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Changelog

v0.3.0 (19/06/2023)

Release version to OpenSTL V0.3.0 as #25.

New Features

  • Support visualization tools in vis_video, config files in configs, and trained files (models, logs, and visualizations) in v0.3.0 of STL methods on various datasets (on updating).
  • Support the dataloader of video classification datasets Kinetics and BAIR, which has a similar setting as the Human3.6M and KTH dataloaders. Relevant video transforms in Kinetics are supported according to VideoMAE, and config files are provided. Add data preparation of TaxiBJ as issue #34.
  • Update STL results visualization by vis_video for video prediction, traffic prediction, weather prediction tasks in video_visualization, traffic_visualization, and weather_visualization.
  • Support Jupyter notebook tutorials and video examples in examples.
  • Support early-stop training with --early_stop_epoch as issue #36.

Update Documents

  • Release arXiv preprint of OpenSTL, which describes the overall framework, benchmark results, and experimental settings, etc.
  • Update benchmark results of video prediction, traffic prediction, and weather prediction benchmarks in docs/en/model_zoos.
  • Add the Huggingface organization for OpenSTL🤗, where users can join it by invitation link.

Fix Bugs

  • Fix bugs in the dataloader (issue #26) and dataset prepration tools (issue #27 and #28).
  • Fix bugs of overwrite config values during training, where utils/main_utils/update_config will overwrite the config file with the default values in utils/main_utils/parser in mistake (issue #42). Using default_parser() to provide the default values and fulfill the config after updating values in the given config file (solving pull request #47).

v0.2.0 (21/04/2023)

Release version to OpenSTL V0.2.0 as #20.

Code Refactoring

  • Rename the project to OpenSTL instead of SimVPv2 with module name refactoring.
  • Refactor the code structure thoroughly to support non-distributed and distributed (DDP) training & testing with tools/train.py and tools/test.py.
  • Refactor _dist_forward_collect and _non_dist_forward_collect to support collection of metrics.

New Features

  • Update the Weather Bench dataloader with 5.625deg, 2.8125deg, and 1.40625deg settings. Add Human3.6M dataloader (supporting augmentations) and config files. Add Moving FMNIST and MMNIST_CIFAR as two advanced variants of MMNIST datasets.
  • Update tools for dataset preparation of Human3.6M, Weather Bench, and Moving FMNIST.
  • Support PredNet, TAU, and DMVFN with configs and benchmark results. And fix bugs in these new STL methods.
  • Support multi-variant versions of Weather Bench with dataloader and metrics.
  • Support lpips metric for video prediction benchmarks.
  • Support STL results visualization by vis_video for video prediction, traffic prediction, weather prediction tasks.
  • Support visualization of STL methods on various datasets (on updating).

Update Documents

  • Update documents of video prediction, traffic prediction, and weather prediction benchmarks with benchmark results and spesific GPU settings (e.g., single GPU). Provide config files for supported STL methods.
  • Update docs/en documents for the basic usages and new features of V0.2.0. Adding detailed steps of installation and preparation datasets.
  • Clean-up STL benchmarks and update to the latest results with config files provided.

Fix Bugs

  • Fix bugs in training loops and validation loops to save GPU memory.
  • There might be some bugs in not using all parameters for calculating losses in ConvLSTM CrevNet, which should use --find_unused_parameters for DDP training.
  • Fig bugs of building distributed dataloaders and preparation of DDP training.
  • Fix bugs of some STL methods (CrevNet, DMVFN, PreDNet, and TAU).
  • Fix bugs in datasets: fixing Caltech dataset for evaluation (28/05/2023 updating Baidu Cloud).
  • Fix the bug of PSNR (changing the implementation from E3D-LSTM to the current version) and update results in the benchmarks.

v0.1.0 (18/02/2023)

Release version to V0.1.0 with code refactoring.

Code Refactoring

  • Refactor code structures as simvp/api, simvp/core, simvp/datasets, simvp/methods, simvp/models, simvp/modules. We support non-distributed training and evaluation by the executable python file tools/non_dist_train.py. Refactor config files for SimVP models.
  • Fix bugs in tools/nondist_train.py, simvp/utils, environment.yml, and .gitignore, etc.

New Features

Update Documents

  • Release arXiv preprint of SimVPv2. This version supports the morst experiments in SimVPv2, which is the extend version of SimVP.
  • Upload readthedocs documents. Summarize video prediction benchmark results on MMNIST in video_benchmarks.md.
  • Update benchmark results of video prediction baselines and MetaFormer architectures based on SimVP on MMNIST, TaxiBJ, and WeatherBench datasets.
  • Update README and add a license.