OBJECT TRAJECTORY FORECASTING USING LSTM-BASED RECURRENT NEURAL NETWORKS

Training, testing and inference with multi-input multi-output LSTM-based Recurrent Neural Networks for trajectory forecasting

Setup Your Anaconda Environment

In order to use the training, testing, inference scripts and utility tools, you need to create/clone an Anaconda dedicated environment containing predifined packages (i.e. tensorflow-gpu). If you do not already have Anaconda installed on your machine, please follow the installation steps avalaible at: https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart

Once you have successfully installed Anaconda, create a new environment with from a previously created Anaconda environment by using the environment.yaml file. Then, please activate your recently created environment.

# Create a new environment based on `environment.yaml` file
conda env create -f environment.yaml
# Activate your recently created environment
conda activate <my_environment>

Training, Testing and Inference

Training

CLI for Training Path Prediction LSTM-based neural models.

Usage:

train_lstm.py [-h] dataset output config
Positional arguments:
  dataset     Path to input train-test dataset in .hdf5 format
  output      Path to the output directory where resulting models, graphs and history results are saved.
  config      Path to configuration file used for training.
optional arguments:
  -h, --help  show this help message and exit

Example:

python3 train_lstm.py path/to/training_lstm_dataset.hdf5 path/to/output/directory config_lstm.json

Inference

Command line tool for making Path Predictions using trained models

Usage:

evaluate_lstm.py [-h] dataset output configuration_file
positional arguments:
  dataset     Path to input test dataset in .hdf5 format
  output      Path to the output directory where resulting training graphs and prediction results are computed.
  config      Path to configuration file used for testing.
optional arguments:
  -h, --help  show this help message and exit

Example:

python3 evaluate_lstm.py  path/to/test_lstm_dataset.hdf5 path/to/trained/model/output/directory config_lstm.json

Utility Tools:

Dataset Creator

Command line utility to create a training dataset in HDF5 format for the track recurrent neural network from CSV files created by the classifier + Kalman filter tracker.

Usage:

dataset-creator [-h] [-a] input_directory output
positional arguments:
	input_directory  Path of a directory with CSV files to extract tracks.
	output           Path of the output training dataset file with .hdf5 extension.
optional arguments:
	-h, --help       show this help message and exit
	-a, --append     Flag to indicate that an existing HDF5 file can be used and new datasets should be appended to it.

Example:

python3 dataset-creator path/to/raw/classifier/detection/files/ path/to/output_raw_dataset.hdf5

Train and Test Dataset transformator

Command line tool for generating train and test sets for LSTM-based models from an input raw dataset created using Raw Dataset Creator tool.

Usage:

dataset_transformer.py [-h] raw_dataset out_dataset configuration_file
positional arguments:
  raw_dataset  Path to input raw dataset in .h5 format
  out_dataset  Path to output train/test dataset in .h5 format
  config       Path to configuration file used for dataset transformer tool.
optional arguments:
  -h, --help   show this help message and exit

Example:

python3 dataset_transformer.py path/to/raw_dataset.hdf5 path/to/output/train_test_dataset.hdf5 config_transformer.json

HDF5 Dataset Merger

Command line tool for merging two HDF5 datasets already generated by dataset_transformer.py .

Usage:

dataset_merger.py [-h] input_dataset_1 input_dataset_2 out_dataset
positional arguments:
  input_dataset_1  Full path to first input  dataset in .hdf5 format to merge
  input_dataset_2  Full path to second input dataset in .hdf5 format to merge
  out_dataset       Path to output train/test dataset in .h5 format
optional arguments:
  -h, --help   show this help message and exit

Example:

python3 dataset_merger.py path/to/input_dataset_1.hdf5 path/to/input_dataset_2.hdf5 path/to/output/merged_dataset.hdf5

Keras HDF5 to Tensorflow PB model converter (3rd Party Tool)

The keras_to_tensorflow.py is a CLI that converts a trained keras model into a ready-for-inference TensorFlow PB model.

Summary

In the default behaviour, this tool freezes the nodes (converts all TF variables to TF constants), and saves the inference graph and weights into a binary protobuf (.pb) file. During freezing, TensorFlow also applies node pruning which removes nodes with no contribution to the output tensor.
This tool supports multiple output networks and enables the user to rename the output tensors via the --output_nodes_prefix flag.
If the --output_meta_ckpt flag is set, the checkpoint and metagraph files for TensorFlow will also be exported which can later be used in the tf.train.Saver class to continue training.

How to use

Keras models can be saved as a single [.hdf5 or h5] file, which stores both the architecture and weights, using the model.save() function. This model can be then converted to a TensorFlow model by calling this tool as follows:

python keras_to_tensorflow.py 
    --input_model="path/to/keras/model.h5" 
    --output_model="path/to/save/model.pb"

Keras models can also be saved in two separate files where a [.hdf5 or h5] file stores the weights, using the model.save_weights() function, and another .json file stores the network architecture using the model.to_json() function. In this case, the model can be converted as follows:

python keras_to_tensorflow.py 
    --input_model="path/to/keras/model.h5" 
    --input_model_json="path/to/keras/model.json" 
    --output_model="path/to/save/model.pb"

Try

python keras_to_tensorflow.py --help

to learn about other supported flags (quantize, output_nodes_prefix, save_graph_def).

Dependencies

keras
tensorflow
absl
pathlib

etarakci-hvl / keras-lstm-trajectory-prediction Goto Github PK

keras-lstm-trajectory-prediction's Introduction

OBJECT TRAJECTORY FORECASTING USING LSTM-BASED RECURRENT NEURAL NETWORKS

Setup Your Anaconda Environment

Training, Testing and Inference

Training

Usage:

Example:

Inference

Usage:

Example:

Utility Tools:

Dataset Creator

Usage:

Example:

Train and Test Dataset transformator

Usage:

Example:

HDF5 Dataset Merger

Usage:

Example:

Keras HDF5 to Tensorflow PB model converter (3rd Party Tool)

Summary

How to use

Dependencies

keras-lstm-trajectory-prediction's People

Contributors

Watchers

Recommend Projects

Recommend Topics

Recommend Org