• Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition (Kim et al., BMVC 2022) Instruction

• An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition (Kim et al., BMVC 2022) Instruction

The only framework that completes your computer vision, action recognition research environment.

** Key features **

• Supports multi-gpu, multi-node training.
• STOA models such as I3D, Non-local, TSN, TRN, TSM, MVFNet, ..., and even ImageNet training!
• Many datasets such as Kinetics-400, EPIC-Kitchens-55, Something-Something-V1/V2, HMDB-51, UCF-101, Diving48, CATER, ...
• Supports both video decoding (straight from .avi/mp4) and frame extracted (.jpg/png) dataloaders, sparse-sample and dense-sample.
• Any popular LR scheduling like Cosine Annealing with Warm Restart, Step LR, and Reduce LR on Plateau.
• Early stopping when training doesn't improve (customise your condition)
• Easily add custom model, optimiser, scheduler, loss and dataloader!
• Telegram bot reporting experiment status.
• TensorBoard reporting stats.
• Colour logging
• All of the above come with no extra setup. Trust me and try some examples.

** Papers implemented **

• ProSelfLC (CVPR 2021).

This package is motivated by PySlowFast from Facebook AI. The PySlowFast is a cool framework, but it depends too much on their config system and it was difficult to add new models (other codes) or reuse part of the modules from the framework.
This framework by Kiyoon, is designed to replace all the configuration systems to Python files, which enables easy-addition of custom models/LR scheduling/dataloader etc.
Just modify the function bodies in the config files!

Difference between the two config systems can be found in CONFIG_SYSTEM.md.

Jupyter Notebook examples to run:

• HMDB-51 data preparation
• Inference on pre-trained model from the model zoo, and visualise model/dataloader/per-class performance.
• Training I3D using Kinetics pretrained model
• Using image model and ImageNet dataset

is provided in the examples!

All of the executable files are in tools/.
dataset_configs/ directory configures datasets. For example, where is the dataset stored, number of classes, single-label or multi-label training, dataset-specific visualisation settings (confusion matrix has different output sizes)
model_configs/ directory configures model architectures. For example, model definition, input preprocessing mean/std.
exp_configs/ directory configures other training settings like optimiser, scheduling, dataloader, number of frames as input. The config file path has to be in exp_configs/[dataset_name]/[model_name]_[experiment_name].py format.

This package supports many action recognition datasets such as HMDB-51, EPIC-Kitchens-55, Something-Something-V1, CATER, etc.
Refer to DATASET.md.

# Single GPU
CUDA_VISIBLE_DEVICES=0 python tools/run_singlenode.sh train 1 -D {dataset_config_name} -M {model_config_name} -E {exp_config_name}
# Multi GPUs, single node
CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/run_singlenode.sh train {num_gpus} -D {dataset_config_name} -M {model_config_name} -E {exp_config_name}
# Multi GPU, multi node (run on every node)
CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/run_multinode.sh train {num_gpus_per_node} {num_nodes} {node_rank} {master_address} {master_port} -D {dataset_config_name} -M {model_config_name} -E {exp_config_name}

You can preview experiment results using Telegram bots!

If your code raises an exception, it will report you too.

You can quickly take a look at example video inputs (as GIF or JPEGs) from the dataloader.
Use tools/visualisations/model_and_dataloader_visualiser.py

• Talk to BotFather and make a bot.
• Go to your bot and type anything (/start)
• Find chat_id at https://api.telegram.org/bot{token}/getUpdates (replace {token} with your token, excluding braces.)
• Add your token and chat_id to tools/key.ini.

[Telegram0]
token=
chat_id=

Refer to MODEL_ZOO.md

Refer to INSTALL.md.

TL;DR,

conda create -n videoai python=3.9
conda activate videoai
conda install pytorch==1.12.1 torchvision cudatoolkit=10.2 -c pytorch
### For RTX 30xx GPUs,
#conda install pytorch==1.12.1 torchvision cudatoolkit=11.3 -c pytorch
 

git clone --recurse-submodules https://github.com/kiyoon/PyVideoAI.git
cd PyVideoAI
git checkout v0.4
git submodule update --recursive
cd submodules/video_datasets_api
pip install -e .
cd ../experiment_utils
pip install -e .
cd ../..
pip install -e .

Optional: Pillow-SIMD and libjepg-turbo to improve dataloading performance.
Run this at the end of the installation:

conda uninstall -y --force pillow pil jpeg libtiff libjpeg-turbo
pip   uninstall -y         pillow pil jpeg libtiff libjpeg-turbo
conda install -yc conda-forge libjpeg-turbo
CFLAGS="${CFLAGS} -mavx2" pip install --upgrade --no-cache-dir --force-reinstall --no-binary :all: --compile pillow-simd
conda install -y jpeg libtiff

The experiment results (log, training stats, weights, tensorboard, plots, etc.) are saved to data/experiments by default. This can be huge, so make sure you make a softlink of a directory you really want to use. (recommended)

Otherwise, you can change pyvideoai/config.py's DEFAULT_EXPERIMENT_ROOT value. Or, you can also set --experiment_root/-R argument manually when executing.