Official Pytorch implementation for video neural representation (NeRV)

Last update: Dec 28, 2022

Related tags

Overview

NeRV: Neural Representations for Videos (NeurIPS 2021)

Project Page | Paper | UVG Data

Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
This is the official implementation of the paper "NeRV: Neural Representations for Videos ".

Get started

We run with Python 3.8, you can set up a conda environment with all dependencies like so:

pip install -r requirements.txt

High-Level structure

The code is organized as follows:

train_nerv.py includes a generic traiing routine.
model_nerv.py contains the dataloader and neural network architecure
data/ directory video/imae dataset, we provide big buck bunny here
checkpoint/ directory contains some pre-trained model on big buck bunny dataset
log files (tensorboard, txt, state_dict etc.) will be saved in output directory (specified by --outf)

Reproducing experiments

Training experiments

The NeRV-S experiment on 'big buck bunny' can be reproduced with

python train_nerv.py -e 300 --cycles 1  --lower-width 96 --num-blocks 1 --dataset bunny --frame_gap 1 \
    --outf bunny_ab --embed 1.25_40 --stem_dim_num 512_1  --reduction 2  --fc_hw_dim 9_16_26 --expansion 1  \
    --single_res --loss Fusion6   --warmup 0.2 --lr_type cosine  --strides 5 2 2 2 2  --conv_type conv \
    -b 1  --lr 0.0005 --norm none --act swish

Evaluation experiments

To evaluate pre-trained model, just add --eval_Only and specify model path with --weight, you can specify model quantization with --quant_bit [bit_lenght], yuo can test decoding speed with --eval_fps, below we preovide sample commends for NeRV-S on bunny dataset

python train_nerv.py -e 300 --cycles 1  --lower-width 96 --num-blocks 1 --dataset bunny --frame_gap 1 \
    --outf bunny_ab --embed 1.25_40 --stem_dim_num 512_1  --reduction 2  --fc_hw_dim 9_16_26 --expansion 1  \
    --single_res --loss Fusion6   --warmup 0.2 --lr_type cosine  --strides 5 2 2 2 2  --conv_type conv \
    -b 1  --lr 0.0005 --norm none  --act swish \
    --weight checkpoints/nerv_S.pth --eval_only

Dump predictions with pre-trained model

To evaluate pre-trained model, just add --eval_Only and specify model path with --weight

python train_nerv.py -e 300 --cycles 1  --lower-width 96 --num-blocks 1 --dataset bunny --frame_gap 1 \
    --outf bunny_ab --embed 1.25_40 --stem_dim_num 512_1  --reduction 2  --fc_hw_dim 9_16_26 --expansion 1  \
    --single_res --loss Fusion6   --warmup 0.2 --lr_type cosine  --strides 5 2 2 2 2  --conv_type conv \
    -b 1  --lr 0.0005 --norm none  --act swish \
   --weight checkpoints/nerv_S.pth --eval_only  --dump_images

Citation

If you find our work useful in your research, please cite:

@inproceedings{hao2021nerv,
    author = {Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava },
    title = {NeRV: Neural Representations for Videos s},
    booktitle = {NeurIPS},
    year={2021}
}

Contact

If you have any questions, please feel free to email the authors.

Official Pytorch implementation for video neural representation (NeRV)

Related tags

Overview

NeRV: Neural Representations for Videos (NeurIPS 2021)

Project Page | Paper | UVG Data

Get started

High-Level structure

Reproducing experiments

Training experiments

Evaluation experiments

Dump predictions with pre-trained model

Citation

Contact

Owner

hao

PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Hysterese plugin with two temperature offset areas

Hyperbolic Procrustes Analysis Using Riemannian Geometry

YOLOX-RMPOLY

Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"

Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Official PyTorch implementation of SyntaSpeech (IJCAI 2022)

Repository to run object detection on a model trained on an autonomous driving dataset.

DROPO: Sim-to-Real Transfer with Offline Domain Randomization

Python implementation of Wu et al (2018)'s registration fusion

Tandem Mass Spectrum Prediction with Graph Transformers

Code for Efficient Visual Pretraining with Contrastive Detection

Unadversarial Examples: Designing Objects for Robust Vision

Implementation of Nyström Self-attention, from the paper Nyströmformer

Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

OCR-D wrapper for detectron2 based segmentation models

A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.

Python implementation of Project Fluent

BlockUnexpectedPackets - Preventing BungeeCord CPU overload due to Layer 7 DDoS attacks by scanning BungeeCord's logs