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NeurIPS 2019 MicroNet Challenge

We encourage our readers to take a look at the latest improvements

You may also want to read the arXiv.org preprint: https://arxiv.org/abs/2004.01077

If you find this code useful in your research, please cite:

@InProceedings{Marban_2020_EC2T,
author = {Marban, Arturo and Becking, Daniel and Wiedemann, Simon and Samek, Wojciech},
title = {Learning Sparse & Ternary Neural Networks With Entropy-Constrained Trained Ternarization (EC2T)},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020},
pages={3105-3113},
doi = {10.1109/CVPRW50498.2020.00369}
}

In this project two algorithms are implemented: first, an algorithm which compresses neural networks by applying an entropy controlled ternary quantization and, second, a compound scaling approach which finds efficient architectures in terms of scaling model width and depth.

The resulting neural networks solve the NeurIPS MicroNet Challenge's target tasks to a specified quality level:

https://micronet-challenge.github.io/

Further information on the compound scaling and quantization concept can be found in the writeup.pdf file in this git repository.

Folder structure:

  • model package: Here the MicroNet and EfficientNet model builders can be found. Not executing tasks.
  • model_compound_scaling package: Here a grid search on width and depth scaling factors is applied to the MicroNet.
  • model_quantization package: Here the MicroNet found by the compound scaling approach (or an existing EfficientNet) are quantized ternary. To make the model as sparse as possible a grid search on lambda dividers is applied with lambda controlling the intensity of the entropy constraint which is applied to the weights to push them into the zero valued cluster (cluster of lowest information content).
  • model_scoring package: Here code can be found which calculates the number of scoring parameters and math operations for inference as declined on the challenge's website. Furthermore a summary of the model statistics is printed.

Scores:

Counting additions and multiplications as FP16 ops

Task # Params Top-1 Acc. Sparsity # Scoring params # Inference FLOP Overall Score
CIFAR-100 8.1M 80.13% 90.49% 0.43M 66.77M 0.0182
ImageNet 7.8M 75.03% 46.33% 1.33M 250.31M 0.4070

Counting additions as FP32 ops and multiplications as FP16 ops

Task # Params Top-1 Acc. Sparsity # Scoring params # Inference FLOP Overall Score
CIFAR-100 8.1M 80.13% 90.49% 0.43M 129.83M 0.0242
ImageNet 7.8M 75.03% 46.33% 1.33M 455.19M 0.5821

Code execution:

To execute compound scaling, quantization or scoring run the according python run files at the top level of this project, e.g. python -u run_compound_scaling.py. Hyperparameters can be altered via the parser. To get all parser arguments execute

python -u run_compound_scaling.py --help
python -u run_quantization.py --help
python -u run_scoring.py --help

The --help flag will list all (optional) arguments for each run file. As an example

python -u run_scoring.py --no-halfprecision --t-model best_ternary_imagenet_micronet.pt --image-size 200 
--no-cuda > console.txt

executes the scoring procedure with full precision parameters (32 bit), the quantized network which solved ImageNet best, with an input image size of 200x200 px and a CPU mapping instead of GPU usage. The console output can be saved optionally in a text file by appending > console.txt.

For using multiple GPUs set the CUDA_VISIBLE_DEVICES environment variable before executing the packages

export CUDA_VISIBLE_DEVICES=0,1

Full example to reproduce the results of the CIFAR-100 task:

python -u run_compound_scaling.py --epochs 250 --batch-size 128 --grid 1.4 1.2 --phi 3.5 --dataset CIFAR100 
--image-size 32

Copy the best full-precision model from ./model_compound_scaling/saved_models/best_model.pt to ./model_quantization/trained_fp_models and optionally rename it, e.g. MicroNet_d14_w12_phi35_acc8146_params8_06m.th.

python -u run_quantization.py --model-dict MicroNet_d14_w12_phi35_acc8146_params8_06m.th --batch-size 128 --epochs 20 
--retrain-epochs 20 --ini-c-divrs 0.45 --lambda-max-divrs 0.15 --model cifar_micronet --dw-multps 1.4 1.2 --phi 3.5 
--dataset CIFAR100

The best quantized model can be found in ./model_quantization/saved_models/Ternary_best_acc.pt. Copy it to ./model_scoring/trained_t_models and optionally rename it, e.g. best_ternary_cifar_micronet.pt. Execute:

python -u run_scoring.py --t-model best_ternary_cifar_micronet.pt  --model cifar_micronet  
--dataset CIFAR100 --eval --halfprecision --add-bits 16

Full example to reproduce the results of the ImageNet task:

Download the pretrained EfficientNet-B1 by Luke Melas-Kyriazi (https://github.com/lukemelas/EfficientNet-PyTorch) or train it from scratch. In our environment EfficientNet-B1, which can be found in ./model_quantization/trained_fp_models, achieves an ImageNet accuracy of 78.4% which is 0.4% less than described in the paper.

In a first step we quantize the "expand", "projection" and "head" convolutions of EfficientNet-B1 with our entropy controlled ternary approach:

python -u run_quantization.py --batch-size 128 --val-batch-size 256 --ini-c-divrs 0.2 --lambda-max-divrs 0.125 
--model efficientnet-b1 --model-dict efficientnet_b1.pt --dataset ImageNet --image-size 224 
--data-path [your_path] --epochs 20 --retrain-epochs 15

In a second step the non-ternary Squeeze-and-Excitation layers plus the fully connected layer run through the same algorithm but only the assignment to the zero cluster is executed (entropy controlled pruning, important: set --do-prune flag). Copy the best model from ./model_quantization/saved_models/ to ./model_quantization/trained_fp_models and optionally rename it, e.g. B1_ternary_exp_proj_head.pt. As the non-zero values remain in full precision, hyperparameters ini-c-divrs and lambda-max-divrs can be increased:

python -u run_quantization.py --batch-size 128 --val-batch-size 256 --ini-c-divrs 0.25 --lambda-max-divrs 0.15 
--model efficientnet-b1 --model-dict B1_ternary_exp_proj_head.pt --dataset ImageNet --image-size 224 
--data-path [your_path] --epochs 10 --retrain-epochs 0 --do-prune --no-resume

Copy the best model from ./model_quantization/saved_models/ to ./model_scoring/trained_t_models and optionally rename it, e.g. best_ternary_and_pruned_imagenet_efficientnet.pt. Execute:

python -u run_scoring.py --dataset ImageNet --data-path [your_path] --val-batch-size 256 
--image-size 256 --model efficientnet-b1 --t-model best_ternary_and_pruned_imagenet_efficientnet.pt 
--prune-threshold 0.01 --eval --halfprecision --add-bits 16