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SSD MobileNet v2

Setup AI Model Efficiency Toolkit (AIMET)

Please install and setup AIMET before proceeding further.

Obtaining model checkpoint and dataset

  • SSD MobileNet v2 checkpoint used for AIMET quantization can be downloaded from the Releases page.
  • OR you could follow the steps below to obtain the checkpoint

export inference graph

The following steps are need to have a model ready for AIMET quantization

  • git clone https://github.com/tensorflow/models.git
cd models && git checkout r1.12.0
cd research && protoc object_detection/protos/*.proto --python_out=.

  • Download ssd_mobilenet_v2

    • tar xfvz ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz

    • remove following parameters from pipeline.config that come with the tarball

      graph_rewriter {
      quantization {
      delay: 48000
      weight_bits: 8
      activation_bits: 8
    }

  • Add the following code snippet to Line 147, models/research/object_detection/export_inference_graph.py

    import os
saver = tf.train.Saver()
with tf.Session() as sess:
  saver.restore(sess, os.path.join(FLAGS.output_directory, "model.ckpt"))
  aimet_model_output_dir = os.path.join(FLAGS.output_directory, "AIMET")
os.mkdir(aimet_model_output_dir)
saver.save(sess, os.path.join(aimet_model_output_dir, "model.ckpt")

  • tensorflow v1.10 is need to run the script, we could use the offical tensorflow 1.10.1 docker image

    • docker pull tensorflow/tensorflow:1.10.1-devel-py3
export WORKSPACE=<path_to_workspace>
docker run -it --rm -v $WORKSPACE:$WORKSPACE tensorflow/tensorflow:1.10.1-devel-py3

  • run export_inference_graph.py to obtain model checkpoint ready for AIMET quantization

    cd models/research
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python ./object_detection/export_inference_graph.py \
    --input_type image_tensor \
    --pipeline_config_path <path/to/modified_pipeline.config> \
    --trained_checkpoint_prefix <path/to/model.ckpt> \
    --output_directory <path/to/exported_model_directory> \
    • model checkpoint will be available at <path/to/exported_model_directory>/AIMET/model.ckpt

COCO dataset TFRecord

TFRecord format of COCO dataset is need

  • download_and_preprocess_mscoco.sh can be used to download and convert coco dataset into TFRecord

    git clone https://github.com/tensorflow/models.git
git checkout master
cd models/research/object_detection/dataset_tools
./download_and_preprocess_mscoco.sh <mscoco_dir>

  • If COCO dataset is already available or you want to download COCO dataset separately

    • COCO dataset can be download here: COCO
      • Please download the 2017 Version
    • create_coco_tf_record.py can be used to convert dataset into TFRecord

Additional Dependencies

Package
tensorflow/models
pycocotools

Setup models Repo

  • Clone the tensorflow models repository from github:

    git clone https://github.com/tensorflow/models.git
cd models && git checkout r1.12.0

  • Append the repo location to your PYTHONPATH by doing the following:

    export PYTHONPATH=<path to tensorflow models repo>/models/research:$PYTHONPATH

Pip install pycocotools

  • Install pycocotools by running the following:

    pip install --user git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

Adding dependencies within Docker Image

  • If you are using a docker image, e.g. AIMET development docker, please add the following lines to the Dockerfile and rebuild the Docker image
RUN pip install git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

Usage

  • ssd_mobilenet_v2_quanteval.py has four required arguments, an example usage is shown below
./ssd_mobilenet_v2_quanteval.py --model-checkpoint <path to model ckpt>/model.ckpt --dataset-dir <path to tfrecord dataset> --TFRecord-file-pattern 'coco_val.record-*-of-00010' --annotation-json-file <path to instances json file>/instances_val2017.json
  • --quantsim-output-dir option can be used if want to save the quantized graph

Quantizer Op Assumptions

In the evaluation script included, we have manually configured the quantizer ops with the following assumptions:

  • Weight quantization: 8 bits, asymmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits, asymmetric quantization
  • Model inputs are not quantized