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Imitation Learning from Logs

✎

Modified 2021-11-03 by tanij

In this part, you can find the required steps to make a submission based on Imitation Learning with Tensorflow for the lane following task, using real log data. It can be used as a starting point for any of the LF, [LFV](#challenge-LFV, and LFI challenges.

That you have made a submission with the tensorflow template.

You run a submission that won a previous edition of the AI-DO.

Quickstart

✎

Modified 2019-11-04 by Liam Paull

Clone the baseline Tensorflow imitation learning from logs repository:

$ git clone -b daffy https://github.com/duckietown/challenge-aido_LF-baseline-IL-logs-tensorflow.git

$ cd challenge-aido_LF-baseline-IL-logs-tensorflow

The code you find is structured into 3 folders.

Extracting data (folder extract_data)
Learning from the data (folder learning)
Submitting learned model (folder imitation_agent)

Step 2 can be run either using Docker or without. All other steps require Docker.

Extract data

✎

Modified 2018-10-28 by liampaull

Go to the extract_data folder:

$ cd extract_data

Docker data extraction

✎

Type:

$ make docker_extract_data

Once extraction is completed, move the extracted data to the learning folder. Type:

$ make docker_copy_for_learning

Learning

✎

Modified 2018-10-28 by liampaull

Go to the learning folder

$ cd ../learning

Docker learning

✎

Before being able to conduct the learning experiments in docker, you will need the Nvidia-runtime environment. To get this dependency type:

$ make prepare-docker

If using Docker (to avoid a ROS installation), type:

$ make learn-docker

to train a small convolutional neural network for imitation learning. This assumes that you have extracted data in the previous step.

Once learning is completed, move the learned model to the submission folder. Type:

$ make copy_for_submission

ROS+Python2.7 learning

✎

If you already have your Tensorflow learning pipeline on your system setup and do not use Docker. Then type:

$ make install-dependencies

To install some dependencies in a Python 2.7 virtual environment.

To train a small convolutional neural network for imitation learning, type:

$ make learn-regular

Once learning is completed, move the learned model to the imitation agent folder. Type:

$ make regular_copy_for_submission

Submission

✎

Modified 2019-10-24 by Manfred Diaz

Go to the imitation_agent folder

$ cd ../imitation_agent

If you have completed the previous steps, you will be able to submit this as is, with:

$ dts challenges submit --challenge aido3-LF-sim-validation

Or, run local evaluation with:

$ dts challenges evaluate

Details and Workflow

✎

Modified 2018-10-28 by liampaull

Changing the code

✎

Modified 2018-10-28 by liampaull

You may change the code to your needs. The code is located in the src folders in the respective folders.

Neural network design

✎

In particular you can adapt the neural network model in learning/src/cnn_training_functions.py.

The type of neural network, its architecture and hyperparameters are choices that you are asked to make, but as a baseline a CNN model which takes as inputs images and predicts angular velocities is provided. This model is not only relatively simple but also takes as input low resolution images indicating that extremely complex models may not necessarily be required to solve the task of lane following without dynamic objects. In any case, during training it is necesary to adhere to the following actions:

Name all your layers in order to be able to tell later which is the output layer of your neural network
Prepare model for mobile deployment generating a graph.pb file (TensorFlow GraphDef file in binary format)

Logs selection

✎

Which logs are downloaded is specified at the bottom of extract_data/src/download_logs.py.

Such data are saved in .bag files which are available in Duckietown Logs Database. The important note here is to feed your agent with appropriate data. Appropriate log data are considered those which:

Are relevant to the lane following task
Execute this task well the whole time and
Present smooth driving of the Duckiebots around the city.

One hint, is to search for data which were collected using the lane controller. In order to check if the lane controller was enabled, use rqt_bag to see if there is inside the node /duckiebot_name/lane_controller/. The LF_IL_tensorflow baseline, provides 5 bag files with approximately 10 minutes of appropriate lane following data.

How to prepare your data for training

✎

When working with real log data, this can be splitted into the following two subtasks:

Extract desired topics from bag files
Synchronize pairs of data from different topics

The first part should be clear, so let us focus to the second one. ROS messages from different topics are neither always of the same number nor properly arranged in the bag files. In this implementation the topics of interest are:

/camera_node/camera/compressed
/lane_controller_node/car_cmd

and the aforementioned problem is visualized in Figure Figure 4.1. Although a continuous sequence of images and car commands is expected, images and/or velocities could be omitted or even saved in slighlty translated timestamps. For this reason, in order to ensure the validity of our pairs of data, a synchronization is necessary.

It is stated that in this case the synchronization is based on the fact that when using the lane controller images cause the car commands and not the other way around, while between two consecutive images there should be only one car command. For your convenience, in the provided baseline there is a script that takes care of these two steps for you and eventually saves the images with their respective car commands to HDF5 files.

Image preprocessing

✎

Additionally, the way images are preprocessed is specified in extract_data/src/extract_data_functions.py and learning/src/cnn_predictions.py.

A submission with the Movidius Neural Compute Stick

✎

Modified 2018-10-31 by julian121266

At this point your model is trained and you are asked to compile it to a Movidius graph. First, you should freeze the TensorFlow graph. This procedure consists of the following steps:

combine the TensorFlow graph of your CNN model and its weights in a single file
convert variables into inline constants
given the output node of your output layer, keep only those operations that are necessary for the predictions
get rid of all training node
and apply optimization flags.

The script /learning/src/freeze_graph.py is part of the baseline and can be your guide for this procedure. The only tricky part here is to define correctly the output node which should not be confused with the output layer. In general, the output node should be easily found by the following name scope_name(if exists)/output_layer_name/BiasAdd.

Once you have the frozen graph, you are only one step away from the Movidius graph. The last step is to install NCSDK v2.05 in order to use its SDK tools to compile the TensorFlow frozen graph to a Movidius graph. After installing NCSDK v2.05, you just have to execute the command:

$ mvNCCompile -s 12 path_to_frozen_graph.pb -in input_node_of_TensorFlow_model -on output_node_of_TensorFlow_model -o path_to_save_the_movidius_graph.graph