pax_global_header00006660000000000000000000000064136107507340014520gustar00rootroot0000000000000052 comment=2155f8d6135622dfa2474b1bc586ce68587ae9cc DLModelBox-1.1.1/000077500000000000000000000000001361075073400134515ustar00rootroot00000000000000DLModelBox-1.1.1/LICENSE000066400000000000000000000027271361075073400144660ustar00rootroot00000000000000Copyright (c) 2017 DT42 CO., Ltd. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. - Neither the name of Thomas J Bradley nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. DLModelBox-1.1.1/README.md000066400000000000000000000006751361075073400147400ustar00rootroot00000000000000DLModelBox - The Swiss Army Knife of Deep Learning Models This software consists of the utilities: * dlmodel_source: Interactive CLI helps user to create structured DL model directory. * dlmodel2deb: Create Debian package from given structured DL model directory. # Setup 1. Add Debian environment variables to your shell config (e.g. `~/.bashrc`) ``` $ export DEBEMAIL="foo@your.email" $ export DEBFULLNAME="YOUR NAME" ``` DLModelBox-1.1.1/bin/000077500000000000000000000000001361075073400142215ustar00rootroot00000000000000DLModelBox-1.1.1/bin/dlmodel2deb000077500000000000000000000034231361075073400163260ustar00rootroot00000000000000#!/usr/bin/python3 import argparse import json import os import subprocess def create_debianization(): cmd = ( 'dh_make -n -i -y;' 'cd debian;' 'rm *ex *EX README.*;' 'cd ..' ) subprocess.call(cmd, shell=True) def create_install(work_dirpath): with open(os.path.join(work_dirpath, 'meta.json'), 'r') as f: meta = json.load(f) print('meta: {}'.format(meta)) with open('debian/install', 'w') as f: package_name = meta['name'] package_dirpath = os.path.join('/usr/share/dlmodels', package_name) # add meta f.write('{meta} {pkgdir}\n'.format( meta='meta.json', pkgdir=package_dirpath)) # add model f.write('{model} {pkgdir}\n'.format( model=meta['model'], pkgdir=package_dirpath)) # add label f.write('{label} {pkgdir}\n'.format( label=meta['label'], pkgdir=package_dirpath)) # add config(s) for k, v in meta['config'].items(): f.write('{config} {pkgdir}\n'.format( config=v, pkgdir=package_dirpath)) def build_debian_binary_package(): cmd = 'debuild -us -uc' subprocess.call(cmd, shell=True) def main(): parser = argparse.ArgumentParser() parser.add_argument('modeldir_path', help='DL model directory path') args = parser.parse_args() work_dirpath = os.path.abspath(args.modeldir_path) dirname_components = os.path.basename(args.modeldir_path).split('-') model_name = '-'.join(dirname_components[:-1]) model_version = dirname_components[-1] os.chdir(work_dirpath) create_debianization() create_install(work_dirpath) build_debian_binary_package() if __name__ == '__main__': main() DLModelBox-1.1.1/bin/dlmodel_source000077500000000000000000000074631361075073400171610ustar00rootroot00000000000000#!/usr/bin/python3 import glob import hashlib import json import os import readline import shutil import subprocess def init_cli(): def complete(text, state): return (glob.glob(text+'*')+[None])[state] readline.set_completer_delims(' \t\n;') readline.parse_and_bind("tab: complete") readline.set_completer(complete) def get_sha256(filepath): bufsize = 65536 sha256 = hashlib.sha256() with open(filepath, 'rb') as f: while True: data = f.read(bufsize) if not data: break sha256.update(data) return sha256.hexdigest() def get_meta(): """Get metadata from user via CLI. :return: metadata in JSON structure :rtype: dict """ inference_engine_names = { 1: 'tflite', 2: 'openvino', 3: 'darknet', 4: 'tensorflow', 5: 'pytorch', 6: 'keras', 7: 'mxnet', 8: 'caffe2', 9: 'caffe', 10: 'movidius', 11: 'others' } # model, label, config are full paths for copying them into # target package directory. They will be updated to relative paths # in the model package. meta = {} config_files = {} meta['name'] = input('Package name: ') meta['version'] = input('Package version: ') meta['model'] = os.path.abspath(input('Model filepath: ')) meta['label'] = os.path.abspath(input('Label filepath: ')) while True: key = input('Config name (press enter directly to stop): ') if len(key) != 0: value = os.path.abspath(input('Config filepath: ')) config_files[key] = value else: break meta['config'] = config_files engine_index = int(input( ( 'Inference engine\n' '\t 1. TFLite\n' '\t 2. OpenVINO\n' '\t 3. Darknet\n' '\t 4. TensorFlow\n' '\t 5. PyTorch\n' '\t 6. Keras\n' '\t 7. MXNet\n' '\t 8. Caffe2\n' '\t 9. Caffe\n' '\t10. Movidius\n' '\t11. Others\n' ': ' ) )) meta['inference-engine'] = inference_engine_names[engine_index] return meta def create_metafile(meta, package_dirpath): """Create DL model package meta file (meta.json) """ checksums = {} for cksum_key in ['model', 'label']: target_path = os.path.join(package_dirpath, meta[cksum_key]) checksums[meta[cksum_key]] = get_sha256(target_path) for k, v in meta['config'].items(): target_path = os.path.join(package_dirpath, v) checksums[v] = get_sha256(target_path) meta['checksums-sha256'] = checksums with open(os.path.join(package_dirpath, 'meta.json'), 'w') as f: json.dump(meta, f, indent=4) def create_source_package(meta): # copy model contents to model package directory package_name = meta['name'] + '-' + meta['version'] package_dirpath = os.path.join('/tmp', package_name) subprocess.call( 'mkdir -p {pkgdir}'.format(pkgdir=package_dirpath), shell=True) shutil.copy2(meta['model'], package_dirpath) shutil.copy2(meta['label'], package_dirpath) for k, v in meta['config'].items(): shutil.copy2(v, package_dirpath) # update full paths to relative paths in the model package directory meta['model'] = os.path.basename(meta['model']) meta['label'] = os.path.basename(meta['label']) for k, v in meta['config'].items(): meta['config'][k] = os.path.basename(v) # create model description file (meta.json) create_metafile(meta, package_dirpath) print('Model source package is at ' + package_dirpath) def main(): init_cli() meta = get_meta() create_source_package(meta) if __name__ == '__main__': # TODO: capture ctrl+c and confirm exit or not main() DLModelBox-1.1.1/doc/000077500000000000000000000000001361075073400142165ustar00rootroot00000000000000DLModelBox-1.1.1/doc/spec.md000066400000000000000000000015531361075073400154760ustar00rootroot00000000000000# Model Directory Structure ``` |-- assets | |-- labels.txt | `-- |-- LICENSE # optional currently |-- meta.json `-- model.pb ``` # Metadata Format of Model Package Metadata, `meta.json`, describes all the details in a model package. Metadata format v1.1.0: ``` # Example of meta.json # Note: model directory name is fight-detection-1.0.0 { "name": "fight-detection", "version": "1.0.0", "inference-engine": "tensorflow", "model": "model.pb", "label": "assets/labels.txt", # optional configs "config": { "": "", "": "", ... }, "checksums-sha256": { "model.pb": "", "assets/labels.txt": "", "": "", ... } } ```