From a14aade3a94d6acdb0ea1847143279759d360597 Mon Sep 17 00:00:00 2001 From: Artur Toshev Date: Thu, 4 Jan 2024 01:18:10 +0100 Subject: [PATCH 01/13] add batches and ToC --- README.md | 29 ++++++++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7663290..43a8ab9 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,30 @@ -# LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite +
-[![Static Badge](https://img.shields.io/badge/docs-red?style=for-the-badge&logo=readthedocs&link=https%3A%2F%2Flagrangebench.readthedocs.io%2Fen%2Flatest%2Findex.html)](https://lagrangebench.readthedocs.io/en/latest/index.html) -[![Static Badge](https://img.shields.io/badge/arxiv-blue?style=for-the-badge&logo=arxiv&link=https%3A%2F%2Farxiv.org%2Fabs%2F2309.16342)](https://arxiv.org/abs/2309.16342) +# LagrangeBench +### A Lagrangian Fluid Mechanics Benchmarking Suite + + + +[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) +[![Docs](https://img.shields.io/readthedocs/lagrangebench/latest)](https://lagrangebench.readthedocs.io/en/latest/index.html) +[![Unit Tests](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml) +[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) +[![PyPI - Version](https://img.shields.io/pypi/v/lagrangebench)](https://pypi.org/project/lagrangebench/) +[![Python Versions](https://img.shields.io/pypi/pyversions/lagrangebench)](https://pypi.org/project/lagrangebench/) +[![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE) + +
+ +## Table of Contents + +1. [**Installation**](#installation) +1. [**Usage**](#usage) +1. [**Datasets**](#datasets) +1. [**Directory Structure**](#directory-structure) +1. [**Citation**](#citation) ## Installation ### Standalone library From 9edfb43933fbf62fb1f41e3f0ae844c57ce48aea Mon Sep 17 00:00:00 2001 From: Artur Toshev Date: Thu, 4 Jan 2024 01:56:07 +0100 Subject: [PATCH 02/13] try out a logo --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 43a8ab9..530e134 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@
-# LagrangeBench +LagrangeBench Logo + ### A Lagrangian Fluid Mechanics Benchmarking Suite +[![Downloads](https://static.pepy.tech/badge/lagrangebench)](https://pypi.org/project/lagrangebench/) +[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) +[![Python Versions](https://img.shields.io/pypi/pyversions/lagrangebench)](https://pypi.org/project/lagrangebench/) --> -[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) +[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) [![Docs](https://img.shields.io/readthedocs/lagrangebench/latest)](https://lagrangebench.readthedocs.io/en/latest/index.html) -[![Unit Tests](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml) -[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) [![PyPI - Version](https://img.shields.io/pypi/v/lagrangebench)](https://pypi.org/project/lagrangebench/) -[![Python Versions](https://img.shields.io/pypi/pyversions/lagrangebench)](https://pypi.org/project/lagrangebench/) [![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE) +[![codecov](https://codecov.io/gh/tumaer/lagrangebench/graph/badge.svg?token=ULMGSY71R1)](https://codecov.io/gh/tumaer/lagrangebench) +[![Unit Tests](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml) +[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) +
## Table of Contents From e219fddca3dec467a44b87c654c7aaf2d83ac94b Mon Sep 17 00:00:00 2001 From: Artur Toshev Date: Sat, 6 Jan 2024 12:48:40 +0100 Subject: [PATCH 05/13] fix formatting --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5db960d..d62dc81 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ [![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) [![Python Versions](https://img.shields.io/pypi/pyversions/lagrangebench)](https://pypi.org/project/lagrangebench/) --> -[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) +[![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) [![Docs](https://img.shields.io/readthedocs/lagrangebench/latest)](https://lagrangebench.readthedocs.io/en/latest/index.html) [![PyPI - Version](https://img.shields.io/pypi/v/lagrangebench)](https://pypi.org/project/lagrangebench/) [![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE) From 26cad5c2c1609fe9bc094acfb52a7d95403208c3 Mon Sep 17 00:00:00 2001 From: Artur Toshev Date: Sat, 6 Jan 2024 13:01:45 +0100 Subject: [PATCH 06/13] swap order or badges --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index d62dc81..09648c4 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ [![Static Badge](https://img.shields.io/badge/arxiv-blue?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2309.16342) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![Downloads](https://static.pepy.tech/badge/lagrangebench)](https://pypi.org/project/lagrangebench/) -[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) [![Python Versions](https://img.shields.io/pypi/pyversions/lagrangebench)](https://pypi.org/project/lagrangebench/) --> [![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) @@ -16,8 +15,8 @@ [![PyPI - Version](https://img.shields.io/pypi/v/lagrangebench)](https://pypi.org/project/lagrangebench/) [![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE) -[![codecov](https://codecov.io/gh/tumaer/lagrangebench/graph/badge.svg?token=ULMGSY71R1)](https://codecov.io/gh/tumaer/lagrangebench) [![Unit Tests](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml) +[![codecov](https://codecov.io/gh/tumaer/lagrangebench/graph/badge.svg?token=ULMGSY71R1)](https://codecov.io/gh/tumaer/lagrangebench) [![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) From 6b4a4e344eec54e18639a07f31918290d110cfce Mon Sep 17 00:00:00 2001 From: gerkone Date: Sat, 6 Jan 2024 13:26:21 +0100 Subject: [PATCH 07/13] new svg logo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 09648c4..6439e48 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
-LagrangeBench Logo +LagrangeBench Logo ### A Lagrangian Fluid Mechanics Benchmarking Suite From f12f6b4cea7aa1594d5983e7ad75edfe26ca28b8 Mon Sep 17 00:00:00 2001 From: gerkone Date: Sat, 6 Jan 2024 13:39:28 +0100 Subject: [PATCH 08/13] svg logo with description --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 6439e48..50a48f8 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,6 @@
-LagrangeBench Logo - -### A Lagrangian Fluid Mechanics Benchmarking Suite +LagrangeBench Logo: Lagrangian Fluid Mechanics Benchmarking Suite @@ -14,11 +15,11 @@ [![Paper](http://img.shields.io/badge/paper-arxiv.2309.16342-B31B1B.svg)](https://arxiv.org/abs/2309.16342) [![Docs](https://img.shields.io/readthedocs/lagrangebench/latest)](https://lagrangebench.readthedocs.io/en/latest/index.html) [![PyPI - Version](https://img.shields.io/pypi/v/lagrangebench)](https://pypi.org/project/lagrangebench/) -[![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/tutorial.ipynb) [![Unit Tests](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/tests.yml) [![codecov](https://codecov.io/gh/tumaer/lagrangebench/graph/badge.svg?token=ULMGSY71R1)](https://codecov.io/gh/tumaer/lagrangebench) -[![Ruff Linting](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml/badge.svg)](https://github.com/tumaer/lagrangebench/actions/workflows/ruff.yml) +[![License](https://img.shields.io/pypi/l/lagrangebench)](https://github.com/tumaer/lagrangebench/blob/main/LICENSE)
@@ -28,6 +29,7 @@ 1. [**Usage**](#usage) 1. [**Datasets**](#datasets) 1. [**Directory Structure**](#directory-structure) +1. [**Contributing**](#contributing) 1. [**Citation**](#citation) ## Installation @@ -155,6 +157,39 @@ We provide three notebooks that show LagrangeBench functionalities, namely: ``` +## Contributing +Welcome! We highly appreciate [Github issues](https://github.com/tumaer/lagrangebench/issues) and [PRs](https://github.com/tumaer/lagrangebench/pulls). + +You can also chat with us on [**Discord**](https://discord.gg/Ds8jRZ78hU). + +### Contributing Guideline +If you want to contribute to this repository, you will need the dev depencencies, i.e. +install the environment with `poetry install` without the ` --only main` flag. +Then, we also recommend you to install the pre-commit hooks +if you don't want to manually run `pre-commit run` before each commit. To sum up: + +```bash +git clone https://github.com/tumaer/lagrangebench.git +cd lagrangebench +poetry install +source $PATH_TO_LAGRANGEBENCH_VENV/bin/activate + +# install pre-commit hooks defined in .pre-commit-config.yaml +# ruff is configured in pyproject.toml +pre-commit install +``` + +After you have run `git add ` and try to `git commit`, the pre-commit hook will +fix the linting and formatting of `` before you are allowed to commit. + +You should also run the unit tests locally before creating a PR. Do this simply by: + +```bash +# pytest is configured in pyproject.toml +pytest +``` + + ## Citation The paper (at NeurIPS 2023 Datasets and Benchmarks) can be cited as: ```bibtex From c018c5dca20d3fcb4721afb0f5d6e6e86ad8103d Mon Sep 17 00:00:00 2001 From: gerkone Date: Tue, 9 Jan 2024 21:46:12 +0100 Subject: [PATCH 12/13] tutorial docs cleanup --- docs/pages/tutorial.rst | 19 +- docs/pages/tutorials/datasets.rst | 351 ------------------- docs/pages/tutorials/gns_data.rst | 324 ----------------- docs/pages/tutorials/intro.rst | 312 ----------------- docs/pages/tutorials/media/datasets_10_0.png | Bin 43677 -> 0 bytes docs/pages/tutorials/media/datasets_11_0.png | Bin 38221 -> 0 bytes docs/pages/tutorials/media/datasets_13_0.png | Bin 32095 -> 0 bytes docs/pages/tutorials/media/datasets_18_0.png | Bin 27828 -> 0 bytes docs/pages/tutorials/media/datasets_21_0.png | Bin 24435 -> 0 bytes docs/pages/tutorials/media/datasets_8_0.png | Bin 38840 -> 0 bytes docs/pages/tutorials/media/datasets_9_0.png | Bin 36664 -> 0 bytes docs/pages/tutorials/media/gns_data_13_0.png | Bin 30802 -> 0 bytes docs/pages/tutorials/media/gns_data_13_1.png | Bin 32095 -> 0 bytes docs/pages/tutorials/media/gns_data_13_2.png | Bin 29616 -> 0 bytes docs/pages/tutorials/media/gns_data_15_0.png | Bin 141852 -> 0 bytes docs/pages/tutorials/media/scatter.gif | Bin 1006653 -> 0 bytes docs/pages/tutorials/media/tutorial_20_0.png | Bin 37296 -> 0 bytes 17 files changed, 14 insertions(+), 992 deletions(-) delete mode 100644 docs/pages/tutorials/datasets.rst delete mode 100644 docs/pages/tutorials/gns_data.rst delete mode 100644 docs/pages/tutorials/intro.rst delete mode 100644 docs/pages/tutorials/media/datasets_10_0.png delete mode 100644 docs/pages/tutorials/media/datasets_11_0.png delete mode 100644 docs/pages/tutorials/media/datasets_13_0.png delete mode 100644 docs/pages/tutorials/media/datasets_18_0.png delete mode 100644 docs/pages/tutorials/media/datasets_21_0.png delete mode 100644 docs/pages/tutorials/media/datasets_8_0.png delete mode 100644 docs/pages/tutorials/media/datasets_9_0.png delete mode 100644 docs/pages/tutorials/media/gns_data_13_0.png delete mode 100644 docs/pages/tutorials/media/gns_data_13_1.png delete mode 100644 docs/pages/tutorials/media/gns_data_13_2.png delete mode 100644 docs/pages/tutorials/media/gns_data_15_0.png delete mode 100644 docs/pages/tutorials/media/scatter.gif delete mode 100644 docs/pages/tutorials/media/tutorial_20_0.png diff --git a/docs/pages/tutorial.rst b/docs/pages/tutorial.rst index 4034516..07d0172 100644 --- a/docs/pages/tutorial.rst +++ b/docs/pages/tutorial.rst @@ -1,9 +1,18 @@ Tutorial =================================== -.. toctree:: - :maxdepth: 1 +- Basics of LagrangeBench: `Training GNS on the 2D Taylor Green vortex `_ |tutorial| +- Stats and specifics of our data: `Datasets overview `_ |datasets| +- How to include datasets from other works: `Working with other datasets `_ |gns| - tutorials/intro - tutorials/datasets - tutorials/gns_data +.. |tutorial| raw:: html + + + +.. |datasets| raw:: html + + + +.. |gns| raw:: html + + \ No newline at end of file diff --git a/docs/pages/tutorials/datasets.rst b/docs/pages/tutorials/datasets.rst deleted file mode 100644 index 833721d..0000000 --- a/docs/pages/tutorials/datasets.rst +++ /dev/null @@ -1,351 +0,0 @@ -LagrangeBench Datasets -====================== - -This is an overview of the introduced datasets. We show: - -- Dataset animations. -- Evaluation of physical properties. - -The structure of all datasets is the same and consists of four files: - -``metadata.json`` - the data needed to set up the numerical solver and -simulate trajectories - ``train.h5`` - training set - ``valid.h5`` - -validation set - ``test.h5`` - test set - -.. code:: ipython3 - - import os - os.environ["CUDA_VISIBLE_DEVICES"] = "" - - import numpy as np - from jax import vmap - from jax_md import space - import matplotlib.pyplot as plt - - import lagrangebench - - plt.rcParams.update({'font.size': 10}) - -Dataset animations ------------------- - -We animate a few consecutive frames from our datasets using -`ParaView `__ and color the particles by the -magnitude of their velocity. - -- Reverse Poiseuille Flow - - |rpf2d.gif| |rpf3d.gif| - -- Taylor Green Vortex - - |tgv2d.gif| |tgv3d.gif| - -- Lid-Driven Cavity - - |ldc2d.gif| |ldc3d.gif| - -- Dam Break - - .. figure:: https://s11.gifyu.com/images/SceKB.gif - :alt: dam2d.gif - - dam2d.gif - -.. |rpf2d.gif| image:: https://s11.gifyu.com/images/Sce92.gif -.. |rpf3d.gif| image:: https://s11.gifyu.com/images/Sce3X.gif -.. |tgv2d.gif| image:: https://s11.gifyu.com/images/Sce9b.gif -.. |tgv3d.gif| image:: https://s11.gifyu.com/images/Sce9z.gif -.. |ldc2d.gif| image:: https://s11.gifyu.com/images/Sce9S.gif -.. |ldc3d.gif| image:: https://s11.gifyu.com/images/Sce3e.gif - -Dataset shapes --------------- - -Short script to check the shapes of the raw validation datasets. - -If you haven’t downloaded the datasets yet, you can do so by running -this script from the root of the repository: - -.. code:: bash - - bash download_data.sh all notebooks/datasets/ - -.. code:: ipython3 - - import os.path as osp - import h5py - - # the datasets are stored in the following directories, whose names follow the convention: - # {dim}_{case}_{num_particles_max}_{num_steps}every{sampling_rate} - # dim - dimension. One of {2D|3D}. - # case - simulation case. One of {TGV|RPF|LDC|DAM}. - # num_particles_max - maximum number of particles in the dataset. - # num_steps - number of position frames in the training split of the dataset. - # sampling_rate - sampling_rate=100 means that the position frames correspond to every - # 100th SPH simulation step. - path = [ - "2D_TGV_2500_10kevery100", - "2D_RPF_3200_20kevery100", - "2D_LDC_2708_10kevery100", - "2D_DAM_5740_20kevery100", - "3D_TGV_8000_10kevery100", - "3D_RPF_8000_10kevery100", - "3D_LDC_8160_10kevery100", - ] - - # 6 history positions (5 past velocities) + 20-step rollout - valid_seq_length = 6 + 20 # during validation and inference - dataset_root = "datasets/" - split = "valid.h5" - - for p in path: - p = osp.join(dataset_root, p, split) - with h5py.File(p, "r") as f: - pos_shape = f["00000/position"].shape - split_into = pos_shape[0]//valid_seq_length - num_trajs = len(list(f.keys())) - print(f"{p[9:]}, traj_shape= {str(pos_shape):<20}, " - f"split_valid_traj_into_n= {split_into:<5}, " - f"num_trajs: {num_trajs:<5}, " - f"eval_n_trajs_infer: {split_into * num_trajs}") - - -.. parsed-literal:: - - 2D_TGV_2500_10kevery100/valid.h5, traj_shape= (126, 2500, 2) , split_valid_traj_into_n= 4 , num_trajs: 50 , eval_n_trajs_infer: 200 - 2D_RPF_3200_20kevery100/valid.h5, traj_shape= (10001, 3200, 2) , split_valid_traj_into_n= 384 , num_trajs: 1 , eval_n_trajs_infer: 384 - 2D_LDC_2708_10kevery100/valid.h5, traj_shape= (5001, 2708, 2) , split_valid_traj_into_n= 192 , num_trajs: 1 , eval_n_trajs_infer: 192 - 2D_DAM_5740_20kevery100/valid.h5, traj_shape= (401, 5740, 2) , split_valid_traj_into_n= 15 , num_trajs: 25 , eval_n_trajs_infer: 375 - 3D_TGV_8000_10kevery100/valid.h5, traj_shape= (61, 8000, 3) , split_valid_traj_into_n= 2 , num_trajs: 100 , eval_n_trajs_infer: 200 - 3D_RPF_8000_10kevery100/valid.h5, traj_shape= (5001, 8000, 3) , split_valid_traj_into_n= 192 , num_trajs: 1 , eval_n_trajs_infer: 192 - 3D_LDC_8160_10kevery100/valid.h5, traj_shape= (5001, 8160, 3) , split_valid_traj_into_n= 192 , num_trajs: 1 , eval_n_trajs_infer: 192 - - -Evaluation of physical properties ---------------------------------- - -Here we focus on the kinetic energy as a measure of physical behaviour. -We distinguish two cases: - -1. Statistically stationary :math:`E_{kin}` (RPF and LDC) - the kinetic - energy oscillates around an equilibrium value, and the oscillations - are because of the finite number of particle used for the - discretization. -2. Variable :math:`E_{kin}` (TGV, DAM) - In the TGV example it decays - due to viscous interactions and in dam break it first increases by - transfer of potential to kinetic energy, but then also decays due to - viscosity. - -.. code:: ipython3 - - # utils - - def compute_kinetic_energy(metadata, traj): - bounds = np.array(metadata["bounds"]) - displacement_fn, _ = space.periodic(side=bounds[:,1]) - displ_vmap = vmap(displacement_fn, (0, 0)) - displ_dvmap = vmap(displ_vmap, (0, 0)) - - displacement = displ_dvmap(traj[:,1:], traj[:, :-1]) - vel = displacement / (metadata["dt"] * metadata["write_every"]) - vel = np.asarray(vel) # shape: (num_particles, num_steps, dim) - square_absolute_velocity = (vel**2).sum(axis=2) # shape: (num_particles, num_steps) - e_kin = 0.5 * square_absolute_velocity.mean(axis=(0,)) # shape: (num_steps,) - return e_kin - - def plt_e_kin(dataset_path, seq_len, e_kin_ref=None): - dim, name, _, _ = dataset_path.split('/')[1].split('_') - - dataset = lagrangebench.data.H5Dataset( - "valid", - dataset_path=dataset_path, - input_seq_length=seq_len, - extra_seq_length=1, - ) - traj, _ = dataset[0] - e_kin = compute_kinetic_energy(dataset.metadata, traj) - - plt.figure(figsize=(8,4)) - plt.plot(np.arange(len(e_kin)) + 0.5, e_kin, 'xb', label='dataset') - - if e_kin_ref is not None: - plt.plot(e_kin_ref, 'r', label='reference') - - plt.legend() - plt.grid() - plt.title(f"{dim} {name}") - plt.xlabel('step') - plt.ylabel('Ekin') - -RPF and LDC -~~~~~~~~~~~ - -.. code:: ipython3 - - plt_e_kin("datasets/2D_RPF_3200_20kevery100", 500) - - - -.. image:: media/datasets_8_0.png - - -.. code:: ipython3 - - plt_e_kin("datasets/3D_RPF_8000_10kevery100", 500) - - - -.. image:: media/datasets_9_0.png - - -.. code:: ipython3 - - plt_e_kin("datasets/2D_LDC_2708_10kevery100", 500) - - - -.. image:: media/datasets_10_0.png - - -.. code:: ipython3 - - plt_e_kin("datasets/3D_LDC_8160_10kevery100", 500) - - - -.. image:: media/datasets_11_0.png - - -DAM -~~~ - -.. code:: ipython3 - - plt_e_kin("datasets/2D_DAM_5740_20kevery100", 400) - - - -.. image:: media/datasets_13_0.png - - -2D TGV -~~~~~~ - -The two dimensional Taylor-Green vortex has an analytical solution as -outlined in [1]. Here, we compare this solution of the kinetic energy -with the kinetic enery in one of our dataset trajectories. - -[1] - Adami et al., `A transport-velocity formulation for smoothed -particle -hydrodynamics `__, -2013 - -.. code:: ipython3 - - dataset_path = "datasets/2D_TGV_2500_10kevery100" - dataset = lagrangebench.data.H5Dataset( - "valid", - dataset_path=dataset_path, - extra_seq_length=1, - ) - - dt_dataset = (dataset.metadata['dt'] * dataset.metadata['write_every']) - len_traj = 1 + dataset.metadata['t_end'] / dt_dataset - t = np.linspace(0, dataset.metadata['t_end'], int(len_traj)) # physical time axis - - rho_ref = 1. # reference density - u_ref = 1. # reference velocity, i.e. u_max at t=0 - L_ref = 1. # reference length, i.e. domain size - eta = dataset.metadata['viscosity'] # viscosity - Re = rho_ref * u_ref * L_ref / eta # Reynolds number - slope_u_max = - 8 * np.pi ** 2 / Re # analytical decay rate of max velocity - # factor 0.25 = int(int((sin(2 pi x) cos(2 pi y))^2, x from 0 to 1), y from 0 to 1) - e_kin_theory = 0.25 * np.exp(2 * slope_u_max * t) # analytical kinetic energy evolution - -In the above we used information from the ``dataset.metadata`` which is -the content of a metadata file associated with each of our datasets. The -metadata file for 2D TGV looks like this: - -.. code:: ipython3 - - dataset.metadata - - - - -.. parsed-literal:: - - {'case': 'TGV', - 'solver': 'SPH', - 'density_evolution': False, - 'dim': 2, - 'dx': 0.02, - 'dt': 0.0004, - 't_end': 5.0, - 'viscosity': 0.01, - 'p_bg_factor': 0.0, - 'g_ext_magnitude': 0.0, - 'artificial_alpha': 0.0, - 'free_slip': False, - 'write_every': 100, - 'is_bc_trick': False, - 'sequence_length_train': 126, - 'num_trajs_train': 100, - 'sequence_length_test': 126, - 'num_trajs_test': 50, - 'num_particles_max': 2500, - 'periodic_boundary_conditions': [True, True, True], - 'bounds': [[0.0, 1.0], [0.0, 1.0]], - 'default_connectivity_radius': 0.029, - 'vel_mean': [2.9928384037702926e-07, 5.485588872033986e-07], - 'vel_std': [0.006356782279908657, 0.006356848403811455], - 'acc_mean': [1.1536379524465599e-14, -1.9131170172353995e-13], - 'acc_std': [0.0008755344315432012, 0.0008755328599363565]} - - - -.. code:: ipython3 - - plt_e_kin(dataset_path, 125, e_kin_theory) - - - -.. image:: media/datasets_18_0.png - - -We don’t perfectly solve the 2D TGV, but this behaviour is a known -limitation of purely Lagrangian SPH, as shown in [1]. We remind the -reader that we restrict ourselves to purely Lagrangian schemes so that -we are able to recover the velocity of particles by computing the -difference between positions, something that is not valid with improved -semi-Lagrangian SPH schemes. - -3D TGV -~~~~~~ - -The three dimensional Taylor Green system at Re=50 does not have an -analytical solution, but we run a high resolution reference simulation -with the `JAX-Fluids `__ solver and -store the values of the kinetic energy at seconds [0, 0.5, 1, 1.5, … -10]. - -Here, we again don’t perfectly recover the true solution, but the -initial rapid drop in kinetic energy on the 3D TGV is also a documented -limitatation of purely Lagrangian SPH, see [2]. And yet we are rather -close to the reference. - -[2] - Adami et al., `Simulating three-dimensional turbulence with -SPH `__, 2012 - -.. code:: ipython3 - - e_kin_ref = 0.001 * np.array([ - 125, 118, 111, 103, 95.7, 88.0, 80.1, 72.4, 65, 58, 51.5, - 45.5, 40.2, 35.5, 31.3, 27.6, 24.3, 21.5, 19.1, 16.9, 15.1 - ]) - plt_e_kin("datasets/3D_TGV_8000_10kevery100", 59, e_kin_ref) - - - -.. image:: media/datasets_21_0.png - diff --git a/docs/pages/tutorials/gns_data.rst b/docs/pages/tutorials/gns_data.rst deleted file mode 100644 index 982bfa3..0000000 --- a/docs/pages/tutorials/gns_data.rst +++ /dev/null @@ -1,324 +0,0 @@ -Working with other datasets -=========================== - -We demonstrate how to train a GNN on one of the datasets provided by -DeepMind along with the paper [1]. We divide this notebook into three -parts: - -- Download and preprocess -- Inspect the data -- GNN training - -[1] - Sanchez-Gonzalez et al., `“Learning to Simulate Complex Physics -with Graph Networks” `__, ICLR 2020 - -.. code:: ipython3 - - import os - os.environ["CUDA_VISIBLE_DEVICES"] = "" - - import lagrangebench - import haiku as hk - import numpy as np - import matplotlib.pyplot as plt - - -Download and preprocess ------------------------ - -The easiest was to download and preprocess the datasets from the GNS -paper is by following the instructions in -```gns_data/README.md`` <../gns_data/README.md>`__. For our -demonstration, we choose the 2D WaterDrop dataset. - -Note: the size of this dataset is around 4.5GB. - -.. code:: ipython3 - - !mkdir -p ./datasets - !bash ../gns_data/download_dataset.sh WaterDrop ./datasets - - -.. parsed-literal:: - - --2023-10-15 01:39:30-- https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/WaterDrop/metadata.json - Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.186.91, 172.217.18.27, 142.250.186.59, ... - Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.186.91|:443... connected. - HTTP request sent, awaiting response... 200 OK - Length: 361 [application/octet-stream] - Saving to: ‘./datasets/WaterDrop/metadata.json’ - - ./datasets/WaterDro 100%[===================>] 361 --.-KB/s in 0s - - 2023-10-15 01:39:30 (400 MB/s) - ‘./datasets/WaterDrop/metadata.json’ saved [361/361] - - --2023-10-15 01:39:30-- https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/WaterDrop/train.tfrecord - Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.186.91, 142.250.186.187, 142.250.185.123, ... - Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.186.91|:443... connected. - HTTP request sent, awaiting response... 200 OK - Length: 4541246980 (4.2G) [application/octet-stream] - Saving to: ‘./datasets/WaterDrop/train.tfrecord’ - - ./datasets/WaterDro 100%[===================>] 4.23G 30.0MB/s in 2m 41s - - 2023-10-15 01:42:11 (26.9 MB/s) - ‘./datasets/WaterDrop/train.tfrecord’ saved [4541246980/4541246980] - - --2023-10-15 01:42:11-- https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/WaterDrop/valid.tfrecord - Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.184.251, 142.250.184.219, 216.58.206.59, ... - Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.184.251|:443... connected. - HTTP request sent, awaiting response... 200 OK - Length: 137190408 (131M) [application/octet-stream] - Saving to: ‘./datasets/WaterDrop/valid.tfrecord’ - - ./datasets/WaterDro 100%[===================>] 130.83M 28.8MB/s in 5.1s - - 2023-10-15 01:42:17 (25.7 MB/s) - ‘./datasets/WaterDrop/valid.tfrecord’ saved [137190408/137190408] - - --2023-10-15 01:42:17-- https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/WaterDrop/test.tfrecord - Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.16.155, 172.217.23.123, 142.250.74.219, ... - Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.16.155|:443... connected. - HTTP request sent, awaiting response... 200 OK - Length: 136885800 (131M) [application/octet-stream] - Saving to: ‘./datasets/WaterDrop/test.tfrecord’ - - ./datasets/WaterDro 100%[===================>] 130.54M 30.5MB/s in 5.0s - - 2023-10-15 01:42:22 (26.0 MB/s) - ‘./datasets/WaterDrop/test.tfrecord’ saved [136885800/136885800] - - - -To avoid conflicting library dependencies, we recommend installing a -second virtual environment only for the preprocessing. It will have the -lightweight CPU version of TensorFlow, which is needed to open the -original datasets. - -.. code:: ipython3 - - !python3 -m venv venv_tf - !venv_tf/bin/pip install tensorflow tensorflow-datasets - -Finally, we transform the ``*.tfrecord`` files to our ``*.h5`` format. -We automatically add two further features to the ``metadata.json`` file, -namely: - -- ``num_particles_max``: needed for jitability (via padding) -- ``periodic_boundary_conditions``: specifying the type of boundary - conditions per dimension - -Note: This might take a few minutes and will double the space taken by -the dataset. - -.. code:: ipython3 - - !venv_tf/bin/python ../gns_data/tfrecord_to_h5.py --dataset-path=./datasets/WaterDrop - - -.. parsed-literal:: - - 2023-10-15 01:44:07.609558: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered - 2023-10-15 01:44:07.609617: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered - 2023-10-15 01:44:07.609667: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered - 2023-10-15 01:44:08.294080: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT - Start conversion of ./datasets/WaterDrop/train.tfrecord to .h5 - 2023-10-15 01:44:09.073610: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected - Finish conversion to ./datasets/WaterDrop/train.h5 - Start conversion of ./datasets/WaterDrop/valid.tfrecord to .h5 - Finish conversion to ./datasets/WaterDrop/valid.h5 - Start conversion of ./datasets/WaterDrop/test.tfrecord to .h5 - Finish conversion to ./datasets/WaterDrop/test.h5 - Max number of particles in valid.h5: 1044 - Max number of particles in train.h5: 1108 - Max number of particles in test.h5: 1108 - - -If everything worked fine and you see the ``*.h5`` files, you can remove -the ``*.tfrecord`` files and the virtual environment. - -.. code:: ipython3 - - !rm ./datasets/WaterDrop/*.tfrecord - !rm -r venv_tf - -Inspect the data ----------------- - -Because the number of particles in this dataset varies, we are forced to -use ``matscipy`` as the neighbors search implementation. Matscipy runs -on the CPU and can efficiently handle systems with up to milions of -particles. To integrate this function into our jit-able codebase, we use -``jax.pure_callback()`` and pad the non-existing particle entries up to -``num_particles_max``. - -.. code:: ipython3 - - data_train = lagrangebench.data.H5Dataset( - split="train", - dataset_path="./datasets/WaterDrop", - name="waterdrop2d", - nl_backend="matscipy" - ) - - data_valid = lagrangebench.data.H5Dataset( - split="valid", - dataset_path="./datasets/WaterDrop", - name="waterdrop2d", - extra_seq_length=20, - nl_backend="matscipy" - ) - # We can compute the `split_valid_traj_into_n=38` by: - # 1. from [1], Appendix B.1, each trajectory has a length of 1000 steps - # 2. Later, we set `extra_seq_length=20` for the validation loss - # 3. We use 5 historic velocities (or 6 historic positions) - # => This leads to splitting each training trajectory into - # 1000 // (20 + 6) = 38 validation instances - - print( - f"This is a {data_train.metadata['dim']}D dataset from {data_train.dataset_path}.\n" - f"Train frames have shape {data_train[0][0].shape} (n_nodes, seq_len, xy pos).\n" - f"Val frames have shape {data_valid[0][0].shape} (n_nodes, rollout, xy pos).\n" - f"And particle types have shape {data_train[0][1].shape} (n_nodes,).\n" - f"Total of {len(data_train)} train frames and {len(data_valid)} val frames.\n" - ) - - -.. parsed-literal:: - - This is a 2D dataset from ./datasets/WaterDrop. - Train frames have shape (1108, 7, 2) (n_nodes, seq_len, xy pos). - Val frames have shape (1108, 26, 2) (n_nodes, rollout, xy pos). - And particle types have shape (1108,) (n_nodes,). - Total of 995000 train frames and 1140 val frames. - - - -Visualize slices from the first trajectory - -.. code:: ipython3 - - - bounds = np.array(data_train.metadata["bounds"]) - - # from [1], Appendix B.1 we know that the trajectory length is 1000 - # If we take indices < 994, we will see samples from the first trajectory - for j in [0, 500, 993]: - sample = data_train[j] - - # visualize 5 consecutive frames - fig, axs = plt.subplots(1, 5, figsize=(20, 4)) - for i in range(5): - mask = sample[1] != -1 - axs[i].scatter(sample[0][mask, i, 0], sample[0][mask, i, 1], s=1) - axs[i].set_xlim(bounds[0]) - axs[i].set_ylim(bounds[1]) - plt.show() - - - -.. image:: media/gns_data_13_0.png - - - -.. image:: media/gns_data_13_1.png - - - -.. image:: media/gns_data_13_2.png - - -Here we visualize random frames from the dataset. - -.. code:: ipython3 - - np.random.seed(42) - frame_nums = np.random.randint(0, len(data_train), 15) - - # visualize 5 consecutive frames - fig, axs = plt.subplots(3, 5, figsize=(20, 12)) - for ax, i in zip(axs.flatten(), frame_nums): - sample = data_train[i] - mask = sample[1] != -1 - - ax.scatter(sample[0][mask, 0, 0], sample[0][mask, 0, 1], s=1, - label=f"seed={i}, N={np.sum(mask)}") - ax.legend() - ax.set_xlim(bounds[0]) - ax.set_ylim(bounds[1]) - plt.show() - - - -.. image:: media/gns_data_15_0.png - - -GNN training ------------- - -This works as usual. See -```notebooks/tutorial.ipynb`` <./tutorial.ipynb>`__ for more details. - -.. code:: ipython3 - - def gns(x): - return lagrangebench.GNS( - particle_dimension=data_train.metadata["dim"], - latent_size=16, - blocks_per_step=2, - num_mp_steps=4, - particle_type_embedding_size=8, - )(x) - - gns = hk.without_apply_rng(hk.transform_with_state(gns)) - - noise_std = 3e-4 - bounds = np.array(data_train.metadata["bounds"]) - box = bounds[:, 1] - bounds[:, 0] - - case = lagrangebench.case_builder( - box=box, - metadata=data_train.metadata, - input_seq_length=6, - isotropic_norm=False, - noise_std=noise_std, - ) - - trainer = lagrangebench.Trainer( - model=gns, - case=case, - data_train=data_train, - data_valid=data_valid, - noise_std=noise_std, - metrics=["mse"], - n_rollout_steps=20, - eval_n_trajs=2, - lr_start=5e-4, - log_steps=10, - eval_steps=50, - ) - -.. code:: ipython3 - - params, state, _ = trainer(step_max=100) - - -.. parsed-literal:: - - Reallocate neighbors list (2, 170875) at step 0 - To list (2, 442352) - 000, train/loss: 4.31286. - Reallocate neighbors list (2, 442352) at step 3 - To list (2, 684860) - Reallocate neighbors list (2, 684860) at step 6 - To list (2, 915872) - 010, train/loss: 0.12886. - 020, train/loss: 0.40341. - 030, train/loss: 0.04893. - 040, train/loss: 0.05427. - 050, train/loss: 12.54240. - {'val/loss': 1.0891471447393997e-05, 'val/mse1': 1.145147032755034e-09, 'val/mse5': 8.628529357679326e-08, 'val/mse10': 9.226690735886223e-07, 'val/stdloss': 9.959435374184977e-07, 'val/stdmse1': 1.827232254392186e-10, 'val/stdmse5': 1.3568257628548963e-08, 'val/stdmse10': 1.5360808447439922e-07} - 060, train/loss: 0.09790. - 070, train/loss: 0.74478. - 080, train/loss: 0.10917. - 090, train/loss: 0.21384. - 100, train/loss: 0.74520. - {'val/loss': 1.3067739018879365e-05, 'val/mse1': 1.373287872308282e-09, 'val/mse5': 1.0317071641452458e-07, 'val/mse10': 1.100612792015454e-06, 'val/stdloss': 6.564368959516287e-08, 'val/stdmse1': 8.536110707169087e-11, 'val/stdmse5': 5.950870018978094e-09, 'val/stdmse10': 7.309478178285644e-08} - diff --git a/docs/pages/tutorials/intro.rst b/docs/pages/tutorials/intro.rst deleted file mode 100644 index bdf7c1b..0000000 --- a/docs/pages/tutorials/intro.rst +++ /dev/null @@ -1,312 +0,0 @@ -Training GNS on the 2D Taylor Green Vortex -========================================== - -This tutorial notebook will show how to train a GNS model on the 2D -Taylor Green Vortex. It will give a brief overview of LagrangeBench on -these topics: - Datasets and case setup - Models - Training tricks and -strategies (random-walk noise and pushforward) - Training and inference - -.. code:: ipython3 - - import os - os.environ["CUDA_VISIBLE_DEVICES"] = "" - os.environ["JAX_ENABLE_X64"] = "True" - - import lagrangebench - import haiku as hk - import numpy as np - import matplotlib.pyplot as plt - import pickle - import matplotlib.animation as animation - -Datasets --------- - -First thing to do is to load the dataset. The simplest way to do this is -by using e.g. the ``lagrangebench.data.TGV2D`` class for the -2-dimensional Taylor-Green vortex problem. It will automatically -download the HDF5 files if they are not found in the respective folder, -and it will take care of setting up the dataset. Note that for the -validation/test set you need to specify a positive number of rollout -steps, e.g. ``extra_seq_length=20``. This means that the dataset will -not split the trajectory into subsequences and keep whole rollouts for -evaluation. - -.. code:: ipython3 - - tgv2d_train = lagrangebench.data.TGV2D("train", extra_seq_length=5) # extra_seq_length=5 will be clear later - tgv2d_valid = lagrangebench.data.TGV2D("valid", extra_seq_length=20) - - print( - f"This is a {tgv2d_train.metadata['dim']}D dataset " - f"called {tgv2d_train.metadata['case']}.\n" - f"Train snapshot have shape {tgv2d_train[0][0].shape} (n_nodes, seq_len, xy pos).\n" - f"Val snapshot have shape {tgv2d_valid[0][0].shape} (n_nodes, rollout, xy pos).\n" - ) - - -.. parsed-literal:: - - This is a 2D dataset called TGV. - Train snapshot have shape (2500, 12, 2) (n_nodes, seq_len, xy pos). - Val snapshot have shape (2500, 26, 2) (n_nodes, rollout, xy pos). - - - -Similarly, for other datasets one can use the respective subclass, for -example - -.. code:: python - - rpf_3d_data = lagrangebench.data.RPF3D("train") # 3D Reverse Poiseuille flow - dam_2d_data = lagrangebench.data.DAM2D("train") # 2D Dam break - # etc. - # and in general: - lagrangebench.data.H5Dataset("train", dataset_path="path/to/dataset") - -Models ------- - -All models should inherit from -```models.BaseModel`` `__, and generally -include a ``_transform`` function for feature engineering and graph -building. - -Here we use a small GNS model, with latent dimension of 16 and 4 message -passing layers and predicting 2D accelerations. Note that we use a -function wrapper beause ``haiku.Modules`` must be initialized inside -``haiku.transform``. - -.. code:: ipython3 - - def gns(x): - return lagrangebench.GNS( - particle_dimension=tgv2d_train.metadata["dim"], - latent_size=16, - blocks_per_step=2, - num_mp_steps=4, - particle_type_embedding_size=8, - )(x) - -Then apply ``haiku.transform`` to the model function - -.. code:: ipython3 - - gns = hk.without_apply_rng(hk.transform_with_state(gns)) - -Training tricks and strategies ------------------------------- - -To improve performance and stabilize training we can use a number of -tricks and strategies. In this notebook we will cover random-walk noise -and pushforward, which are included by default in LagrangeBench. - -Random-walk noise -~~~~~~~~~~~~~~~~~ - -Briefly, random-walk noise adds noise to the velocities in the input -sequence and adjusts the target positions accordingly. The standard -deviation is passed as a parameter, and each noise step is rescaled so -that the cumulated noise has the input standard deviation. It applied to -GNNs in physics in `“Learning to Simulate Complex Physics with Graph -Networks” `__ by Deepmind (2020). - -Pushforward -~~~~~~~~~~~ - -Instead of training always on the next step, the pushforward trick will -randomly sample a number of “unroll” steps to perform and compute the -loss only on the last one. Ideally this should inject the “right amount” -of noise, since it comes from the physical distribution. Because it adds -some overhead, the maximum number of unroll steps should not be too -large and the probability of unrolling large sequences should be -reasonably small. It comes from `“Message Passing Neural PDE -Solvers” `__ by J Brandstetter et -al. (2022). - -.. code:: ipython3 - - noise_std = 3e-4 - - pf_config = lagrangebench.PushforwardConfig( - steps=[-1, 500, 700], # training steps to unlock the relative stage - unrolls=[0, 2, 5], # number of unroll steps per stage - probs=[7, 2, 1], # relative probabilities to unroll to the relative stage - ) - -For example, this configuration would apply noise with ``std=3e-4`` and -pushforward with three unroll stages (0, 2 and 5), “unlocking” the -second stage after 500 training steps and the third stage after 700 -training steps. After 700 steps, 0-step unroll (normal, 1-step training) -will happen with a probability of 70%, 2-step unroll with a probability -of 20% and finally 5-step unroll with a probability of 10%. - -Pushforward up to 5 steps is the reason why we created the training -dataset as ``lagrangebench.data.TGV2D("train", extra_seq_length=5)``, as -or every sample from the dataset we need up to 5 steps of unroll. - -Case ----- - -The ``CaseSetupFn`` is an essential part in LagrangeBench. Most of the -operations during training, such as data preprocessing, time integration -for the ouput and neighbor computation are managed by ``CaseSetupFn``. -The easies way to initialize it is through the -``lagrangebench.case_builder`` function. - -.. code:: ipython3 - - bounds = np.array(tgv2d_train.metadata["bounds"]) - box = bounds[:, 1] - bounds[:, 0] - - tgv2d_case = lagrangebench.case_builder( - box=box, # (x,y) array with the world size along each axis. (1.0, 1.0) for 2D TGV - metadata=tgv2d_train.metadata, # metadata dictionary - input_seq_length=6, # number of consecutive time steps fed to the model - isotropic_norm=False, # whether to normalize each dimension independently - noise_std=noise_std, # noise standard deviation used by the random-walk noise - ) - -Training and inference ----------------------- - -Finally, to train GNS on Taylor Green (with noise and pushforward) the -``lagrangebench.Trainer`` methods comes to hand - -.. code:: ipython3 - - trainer = lagrangebench.Trainer( - model=gns, - case=tgv2d_case, - data_train=tgv2d_train, - data_valid=tgv2d_valid, - pushforward=pf_config, - noise_std=noise_std, - metrics=["mse"], - n_rollout_steps=20, - eval_n_trajs=1, - lr_start=5e-4, - log_steps=100, - eval_steps=500, - ) - - params, state, _ = trainer(step_max=1000) - - -.. parsed-literal:: - - /home/ggalletti/git/lagrangebench/venv/lib/python3.10/site-packages/jax/_src/ops/scatter.py:94: FutureWarning: scatter inputs have incompatible types: cannot safely cast value from dtype=int64 to dtype=int32 with jax_numpy_dtype_promotion='standard'. In future JAX releases this will result in an error. - warnings.warn("scatter inputs have incompatible types: cannot safely cast " - - -.. parsed-literal:: - - 0000, train/loss: 2.17808. - 0100, train/loss: 0.19394. - 0200, train/loss: 0.19751. - 0300, train/loss: 0.20027. - 0400, train/loss: 0.15017. - 0500, train/loss: 0.14875. - {'val/loss': 0.006475041204928584, 'val/mse1': 3.5806455399026536e-06, 'val/mse5': 0.00014116973568971617, 'val/mse10': 0.0009921582776032162, 'val/stdloss': 0.0, 'val/stdmse1': 0.0, 'val/stdmse5': 0.0, 'val/stdmse10': 0.0} - 0600, train/loss: 0.02190. - 0700, train/loss: 1.62371. - Reallocate neighbors list at step 772 - From (2, 21057) to (2, 20557) - 0800, train/loss: 0.18237. - Reallocate neighbors list at step 804 - From (2, 20557) to (2, 20742) - 0900, train/loss: 0.01483. - 1000, train/loss: 0.19956. - {'val/loss': 0.003817330574772867, 'val/mse1': 2.793629854284794e-06, 'val/mse5': 9.147089474639231e-05, 'val/mse10': 0.0005903546941926859, 'val/stdloss': 0.0, 'val/stdmse1': 0.0, 'val/stdmse5': 0.0, 'val/stdmse10': 0.0} - - -Now let’s see what the trained GNS can do. First, let’s get the test -data. As for the validation set, ``n_rollout_steps=20``. - -.. code:: ipython3 - - tgv2d_test = lagrangebench.TGV2D("test", extra_seq_length=20) - -Then let’s run then evaluation over 20 timesteps. Here we use three -evaluation metrics: **position MSE** and **Sinkhorn** distance. - -.. code:: ipython3 - - metrics = lagrangebench.infer( - gns, - tgv2d_case, - tgv2d_test, - params, - state, - metrics=["mse", "sinkhorn"], - eval_n_trajs=1, - n_rollout_steps=20, - rollout_dir="rollouts/", - out_type="pkl", - )["rollout_0"] - rollout = pickle.load(open("rollouts/rollout_0.pkl", "rb")) - -.. code:: ipython3 - - # plot metrics - fig, ax = plt.subplots(1, 2, figsize=(12, 4)) - - # plot the metrics - for i, m in enumerate(["mse", "sinkhorn"]): - ax[i].plot(metrics[m]) - ax[i].set_title(m) - - - -.. image:: media/tutorial_20_0.png - - -.. code:: ipython3 - - print(f"rollout of shape {rollout['predicted_rollout'].shape} (steps, nodes, xy pos)") - - fig, ax = plt.subplots(1, 2) - ax[0].set_xlim([0, 1.0]) - ax[0].set_ylim([0, 1.0]) - ax[1].set_xlim([0, 1.0]) - ax[1].set_ylim([0, 1.0]) - fig.set_size_inches(10, 5, forward=True) - ax[0].set_title("GNS") - ax[1].set_title("Ground Truth") - - rollout_len = rollout["predicted_rollout"].shape[0] - 1 - - scat0 = ax[0].scatter( - rollout["predicted_rollout"][0, :, 0], rollout["predicted_rollout"][0, :, 1] - ) - scat1 = ax[1].scatter( - rollout["ground_truth_rollout"][0, :, 0], rollout["ground_truth_rollout"][0, :, 1] - ) - - - def animate(i): - scat0.set_offsets(rollout["predicted_rollout"][i]) - scat1.set_offsets(rollout["ground_truth_rollout"][i]) - return scat0, scat1 - - - ani = animation.FuncAnimation( - fig, animate, repeat=True, frames=rollout_len, interval=50 - ) - - plt.close(fig) - - writer = animation.PillowWriter(fps=10, metadata=dict(artist="Me"), bitrate=1800) - ani.save("media/scatter.gif", writer=writer) - - from IPython.display import Image - - Image(url="media/scatter.gif") - - -.. parsed-literal:: - - rollout of shape (26, 2500, 2) (steps, nodes, xy pos) - - -.. image:: media/scatter.gif diff --git a/docs/pages/tutorials/media/datasets_10_0.png b/docs/pages/tutorials/media/datasets_10_0.png deleted file mode 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z)N~lV@%-?$mO?O7sR+up8n!iXN$UEwYiY^4veHmUAUGE=~(a;LUV&2NT~-JX4bUB^2qPoqS~<3(E;)@>%FO zRGxq?G)y)C*gjl}TkF^}XoF6extK)+J2+J`$Q6v1sxK(S#>U=2k_E<$ebA7`Z>k-x z5V?|V84ufp#D-P((2z+D5c44wJ|OhasQQKM9K0Jhun)b?MNby#H+GXoVmeI>l=SrS zAU+{Y!89!_GZmK5G6Dt6UIp{t%3u6!#f5C*DH)kf_de`~o#!bK;u!cIW)A8MIMTuE zu$B^&kT8^TwzT8c&o6t@i!RvZQoEc)uHe&+BM+-|eQ>KS^)x{)#Z%+2r5d8YU?h!O zTr}1V%sW$K+u+p2aCSL4xg)?CGVq%`$6;9pe|zqCl-S_K8~Pe&a#ACkv2Wjh`0yEQ zdzyz%vUQwrklkH2x$r8Y5^2-s&_>2Yp*v@Kxh)e{C-c+Ex9BiCPQ3u5h Date: Tue, 9 Jan 2024 21:50:32 +0100 Subject: [PATCH 13/13] notebooks install for colab and links --- README.md | 6 +++--- notebooks/datasets.ipynb | 11 ++++++++++- notebooks/gns_data.ipynb | 11 ++++++++++- notebooks/tutorial.ipynb | 13 +++++++++++-- 4 files changed, 34 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index b8ef248..377c59e 100644 --- a/README.md +++ b/README.md @@ -124,9 +124,9 @@ The datasets are hosted on Zenodo under the DOI: [10.5281/zenodo.10021925](https ### Notebooks We provide three notebooks that show LagrangeBench functionalities, namely: -- [`tutorial.ipynb`](notebooks/tutorial.ipynb) with a general overview of LagrangeBench library, with training and evaluation of a simple GNS model, -- [`datasets.ipynb`](notebooks/datasets.ipynb) with more details and visualizations on the datasets, and -- [`gns_data.ipynb`](notebooks/gns_data.ipynb) showing how to train models within LagrangeBench on the datasets from the paper [Learning to Simulate Complex Physics with Graph Networks](https://arxiv.org/abs/2002.09405). +- [`tutorial.ipynb`](notebooks/tutorial.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/tutorial.ipynb), with a general overview of LagrangeBench library, with training and evaluation of a simple GNS model, +- [`datasets.ipynb`](notebooks/datasets.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/datasets.ipynb), with more details and visualizations on the datasets, and +- [`gns_data.ipynb`](notebooks/gns_data.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/gns_data.ipynb), showing how to train models within LagrangeBench on the datasets from the paper [Learning to Simulate Complex Physics with Graph Networks](https://arxiv.org/abs/2002.09405). ## Directory structure ``` diff --git a/notebooks/datasets.ipynb b/notebooks/datasets.ipynb index 894126a..7a9296d 100644 --- a/notebooks/datasets.ipynb +++ b/notebooks/datasets.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# LagrangeBench Datasets\n", + "# LagrangeBench Datasets [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/datasets.ipynb)\n", "\n", "This is an overview of the introduced datasets. We show:\n", "\n", @@ -18,6 +18,15 @@ "- `test.h5` - test set" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install lagrangebench --extra-index-url=https://download.pytorch.org/whl/cpu" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/notebooks/gns_data.ipynb b/notebooks/gns_data.ipynb index 4875a99..e48e02e 100644 --- a/notebooks/gns_data.ipynb +++ b/notebooks/gns_data.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working with other datasets\n", + "# Working with other datasets [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/gns_data.ipynb)\n", "\n", "We demonstrate how to train a GNN on one of the datasets provided by DeepMind along with the paper [1]. We divide this notebook into three parts:\n", "\n", @@ -15,6 +15,15 @@ "[1] - Sanchez-Gonzalez et al., [\"Learning to Simulate Complex Physics with Graph Networks\"](https://arxiv.org/abs/2002.09405), ICLR 2020" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install lagrangebench --extra-index-url=https://download.pytorch.org/whl/cpu" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 936a77e..f2e5c36 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Training GNS on the 2D Taylor Green Vortex\n", + "# Training GNS on the 2D Taylor Green Vortex [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tumaer/lagrangebench/blob/main/notebooks/tutorial.ipynb)\n", "This tutorial notebook will show how to train a GNS model on the 2D Taylor Green Vortex. It will give a brief overview of LagrangeBench on these topics:\n", "- Datasets and case setup\n", "- Models\n", @@ -13,6 +13,15 @@ "- Training and inference" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install lagrangebench --extra-index-url=https://download.pytorch.org/whl/cpu" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -494,7 +503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.12" }, "orig_nbformat": 4 },