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

Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

All of the figures and notebooks for my deep learning book, for free!

Conversational text Analysis using various NLP techniques

Measuring Coding Challenge Competence With APPS

PyTorch Lightning + Hydra. A feature-rich template for rapid, scalable and reproducible ML experimentation with best practices. ⚡🔥⚡

duralava is a neural network which can simulate a lava lamp in an infinite loop.

CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network)

A trusty face recognition research platform developed by Tencent Youtu Lab

CodeContests is a competitive programming dataset for machine-learning

Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

A Strong Baseline for Image Semantic Segmentation

Machine Learning toolbox for Humans

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

MWPToolkit is a PyTorch-based toolkit for Math Word Problem (MWP) solving.

SAN for Product Attributes Prediction

Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database.

PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Spherical Confidence Learning for Face Recognition, accepted to CVPR2021.

Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data

The Pytorch code of "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification", CVPR 2022 (Oral).