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This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics and implements novel pruning / compression algorithms for deep learning / neural networks. Amongst others it implements structured pruning before training, its actual parameter shrinking and unstructured before/during training.

Home Page: https://arxiv.org/abs/2006.00896

License: MIT License

Python 100.00%
artificial-intelligence computer-vision deep-learning machine-learning model-compression neural-networks pruning publication science science-research speedup structured-pruning structured-pruning-before-training unstructured-pruning

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snip-it's Issues

How to get the pruned model without pickle and module dependencies ?

First of all, thank for your sharing.

Your experiment codes are perpectly working to reproduce your experiments.

But when I want to get the pruned model which is not trained for my custom experiment,

I get in trouble.

Referring your main script and other codes I found that if I want to load a pruned model in other code base,

I need to call all dependent modules for the neural network with unpickling the pruned weights.

Is there any detour to get a pruned model for using easily as like following ?

import torch
model_path = "Pruned_model.pth"
model = torch.load(model_path)

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