Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/openclimatefix/skillful_nowcasting

Implementation of DeepMind's Deep Generative Model of Radar (DGMR) https://arxiv.org/abs/2104.00954
https://github.com/openclimatefix/skillful_nowcasting

gan nowcasting nowcasting-models nowcasting-precipitation pytorch pytorch-implementation pytorch-lightning

Last synced: about 1 month ago
JSON representation

Implementation of DeepMind's Deep Generative Model of Radar (DGMR) https://arxiv.org/abs/2104.00954

Host: GitHub
URL: https://github.com/openclimatefix/skillful_nowcasting
Owner: openclimatefix
License: mit
Created: 2021-09-02T11:18:14.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-09-23T17:45:26.000Z (about 2 months ago)
Last Synced: 2024-09-28T13:22:32.319Z (about 2 months ago)
Topics: gan, nowcasting, nowcasting-models, nowcasting-precipitation, pytorch, pytorch-implementation, pytorch-lightning
Language: Python
Homepage:
Size: 188 KB
Stars: 214
Watchers: 5
Forks: 59
Open Issues: 25
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # Skillful Nowcasting with Deep Generative Model of Radar (DGMR)

[![All Contributors](https://img.shields.io/badge/all_contributors-12-orange.svg?style=flat-square)](#contributors-)

Implementation of DeepMind's Skillful Nowcasting GAN Deep Generative Model of Radar (DGMR) (https://arxiv.org/abs/2104.00954) in PyTorch Lightning.

This implementation matches as much as possible the pseudocode released by DeepMind. Each of the components (Sampler, Context conditioning stack, Latent conditioning stack, Discriminator, and Generator) are normal PyTorch modules. As the model training is a bit complicated, the overall architecture is wrapped in PyTorch Lightning.

The default parameters match what is written in the paper.

## Installation

Clone the repository, then run

```shell

pip install -r requirements.txt

pip install -e .

````

Alternatively, you can also install through ```pip install dgmr```

## Training Data

The open-sourced UK training dataset has been mirrored to [HuggingFace Datasets!](https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km) This should enable training the original architecture on the original data for reproducing the results from the paper. The full dataset is roughly 1TB in size, and unfortunately, streaming the data from HF Datasets doesn't seem to work, so it has to be cached locally. We have added the sample dataset as well though, which can be directly streamed from GCP without costs.

The dataset can be loaded with

```python

from datasets import load_dataset

dataset = load_dataset("openclimatefix/nimrod-uk-1km")

```

For now, only the sample dataset support streaming in, as its data files are hosted on GCP, not HF, so it can be used with:

```python

from datasets import load_dataset

dataset = load_dataset("openclimatefix/nimrod-uk-1km", "sample", streaming=True)

```

The authors also used [MRMS](https://www.nssl.noaa.gov/projects/mrms/) US precipitation radar data as another comparison. While that dataset was not released, the MRMS data is publicly available, and we have made that data available on HuggingFace Datasets as well [here](https://huggingface.co/datasets/openclimatefix/mrms). This dataset is the raw 3500x7000 contiguous US MRMS data for 2016 through May 2022, is a few hundred GBs in size, with sporadic updates to more recent data planned. This dataset is in Zarr format, and can be streamed without caching locally through

```python

from datasets import load_dataset

dataset = load_dataset("openclimatefix/mrms", "default_sequence", streaming=True)

```

This steams the data with 24 timesteps per example, just like the UK DGMR dataset. To get individual MRMS frames, instead of a sequence, this can be achieved through

```python

from datasets import load_dataset

dataset = load_dataset("openclimatefix/mrms", "default", streaming=True)

```

## Pretrained Weights

Pretrained weights are be available through [HuggingFace Hub](https://huggingface.co/openclimatefix), currently weights trained on the sample dataset. The whole DGMR model or different components can be loaded as the following:

```python

from dgmr import DGMR, Sampler, Generator, Discriminator, LatentConditioningStack, ContextConditioningStack

model = DGMR.from_pretrained("openclimatefix/dgmr")

sampler = Sampler.from_pretrained("openclimatefix/dgmr-sampler")

discriminator = Discriminator.from_pretrained("openclimatefix/dgmr-discriminator")

latent_stack = LatentConditioningStack.from_pretrained("openclimatefix/dgmr-latent-conditioning-stack")

context_stack = ContextConditioningStack.from_pretrained("openclimatefix/dgmr-context-conditioning-stack")

generator = Generator(conditioning_stack=context_stack, latent_stack=latent_stack, sampler=sampler)

```

## Example Usage

```python

from dgmr import DGMR

import torch.nn.functional as F

import torch

model = DGMR(

        forecast_steps=4,

        input_channels=1,

        output_shape=128,

        latent_channels=384,

        context_channels=192,

        num_samples=3,

    )

x = torch.rand((2, 4, 1, 128, 128))

out = model(x)

y = torch.rand((2, 4, 1, 128, 128))

loss = F.mse_loss(y, out)

loss.backward()

```

## Citation

```

@article{ravuris2021skillful,

  author={Suman Ravuri and Karel Lenc and Matthew Willson and Dmitry Kangin and Remi Lam and Piotr Mirowski and Megan Fitzsimons and Maria Athanassiadou and Sheleem Kashem and Sam Madge and Rachel Prudden Amol Mandhane and Aidan Clark and Andrew Brock and Karen Simonyan and Raia Hadsell and Niall Robinson Ellen Clancy and Alberto Arribas† and Shakir Mohamed},

  title={Skillful Precipitation Nowcasting using Deep Generative Models of Radar},

  journal={Nature},

  volume={597},

  pages={672--677},

  year={2021}

}

```

## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

  

    

      
_{Jacob Bieker}
💻

      
_{Johan Mathe}
💻

      
_Z1YUE
🐛

      
_Nan.Y
💬

      
_Taisanai
💬

      
_cameron
💬

      
_zhrli
💬

    

    

      
_{Najeeb Kazmi}
💬

      
_TQRTQ
💬

      
_{Viktor Bordiuzha}
💡

      
_agijsberts
💻

      
_Mews
⚠️

    

  

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!