Getting Started

N2D2 provides a pruning module to perform pruning operations on your model in order to reduce its memory footprint. The module works like the QAT module i.e. it is possible to carry out trainings with pruned weights in order to improve the performance of the network. Only weights can be pruned so far.

Example with Python

Example of code to use the n2d2.quantizer.PruneCell in your scripts:

for cell in model:
    ### Add Pruning ###
    if isinstance(cell, n2d2.cells.Conv) or isinstance(cell, n2d2.cells.Fc):
        cell.quantizer = n2d2.quantizer.PruneCell(prune_mode="Static", threshold=0.3, prune_filler="IterNonStruct")

Some explanations with the differents options of the n2d2.quantizer.PruneCell :

Pruning mode

3 modes are possible:

  • Identity: no pruning is applied to the cell

  • Static: all weights of the cell are pruned to the requested threshold at initialization

  • Gradual: the weights are pruned to the start threshold at initialization and at each update of the current threshold, it is increased by gamma until it reaches threshold. By default, the update is performed at the end of each epoch (possible to change it with stepsize)

Warning: if you use stepsize, please indicate the number of steps and not the number of epochs. For example, to update each two epochs, write:

n2d2.quantizer.PruneCell(prune_mode="Gradual", threshold=0.3, stepsize=2*DATASET_SIZE)

Where DATASET_SIZE is the size of the dataset you are using.

Pruning filler

2 fillers are available to fill the masks:

  • Random: The masks are filled randomly

  • IterNonStruct: all weights below than the delta factor are pruned. If this is not enough to reach threshold, all the weights below 2 “delta” are pruned and so on…

Important: With n2d2.quantizer.PruneCell, quant_mode and range are not used.

Example with INI file

The common set of parameters for any kind of Prune Quantizer.

Option [default value]



Quantization / Pruning method, choose Prune to activate the Pruning mode.

QWeight.PruningMode [Identity]

Pruning mode, can be Identity, Static or Gradual

QWeight.PruningFiller [Random]

Pruning filler for the weights, can be Random, IterNonStruct or None

QWeight.Threshold [0.2]

Weight threshold to be pruned, 0.2 means 20% for example

QWeight.Delta [0.001]

Factor for iterative pruning, use it with IterNonStruct pruning filler

QWeight.StartThreshold [0.1]

Starting threshold, use it with Gradual pruning mode

QWeight.StepSizeThreshold [0]

Step size for the threshold update, use it with Gradual pruning mode

QWeight.GammaThreshold [0.05]

Value to add to current threshold during its update, use it with Gradual pruning mode

Example of code to use the Prune Quantizer in your scripts:

KernelDims=5 5

All explanations in relation to the parameters of Prune Quantizer are provided in the python section of this page.