Deployment: Wrapping and Executing Face Privacy Models

To utilize this transformer model set, it first creates a detect transformer and then a pixelate transformer. Continue to the Demonstrations: Tutorial for Face Privacy to see how to utilize these models with a simple demo API server.

Model Deployment

Following similar use pattens described by the main client library, there are two primary modes to export and deploy the generated classifier: by dumping it to disk or by pushing it to an onboarding server. Please consult the Usage for more specific arguments but the examples below demonstrate basic capabilities.

This single repo has a number of different models that can be composed together for operation.

  • Dump the detect model to disk.

    python face_privacy_filter/ -f detect  -d model_detect
  • Dump the pixelate model to disk.

    python face_privacy_filter/ -f pixelate -d model_pix

Below is an extended for training a model, dumping it to disk, and pushing that model. (recommended)

export ACUMOS_USERNAME="user"; \
export ACUMOS_PASSWORD="password";
export ACUMOS_TOKEN="a_very_long_token";

export ACUMOS_PUSH=""; \
export ACUMOS_AUTH=""; \
python face_privacy_filter/ -f detect

In-place Evaluation

In-place evaluation will utilize a serialized version of the model and load it into memory for use in-place. This mode is handy for quick evaluation of images or image sets for use in other classifiers.

  • Evaluate the detect model from disk and a previously produced detect object

    python face_privacy_filter/ -d model_detect -p output.csv -i web_demo/images/face_DiCaprio.jpg
  • Example for evaluating the pixelate model from disk and a previously produced detect object

    python face_privacy_filter/ -d model_pix -i detect.csv -p output.jpg --csv_input

Using the client model runner

Getting even closer to what it looks like in a deployed model, you can also use the model runner code to run your full cascade (detection + pixelate) transform locally. (added v0.3.0)

  1. First, decide the ports to run your detection and pixelate models. In the example below, detection runs on port 8884 and pixelation runs on port 8885. For the runner to properly forward requests for you, provide a simple JSON file example called runtime.json in the working directory that you run the model runner.
# cat runtime.json
{downstream": [""]}
  1. Second, dump and launch the face detection model. If you modify the ports to run the models, please change them accordingly. This command example assumes that you have cloned the client library in a relative path of ../acumos-python-client. The first line removes any prior model directory, the second dumps the detect model to disk, and the third runs the model.
rm -rf model_detect/;  \
    python face_privacy_filter/ -d model_detect -f detect; \
    python ../acumos-python-client/testing/wrap/ --port 8884 --modeldir model_detect/face_privacy_filter_detect
  1. Finally, dump and launch the face pixelation model. Again, if you modify the ports to run the models, please change them accordingly. Aside from the model and port, the main difference between the above line is that the model runner is instructed to ignore the downstream forward (runtime.json) file so that it doesn’t attempt to forward the request to itself.
rm -rf model_pix;  \
    python face_privacy_filter/ -d model_pix -f pixelate; \
    python ../acumos-python-client/testing/wrap/ --port 8885 --modeldir model_pix/face_privacy_filter_pixelate  --no_downstream

Installation Troubleshoting

Using some environment-based versions of python (e.g. conda), one problem seemed to come up with the installation of the dependent package opencv-python. If you launch your python instance and see an error like the one below, keep reading.

>>> import cv2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: dynamic module does not define module export function (PyInit_cv2)

This is likely because your PYTHONPATH is not correctly configured to point to the additional installed libraries.

  • From the simple example here you can check your environment with echo $PYTHONPATH. If it does not contain the directory that you installed to, then you have a problem.
  • Please check your installation by running python -v -v; import cv2 and checking that the last loaded library is in the right location.
  • In some instances, this variable needed to be blank to work properly (i.e. export PYTHONPATH=) run at some time during start up.