A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

Overview

MUGEN Dataset

Project Page | Paper

Setup

conda create --name MUGEN python=3.6
conda activate MUGEN
pip install --ignore-installed https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.12.0-cp36-cp36m-linux_x86_64.whl 
module load cuda/9.0
module load cudnn/v7.4-cuda.10.0
git clone coinrun_MUGEN
cd coinrun_MUGEN
pip install -r requirements.txt
conda install -c conda-forge mpi4py
pip install -e .

Training Agents

Basic training commands:

python -m coinrun.train_agent --run-id myrun --save-interval 1

After each parameter update, this will save a copy of the agent to ./saved_models/. Results are logged to /tmp/tensorflow by default.

Run parallel training using MPI:

mpiexec -np 8 python -m coinrun.train_agent --run-id myrun

Train an agent on a fixed set of N levels. With N = 0, the training set is unbounded.

python -m coinrun.train_agent --run-id myrun --num-levels N

Continue training an agent from a checkpoint:

python -m coinrun.train_agent --run-id newrun --restore-id myrun

View training options:

python -m coinrun.train_agent --help

Example commands for MUGEN agents:

Base model

python -m coinrun.train_agent --run-id name_your_agent \
                --architecture impala --paint-vel-info 1 --dropout 0.0 --l2-weight 0.0001 \
                --num-levels 0 --use-lstm 1 --num-envs 96 --set-seed 80 \
                --bump-head-penalty 0.25 -kill-monster-reward 10.0

Add squat penalty to reduce excessive squating

python -m coinrun.train_agent --run-id gamev2_fine_tune_m4_squat_penalty \
                --architecture impala --paint-vel-info 1 --dropout 0.0 --l2-weight 0.0001 \
                --num-levels 0 --use-lstm 1 --num-envs 96 --set-seed 811 \
                --bump-head-penalty 0.1 --kill-monster-reward 5.0 --squat-penalty 0.1 \
                --restore-id gamev2_fine_tune_m4_0

Larger model

python -m coinrun.train_agent --run-id gamev2_largearch_bump_head_penalty_0.05_0 \
                --architecture impalalarge --paint-vel-info 1 --dropout 0.0 --l2-weight 0.0001 \
                --num-levels 0 --use-lstm 1 --num-envs 96 --set-seed 51 \
                --bump-head-penalty 0.05 -kill-monster-reward 10.0

Add reward for dying

python -m coinrun.train_agent --run-id gamev2_fine_tune_squat_penalty_die_reward_3.0 \
                --architecture impala --paint-vel-info 1 --dropout 0.0 --l2-weight 0.0001 \
                --num-levels 0 --use-lstm 1 --num-envs 96 --set-seed 857 \
                --bump-head-penalty 0.1 --kill-monster-reward 5.0 --squat-penalty 0.1 \
                --restore-id gamev2_fine_tune_m4_squat_penalty --die-penalty -3.0

Add jump penalty

python -m coinrun.train_agent --run-id gamev2_fine_tune_m4_jump_penalty \
                --architecture impala --paint-vel-info 1 --dropout 0.0 --l2-weight 0.0001 \
                --num-levels 0 --use-lstm 1 --num-envs 96 --set-seed 811 \
                --bump-head-penalty 0.1 --kill-monster-reward 10.0 --jump-penalty 0.1 \
                --restore-id gamev2_fine_tune_m4_0

Data Collection

Collect video data with trained agent. The following command will create a folder {save_dir}/{model_name}_seed_{seed}, which contains the audio semantic maps to reconstruct game audio, as well as the csv containing all game metadata. We use the csv for reconstructing video data in the next step.

python -m coinrun.collect_data --collect_data --paint-vel-info 1 \
                --set-seed 406 --restore-id gamev2_fine_tune_squat_penalty_timeout_300 \
                --save-dir  \
                --level-timeout 600 --num-levels-to-collect 2000

The next step is to create 3.2 second videos with audio by running the script gen_videos.sh. This script first parses the csv metadata of agent gameplay into a json format. Then, we sample 3 second clips, render to RGB, generate audio, and save .mp4s. Note that we apply some sampling logic in gen_videos.py to only generate videos for levels of sufficient length and with interesting game events. You can adjust the sampling logic to your liking here.

There are three outputs from this script:

  1. ./json_metadata - where full level jsons are saved for longer video rendering
  2. ./video_metadata - where 3.2 second video jsons are saved
  3. ./videos - where 3.2s .mp4 videos with audio are saved. We use these videos for human annotation.
bash gen_videos.sh  

For example:

bash gen_videos.sh video_data model_gamev2_fine_tune_squat_penalty_timeout_300_seed_406

License Info

The majority of MUGEN is licensed under CC-BY-NC, however portions of the project are available under separate license terms: CoinRun, VideoGPT, VideoCLIP, and S3D are licensed under the MIT license; Tokenizer is licensed under the Apache 2.0 Pycocoevalcap is licensed under the BSD license; VGGSound is licensed under the CC-BY-4.0 license.

Owner
MUGEN
MUGEN
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
Face Recognize System on camera AI OAK1

FRS on OAK1 Face Recognize System on camera OAK1 This project contains our work that deploy on camera OAK1 Features Anti-Spoofing Face detection Face

Tran Anh Tuan 6 Aug 08, 2022
Complete system for facial identity system

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

4 May 02, 2022
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
This is a official repository of SimViT.

SimViT This is a official repository of SimViT. We will open our models and codes about object detection and semantic segmentation soon. Our code refe

ligang 57 Dec 15, 2022
Memory-Augmented Model Predictive Control

Memory-Augmented Model Predictive Control This repository hosts the source code for the journal article "Composing MPC with LQR and Neural Networks fo

Fangyu Wu 1 Jun 19, 2022
Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022
Starter code for the ICCV 2021 paper, 'Detecting Invisible People'

Detecting Invisible People [ICCV 2021 Paper] [Website] Tarasha Khurana, Achal Dave, Deva Ramanan Introduction This repository contains code for Detect

Tarasha Khurana 28 Sep 16, 2022
A reimplementation of DCGAN in PyTorch

DCGAN in PyTorch A reimplementation of DCGAN in PyTorch. Although there is an abundant source of code and examples found online (as well as an officia

Diego Porres 6 Jan 08, 2022
MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

Burak Bagatarhan 12 Mar 29, 2022
Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

Emirhan BULUT 28 Dec 04, 2021
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

PyTorch implementation of Continuous Augmented Positional Embeddings (CAPE), by Likhomanenko et al. Enhance your Transformer positional embeddings with easy-to-use augmentations!

Guillermo Cámbara 26 Dec 13, 2022
Hardware accelerated, batchable and differentiable optimizers in JAX.

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Google 621 Jan 08, 2023
rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle.

rastrainer rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle. UI TODO Init UI. Add Block. Add l

deepbands 5 Mar 04, 2022
A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

Youzhi Gu 7 Nov 27, 2021
Fast sparse deep learning on CPUs

SPARSEDNN **If you want to use this repo, please send me an email: [email pro

Ziheng Wang 44 Nov 30, 2022
Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi.

Spchcat Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi. Description spchcat is a command-line tool that read

Pete Warden 279 Jan 03, 2023
✨风纪委员会自动投票脚本,利用Github Action帮你进行裁决操作(为了让其他风纪委员有案件可判,本程序从中午12点才开始运行,有需要请自己修改运行时间)

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能,喜欢请给我点个STAR吧! 如果你不是风纪委员,在符合风纪委员申请条件的情况下,本脚本会自动帮你申请 投票时间是早上八点,如果有需要请自行修改.github/workflows/Judge.yml中的时间,

Pesy Wu 25 Feb 17, 2021
nnFormer: Interleaved Transformer for Volumetric Segmentation

nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation ". Please

jsguo 610 Dec 28, 2022