-
-
Notifications
You must be signed in to change notification settings - Fork 49
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
2 changed files
with
187 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -7,3 +7,5 @@ | |
# pixi environments | ||
.pixi | ||
.vscode/ | ||
checkpoints/ | ||
lightning_logs/ |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,185 @@ | ||
import click | ||
import xarray | ||
import numpy as np | ||
import pandas as pd | ||
import pytorch_lightning as pl | ||
import torch | ||
from pytorch_lightning.callbacks import ModelCheckpoint | ||
from torch.utils.data import DataLoader, Dataset | ||
|
||
from graph_weather.models import MetaModel | ||
from graph_weather.models.losses import NormalizedMSELoss | ||
|
||
from einops import rearrange | ||
|
||
|
||
class LitGraphForecaster(pl.LightningModule): | ||
""" | ||
LightningModule for graph-based weather forecasting. | ||
Attributes: | ||
model (GraphWeatherForecaster): Graph weather forecaster model. | ||
criterion (NormalizedMSELoss): Loss criterion for training. | ||
lr : Learning rate for optimizer. | ||
Methods: | ||
__init__: Initialize the LitGraphForecaster object. | ||
forward: Forward pass of the model. | ||
training_step: Training step. | ||
configure_optimizers: Configure the optimizer for training. | ||
""" | ||
|
||
def __init__( | ||
self, | ||
lat_lons: list, | ||
*, | ||
channels: int, | ||
image_size, | ||
patch_size=4, | ||
depth=5, | ||
heads=4, | ||
mlp_dim=5, | ||
feature_dim: int = 605, | ||
lr: float = 3e-4, | ||
|
||
): | ||
""" | ||
Initialize the LitGraphForecaster object with the required args. | ||
Args: | ||
lat_lons : List of latitude and longitude values. | ||
feature_dim : Dimensionality of the input features. | ||
aux_dim : Dimensionality of auxiliary features. | ||
hidden_dim : Dimensionality of hidden layers in the model. | ||
num_blocks : Number of graph convolutional blocks in the model. | ||
lr (float): Learning rate for optimizer. | ||
""" | ||
super().__init__() | ||
self.model = MetaModel( | ||
lat_lons, | ||
image_size=image_size, | ||
patch_size=patch_size, | ||
depth=depth, | ||
heads=heads, | ||
mlp_dim=mlp_dim, | ||
channels=channels | ||
) | ||
self.criterion = NormalizedMSELoss( | ||
lat_lons=lat_lons, feature_variance=np.ones((feature_dim,)) | ||
) | ||
self.lr = lr | ||
self.save_hyperparameters() | ||
|
||
def forward(self, x): | ||
""" | ||
Forward pass . | ||
Args: | ||
x (torch.Tensor): Input tensor. | ||
Returns: | ||
torch.Tensor: Output tensor. | ||
""" | ||
return self.model(x) | ||
|
||
def training_step(self, batch, batch_idx): | ||
""" | ||
Training step. | ||
Args: | ||
batch (array): Batch of data containing input and output tensors. | ||
batch_idx (int): Index of the current batch. | ||
Returns: | ||
torch.Tensor: Loss tensor. | ||
""" | ||
x, y = batch[:, 0], batch[:, 1] | ||
if torch.isnan(x).any() or torch.isnan(y).any(): | ||
return None | ||
y_hat = self.forward(x) | ||
loss = self.criterion(y_hat, y) | ||
self.log('loss', loss, prog_bar=True) | ||
return loss | ||
|
||
def configure_optimizers(self): | ||
""" | ||
Configure the optimizer. | ||
Returns: | ||
torch.optim.Optimizer: Optimizer instance. | ||
""" | ||
return torch.optim.AdamW(self.parameters(), lr=self.lr) | ||
|
||
|
||
class Era5Dataset(Dataset): | ||
"""Era5 dataset.""" | ||
|
||
def __init__(self, xarr, transform=None): | ||
""" | ||
Arguments: | ||
#TODO | ||
""" | ||
ds = np.asarray(xarr.to_array()) | ||
ds = torch.from_numpy(ds) | ||
ds -= ds.min(0, keepdim=True)[0] | ||
ds /= ds.max(0, keepdim=True)[0] | ||
ds = rearrange(ds, "C T H W -> T (H W) C") | ||
self.ds = ds | ||
|
||
def __len__(self): | ||
return len(self.ds) - 1 | ||
|
||
def __getitem__(self, index): | ||
return self.ds[index:index+2] | ||
|
||
|
||
if __name__ == "__main__": | ||
|
||
patch_size = 4 | ||
grid_step = 20 | ||
|
||
reanalysis = xarray.open_zarr( | ||
'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3', | ||
storage_options=dict(token='anon'), | ||
|
||
) | ||
reanalysis = reanalysis.isel(time=slice(100, 400), longitude=slice( | ||
0, 1440, grid_step), latitude=slice(0, 721, grid_step)) | ||
print(f'size: {reanalysis.nbytes / (1024 ** 3)} GiB') | ||
|
||
lat_lons = np.array( | ||
np.meshgrid( | ||
np.asarray(reanalysis["latitude"]).flatten(), | ||
np.asarray(reanalysis["longitude"]).flatten(), | ||
) | ||
).T.reshape((-1, 2)) | ||
|
||
checkpoint_callback = ModelCheckpoint( | ||
dirpath="./checkpoints", save_top_k=1, monitor="loss") | ||
reanalysis = reanalysis[["2m_temperature", | ||
"surface_pressure", | ||
"10m_u_component_of_wind", | ||
"10m_v_component_of_wind"]] | ||
|
||
shape = np.asarray(reanalysis.to_array()).shape | ||
channels = shape[0] | ||
|
||
dset = DataLoader(Era5Dataset(reanalysis), batch_size=10, num_workers=8) | ||
model = LitGraphForecaster(lat_lons=lat_lons, | ||
channels=channels, | ||
image_size=(721//grid_step, 1440//grid_step), | ||
patch_size=patch_size, | ||
depth=5, | ||
heads=4, | ||
mlp_dim=5) | ||
trainer = pl.Trainer( | ||
accelerator="gpu", | ||
devices=-1, | ||
max_epochs=1000, | ||
precision="16-mixed", | ||
callbacks=[checkpoint_callback], | ||
log_every_n_steps=3 | ||
|
||
) | ||
|
||
trainer.fit(model, dset) |