| /** |
| @page data_import Importing data from existing models |
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| @tableofcontents |
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| @section caffe_data_extractor Extract data from pre-trained caffe model |
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| One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on |
| caffe's official github repository. |
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| The caffe_data_extractor.py provided in the @ref scripts folder is an example script that shows how to |
| extract the parameter values from a trained model. |
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| @note complex networks might require altering the script to properly work. |
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| @subsection caffe_how_to How to use the script |
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| 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. |
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| Download the pre-trained caffe model. |
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| Run the caffe_data_extractor.py script by |
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| python caffe_data_extractor.py -m <caffe model> -n <caffe netlist> |
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| For example, to extract the data from pre-trained caffe Alex model to binary file: |
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| python caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt |
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| The script has been tested under Python2.7. |
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| @subsection caffe_result What is the expected output from the script |
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| 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. |
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| The @ref 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. |
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| @section tensorflow_data_extractor Extract data from pre-trained tensorflow model |
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| 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: |
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| {model_name}.data-{step}-{global_step}: A binary file containing values of each variable. |
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| {model_name}.meta: A binary file containing a MetaGraph struct which defines the graph structure of the neural |
| network. |
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| @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. |
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| @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. |
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| @subsection tensorflow_how_to How to use the script |
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| Install tensorflow and numpy. |
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| Download the pre-trained tensorflow model. |
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| Run tensorflow_data_extractor.py with |
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| python tensorflow_data_extractor -m <path_to_binary_checkpoint_file> -n <path_to_metagraph_file> |
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| For example, to extract the data from pre-trained tensorflow Alex model to binary files: |
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| python tensorflow_data_extractor -m /path/to/bvlc_alexnet -n /path/to/bvlc_alexnet.meta |
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| Or for binary checkpoint files before Tensorflow 0.11: |
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| python tensorflow_data_extractor -m /path/to/bvlc_alexnet.ckpt -n /path/to/bvlc_alexnet.meta |
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| @note with versions >= Tensorflow 0.11 only model name is passed to the -m option |
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| The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3.4.3. |
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| @subsection tensorflow_result What is the expected output from the script |
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| 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 @ref 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. |
| */ |