%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
[](#contributors-)
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)):
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
[](#contributors-)
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)):
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
[](#contributors-)
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)):
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 - 4.1.14-1
- Package Spec generated
