vp_suite.defaults

This module contains the Settings class, which contains constants; and pre-set program configurations, such as default run config parameters.

DEFAULT_RUN_CONFIG = {'batch_size': 32, 'context_frames': 10, 'epochs': 1000000, 'losses_and_scales': {'mse': 1.0}, 'lr': 0.0001, 'max_training_hours': 48, 'metrics': ['mse', 'lpips', 'psnr', 'ssim'], 'n_vis': 5, 'no_train': False, 'no_val': False, 'no_vis': False, 'no_wandb': False, 'out_dir': None, 'pred_frames': 10, 'seed': 42, 'seq_step': 1, 'use_actions': False, 'val_rec_criterion': 'mse', 'vis_compare': False, 'vis_context_frame_idx': None, 'vis_every': 10, 'vis_mode': 'gif'}

A dictionary containing the default run configuration specified in the DefaultRunConfig class.

class DefaultRunConfig

Bases: object

This class holds the default run configuration parameters, specifying behaviour during training and testing. All parameters can be overridden by supplementing keyword args in the corresponding training/testing call.

batch_size: int = 32

The batch size used for training.

context_frames: int = 10

The number of context frames given to the prediction models. Also used in determining the needed sequence length for dataset usage.

epochs: int = 1000000

Number of epochs the model is trained before finalizing the training procedure. By default, this is set to a large number to let the training run terminate by time-outing.

losses_and_scales: dict = {'mse': 1.0}

A dictionary where the keys denote all losses that should be calculated and logged during training, and their corresponding values denote the factor with which to multiply and add these losses to the overall loss used for backpropagation.

lr: float = 0.0001

The learning rate for the models.

max_training_hours: float = 48

Maximum number of training hours before finalizing the training procedure. When the training time is exceeded, the current training iteration is continued but becomes the last training iteration.

metrics = ['mse', 'lpips', 'psnr', 'ssim']

A list of the metrics used for testing. If instead of a list, “all” is specified, all mavailable metrics are calculated.

n_vis: int = 5

Number of visualizations generated each time the model is used for visualization.

no_train: bool = False

If set to True, the training loop is skipped.

no_val: bool = False

If set to True, the validation loop is skipped during the training procedure. Instead the best model is saved after every epoch.

no_vis: bool = False

If set to True, no visualizations are generated during training/testing.

no_wandb: bool = False

If set to True, don’t log the run data to Weights and Biases.

out_dir = None

A file path for the output directory of the model. If none is specified, creates a suitable directory at runtime.

pred_frames: int = 10

The number of frames the prediction model shall predict. Also used in determining the needed sequence length for dataset usage.

seed: int = 42

The seed for all random number generators (python, numpy, pytorch) used throughout training/testing.

seq_step: int = 1

Sequences taken from the dataset use every Nth frame, where N is this value (Default value is 1, meaning that every frame is taken for the sequence).

use_actions: bool = False

If set to True, and the model supports actions, and the dataset contains actions, these actions will be used by the model for prediction.

val_rec_criterion: str = 'mse'

The measure that is used to determine the model quality during validation. Every time the resulting measurement is improved, the current model snapshot is saved as the current ‘best model’.

vis_compare: bool = False

If set to True, during testing, also generate visualization figures where the predicted frames of all tested models are laid out side-by-side.

vis_context_frame_idx = None

If not None, during testing, this parameter specifies which context frame to include in the visualization figure that lays out the predictions of all models.

vis_every: int = 10

After this many training epochs, model predictions on randomly sampled validation sequences are visualized and saved (if no_vis is not set to False).

vis_mode: str = 'gif'

Specifies how to save the generated visualization videos.

SETTINGS = <vp_suite.defaults._PackageSettings object>

A settings instance that can be imported by other modules. It contains program-internal settings such as save paths (for the default values please see the source code).