| /// |
| /// Copyright (c) 2017-2018 ARM Limited. |
| /// |
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| namespace arm_compute |
| { |
| /** |
| @page data_import Importing data from existing models |
| |
| @tableofcontents |
| |
| @section caffe_data_extractor Extract data from pre-trained caffe model |
| |
| One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on |
| caffe's official github repository. |
| |
| The caffe_data_extractor.py provided in the scripts folder is an example script that shows how to |
| extract the parameter values from a trained model. |
| |
| @note complex networks might require altering the script to properly work. |
| |
| @subsection caffe_how_to How to use the script |
| |
| Install caffe following <a href="http://caffe.berkeleyvision.org/installation.html">caffe's document</a>. |
| Make sure the pycaffe has been added into the PYTHONPATH. |
| |
| Download the pre-trained caffe model. |
| |
| Run the caffe_data_extractor.py script by |
| |
| python caffe_data_extractor.py -m <caffe model> -n <caffe netlist> |
| |
| For example, to extract the data from pre-trained caffe Alex model to binary file: |
| |
| python caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt |
| |
| The script has been tested under Python2.7. |
| |
| @subsection caffe_result What is the expected output from the script |
| |
| If the script runs successfully, it prints the names and shapes of each layer onto the standard |
| output and generates *.npy files containing the weights and biases of each layer. |
| |
| The arm_compute::utils::load_trained_data shows how one could load |
| the weights and biases into tensor from the .npy file by the help of Accessor. |
| |
| @section tensorflow_data_extractor Extract data from pre-trained tensorflow model |
| |
| The script tensorflow_data_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a |
| trained tensorflow model. A tensorflow model consists of the following two files: |
| |
| {model_name}.data-{step}-{global_step}: A binary file containing values of each variable. |
| |
| {model_name}.meta: A binary file containing a MetaGraph struct which defines the graph structure of the neural |
| network. |
| |
| @note Since Tensorflow version 0.11 the binary checkpoint file which contains the values for each parameter has the format of: |
| {model_name}.data-{step}-of-{max_step} |
| instead of: |
| {model_name}.ckpt |
| When dealing with binary files with version >= 0.11, only pass {model_name} to -m option; |
| when dealing with binary files with version < 0.11, pass the whole file name {model_name}.ckpt to -m option. |
| |
| @note This script relies on the parameters to be extracted being in the |
| 'trainable_variables' tensor collection. By default all variables are automatically added to this collection unless |
| specified otherwise by the user. Thus should a user alter this default behavior and/or want to extract parameters from other |
| collections, tf.GraphKeys.TRAINABLE_VARIABLES should be replaced accordingly. |
| |
| @subsection tensorflow_how_to How to use the script |
| |
| Install tensorflow and numpy. |
| |
| Download the pre-trained tensorflow model. |
| |
| Run tensorflow_data_extractor.py with |
| |
| python tensorflow_data_extractor -m <path_to_binary_checkpoint_file> -n <path_to_metagraph_file> |
| |
| For example, to extract the data from pre-trained tensorflow Alex model to binary files: |
| |
| python tensorflow_data_extractor -m /path/to/bvlc_alexnet -n /path/to/bvlc_alexnet.meta |
| |
| Or for binary checkpoint files before Tensorflow 0.11: |
| |
| python tensorflow_data_extractor -m /path/to/bvlc_alexnet.ckpt -n /path/to/bvlc_alexnet.meta |
| |
| @note with versions >= Tensorflow 0.11 only model name is passed to the -m option |
| |
| The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3. |
| |
| @subsection tensorflow_result What is the expected output from the script |
| |
| If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates |
| *.npy files containing the weights and biases of each layer. |
| |
| The arm_compute::utils::load_trained_data shows how one could load |
| the weights and biases into tensor from the .npy file by the help of Accessor. |
| |
| @section tf_frozen_model_extractor Extract data from pre-trained frozen tensorflow model |
| |
| The script tf_frozen_model_extractor.py extracts trainable parameters (e.g. values of weights and biases) from a |
| frozen trained Tensorflow model. |
| |
| @subsection tensorflow_frozen_how_to How to use the script |
| |
| Install Tensorflow and NumPy. |
| |
| Download the pre-trained Tensorflow model and freeze the model using the architecture and the checkpoint file. |
| |
| Run tf_frozen_model_extractor.py with |
| |
| python tf_frozen_model_extractor -m <path_to_frozen_pb_model_file> -d <path_to_store_parameters> |
| |
| For example, to extract the data from pre-trained Tensorflow model to binary files: |
| |
| python tf_frozen_model_extractor -m /path/to/inceptionv3.pb -d ./data |
| |
| @subsection tensorflow_frozen_result What is the expected output from the script |
| |
| If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates |
| *.npy files containing the weights and biases of each layer. |
| |
| The arm_compute::utils::load_trained_data shows how one could load |
| the weights and biases into tensor from the .npy file by the help of Accessor. |
| |
| @section validate_examples Validating examples |
| Using one of the provided scripts will generate files containing the trainable parameters. |
| |
| You can validate a given graph example on a list of inputs by running: |
| |
| LD_LIBRARY_PATH=lib ./<graph_example> --validation-range='<validation_range>' --validation-file='<validation_file>' --validation-path='/path/to/test/images/' --data='/path/to/weights/' |
| |
| e.g: |
| |
| LD_LIBRARY_PATH=lib ./bin/graph_alexnet --target=CL --layout=NHWC --type=F32 --threads=4 --validation-range='16666,24998' --validation-file='val.txt' --validation-path='images/' --data='data/' |
| |
| where: |
| validation file is a plain document containing a list of images along with their expected label value. |
| e.g: |
| |
| val_00000001.JPEG 65 |
| val_00000002.JPEG 970 |
| val_00000003.JPEG 230 |
| val_00000004.JPEG 809 |
| val_00000005.JPEG 516 |
| |
| --validation-range is the index range of the images within the validation file you want to check: |
| e.g: |
| |
| --validation-range='100,200' will validate 100 images starting from 100th one in the validation file. |
| |
| This can be useful when parallelizing the validation process is needed. |
| */ |
| } |