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models/keyword_spotting/cnn_large/model_package_tf/README.md
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| # CNN Large model package | ||
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| This folder contains code that will allow you to recreate the CNN Large keyword spotting model from | ||
| the [Hello Edge paper](https://arxiv.org/pdf/1711.07128.pdf). | ||
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| ## License | ||
| [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) | ||
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| ## Model Package Overview | ||
| | Model | CNN_Large | | ||
| |:---------------: |:------------------------------------------:| | ||
| | <u>**Format**</u>: | Keras, Saved Model, TensorFlow Lite int8, TensorFlow Lite fp32 | | ||
| | <u>**Feature**</u>: | Keyword spotting for Arm Cortex-M CPUs | | ||
| | <u>**Architectural Delta w.r.t. Vanilla**</u>: | None | | ||
| | <u>**Domain**</u>: | Keyword spotting | | ||
| | <u>**Package Quality**</u>: | Optimised | | ||
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| ## Model Recreation | ||
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| In order to recreate the model you will first need to be using ```Python3.7``` and install the requirements in ```requirements.txt```. | ||
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| Once you have these requirements satisfied you can execute the recreation script contained within this folder, just run: | ||
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| ```bash | ||
| bash ./recreate_model.sh | ||
| ``` | ||
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| Running this script will use the pre-trained checkpoint files supplied in the ```./model_archive/model_source/weights``` folder | ||
| to generate the TFLite files and perform evaluation on the test sets. Both an fp32 version and a quantized version will be produced. | ||
| The quantized version will use post-training quantization to fully quantize it. | ||
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| If you want to run training from scratch you can do this by supplying ```--train``` when running the script. For example: | ||
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| ```bash | ||
| bash ./recreate_model.sh --train | ||
| ``` | ||
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| Training is then performed and should produce a model to the stated accuracy in this repository. | ||
| Note that exporting to TFLite will still happen with the pre-trained checkpoint files so you will need to re-run the script | ||
| and this time supply the path to the new checkpoint files you want to use, for example: | ||
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| ```bash | ||
| bash ./recreate_model.sh --ckpt <checkpoint_path> | ||
| ``` | ||
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| ## Training | ||
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| To train a DNN with 3 fully-connected layers with 128 neurons in each layer, run: | ||
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| ``` | ||
| python train.py --model_architecture dnn --model_size_info 128 128 128 | ||
| ``` | ||
| The command line argument *--model_size_info* is used to pass the neural network layer | ||
| dimensions such as number of layers, convolution filter size/stride as a list to models.py, | ||
| which builds the TensorFlow graph based on the provided model architecture | ||
| and layer dimensions. For more info on *model_size_info* for each network architecture see | ||
| [models.py](models.py). | ||
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| The training commands with all the hyperparameters to reproduce the models shown in the | ||
| [paper](https://arxiv.org/pdf/1711.07128.pdf) are given [here](recreate_model.sh). | ||
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| ## Testing | ||
| To run inference on the trained model from a checkpoint and get accuracy on validation and test sets, run: | ||
| ``` | ||
| python evaluation.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint <checkpoint_path> | ||
| ``` | ||
| The parameters used here should match those used in the Training step. | ||
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| ## Optimization | ||
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| We introduce a new *optional* step to optimize the trained keyword spotting model for deployment. | ||
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| Here we use TensorFlow's [weight clustering API](https://www.tensorflow.org/model_optimization/guide/clustering) to reduce the compressed model size and optimize inference on supported hardware. 32 weight clusters and kmeans++ cluster intialization method are used as the clustering hyperparameters. | ||
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| To optimize your trained model (e.g. a DNN), a trained model checkpoint is needed to run clustering and fine-tuning on. | ||
| You can use the pre-trained checkpoints provided, or train your own model and use the resulting checkpoint. | ||
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| To apply the optimization and fine-tuning, run the following command: | ||
| ``` | ||
| python optimisations.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint <checkpoint_path> | ||
| ``` | ||
| The parameters used here should match those used in the Training step, except for the number of training steps. | ||
| The number of training steps is reduced since the optimization step only requires fine-tuning. | ||
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| This will generate a clustered model checkpoint that can be used in the quantization step to generate a quantized and clustered TFLite model. | ||
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| ## Quantization and TFLite Conversion | ||
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| As part of the update we now use TensorFlow's | ||
| [post training quantization](https://www.tensorflow.org/lite/performance/post_training_quantization) to | ||
| make quantization of the trained models super simple. | ||
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| To quantize your trained model (e.g. a DNN) run: | ||
| ``` | ||
| python convert_to_tflite.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint <checkpoint_path> [--inference_type int8|int16] | ||
| ``` | ||
| The parameters used here should match those used in the Training step. | ||
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| The inference_type parameter is *optional* and to be used if a fully quantized model with inputs and outputs of type int8 or int16 is needed. It defaults to fp32. | ||
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| This step will produce a quantized TFLite file *dnn_quantized.tflite*. | ||
| You can test the accuracy of this quantized model on the test set by running: | ||
| ``` | ||
| python evaluation.py --tflite_path dnn_quantized.tflite | ||
| ``` | ||
| The parameters used here should match those used in the Training step. | ||
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| `convert_to_tflite.py` uses post-training quantization to generate a quantized model by default. If you wish to convert to a floating point TFLite model, use the command below: | ||
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| ``` | ||
| python convert_to_tflite.py --model_architecture dnn --model_size_info 128 128 128 --checkpoint <checkpoint_path> --no-quantize | ||
| ``` | ||
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| This will produce a floating point TFLite file *dnn.tflite*. You can test the accuracy of this floating point model using `evaluation.py` as above. |
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