55 lines
2.2 KiB
Markdown
Executable File
55 lines
2.2 KiB
Markdown
Executable File
# Stable-Time Path Planning
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## 1. Installation
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To reproduce our ablation results, please install [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html#) environment on a Linux machine with Nvidia GPU.<br>
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You may need to install the following apt packages:
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```bash
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sudo apt-get install libboost-dev
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```
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Please config the conda environment:
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```bash
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cd ~/DPtraj/deepPathPlan
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conda create -n <your_env_name> python=3.8
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conda activate <your_env_name>
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pip install -r requirements.txt
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```
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## 2. Reproducing the Model
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### 2.1 Download Training Data
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Please download, unzip and place the [Data](https://drive.google.com/file/d/1uuQsWTBYMzHI0RcXgpFg6Ft-3lf6-fRD/view?usp=drive_link) as the directory `~/totalData`. The completed dataset is suggested for reproducing the model as we found rich data makes performances better.
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### 2.2 Training
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You can easily retrain the model by running `PathNet/train_ours.py`:
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```bash
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cd ~/DPtraj/deepPathPlan
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python PathNet/train_ours.py
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```
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### 2.3 Visualizing the Results
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Once the model converges, you can visualize it:
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```bash
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cd ~/DPtraj/deepPathPlan
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python PathNet/visualizer_tojit.py
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```
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> **Note:** This script will utilize `torch.jit.trace` to generate a model file that can be directly invoked by LibTorch, allowing you to seamlessly integrate it into our ROS program.
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## 3. Checkout our experiment logs
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To check similar results in table Table.S1 and Fig.S2 of Supplementary Materials, we provide:<br>
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1. Detailed eval log [`model.pkl.txt`](deepPathPlan/models/model.pkl.txt).
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2. Detailed training log [`model.pklStep.txt`](deepPathPlan/models/model.pklStep.txt).
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3. Reproduced model [`model.pkl`](https://drive.google.com/file/d/13o9flu4yo451FzMRhiEFF8PYq2dcNoDo/view?usp=sharing).
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> **Note:**
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> Please note that slight differences in the results compared to the paper are normal, due to variations in training configuration and package versions.<br>
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> For example, the batch size (`bz`) is set to 32 in this repo for easier reproduction on GPUs with 16 GB memory (the bz used in the paper is 64 based on 32 GB memory).
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## 4. Contact
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If you have any questions, please feel free to contact Zhichao HAN (<zhichaohan@zju.edu.cn>) or Mengze TIAN(<mengze.tian@epfl.ch>).
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