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CMSIS-NN CIFAR10 Example

This example shows how to quantize a trained Caffe model to 8-bits and deploy it on Arm Cortex-M CPUs using CMSIS-NN.

Getting started

Code generation from a trained Caffe model is performed in the following steps:

  1. Network parsing from caffe prototxt
  2. Quantization to 8-bit weights and activations
  3. Code generation using optimized NN Functions

nn_quantizer.py: Needs Caffe model definition (.prototxt) used for training/testing the model that consists of valid paths to datasets (lmdb) and trained model file (.caffemodel). It parses the network graph connectivity, quantize the caffemodel to 8-bit weights/activations layer-by-layer incrementally with minimal loss in accuracy on the test dataset. It dumps the network graph connectivity, quantization parameters into a pickle file.

code_gen.py: Gets the quantization parameters and network graph connectivity from previous step and generates the code consisting of NN function calls. Supported layers: convolution, innerproduct, pooling (max/average) and relu. It generates (a) weights.h (b) parameter.h: consisting of quantization ranges and (c) main.cpp: the network code.

Note: Make sure caffe is installed and it's python path is added in $PYTHONPATH environment variable.

Usage

  1. Update the dataset path (lmdb) in .prototxt file and run nn_quantizer.py to parse and quantize the network. This step takes a while if run on CPU as it quantizes the network layer-by-layer while validating the accuracy on test dataset
python nn_quantizer.py --model models/cifar10_m4_train_test.prototxt \ 
  --weights models/cifar10_m4_iter_70000.caffemodel.h5 \
  --save models/cifar10_m4.pkl

Note: To enable GPU for quantization sweeps, use --gpu argument.

  1. Generate code to run on Arm Cortex-M CPUs.
python code_gen.py --model models/cifar10_m4.pkl --out_dir code/m4

Common Problems

  1. ImportError: No module named caffe Add Caffe python installation path to $PYTHONPATH environment variable, e.g., export PYTHONPATH="/home/ubuntu_user/caffe/python:$PYTHONPATH"
  2. F0906 15:49:48.701362 11933 db_lmdb.hpp:15] Check failed: mdb_status == 0 (2 vs. 0) No such file or directory Make sure valid dataset (lmdb) is present in the model prototxt definition, as the dataset is required to find the quantization ranges for activations.

Known Limitations

  1. Parser supports conv, pool, relu, fc layers only.
  2. Quantizer supports only networks with feed-forward structures (e.g. conv-relu-pool-fc) without branch-out/branch-in (as in inception/squeezeNet, etc.).