torch-simplify 1.1.1

Simplification of pruned models for accelerated inference

Simplify

Simplification of pruned models for accelerated inference.

Installation

Simplify can be installed using pip:

pip3 install torch-simplify

or if you want to run the latest version of the code, you can install from git:

git clone https://github.com/EIDOSlab/simplify
cd simplify
pip3 install -r requirements.txt

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Usage

Main function

For most scenarios the main simplify function will suffice. This function returns the simplified model.

Arguments

The expected arguments are:

• model (torch.nn.Module): Module to be simplified i.e. the PyTorch's model.
• x (torch.Tensor): zero-tensor of shape [1, C, N, M], same as the model usual input.
• bn_folding (List): List of tuple (nn.Conv2d, nn.BatchNorm2d) to be fused. If None it tries to evaluate them given the model. Default None.
• fuse_bn (bool): If True, fuse the conv-bn tuple.
• pinned_out (List): List of nn.Modules which output needs to remain of the original shape (e.g. layers related to a residual connection with a sum operation).

Minimal working example

import torch
from torchvision import models
from simplify import simplify

model = models.resnet18()

# Apply some pruning strategy or load a pruned checkpoint

dummy_input = torch.zeros(1, 3, 224, 224)  # Tensor shape is that of a standard input for the given model
simplified_model = simplify(model, dummy_input)

Submodules

The simplify function is composed of three different submodules: fuse, propagate and remove. Each module can be used independently as needed.

fuse

Fuses adjacent Conv (or Linear) and BatchNorm layers.

propagate

Propagates non-zero bias of pruned neurons to remaining neurons of the next layers.

remove

Removes zeroed neurons from the architecture.

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Citing

If you use this software for research or application purposes, please use the following citation:

@article{bragagnolo2021simplify,
  title = {Simplify: A Python library for optimizing pruned neural networks},
  journal = {SoftwareX},
  volume = {17},
  pages = {100907},
  year = {2022},
  issn = {2352-7110},
  doi = {https://doi.org/10.1016/j.softx.2021.100907},
  url = {https://www.sciencedirect.com/science/article/pii/S2352711021001576},
  author = {Andrea Bragagnolo and Carlo Alberto Barbano},
}