Open source PyTorch library to analyse effect of neural network pruning methods on model recall and class intensification. Modified from Original Shrinkbench Git Repository
First, install the dependencies found in the requirements.txt file
The modules are organized as follows:
| submodule | Description |
|---|---|
datasets/ |
Standardized dataloaders for supported datasets |
experiment/ |
Main experiment class with the data loading, pruning, finetuning & evaluation |
metrics/ |
Utils for measuring accuracy, model size, flops & memory footprint |
models/ |
Custom architectures not included in torchvision |
plot/ |
Utils for plotting across the logged dimensions |
pruning/ |
General pruning and masking API. |
scripts/ |
Executable scripts for running experiments (see experiment/) |
strategies/ |
Baselines pruning methods, mainly magnitude pruning based |