├── .gitignore
├── MANIFEST
├── README.md
├── dist
    └── tinymlgen-0.2.tar.gz
├── setup.py
└── tinymlgen
    ├── __init__.py
    └── tinymlgen.py


/.gitignore:
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1 | try.py
2 | publish
3 | venv
4 | .idea
5 | tinymlgen/__pycache__


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/MANIFEST:
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1 | # file GENERATED by distutils, do NOT edit
2 | setup.py
3 | tinymlgen/__init__.py
4 | tinymlgen/tinymlgen.py
5 | 


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/README.md:
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 1 | # TinyML gen
 2 | 
 3 | This is a simple package to export a model trained in Tensorflow Lite
 4 | to a plain C array, ready to be used for inference on microcontrollers.
 5 | 
 6 | ### Install
 7 | 
 8 | ```shell script
 9 | pip install tinymlgen
10 | ```
11 | 
12 | ### Use
13 | 
14 | ```python
15 | from tinymlgen import port
16 | 
17 | if __name__ == '__main__':
18 |     tf_model = create_tf_model()
19 |     c_code = port(tf_model)
20 | ```
21 | 
22 | ### Configuration
23 | 
24 | You can pass a few parameters to the `port` function:
25 | 
26 |  - `optimize (=True)`: apply optimizers to the exported model.
27 |     Can either be a list of optimizers or a boolean, in which case
28 |     `OPTIMIZE_FOR_SIZE` is applied
29 |  - `variable_name (='model_data')`: give the exported array a custom name
30 |  - `pretty_print (=False)`: print the array in a nicely formatted arrangement
31 | 
32 | 
33 | 


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/dist/tinymlgen-0.2.tar.gz:
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https://raw.githubusercontent.com/eloquentarduino/tinymlgen/5798ed1857b1f9dc126f94537169d219db2d7cf0/dist/tinymlgen-0.2.tar.gz


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/setup.py:
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 1 | from distutils.core import setup
 2 | setup(
 3 |   name = 'tinymlgen',
 4 |   packages = ['tinymlgen'],
 5 |   version = '0.3',
 6 |   license='MIT',
 7 |   description = 'Generate C code for microcontrollers from Tensorflow models',
 8 |   author = 'Simone Salerno',
 9 |   author_email = 'eloquentarduino@gmail.com',
10 |   url = 'https://github.com/eloquentarduino/tinymlgen',
11 |   download_url = 'https://github.com/eloquentarduino/tinymlgen/archive/v_03.tar.gz',
12 |   keywords = ['ML', 'microcontrollers', 'tensorflow', 'machine learning'],
13 |   install_requires=[
14 |     'tensorflow',
15 |     'hexdump'
16 |   ],
17 |   classifiers=[
18 |     'Development Status :: 3 - Alpha',
19 |     'Intended Audience :: Developers',
20 |     'Topic :: Software Development :: Code Generators',
21 |     'License :: OSI Approved :: MIT License',
22 |     'Programming Language :: Python :: 3',
23 |     'Programming Language :: Python :: 3.4',
24 |     'Programming Language :: Python :: 3.5',
25 |     'Programming Language :: Python :: 3.6',
26 |   ],
27 | )


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/tinymlgen/__init__.py:
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1 | from tinymlgen.tinymlgen import port


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/tinymlgen/tinymlgen.py:
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 1 | import re
 2 | import hexdump
 3 | import tensorflow as tf
 4 | 
 5 | 
 6 | def port(model, optimize=False, variable_name='model_data', pretty_print=False):
 7 |     converter = tf.lite.TFLiteConverter.from_keras_model(model)
 8 |     if optimize:
 9 |         if isinstance(optimize, bool):
10 |             optimizers = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
11 |         else:
12 |             optimizers = optimize
13 |         converter.optimizations = optimizers
14 |     tflite_model = converter.convert()
15 |     bytes = hexdump.dump(tflite_model).split(' ')
16 |     c_array = ', '.join(['0x%02x' % int(byte, 16) for byte in bytes])
17 |     c = 'const unsigned char %s[] DATA_ALIGN_ATTRIBUTE = {%s};' % (variable_name, c_array)
18 |     if pretty_print:
19 |         c = c.replace('{', '{\n\t').replace('}', '\n}')
20 |         c = re.sub(r'(0x..?, ){12}', lambda x: '%s\n\t' % x.group(0), c)
21 |     c += '\nconst int %s_len = %d;' % (variable_name, len(bytes))
22 |     preamble = '''
23 | // if having troubles with min/max, uncomment the following
24 | // #undef min    
25 | // #undef max
26 | 
27 | #ifdef __has_attribute
28 | #define HAVE_ATTRIBUTE(x) __has_attribute(x)
29 | #else
30 | #define HAVE_ATTRIBUTE(x) 0
31 | #endif
32 | #if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
33 | #define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
34 | #else
35 | #define DATA_ALIGN_ATTRIBUTE
36 | #endif
37 | 
38 | '''
39 |     return preamble + c


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