Videocaptioning.pytorch - A simple implementation of video captioning

Overview

pytorch implementation of video captioning

recommend installing pytorch and python packages using Anaconda

This code is based on video-caption.pytorch

requirements (my environment, other versions of pytorch and torchvision should also support this code (not been verified!))

  • cuda
  • pytorch 1.7.1
  • torchvision 0.8.2
  • python 3
  • ffmpeg (can install using anaconda)

python packages

  • tqdm
  • pillow
  • nltk

Data

MSR-VTT. Download and put them in ./data/msr-vtt-data directory

|-data
  |-msr-vtt-data
    |-train-video
    |-test-video
    |-annotations
      |-train_val_videodatainfo.json
      |-test_videodatainfo.json

MSVD. Download and put them in ./data/msvd-data directory

|-data
  |-msvd-data
    |-YouTubeClips
    |-annotations
      |-AllVideoDescriptions.txt

Options

all default options are defined in opt.py or corresponding code file, change them for your like.

Acknowledgements

Some code refers to ImageCaptioning.pytorch

Usage

(Optional) c3d features (not verified)

you can use video-classification-3d-cnn-pytorch to extract features from video.

Steps

  1. preprocess MSVD annotations (convert txt file to json file)

refer to data/msvd-data/annotations/prepro_annotations.ipynb

  1. preprocess videos and labels
# For MSR-VTT dataset
# Train and Validata set
CUDA_VISIBLE_DEVICES=0 python prepro_feats.py \
    --video_path ./data/msr-vtt-data/train-video \
    --video_suffix mp4 \
    --output_dir ./data/msr-vtt-data/resnet152 \
    --model resnet152 \
    --n_frame_steps 40

# Test set
CUDA_VISIBLE_DEVICES=0 python prepro_feats.py \
    --video_path ./data/msr-vtt-data/test-video \
    --video_suffix mp4 \
    --output_dir ./data/msr-vtt-data/resnet152 \
    --model resnet152 \
    --n_frame_steps 40

python prepro_vocab.py \
    --input_json data/msr-vtt-data/annotations/train_val_videodatainfo.json data/msr-vtt-data/annotations/test_videodatainfo.json \
    --info_json data/msr-vtt-data/info.json \
    --caption_json data/msr-vtt-data/caption.json \
    --word_count_threshold 4

# For MSVD dataset
CUDA_VISIBLE_DEVICES=0 python prepro_feats.py \
    --video_path ./data/msvd-data/YouTubeClips \
    --video_suffix avi \
    --output_dir ./data/msvd-data/resnet152 \
    --model resnet152 \
    --n_frame_steps 40

python prepro_vocab.py \
    --input_json data/msvd-data/annotations/MSVD_annotations.json \
    --info_json data/msvd-data/info.json \
    --caption_json data/msvd-data/caption.json \
    --word_count_threshold 2
  1. Training a model
# For MSR-VTT dataset
CUDA_VISIBLE_DEVICES=0 python train.py \
    --epochs 1000 \
    --batch_size 300 \
    --checkpoint_path data/msr-vtt-data/save \
    --input_json data/msr-vtt-data/annotations/train_val_videodatainfo.json \
    --info_json data/msr-vtt-data/info.json \
    --caption_json data/msr-vtt-data/caption.json \
    --feats_dir data/msr-vtt-data/resnet152 \
    --model S2VTAttModel \
    --with_c3d 0 \
    --dim_vid 2048

# For MSVD dataset
CUDA_VISIBLE_DEVICES=0 python train.py \
    --epochs 1000 \
    --batch_size 300 \
    --checkpoint_path data/msvd-data/save \
    --input_json data/msvd-data/annotations/train_val_videodatainfo.json \
    --info_json data/msvd-data/info.json \
    --caption_json data/msvd-data/caption.json \
    --feats_dir data/msvd-data/resnet152 \
    --model S2VTAttModel \
    --with_c3d 0 \
    --dim_vid 2048
  1. test

    opt_info.json will be in same directory as saved model.

# For MSR-VTT dataset
CUDA_VISIBLE_DEVICES=0 python eval.py \
    --input_json data/msr-vtt-data/annotations/test_videodatainfo.json \
    --recover_opt data/msr-vtt-data/save/opt_info.json \
    --saved_model data/msr-vtt-data/save/model_xxx.pth \
    --batch_size 100

# For MSVD dataset
CUDA_VISIBLE_DEVICES=0 python eval.py \
    --input_json data/msvd-data/annotations/test_videodatainfo.json \
    --recover_opt data/msvd-data/save/opt_info.json \
    --saved_model data/msvd-data/save/model_xxx.pth \
    --batch_size 100

NOTE

This code is just a simple implementation of video captioning. And I have not verify whether the SCST training process and C3D feature are useful!

Acknowledgements

Some code refers to ImageCaptioning.pytorch

Owner
Yiyu Wang
Yiyu Wang
Official Implementation for HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing Yuval Alaluf*, Omer Tov*, Ron Mokady, Rinon Gal, Amit H. Bermano *Denotes equ

885 Jan 06, 2023
A Keras implementation of CapsNet in the paper: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules

NOTE This implementation is fork of https://github.com/XifengGuo/CapsNet-Keras , applied to IMDB texts reviews dataset. CapsNet-Keras A Keras implemen

Lauro Moraes 5 Oct 23, 2022
Simple node deletion tool for onnx.

snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs

Katsuya Hyodo 6 May 15, 2022
Deep Learning ❤️ OneFlow

Deep Learning with OneFlow made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side Computer V

21 Oct 27, 2022
This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Robust Counterfactual Explanations This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations". I

Marco 5 Dec 20, 2022
Recurrent Conditional Query Learning

Recurrent Conditional Query Learning (RCQL) This repository contains the Pytorch implementation of One Model Packs Thousands of Items with Recurrent C

Dongda 4 Nov 28, 2022
D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos This repository contains the implementation for "D²Conv3D: Dynamic Dilated Co

17 Oct 20, 2022
Multi-robot collaborative exploration and mapping through Voronoi partition and DRL in unknown environment

Voronoi Multi_Robot Collaborate Exploration Introduction In the unknown environment, the cooperative exploration of multiple robots is completed by Vo

PeaceWord 6 Nov 22, 2022
Matching python environment code for Lux AI 2021 Kaggle competition, and a gym interface for RL models.

Lux AI 2021 python game engine and gym This is a replica of the Lux AI 2021 game ported directly over to python. It also sets up a classic Reinforceme

Geoff McDonald 74 Nov 03, 2022
Repository for training material for the 2022 SDSC HPC/CI User Training Course

hpc-training-2022 Repository for training material for the 2022 SDSC HPC/CI Training Series HPC/CI Training Series home https://www.sdsc.edu/event_ite

sdsc-hpc-training-org 21 Jul 27, 2022
A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks

PixelSSL is a PyTorch-based semi-supervised learning (SSL) codebase for pixel-wise (Pixel) vision tasks. The purpose of this project is to promote the

Zhanghan Ke 255 Dec 11, 2022
Count GitHub Stars ⭐

Count GitHub Stars per Day ⭐ Track GitHub stars per day over a date range to measure the open-source popularity of different repositories. Requirement

Ultralytics 20 Nov 20, 2022
NLU Dataset Diagnostics

NLU Dataset Diagnostics This repository contains data and scripts to reproduce the results from our paper: Aarne Talman, Marianna Apidianaki, Stergios

Language Technology at the University of Helsinki 1 Jul 20, 2022
NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

NCVX NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning. Please check https://ncvx.org for detailed instruction

SUN Group @ UMN 28 Aug 03, 2022
GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

GLODISMO: Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery This is the code to the paper: Gradient-Based Learn

3 Feb 15, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
Self-driving car env with PPO algorithm from stable baseline3

Self-driving car with RL stable baseline3 Most of the project develop from https://github.com/GerardMaggiolino/Gym-Medium-Post Please check it out! Th

Sornsiri.P 7 Dec 22, 2022
Sequence lineage information extracted from RKI sequence data repo

Pango lineage information for German SARS-CoV-2 sequences This repository contains a join of the metadata and pango lineage tables of all German SARS-

Cornelius Roemer 24 Oct 26, 2022
Implementation of Multistream Transformers in Pytorch

Multistream Transformers Implementation of Multistream Transformers in Pytorch. This repository deviates slightly from the paper, where instead of usi

Phil Wang 47 Jul 26, 2022