https://github.com/srama2512/sidekicks
Sidekick Policy Learning for Active Visual Exploration (ECCV 2018)
https://github.com/srama2512/sidekicks
exploration learning reinforcement visual
Last synced: 6 months ago
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Sidekick Policy Learning for Active Visual Exploration (ECCV 2018)
- Host: GitHub
- URL: https://github.com/srama2512/sidekicks
- Owner: srama2512
- License: mit
- Created: 2018-07-08T04:56:13.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-01-21T19:42:38.000Z (over 3 years ago)
- Last Synced: 2025-03-23T17:51:28.344Z (7 months ago)
- Topics: exploration, learning, reinforcement, visual
- Language: Python
- Size: 89.8 KB
- Stars: 26
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Emergence of exploratory look-around behaviors through active observation completion
A journal version of this work in conjunction with our prior work on [Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks](https://arxiv.org/abs/1709.00507) has been published in Science Robotics 2019.
[Emergence of exploratory look-around behaviors through active observation completion](http://vision.cs.utexas.edu/projects/visual-exploration/)
Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman
Science Robotics 2019
A cleaned version of this codebase along with new transfer tasks are available at https://github.com/srama2512/visual-exploration.
# Sidekick Policy Learning
This repository contains code and data for the paper
[Sidekick Policy Learning for Active Visual Exploration](http://vision.cs.utexas.edu/projects/sidekicks/)
Santhosh K. Ramakrishnan, Kristen Grauman
ECCV 2018
## Setup
- First install anaconda and setup a new environment. Install anaconda from: https://www.anaconda.com/download/
```
conda create -n spl python=2.7
source activate spl
```
- Clone this project repository and setup requirements using pip.
```
git clone https://github.com/srama2512/sidekicks.git
cd sidekicks
pip install -r requirements.txt
```
- Download preprocessed SUN360 and ModelNet data.
```
wget http://vision.cs.utexas.edu/projects/sidekicks/data.zip
unzip data.zip
```
- Sidekick scores for `ours-rew`, `ours-demo`, `rnd-rewards` on both datasets have been provided [here](http://vision.cs.utexas.edu/projects/sidekicks/scores.zip). The `one-view` model used to generate them have also been provided.
## Evaluating pre-trained models
All the pre-trained models have been provided [here](http://vision.cs.utexas.edu/projects/sidekicks/models.zip). To evaluate them, download them to the `models` directory. To reproduce results from the paper:
```
wget http://vision.cs.utexas.edu/projects/sidekicks/models.zip
unzip models.zip
sh evaluation_script_final.sh
```
### Evaluation examples
- Evaluating SUN360 `one-view` baseline on the test data with `avg` metric:
```
python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
--model_path models/sun360/one-view.net --T 1 --M 8 --N 4 \
--start_view 2 --save_path dummy/
```
- Evaluating SUN360 `ltla` baseline on the test data with `avg` metric:
```
python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
--model_path models/sun360/ltla.net --T 4 --M 8 --N 4 \
--start_view 2 --save_path dummy/
```
- Evaluating SUN360 `ltla` baseline on the test data with `adv` metric:
```
python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
--model_path models/sun360/ltla.net --T 4 --M 8 --N 4 \
--start_view 2 --save_path dummy/
```
- Evaluating SUN360 `rnd-actions` baseline on test data with `avg` metric:
```
python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
--model_path models/sun360/rnd-actions.net --T 4 --M 8 --N 4 \
--start_view 2 --actorType random --save_path dummy/
```
- Evaluating ModelNet Hard `one-view` baseline on test (seen and unseen) data with `avg` metric:
```
python eval.py --h5_path modelnet30_processed.h5 \
--h5_path_unseen modelnet10_processed.h5 --dataset 1 \
--model_path models/modelnet_hard/one-view.net --T 1 --M 9 --N 5 \
--start_view 2 --save_path dummy/
```
## Training models
Ensure that the [pre-trained models](http://vision.cs.utexas.edu/projects/sidekicks/models.zip) and [pre-computed scores](http://vision.cs.utexas.edu/projects/sidekicks/scores.zip) are downloaded and extracted.
- Training `one-view` model on SUN360 with default settings:
```
python main.py --T 1 --training_setting 0 --epochs 100 \
--save_path saved_models/sun360/one-view
```
- Training `ltla` baseline on SUN360 with default settings (starting from pre-trained `one-view` model):
```
python main.py --T 4 --training_setting 1 --epochs 1000 \
--save_path saved_models/sun360/ltla/ \
--load_model models/sun360/one-view.net
```
- Training `ours-rew` on SUN360 with default settings (with pre-computed score):
```
python main.py --T 4 --training_setting 1 --epochs 1000 \
--save_path saved_models/sun360/ours-rew/ \
--load_model models/sun360/one-view.net --expert_rewards True \
--rewards_h5_path scores/sun360/ours-rew-scores.h5
```
- Training `ours-demo` on SUN360 with default settings (with pre-computed score):
```
python main.py --T 4 --training_setting 1 --epochs 1000 \
--save_path saved_models/sun360/ours-demo/ \
--load_model models/sun360/one-view.net --expert_trajectories True \
--utility_h5_path scores/sun360/ours-demo-scores.h5
```
- Training `ltla` baseline on ModelNet Hard with default settings (starting from pre-trained `one-view` model):
```
python main.py --h5_path data/modelnet_hard/modelnet30_processed.h5 \
--training_setting 1 --dataset 1 --T 4 --M 9 --N 5 \
--load_model models/modelnet_hard/one-view.net \
--save_path saved_models/modelnet_hard/ltla/
```
The other ModelNet Hard models can be trained similar to SUN360 models. To train actor critic models, set `--baselineType critic`. To add full observability to the critic (for `asymm-ac`), set `--critic_full_obs True`.
## Visualization
From the repository directory, start jupyter notebook and open `visualize_policy_paper.ipynb`. Perform the TODOs mentioned in the comments (setting the correct paths) and run the entire script. It will generate tensorboard files contained visualized heatmaps on several examples.