermore, additional plots (averaged accuracy, averaged loss, etc) will be available. With is_continuation=False the output folder will be archived and moved into the history folder with the current timestamp as its name prior to starting the run.

Note that trials is treated as the minimum number of trials to perform. This will attempt to use all available cores (i.e., the number specified in cores), which may mean multiple trials can be run in parallel without any significant difference in runtime. This can be suppressed with the parameter trials_strict=True.

Validation sets

For automatic dataset splitting into training and validiation, one can use ignite_simple.utils.split as follows:

import ignite_simple.utils
import torch.utils.data as data

full: data.Dataset  # dataset to split
val_perc: float = 0.1  # perc in the validation set


train_set, val_set = ignite_simple.utils.split(
    full, val_perc, filen='mydataset/train_val_split')

The split is random and stored in the given file (extensionless is recommended, in which the appropriate extension will be added). If the file already exists, this returns the split stored in the specified file. This makes it easier to verify the training and validation accuracy after the fact and simplifies comparisons of models on the same dataset.

Accuracy style

Valid values are classification, multiclass, and inv-loss. Classification is for MNIST-style labels (labels are one-hot and the output of the network is a one-hot encoding of the label). Multi-class is for when the labels are one-hot encoded class labels extended to potentially multiple ones. inv-loss uses 1/(loss+1) as the performance metric instead of accuracy.

Note: both classification and multiclass support more an arbitrary number of classes of images, but classification says that each image has exactly one class and multiclass says that each image may have more than one class. Multiclass uses a >=0.5 thresholding on the output, classification uses argmax. This does not effect training or model selection, only output.

In both cases, the output of the network should be (batch, num labels) and the targets should be (batch, num labels).

Automatic hyperparameter tuning

Valid presets are fastest, fast, slow, and slowest.

The learning rate and batch size are automatically tuned since they can dramatically effect model performance. The methodology is inspired by Cyclical Learning Rates for Training Neural Networks, 2017.

For tuning the learning rate, a reasonable cycle size is used and the process is analagous to that described by the paper.

The batch size is found in a similar way - vary the batch size upward linearly over a few epochs. The range of batch sizes where the accuracy increased is found, and then batch sizes are randomly drawn from that range and tested for a few epochs. The batch size with the highest accuracy at the end of the short test is used.

The hyperparameter presets correspond roughly to: - How many trials to average tests over (i.e., for learning rate it can be beneficial to find the range via an average of several runs rather than the very noisy output of a single run) - How many batch sizes are attempted within the reasonable range - How many times to repeat the process for checking for batch size and learning rate interactions.

It is intended that the preset 'fast' is used while making rapid adjustments and then 'slow' used occassionally as a sanity check. Then 'slowest' can be used if you don't require any additional features and want to use this package for a final model.

Automatic analysis

Valid presets are none, text, images, images-min, animations-draft, animations, video-draft, and video. See ignite_simple.analarams for details.

If unsure, choose images-min and then upgrade to images or video for final analysis or for additional information as necessary.

This package is capable of producing some explanation about how the model was trained and some information about it's solution. The analysis includes:

• An explanation of the learning rules
• All relevant hyperparameters for the network and brief explanations of each
• Where relevant, how hyperparameters were selected
• Network representation in 3d principal-component-space

The analysis can be provided in text form, all the previous plus image references, all the previous plus animation references, or all the previous plus a video guide. The -draft settings produce lower-quality (FPS and resolution) versions that are somewhat faster to generate.

Analysis can be performed after-the-fact assuming that sufficient data was collected (which is specified in the allow_later_analysis_up_to parameter). The following snippet performs video analysis on the first example without repeating training, assuming its in the same file:

def reanalyze():
    ignite_simple.analyze(
        (__name__, 'dataset', tuple(), dict()),
        (__name__, 'loss', tuple(), dict()),
        folder='out',
        settings='video',
        accuracy_style='classification',
        cores='all')

This can be done automatically with the --reanalyze option in the helper module.

Note that reanalysis does not reproduce unless it believes the result would be different from that which exists on the file system. The analysis output is in the analysis subdirectory of the output folder, and then in a folder indexed by the number of models trained in the current hyperparameter settings. To ensure you get the most up-to-date analysis you can delete the analysis folder before calling analyze.

Implementation details

This package trains with SGD without momentum on a linear cyclical learning rate rule for a fixed number of epochs. The batch size is fixed throughout training, and the validation dataset is not used during hyperparameter selection nor model training, however it is measured and reported for analysis. In the output folder, analysis/html/index.html is produced which explains the details of training to a reasonable degree. Further details can be found by checking the ignite_simple.tuner and ignite_simple.model_manager documentation and source code.