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#torchmanager

callbacks

Contains callbacks for fit method in the manager

configs

Contains organized configurations tools

losses

Contains wrapped loss functions

metrics

Contains wrapped metric functions

Classes

BaseManager

The basic manager

• Properties: - compiled: A bool flag of if the manager has been compiled - loss_fn: A Callable method that takes the truth and predictions in torch.Tensor and returns a loss torch.Tensor - metrics: A dict of metrics with a name in str and a Callable method that takes the truth and predictions in torch.Tensor and returns a loss torch.Tensor - model: A target torch.nn.Module to be trained - optimizer: A torch.optim.Optimizer to train the model

• Methods:
  • Constructor - Parameters: - loss_fn: An optional Loss object to calculate the loss for single loss or a dict of losses in Loss with their names in str to calculate multiple losses - metrics: An optional dict of metrics with a name in str and a Metric object to calculate the metric - model: An optional target torch.nn.Module to be trained - optimizer: An optional torch.optim.Optimizer to train the model
  • compile
    • Compiles the manager
      • Parameters:
        • loss_fn: A Loss object to calculate the loss for single loss or a dict of losses in Loss with their names in str to calculate multiple losses
        • metrics: A dict of metrics with a name in str and a Metric object to calculate the metric
        • optimizer: A torch.optim.Optimizer to train the model
  • from_checkpoint
    • Method to load a manager from a saved Checkpoint. The manager will not be compiled with a loss function and its metrics.
    • classmethod
      • Returns: A loaded Manager
  • to_checkpoint Convert the current manager to a checkpoint - Returns: A Checkpoint with its model in Module type

DataManager

The manager to load data during training or testing

• Methods:
  • unpack_data Unpacks data to input and target - Parameters: - data: Any kind of data object - Returns: A tuple of Any kind of input and Any kind of target

TestingManager

A testing manager, only used for testing

• extends: BaseManager, DataManager

• Properties
  • compiled_losses: The loss function in Loss that must be exist
  • compiled_metrics: The dict of metrics in Metric that does not contain losses

• Methods
  • test
    • Test target model
      • Parameters:
        • dataset: Either SizedIterable or data.DataLoader to load the dataset
        • device: An optional torch.device to test on
        • use_multi_gpus: A bool flag to use multi gpus during testing
        • show_verbose: A bool flag to show the progress bar during testing
      • Returns: A dict of validation summary
  • test_step
    • A single testing step
    • Parameters:
      • x_train: The testing data in torch.Tensor
      • y_train: The testing label in torch.Tensor
    • Returns: A dict of validation summary

Manager

A training manager

• extends: TestingManager
• [Deprecation Warning]: Method train becomes protected from v1.0.2, the public method will be removed from v1.2.0. Override _train method instead.
• Compile a model, optimizer, loss function, and metrics into the manager:

import torch
from torchmanager import losses, metrics
class SomeModel(torch.nn.Module): ...
model = SomeModel()
optimizer = torch.optim.SGD(...)
loss_fn = losses.Loss(...)
metric_fns = {
...    ...
... }
manager = Manager(model, optimizer, loss_fn, metric_fns=metric_fns)

• Train using fit method:

from torch.utils.data import Dataset, DataLoader
dataset = Dataset(...)
dataset = DataLoader(dataset, ...)
epochs: int = ...
manager.fit(dataset, epochs, ...)

• Properties
  • current_epoch: The int index of current training epoch
  • compiled_optimizer: The torch.optim.Optimizer that must be exist

• Methods
  • _train
    • The single training step for an epoch
      • Parameters:
        • dataset: A SizedIterable training dataset
        • iterations: An optional int of total training iterations, must be smaller than the size of dataset
        • device: A torch.device where the data is moved to, should be same as the model
        • use_multi_gpus: A bool flag of if using multi gpus
        • show_verbose: A bool flag of if showing progress bar
        • verbose_type: A view.VerboseType that controls the display of verbose
        • callbacks_list: A list of callbacks in Callback
      • Returns: A summary of dict with keys as str and values as float
  • fit
    • Training algorithm
      • Parameters:
        • training_dataset: Any kind of training dataset, must performs to SizedIterable
        • epochs: An optional int number of training epochs
        • iterations: An optional int number of training iterations
        • lr_scheduelr: An optioanl torch.optim.lr_scheduler._LRScheduler to update the lr per epoch
        • is_dynamic_pruning: A bool flag of if using dynamic pruning
        • val_dataset: An optional validation Any
        • device: An optional torch.device where the data is moved to, gpu will be used when available if not specified.
        • use_multi_gpus: A bool flag of if using multi gpus
        • callbacks_list: A list of callbacks in Callback
        • **kwargs: Additional keyword arguments that will be passed to train method.
      • Returns: A trained torch.nn.Module
  • train_step
    • A single training step
      • Parameters:
        • x_train: The training data
        • y_train: The training label
      • Returns: A summary of dict with keys as str and values as float