path: root/python-metnet.spec

%global _empty_manifest_terminate_build 0
Name:		python-metnet
Version:	4.1.14
Release:	1
Summary:	PyTorch MetNet Implementation
License:	MIT License
URL:		https://github.com/openclimatefix/metnet
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3a/4f/bffd6422c606b1f26da39bff2881d626c85bf86f729497cb4f7bac08bed3/metnet-4.1.14.tar.gz
BuildArch:	noarch

Requires:	python3-einops
Requires:	python3-numpy
Requires:	python3-torchvision
Requires:	python3-antialiased-cnns
Requires:	python3-axial-attention
Requires:	python3-pytorch-msssim
Requires:	python3-huggingface-hub
Requires:	python3-ocf-datapipes
Requires:	python3-pytorch-lightning

%description
# MetNet and MetNet-2
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-6-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->

PyTorch Implementation of Google Research's MetNet for short term weather forecasting (https://arxiv.org/abs/2003.12140), inspired from https://github.com/tcapelle/metnet_pytorch/tree/master/metnet_pytorch

MetNet-2 (https://arxiv.org/pdf/2111.07470.pdf) is a further extension of MetNet that takes in a larger context image to predict up to 12 hours ahead, and is also implemented in PyTorch here.

## Installation

Clone the repository, then run
```shell
pip install -r requirements.txt
pip install -e .
````

Alternatively, you can also install a usually older version through ```pip install metnet```

Please ensure that you're using Python version 3.9 or above.

## Data

While the exact training data used for both MetNet and MetNet-2 haven't been released, the papers do go into some detail as to the inputs, which were GOES-16 and MRMS precipitation data, as well as the time period covered. We will be making those splits available, as well as a larger dataset that covers a longer time period, with [HuggingFace Datasets](https://huggingface.co/datasets/openclimatefix/goes-mrms)! Note: The dataset is not available yet, we are still processing data!

```python
from datasets import load_dataset

dataset = load_dataset("openclimatefix/goes-mrms")
```

This uses the publicly avaiilable GOES-16 data and the MRMS archive to create a similar set of data to train and test on, with various other splits available as well.

## Pretrained Weights
Pretrained model weights for MetNet and MetNet-2 have not been publicly released, and there is some difficulty in reproducing their training. We release weights for both MetNet and MetNet-2 trained on cloud mask and satellite imagery data with the same parameters as detailed in the papers on HuggingFace Hub for [MetNet](https://huggingface.co/openclimatefix/metnet) and [MetNet-2](https://huggingface.co/openclimatefix/metnet-2). These weights can be downloaded and used using:

```python
from metnet import MetNet, MetNet2
model = MetNet().from_pretrained("openclimatefix/metnet")
model = MetNet2().from_pretrained("openclimatefix/metnet-2")
```

## Example Usage

MetNet can be used with:

```python
from metnet import MetNet
import torch
import torch.nn.functional as F

model = MetNet(
        hidden_dim=32,
        forecast_steps=24,
        input_channels=16,
        output_channels=12,
        sat_channels=12,
        input_size=32,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 12, 16, 128, 128))
out = []
for lead_time in range(24):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
# MetNet creates predictions for the center 1/4th
y = torch.randn((2, 24, 12, 8, 8))
F.mse_loss(out, y).backward()
```

And MetNet-2 with:

```python
from metnet import MetNet2
import torch
import torch.nn.functional as F

model = MetNet2(
        forecast_steps=8,
        input_size=64,
        num_input_timesteps=6,
        upsampler_channels=128,
        lstm_channels=32,
        encoder_channels=64,
        center_crop_size=16,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 6, 12, 256, 256))
out = []
for lead_time in range(8):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
y = torch.rand((2,8,12,64,64))
F.mse_loss(out, y).backward()
```

## Contributors ✨

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

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
  <tbody>
    <tr>
      <td align="center"><a href="https://www.jacobbieker.com"><img src="https://avatars.githubusercontent.com/u/7170359?v=4?s=100" width="100px;" alt="Jacob Bieker"/><br /><sub><b>Jacob Bieker</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=jacobbieker" title="Code">💻</a></td>
      <td align="center"><a href="http://jack-kelly.com"><img src="https://avatars.githubusercontent.com/u/460756?v=4?s=100" width="100px;" alt="Jack Kelly"/><br /><sub><b>Jack Kelly</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=JackKelly" title="Code">💻</a></td>
      <td align="center"><a href="https://github.com/ValterFallenius"><img src="https://avatars.githubusercontent.com/u/21970939?v=4?s=100" width="100px;" alt="Valter Fallenius"/><br /><sub><b>Valter Fallenius</b></sub></a><br /><a href="#userTesting-ValterFallenius" title="User Testing">📓</a></td>
      <td align="center"><a href="https://github.com/terigenbuaa"><img src="https://avatars.githubusercontent.com/u/91317406?v=4?s=100" width="100px;" alt="terigenbuaa"/><br /><sub><b>terigenbuaa</b></sub></a><br /><a href="#question-terigenbuaa" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/NMC-DAVE"><img src="https://avatars.githubusercontent.com/u/26354668?v=4?s=100" width="100px;" alt="Kan.Dai"/><br /><sub><b>Kan.Dai</b></sub></a><br /><a href="#question-NMC-DAVE" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/SaileshBechar"><img src="https://avatars.githubusercontent.com/u/38445041?v=4?s=100" width="100px;" alt="Sailesh Bechar"/><br /><sub><b>Sailesh Bechar</b></sub></a><br /><a href="#question-SaileshBechar" title="Answering Questions">💬</a></td>
    </tr>
  </tbody>
</table>

<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->

<!-- ALL-CONTRIBUTORS-LIST:END -->

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


%package -n python3-metnet
Summary:	PyTorch MetNet Implementation
Provides:	python-metnet
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-metnet
# MetNet and MetNet-2
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-6-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->

PyTorch Implementation of Google Research's MetNet for short term weather forecasting (https://arxiv.org/abs/2003.12140), inspired from https://github.com/tcapelle/metnet_pytorch/tree/master/metnet_pytorch

MetNet-2 (https://arxiv.org/pdf/2111.07470.pdf) is a further extension of MetNet that takes in a larger context image to predict up to 12 hours ahead, and is also implemented in PyTorch here.

## Installation

Clone the repository, then run
```shell
pip install -r requirements.txt
pip install -e .
````

Alternatively, you can also install a usually older version through ```pip install metnet```

Please ensure that you're using Python version 3.9 or above.

## Data

While the exact training data used for both MetNet and MetNet-2 haven't been released, the papers do go into some detail as to the inputs, which were GOES-16 and MRMS precipitation data, as well as the time period covered. We will be making those splits available, as well as a larger dataset that covers a longer time period, with [HuggingFace Datasets](https://huggingface.co/datasets/openclimatefix/goes-mrms)! Note: The dataset is not available yet, we are still processing data!

```python
from datasets import load_dataset

dataset = load_dataset("openclimatefix/goes-mrms")
```

This uses the publicly avaiilable GOES-16 data and the MRMS archive to create a similar set of data to train and test on, with various other splits available as well.

## Pretrained Weights
Pretrained model weights for MetNet and MetNet-2 have not been publicly released, and there is some difficulty in reproducing their training. We release weights for both MetNet and MetNet-2 trained on cloud mask and satellite imagery data with the same parameters as detailed in the papers on HuggingFace Hub for [MetNet](https://huggingface.co/openclimatefix/metnet) and [MetNet-2](https://huggingface.co/openclimatefix/metnet-2). These weights can be downloaded and used using:

```python
from metnet import MetNet, MetNet2
model = MetNet().from_pretrained("openclimatefix/metnet")
model = MetNet2().from_pretrained("openclimatefix/metnet-2")
```

## Example Usage

MetNet can be used with:

```python
from metnet import MetNet
import torch
import torch.nn.functional as F

model = MetNet(
        hidden_dim=32,
        forecast_steps=24,
        input_channels=16,
        output_channels=12,
        sat_channels=12,
        input_size=32,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 12, 16, 128, 128))
out = []
for lead_time in range(24):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
# MetNet creates predictions for the center 1/4th
y = torch.randn((2, 24, 12, 8, 8))
F.mse_loss(out, y).backward()
```

And MetNet-2 with:

```python
from metnet import MetNet2
import torch
import torch.nn.functional as F

model = MetNet2(
        forecast_steps=8,
        input_size=64,
        num_input_timesteps=6,
        upsampler_channels=128,
        lstm_channels=32,
        encoder_channels=64,
        center_crop_size=16,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 6, 12, 256, 256))
out = []
for lead_time in range(8):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
y = torch.rand((2,8,12,64,64))
F.mse_loss(out, y).backward()
```

## Contributors ✨

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

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
  <tbody>
    <tr>
      <td align="center"><a href="https://www.jacobbieker.com"><img src="https://avatars.githubusercontent.com/u/7170359?v=4?s=100" width="100px;" alt="Jacob Bieker"/><br /><sub><b>Jacob Bieker</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=jacobbieker" title="Code">💻</a></td>
      <td align="center"><a href="http://jack-kelly.com"><img src="https://avatars.githubusercontent.com/u/460756?v=4?s=100" width="100px;" alt="Jack Kelly"/><br /><sub><b>Jack Kelly</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=JackKelly" title="Code">💻</a></td>
      <td align="center"><a href="https://github.com/ValterFallenius"><img src="https://avatars.githubusercontent.com/u/21970939?v=4?s=100" width="100px;" alt="Valter Fallenius"/><br /><sub><b>Valter Fallenius</b></sub></a><br /><a href="#userTesting-ValterFallenius" title="User Testing">📓</a></td>
      <td align="center"><a href="https://github.com/terigenbuaa"><img src="https://avatars.githubusercontent.com/u/91317406?v=4?s=100" width="100px;" alt="terigenbuaa"/><br /><sub><b>terigenbuaa</b></sub></a><br /><a href="#question-terigenbuaa" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/NMC-DAVE"><img src="https://avatars.githubusercontent.com/u/26354668?v=4?s=100" width="100px;" alt="Kan.Dai"/><br /><sub><b>Kan.Dai</b></sub></a><br /><a href="#question-NMC-DAVE" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/SaileshBechar"><img src="https://avatars.githubusercontent.com/u/38445041?v=4?s=100" width="100px;" alt="Sailesh Bechar"/><br /><sub><b>Sailesh Bechar</b></sub></a><br /><a href="#question-SaileshBechar" title="Answering Questions">💬</a></td>
    </tr>
  </tbody>
</table>

<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->

<!-- ALL-CONTRIBUTORS-LIST:END -->

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


%package help
Summary:	Development documents and examples for metnet
Provides:	python3-metnet-doc
%description help
# MetNet and MetNet-2
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/all_contributors-6-orange.svg?style=flat-square)](#contributors-)
<!-- ALL-CONTRIBUTORS-BADGE:END -->

PyTorch Implementation of Google Research's MetNet for short term weather forecasting (https://arxiv.org/abs/2003.12140), inspired from https://github.com/tcapelle/metnet_pytorch/tree/master/metnet_pytorch

MetNet-2 (https://arxiv.org/pdf/2111.07470.pdf) is a further extension of MetNet that takes in a larger context image to predict up to 12 hours ahead, and is also implemented in PyTorch here.

## Installation

Clone the repository, then run
```shell
pip install -r requirements.txt
pip install -e .
````

Alternatively, you can also install a usually older version through ```pip install metnet```

Please ensure that you're using Python version 3.9 or above.

## Data

While the exact training data used for both MetNet and MetNet-2 haven't been released, the papers do go into some detail as to the inputs, which were GOES-16 and MRMS precipitation data, as well as the time period covered. We will be making those splits available, as well as a larger dataset that covers a longer time period, with [HuggingFace Datasets](https://huggingface.co/datasets/openclimatefix/goes-mrms)! Note: The dataset is not available yet, we are still processing data!

```python
from datasets import load_dataset

dataset = load_dataset("openclimatefix/goes-mrms")
```

This uses the publicly avaiilable GOES-16 data and the MRMS archive to create a similar set of data to train and test on, with various other splits available as well.

## Pretrained Weights
Pretrained model weights for MetNet and MetNet-2 have not been publicly released, and there is some difficulty in reproducing their training. We release weights for both MetNet and MetNet-2 trained on cloud mask and satellite imagery data with the same parameters as detailed in the papers on HuggingFace Hub for [MetNet](https://huggingface.co/openclimatefix/metnet) and [MetNet-2](https://huggingface.co/openclimatefix/metnet-2). These weights can be downloaded and used using:

```python
from metnet import MetNet, MetNet2
model = MetNet().from_pretrained("openclimatefix/metnet")
model = MetNet2().from_pretrained("openclimatefix/metnet-2")
```

## Example Usage

MetNet can be used with:

```python
from metnet import MetNet
import torch
import torch.nn.functional as F

model = MetNet(
        hidden_dim=32,
        forecast_steps=24,
        input_channels=16,
        output_channels=12,
        sat_channels=12,
        input_size=32,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 12, 16, 128, 128))
out = []
for lead_time in range(24):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
# MetNet creates predictions for the center 1/4th
y = torch.randn((2, 24, 12, 8, 8))
F.mse_loss(out, y).backward()
```

And MetNet-2 with:

```python
from metnet import MetNet2
import torch
import torch.nn.functional as F

model = MetNet2(
        forecast_steps=8,
        input_size=64,
        num_input_timesteps=6,
        upsampler_channels=128,
        lstm_channels=32,
        encoder_channels=64,
        center_crop_size=16,
        )
# MetNet expects original HxW to be 4x the input size
x = torch.randn((2, 6, 12, 256, 256))
out = []
for lead_time in range(8):
        out.append(model(x, lead_time))
out = torch.stack(out, dim=1)
y = torch.rand((2,8,12,64,64))
F.mse_loss(out, y).backward()
```

## Contributors ✨

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

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
  <tbody>
    <tr>
      <td align="center"><a href="https://www.jacobbieker.com"><img src="https://avatars.githubusercontent.com/u/7170359?v=4?s=100" width="100px;" alt="Jacob Bieker"/><br /><sub><b>Jacob Bieker</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=jacobbieker" title="Code">💻</a></td>
      <td align="center"><a href="http://jack-kelly.com"><img src="https://avatars.githubusercontent.com/u/460756?v=4?s=100" width="100px;" alt="Jack Kelly"/><br /><sub><b>Jack Kelly</b></sub></a><br /><a href="https://github.com/openclimatefix/metnet/commits?author=JackKelly" title="Code">💻</a></td>
      <td align="center"><a href="https://github.com/ValterFallenius"><img src="https://avatars.githubusercontent.com/u/21970939?v=4?s=100" width="100px;" alt="Valter Fallenius"/><br /><sub><b>Valter Fallenius</b></sub></a><br /><a href="#userTesting-ValterFallenius" title="User Testing">📓</a></td>
      <td align="center"><a href="https://github.com/terigenbuaa"><img src="https://avatars.githubusercontent.com/u/91317406?v=4?s=100" width="100px;" alt="terigenbuaa"/><br /><sub><b>terigenbuaa</b></sub></a><br /><a href="#question-terigenbuaa" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/NMC-DAVE"><img src="https://avatars.githubusercontent.com/u/26354668?v=4?s=100" width="100px;" alt="Kan.Dai"/><br /><sub><b>Kan.Dai</b></sub></a><br /><a href="#question-NMC-DAVE" title="Answering Questions">💬</a></td>
      <td align="center"><a href="https://github.com/SaileshBechar"><img src="https://avatars.githubusercontent.com/u/38445041?v=4?s=100" width="100px;" alt="Sailesh Bechar"/><br /><sub><b>Sailesh Bechar</b></sub></a><br /><a href="#question-SaileshBechar" title="Answering Questions">💬</a></td>
    </tr>
  </tbody>
</table>

<!-- markdownlint-restore -->
<!-- prettier-ignore-end -->

<!-- ALL-CONTRIBUTORS-LIST:END -->

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


%prep
%autosetup -n metnet-4.1.14

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-metnet -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 4.1.14-1
- Package Spec generated