lpd 0.4.12

A Fast, Flexible Trainer with Callbacks and Extensions for PyTorch

lpd

A Fast, Flexible Trainer with Callbacks and Extensions for PyTorch

lpd derives from the Hebrew word lapid (לפיד) which means "torch".

For latest PyPI stable release

There are 2 types of lpd packages available

• lpd which brings dependencies for pytorch, numpy and tensorboard

    pip install lpd

• lpd-nodeps which you provide your own dependencies for pytorch, numpy and tensorboard

    pip install lpd-nodeps

v0.4.12-beta Release - contains the following:

• ThresholdChecker is updated to compute improvement according to last improved step and not to the best received metric
• Some minor cosmetic changes

Previously on lpd:

• Dense custom layer to support apply norm (configurable to before or after activation)
• StatsPrint callback to support printing best confusion matrix when at least one of the metrics is of type MetricConfusionMatrixBase
• TransformerEncoderStack to support activation as input
• PositionalEncoding to support more than 3 dimensions input
• Updated Pipfile
• Fixed confusion matrix cpu/gpu device error
• Better handling on callbacks where apply_on_states=None (apply on all states)
• Bug fix in case validation samples are empty

Usage

lpd intended to properly structure your PyTorch model training.
The main usages are given below.

Training your model

    from lpd.trainer import Trainer
    from lpd.enums import Phase, State, MonitorType, MonitorMode, StatsType
    from lpd.callbacks import LossOptimizerHandler, StatsPrint, ModelCheckPoint, Tensorboard, EarlyStopping, SchedulerStep, CallbackMonitor
    from lpd.extensions.custom_schedulers import KerasDecay
    from lpd.metrics import BinaryAccuracyWithLogits, FalsePositives
    from lpd.utils.torch_utils import get_gpu_device_if_available
    from lpd.utils.general_utils import seed_all
    from lpd.utils.threshold_checker import AbsoluteThresholdChecker

    seed_all(seed=42) # because its the answer to life and the universe

    device = get_gpu_device_if_available() # with fallback to CPU if GPU not available
    model = MyModel().to(device) # this is your model class, and its being sent to the relevant device
    optimizer = torch.optim.SGD(params=model.parameters())
    scheduler = KerasDecay(optimizer, decay=0.01, last_step=-1) # decay scheduler using keras formula 
    loss_func = torch.nn.BCEWithLogitsLoss().to(device) # this is your loss class, already sent to the relevant device
    metrics = [BinaryAccuracyWithLogits(name='Accuracy'), FalsePositives(name='FP', num_class=2, threshold=0)] # define your metrics
                           

    # you can use some of the defined callbacks, or you can create your own
    callbacks = [
                LossOptimizerHandler(),
                SchedulerStep(apply_on_phase=Phase.BATCH_END, apply_on_states=State.TRAIN),
                ModelCheckPoint(checkpoint_dir, 
                                checkpoint_file_name, 
                                CallbackMonitor(monitor_type=MonitorType.LOSS, 
                                                stats_type=StatsType.VAL, 
                                                monitor_mode=MonitorMode.MIN),
                                save_best_only=True), 
                Tensorboard(summary_writer_dir=summary_writer_dir),
                EarlyStopping(CallbackMonitor(monitor_type=MonitorType.METRIC, 
                                              stats_type=StatsType.VAL, 
                                              monitor_mode=MonitorMode.MAX,
                                              patience=10,
                                              metric_name='Accuracy'),
                                              threshold_checker=AbsoluteThresholdChecker(monitor_mode=MonitorMode.MAX, threshold=0.01)),
                StatsPrint(train_metrics_monitors=[CallbackMonitor(monitor_type=MonitorType.METRIC,
                                                                   stats_type=StatsType.TRAIN,
                                                                   monitor_mode=MonitorMode.MAX,  # <-- notice MAX
                                                                   metric_name='Accuracy'),
                                                   CallbackMonitor(monitor_type=MonitorType.METRIC,
                                                                   stats_type=StatsType.TRAIN,
                                                                   monitor_mode=MonitorMode.MIN, # <-- notice MIN
                                                                   metric_name='FP')],
                           print_confusion_matrix=True) # since one of the metric (FalsePositives) is confusion matrix based, lets print the whole confusion matrix
                ]

    trainer = Trainer(model, 
                      device, 
                      loss_func, 
                      optimizer,
                      scheduler,
                      metrics, 
                      train_data_loader,  # DataLoader, Iterable or Generator
                      val_data_loader,    # DataLoader, Iterable or Generator
                      train_steps,
                      val_steps,
                      callbacks,
                      name='Readme-Example')
    
    trainer.train(num_epochs)

Evaluating your model

trainer.evaluate will return StatsResult that stores the loss and metrics results for the test set

    evaluation_result = trainer.evaluate(test_data_loader, test_steps)

Making predictions

Predictor class will generate output predictions from input samples.

Predictor class can be created from Trainer

    predictor_from_trainer = Predictor.from_trainer(trainer)
    predictions = predictor_from_trainer.predict_batch(batch)

Predictor class can also be created from saved checkpoint

    predictor_from_checkpoint = Predictor.from_checkpoint(checkpoint_dir,
                                                          checkpoint_file_name,
                                                          model, # nn.Module, weights will be loaded from checkpoint
                                                          device)
    prediction = predictor_from_checkpoint.predict_sample(sample)

Lastly, Predictor class can be initialized explicitly

    predictor = Predictor(model,
                          device,
                          callbacks, # relevant only for prediction callbacks (see callbacks Phases and States)
                          name='lpd predictor')
    predictions = predictor.predict_data_loader(data_loader, steps)

Just to be fair, you can also predict directly from Trainer class

    # On single sample:
    prediction = trainer.predict_sample(sample)
    # On batch:
    predictions = trainer.predict_batch(batch)
    # On Dataloader/Iterable/Generator:
    predictions = trainer.predict_data_loader(data_loader, steps)

TrainerStats

Trainer tracks stats for train/validate/test and you can access them in your custom callbacks or any other place that has access to your trainer.

Here are some examples

    train_loss = trainer.train_stats.get_loss()         # the mean of the last epoch's train losses
    val_loss = trainer.val_stats.get_loss()             # the mean of the last epoch's validation losses
    test_loss = trainer.test_stats.get_loss()           # the mean of the test losses (available only after calling evaluate)

    train_metrics = trainer.train_stats.get_metrics()   # dict(metric_name, MetricMethod(values)) of the current epoch in train state
    val_metrics = trainer.val_stats.get_metrics()       # dict(metric_name, MetricMethod(values)) of the current epoch in validation state
    test_metrics = trainer.test_stats.get_metrics()     # dict(metric_name, MetricMethod(values)) of the test (available only after calling evaluate)

Callbacks

Will be used to perform actions at various stages.
Some common callbacks are available under lpd.callbacks, and you can also create your own, more details below.
In a callback, apply_on_phase (lpd.enums.Phase) will determine the execution phase,
and apply_on_states (lpd.enums.State or list(lpd.enums.State)) will determine the execution states
These are the current available phases and states, more might be added in future releases

Training and Validation phases and states will behave as follow

        State.EXTERNAL
        Phase.TRAIN_BEGIN
        # train loop:
            Phase.EPOCH_BEGIN

            State.TRAIN
            # batches loop:
                Phase.BATCH_BEGIN
                # batch
                Phase.BATCH_END
            State.VAL
            # batches loop:
                Phase.BATCH_BEGIN
                # batch
                Phase.BATCH_END
            State.EXTERNAL

            Phase.EPOCH_END
        Phase.TRAIN_END

Evaluation phases and states will behave as follow

        State.EXTERNAL
        Phase.TEST_BEGIN
        State.TEST
        # batches loop:
            Phase.BATCH_BEGIN
            # batch
            Phase.BATCH_END
        State.EXTERNAL
        Phase.TEST_END

Predict phases and states will behave as follow

        State.EXTERNAL
        Phase.PREDICT_BEGIN
        State.PREDICT
        # batches loop:
            Phase.BATCH_BEGIN
            # batch
            Phase.BATCH_END
        State.EXTERNAL
        Phase.PREDICT_END

Callbacks will be executed under the relevant phase and state, and by their order.
With phases and states, you have full control over the timing of your callbacks.
Let's take a look at some of the callbacks lpd provides:

LossOptimizerHandler Callback

Derives from LossOptimizerHandlerBase, probably the most important callback during training 😎
Use LossOptimizerHandler to determine when to call:

    loss.backward(...)
    optimizer.step(...)
    optimizer.zero_grad(...)

Or, you may choose to create your own AwesomeLossOptimizerHandler class by deriving from LossOptimizerHandlerBase.
Trainer.train(...) will validate that at least one LossOptimizerHandlerBase callback was provided.

LossOptimizerHandlerAccumulateBatches Callback

As well as LossOptimizerHandlerAccumulateSamples will call loss.backward() every batch, but invoke optimizer.step() and optimizer.zero_grad()
only after the defined num of batches (or samples) were accumulated

StatsPrint Callback

StatsPrint callback prints informative summary of the trainer stats including loss and metrics.

• CallbackMonitor can add nicer look with IMPROVED indication on improved loss or metric, see output example below.
• Loss (for all states) will be monitored as MonitorMode.MIN
• For train metrics, provide your own monitors via train_metrics_monitors argument
• Validation metrics monitors will be added automatically according to train_metrics_monitors argument

    from lpd.enums import Phase, State, MonitorType, StatsType, MonitorMode

    StatsPrint(apply_on_phase=Phase.EPOCH_END, 
               apply_on_states=State.EXTERNAL, 
               train_metrics_monitors=CallbackMonitor(monitor_type=MonitorType.METRIC,
                                                      stats_type=StatsType.TRAIN,
                                                      monitor_mode=MonitorMode.MAX,
                                                      metric_name='TruePositives'),
               print_confusion_matrix_normalized=True) # in case you use one of the ConfusionMatrix metrics (e.g. TruePositives), you may also print the confusion matrix 

Output example:

ModelCheckPoint Callback

Saving a checkpoint when a monitored loss/metric has improved.
The callback will save the model, optimizer, scheduler, and epoch number.
You can also configure it to save Full Trainer.
For example, ModelCheckPoint that will save a new full trainer checkpoint every time the validation metric_name my_metric
is getting higher than the highest value so far.

    ModelCheckPoint(Phase.EPOCH_END, 
                    State.EXTERNAL,
                    checkpoint_dir, 
                    checkpoint_file_name,
                    CallbackMonitor(monitor_type=MonitorType.METRIC,    # It's a Metric and not a Loss 
                                    stats_type=StatsType.VAL,           # check the value on the Validation set
                                    monitor_mode=MonitorMode.MAX,       # MAX indicates higher is better
                                    metric_name='my_metric'),           # since it's a Metric, mention its name
                    save_best_only=False, 
                    save_full_trainer=True)

EarlyStopping Callback

Stops the trainer when a monitored loss/metric has stopped improving.
For example, EarlyStopping that will monitor at the end of every epoch, and stop the trainer if the validation loss didn't improve (decrease) for the last 10 epochs.

EarlyStopping(Phase.EPOCH_END, 
              State.EXTERNAL,
              CallbackMonitor(monitor_type=MonitorType.LOSS, 
                              stats_type=StatsType.VAL, 
                              monitor_mode=MonitorMode.MIN,
                              patience=10))

SchedulerStep Callback

Will invoke step() on your scheduler in the desired phase and state.
For example, SchedulerStep callback to invoke scheduler.step() at the end of every batch, in train state (as opposed to validation and test):

    from lpd.callbacks import SchedulerStep
    from lpd.enums import Phase, State
    SchedulerStep(apply_on_phase=Phase.BATCH_END, apply_on_states=State.TRAIN)

Tensorboard Callback

Will export the loss and the metrics at a given phase and state, in a format that can be viewed on Tensorboard

    from lpd.callbacks import Tensorboard
    Tensorboard(apply_on_phase=Phase.EPOCH_END, 
                apply_on_states=State.EXTERNAL, 
                summary_writer_dir=dir_path)

TensorboardImage Callback

Will export images, in a format that can be viewed on Tensorboard.
For example, a TensorboardImage callback that will output all the images generated in validation

    from lpd.callbacks import TensorboardImage
    TensorboardImage(apply_on_phase=Phase.BATCH_END, 
                     apply_on_states=State.VAL, 
                     summary_writer_dir=dir_path,
                     description='Generated Images',
                     outputs_parser=None)

Lets pass outputs_parser that will change the range of the outputs from [-1,1] to [0,255]

    from lpd.callbacks import TensorboardImage

    def outputs_parser(input_output_label: InputOutputLabel):
        outputs_scaled = (input_output_label.outputs + 1.0) / 2.0 * 255
        outputs_scaled = torchvision.utils.make_grid(input_output_label.output)
        return outputs_scaled

    TensorboardImage(apply_on_phase=Phase.BATCH_END, 
                     apply_on_states=State.VAL, 
                     summary_writer_dir=dir_path,
                     description='Generated Images',
                     outputs_parser=outputs_parser)

CollectOutputs Callback

Will collect model's outputs for the defined states.
CollectOutputs is automatically used by Trainer to collect the predictions when calling one of the predict methods.

    CollectOutputs(apply_on_phase=Phase.BATCH_END, apply_on_states=State.VAL)

Create your custom callbacks

    from lpd.enums import Phase, State
    from lpd.callbacks import CallbackBase

    class MyAwesomeCallback(CallbackBase):
        def __init__(self, apply_on_phase=Phase.BATCH_END, apply_on_states=[State.TRAIN, State.VAL]):
            # make sure to call init parent class
            super(MyAwesomeCallback, self).__init__(apply_on_phase, apply_on_states)

        def __call__(self, callback_context): # <=== implement this method!
            # your implementation here
            # using callback_context, you can access anything in your trainer
            # below are some examples to get the hang of it
            val_loss = callback_context.val_stats.get_loss()
            train_loss = callback_context.train_stats.get_loss()
            train_metrics = callback_context.train_stats.get_metrics()
            val_metrics = callback_context.val_stats.get_metrics()
            optimizer = callback_context.optimizer
            scheduler = callback_context.scheduler
            trainer = callback_context.trainer

            if val_loss < 0.0001:
                # you can also mark the trainer to STOP training by calling stop()
                trainer.stop()

Lets expand MyAwesomeCallback with CallbackMonitor to track if our validation loss is getting better

    from lpd.callbacks import CallbackBase, CallbackMonitor # <== CallbackMonitor added
    from lpd.enums import Phase, State, MonitorType, StatsType, MonitorMode # <== added few needed enums to configure CallbackMonitor

    class MyAwesomeCallback(CallbackBase):
        def __init__(self, apply_on_phase=Phase.BATCH_END, apply_on_states=[State.TRAIN, State.VAL]):
            super(MyAwesomeCallback, self).__init__(apply_on_phase, apply_on_states)
            
            # adding CallbackMonitor to track VAL LOSS with regards to MIN (lower is better) and patience of 20 epochs
            self.val_loss_monitor = CallbackMonitor(MonitorType.LOSS, StatsType.VAL, MonitorMode.MIN, patience=20)

        def __call__(self, callback_context: CallbackContext): # <=== implement this method!
            # same as before, using callback_context, you can access anything in your trainer
            train_metrics = callback_context.train_stats.get_metrics()
            val_metrics = callback_context.val_stats.get_metrics()

            # invoke track() method on your monitor and pass callback_context as parameter
            # since you configured your val_loss_monitor, it will get the relevant parameters from callback_context
            monitor_result = self.val_loss_monitor.track(callback_context)

            # monitor_result (lpd.callbacks.CallbackMonitorResult) contains informative properties
            # for example lets check the status of the patience countdown

            if monitor_result.has_patience():
                print(f'[MyAwesomeCallback] - patience left: {monitor_result.patience_left}')

            # Or, let's stop the trainer, by calling the trainer.stop()
            # if our monitored value did not improve

            if not monitor_result.has_improved():
                print(f'[MyAwesomeCallback] - {monitor_result.description} has stopped improving')
                callback_context.trainer.stop()

CallbackMonitor, AbsoluteThresholdChecker and RelativeThresholdChecker

When using callbacks such as EarlyStopping, a CallbackMonitor is provided to track
a certain metric and reset/trigger the stopping event (or any event in other callbacks).

CallbackMonitor will internally use ThresholdChecker when comparing new value to old value
for the tracked metric, and AbsoluteThresholdChecker or RelativeThresholdChecker will be used
to check if the criteria was met.
The following example creates a CallbackMonitor that will track if the metric 'accuracy'
has increased with more then 1% using RelativeThresholdChecker

    from lpd.utils.threshold_checker import RelativeThresholdChecker
    relative_threshold_checker_1_percent = RelativeThresholdChecker(monitor_mode=MonitorMode.MAX, threshold=0.01)

    CallbackMonitor(monitor_type=MonitorType.METRIC,                        # It's a Metric and not a Loss 
                    stats_type=StatsType.VAL,                               # check the value on the Validation set
                    monitor_mode=MonitorMode.MAX,                           # MAX indicates higher is better
                    metric_name='accuracy',                                 # since it's a Metric, mention its name
                    threshold_checker=relative_threshold_checker_1_percent) # track 1% increase from last highest value     

Metrics

lpd.metrics provides metrics to check the accuracy of your model.
Let's create a custom metric using MetricBase and also show the use of BinaryAccuracyWithLogits in this example

    from lpd.metrics import BinaryAccuracyWithLogits, MetricBase
    from lpd.enums import MetricMethod

    # our custom metric
    class InaccuracyWithLogits(MetricBase):
        def __init__(self):
            super(InaccuracyWithLogits, self).__init__(MetricMethod.MEAN) # use mean over the batches
            self.bawl = BinaryAccuracyWithLogits() # we exploit BinaryAccuracyWithLogits for the computation

        def __call__(self, y_pred, y_true): # <=== implement this method!
            # your implementation here
            acc = self.bawl(y_pred, y_true)
            return 1 - acc  # return the inaccuracy

    # we can now define our metrics and pass them to the trainer
    metrics = [BinaryAccuracyWithLogits(name='accuracy'), InaccuracyWithLogits(name='inaccuracy')]

Let's do another example, a custom metric Truthfulness based on confusion matrix using MetricConfusionMatrixBase

    from lpd.metrics import MetricConfusionMatrixBase, TruePositives, TrueNegatives
    from lpd.enums import ConfusionMatrixBasedMetric

    # our custom metric
    class Truthfulness(MetricConfusionMatrixBase):
        def __init__(self, num_classes, labels=None, predictions_to_classes_convertor=None, threshold=0.5):
            super(Truthfulness, self).__init__(num_classes, labels, predictions_to_classes_convertor, threshold)
            self.tp = TruePositives(num_classes, labels, predictions_to_classes_convertor, threshold) # we exploit TruePositives for the computation
            self.tn = TrueNegatives(num_classes, labels, predictions_to_classes_convertor, threshold) # we exploit TrueNegatives for the computation

        def __call__(self, y_pred, y_true):  # <=== implement this method!
            tp_per_class = self.tp(y_pred, y_true)
            tn_per_class = self.tn(y_pred, y_true)

            # you can also access more confusion matrix metrics such as
            f1score = self.get_stats(ConfusionMatrixBasedMetric.F1SCORE)
            precision = self.get_stats(ConfusionMatrixBasedMetric.PRECISION)
            recall = self.get_stats(ConfusionMatrixBasedMetric.RECALL)
            # see ConfusionMatrixBasedMetric enum for more             

            return tp_per_class + tn_per_class

Save and Load full Trainer

Sometimes you just want to save everything so you can continue training where you left off.
To do so, you may use ModelCheckPoint for saving full trainer by setting parameter

    save_full_trainer=True

Or, you can invoke it directly from your trainer

    your_trainer.save_trainer(dir_path, file_name)

Loading a trainer from checkpoint is as simple as:

    loaded_trainer = Trainer.load_trainer(dir_path,             # the folder where the saved trainer file exists 
                                          trainer_file_name,    # the saved trainer file name 
                                          model,                # state_dict will be loaded
                                          device,
                                          loss_func,            # state_dict will be loaded
                                          optimizer,            # state_dict will be loaded
                                          scheduler,            # state_dict will be loaded
                                          train_data_loader,    # provide new/previous data_loader
                                          val_data_loader,      # provide new/previous data_loader
                                          train_steps,
                                          val_steps)

Utils

lpd.utils provides torch_utils, file_utils and general_utils
For example, a good practice is to use seed_all as early as possible in your code, to make sure that results are reproducible:

    import lpd.utils.general_utils as gu
    gu.seed_all(seed=42)  # because its the answer to life and the universe

Extensions

lpd.extensions provides some custom PyTorch layers, and schedulers, these are just some stuff we like using when we create our models, to gain better flexibility.
So you can use them at your own will, more extensions are added from time to time.

Something is missing?! please share with us

You can open an issue, but also feel free to email us at [e-mail]