wah 1.8.15

a library so simple you will learn Within An Hour

Install

pip install wah

Requirements

You might want to manually install PyTorch for GPU computation.

lightning
matplotlib
numpy
pandas
pyperclip
PyYAML
selenium
tensorboard
timm
torch
torchaudio
torchmetrics
torchvision
webdriver_manager

Examples

• Model Training
• Model Evaluation
• Geodesic Optimization

Structure

classification

• attacks
  • fgsm: FGSM, IFGSM
• datasets
  • base: ClassificationDataset
  • cifar10: CIFAR10
  • cifar100: CIFAR100
  • dataloader
    • __init__: to_dataloader
    • transforms: CollateFunction
  • imagenet: ImageNet
  • stl10: STL10
  • utils: compute_mean_and_std, DeNormalize, Normalize, portion_dataset, tensor_to_dataset
• models
  • feature_extraction: FeatureExtractor
  • load: add_preprocess, load_model, load_state_dict
  • replace:
    • __init__: Replacer
• test
  • accuracy: AccuracyTest
  • eval: EvalTest
  • hessian_max_eigval_spectrum: HessianMaxEigValSpectrum
  • loss: LossTest
  • pred: PredTest
  • tid: TIDTest
• train
  • plot: proj_train_path_to_2d, TrainPathPlot2D
  • train: Wrapper, load_trainer

module

_getattr, get_attrs, get_module_name, get_module_params, get_named_modules, get_valid_attr

np

path

basename, clean, dirname, exists, isdir, join, ls, mkdir, rmdir, rmfile, split, splitext

plot

• dist: DistPlot2D
• hist: HistPlot2D
• image: ImShow
• mat: MatShow2D
• quiver: QuiverPlot2D, TrajPlot2D
• scatter: GridPlot2D, ScatterPlot2D

riemann

• geodesic: optimize_geodesic
• grad: compute_jacobian, compute_hessian
• jacobian_sigvals: JacobianSigVals

tensor

broadcasted_elementwise_mul, create_1d_traj, create_2d_grid, flatten_batch, repeat, stretch

torch

utils

• args: ArgumentParser
• dictionary: dict_to_df, dict_to_tensor, load_csv_to_dict, load_yaml_to_dict, save_dict_to_csv
• download: disable_ssl_verification, download_url, md5_check
• logs: disable_lightning_logging
• lst: load_txt_to_list, save_list_to_txt, sort_str_list
• random: seed, unseed
• time: time
• zip: extract