Document the model quantizer

Author: Roman Donchenko

tools/downloader/README.md

@@ -11,6 +11,9 @@ based on configuration files in the models' directories.
 * `converter.py` (model converter) converts the models that are not in the
   Inference Engine IR format into that format using Model Optimizer.
 
+* `quantizer.py` (model quantizer) quantizes full-precision models in the IR
+  format into low-precision versions using Post-Training Optimization Toolkit.
+
 * `info_dumper.py` (model information dumper) prints information about the models
   in a stable machine-readable format.
 
@@ -296,6 +299,100 @@ To do this, use the `--dry_run` option:
 See the "Shared options" section for information on other options accepted by
 the script.
 
+Model quantizer usage
+---------------------
+
+Before you run the model quantizer, you must prepare a directory with
+the datasets required for the quantization process. This directory will be
+referred to as `<DATASET_DIR>` below. See the "Dataset directory layout"
+section for information on the expected contents of that directory.
+
+The basic usage is to run the script like this:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR>
+```
+
+This will quantize all models for which quantization is supported. Other models
+are ignored.
+
+The `--all` option can be replaced with other filter options to quantize only
+a subset of models. See the "Shared options" section.
+
+The current directory must be the root of a tree of model files create by the model
+converter. To specify a different model tree path, use the `--model_dir` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --model_dir my/model/directory
+```
+
+By default, the quantized models are placed into the same model tree. To place them
+into a different directory tree, use the `-o`/`--output_dir` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --output_dir my/output/directory
+```
+
+By default, the script will produce models in every precision that is supported
+as a quantization output. To only produce models in a specific precision, use
+the `--precisions` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --precisions=FP16-INT8
+```
+
+The script will attempt to locate Post-Training Optimization Toolkit using
+the environment variables set by the OpenVINO&trade; toolkit's `setupvars.sh`/`setupvars.bat`
+script. You can override this heuristic with the `--pot` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --pot my/openvino/path/post_training_optimization_toolkit/main.py
+```
+
+By default, the script will run Post-Training Optimization Toolkit using the same
+Python executable that was used to run the script itself. To use a different
+Python executable, use the `-p`/`--python` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --python my/python
+```
+
+It's possible to specify a target device for Post-Training Optimization Toolkit
+to optimize for, by using the `--target_device` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --target_device VPU
+```
+
+The supported values are those accepted by the "target_device" option in
+Post-Training Optimization Toolkit's config files. If this option is unspecified,
+Post-Training Optimization Toolkit's default is used.
+
+The script can print the quantization commands without actually running them.
+To do this, use the `--dry_run` option:
+
+```sh
+./quantizer.py --all --dataset_dir <DATASET_DIR> --dry_run
+```
+
+With this option specified, the configuration file for Post-Training Optimization
+Toolkit will still be created, so that you can inspect it.
+
+See the "Shared options" section for information on other options accepted by
+the script.
+
+### Dataset directory layout
+
+Currently, all models for which quantization is supported require the
+[ILSVRC 2012](http://image-net.org/challenges/LSVRC/2012/index) validation
+dataset. This means that `<DATASET_DIR>` must contain the following entries:
+
+* A subdirectory named `ILSVRC2012_img_val` containing the ILSVRC 2012
+  validation images. To obtain these images, follow the
+  [instructions at the ILSVRC 2012 website](http://image-net.org/challenges/LSVRC/2012/signup).
+
+* `val.txt` from <http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz>.
+
 Model information dumper usage
 ------------------------------
 
@@ -364,7 +461,7 @@ describing a single model. Each such object has the following keys:
 Shared options
 --------------
 
-The are certain options that both tools accept.
+The are certain options that all tools accept.
 
 `-h`/`--help` can be used to print a help message: