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README.md

Public Topologies Downloader

The script is designed to download popular public deep learning topologies and prepare models for the Model Optimizer tool.

Prerequisites

  1. Install python3 (version 3.5.2 or higher)
  2. Install yaml and requests modules with the command
sudo -E pip3 install pyyaml requests

Usage

  • Run the script with -h key to see the help message:

    ./downloader.py -h
    
    
       usage: downloader.py [-h] [-c CONFIG.YML] [--name PAT[,PAT...]]
                            [--list FILE.LST] [--all] [--print_all] [-o DIR]
                            [--cache_dir DIR] [--num_attempts N]
    
       optional arguments:
         -h, --help            show this help message and exit
         -c CONFIG.YML, --config CONFIG.YML
                               path to YML configuration file
         --name PAT[,PAT...]   download only topologies whose names match at least
                               one of the specified patterns
         --list FILE.LST       download only topologies whose names match at least
                               one of the patterns in the specified file
         --all                 download all topologies from the configuration file
         --print_all           print all available topologies
         -o DIR, --output_dir DIR
                               path where to save topologies
         --cache_dir DIR       directory to use as a cache for downloaded files
         --num_attempts N      attempt each download up to N times
    
       list_topologies.yml - default configuration file
  • Run the script with the default configuration file:

    ./downloader.py --all

    or with a custom configuration file:

    ./downloader.py --all -c <path_to_configuration_file>
  • Run the script with the --print_all option to see the available topologies:

    ./downloader.py --print_all
    
    densenet-121
    densenet-161
    densenet-169
    densenet-201
    squeezenet1.0
    squeezenet1.1
    mtcnn-p
    mtcnn-r
    mtcnn-o
    mobilenet-ssd
    vgg19
    vgg16
    ssd512
    ssd300
    inception-resnet-v2
    dilation
    googlenet-v1
    googlenet-v2
    googlenet-v4
    alexnet
    ssd_mobilenet_v2_coco
    resnet-50
    resnet-101
    resnet-152
    googlenet-v3
    se-inception
    se-resnet-101
    se-resnet-152
    se-resnet-50
    se-resnext-50
    se-resnext-101
    Sphereface
    license-plate-recognition-barrier-0007
    mobilenet-v1-1.0-224
    mobilenet-v2
    faster_rcnn_inception_v2_coco
    deeplabv3
    ctpn
    ssd_mobilenet_v1_coco
    faster_rcnn_resnet101_coco
    mobilenet-v2-1.4-224
    age-gender-recognition-retail-0013
    age-gender-recognition-retail-0013-fp16
    emotions-recognition-retail-0003
    emotions-recognition-retail-0003-fp16
    face-detection-adas-0001
    face-detection-adas-0001-fp16
    face-detection-retail-0004
    face-detection-retail-0004-fp16
    face-person-detection-retail-0002
    face-person-detection-retail-0002-fp16
    face-reidentification-retail-0095
    face-reidentification-retail-0095-fp16
    facial-landmarks-35-adas-0002
    facial-landmarks-35-adas-0002-fp16
    head-pose-estimation-adas-0001
    head-pose-estimation-adas-0001-fp16
    human-pose-estimation-0001
    human-pose-estimation-0001-fp16
    landmarks-regression-retail-0009
    landmarks-regression-retail-0009-fp16
    license-plate-recognition-barrier-0001
    license-plate-recognition-barrier-0001-fp16
    pedestrian-and-vehicle-detector-adas-0001
    pedestrian-and-vehicle-detector-adas-0001-fp16
    pedestrian-detection-adas-0002
    pedestrian-detection-adas-0002-fp16
    person-attributes-recognition-crossroad-0230
    person-attributes-recognition-crossroad-0230-fp16
    person-detection-action-recognition-0005
    person-detection-action-recognition-0005-fp16
    person-detection-retail-0002
    person-detection-retail-0002-fp16
    person-detection-retail-0013
    person-detection-retail-0013-fp16
    person-reidentification-retail-0031
    person-reidentification-retail-0031-fp16
    person-reidentification-retail-0076
    person-reidentification-retail-0076-fp16
    person-reidentification-retail-0079
    person-reidentification-retail-0079-fp16
    person-vehicle-bike-detection-crossroad-0078
    person-vehicle-bike-detection-crossroad-0078-fp16
    road-segmentation-adas-0001
    road-segmentation-adas-0001-fp16
    semantic-segmentation-adas-0001
    semantic-segmentation-adas-0001-fp16
    single-image-super-resolution-1033
    single-image-super-resolution-1033-fp16
    text-detection-0002
    text-detection-0002-fp16
    vehicle-attributes-recognition-barrier-0039
    vehicle-attributes-recognition-barrier-0039-fp16
    vehicle-detection-adas-0002
    vehicle-detection-adas-0002-fp16
    vehicle-license-plate-detection-barrier-0106
    vehicle-license-plate-detection-barrier-0106-fp16
    face-detection-adas-binary-0001
    single-image-super-resolution-1032
    single-image-super-resolution-1032-fp16
    action-recognition-0001-encoder
    action-recognition-0001-encoder-fp16
    instance-segmentation-security-0049
    instance-segmentation-security-0049-fp16
    vehicle-detection-adas-binary-0001 
    driver-action-recognition-adas-0002-decoder
    driver-action-recognition-adas-0002-decoder-fp16
    pedestrian-detection-adas-binary-0001
    person-detection-action-recognition-teacher-0002
    person-detection-action-recognition-teacher-0002-fp16
    instance-segmentation-security-0033
    instance-segmentation-security-0033-fp16
    action-recognition-0001-decoder
    action-recognition-0001-decoder-fp16
    text-recognition-0012
    text-recognition-0012-fp16
    driver-action-recognition-adas-0002-encoder
    driver-action-recognition-adas-0002-encoder-fp16
    gaze-estimation-adas-0002
    gaze-estimation-adas-0002-fp16
  • Download only some topologies (mtcnn-p and all topologies starting with "densenet-" in the following code example):

    ./downloader.py --name 'mtcnn-p,densenet-*'

    The argument to --name must be a comma-separated list of patterns, which may contain shell-style wildcards. See https://docs.python.org/3/library/fnmatch.html for a full description of the pattern syntax.

    Alternatively, you can get the list of patterns from a file:

    ./downloader.py --list my.lst

    The specified file must list one pattern per line. Blank lines and comments starting with # will be ignored. For example:

    mtcnn-p
    densenet-* # get all DenseNet variants
    

Expected free space to download all the topologies with the default configuration file is around 7.5 GB.


Copyright © 2018 Intel Corporation

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at

 http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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