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https://github.com/openclimatefix/metnet
PyTorch Implementation of Google Research's MetNet and MetNet-2
https://github.com/openclimatefix/metnet
pytorch
Last synced: about 2 months ago
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PyTorch Implementation of Google Research's MetNet and MetNet-2
- Host: GitHub
- URL: https://github.com/openclimatefix/metnet
- Owner: openclimatefix
- License: mit
- Created: 2021-09-02T11:20:11.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-08T17:52:11.000Z (3 months ago)
- Last Synced: 2024-04-14T11:50:43.060Z (2 months ago)
- Topics: pytorch
- Language: Python
- Homepage:
- Size: 188 KB
- Stars: 217
- Watchers: 7
- Forks: 46
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- open-sustainable-technology - MetNet - A neural network that forecasts precipitation up to 8 hours into the future at the high spatial resolution of 1 km² and at the temporal resolution of 2 minutes with a latency in the order of second. (Atmosphere / Meteorological Observation and Forecast)
- awesome-meteo - MetNet
README
# 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)):
Jacob Bieker
💻
Jack Kelly
💻
Valter Fallenius
📓
terigenbuaa
💬
Kan.Dai
💬
Sailesh Bechar
💬
Rahul Maurya
⚠️
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!