era5 training bugfix

train/era5.py

@@ -12,8 +12,10 @@
 
 from einops import rearrange
 
+from pathlib import Path
 
-class LitGraphForecaster(pl.LightningModule):
+
+class LitFengWuGHR(pl.LightningModule):
     """
     LightningModule for graph-based weather forecasting.
 
@@ -23,7 +25,7 @@ class LitGraphForecaster(pl.LightningModule):
         lr : Learning rate for optimizer.
 
     Methods:
-        __init__: Initialize the LitGraphForecaster object.
+        __init__: Initialize the LitFengWuGHR object.
         forward: Forward pass of the model.
         training_step: Training step.
         configure_optimizers: Configure the optimizer for training.
@@ -44,7 +46,7 @@ def __init__(
 
     ):
         """
-        Initialize the LitGraphForecaster object with the required args.
+        Initialize the LitFengWuGHR object with the required args.
 
         Args:
             lat_lons : List of latitude and longitude values.
@@ -135,16 +137,27 @@ def __getitem__(self, index):
 
 if __name__ == "__main__":
 
+    ckpt_path = Path("./checkpoints")
     patch_size = 4
     grid_step = 20
+    variables = ["2m_temperature",
+                 "surface_pressure",
+                 "10m_u_component_of_wind",
+                 "10m_v_component_of_wind"]
+
+    channels = len(variables)
+    ckpt_path.mkdir(parents=True, exist_ok=True)
 
     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(
+
+    reanalysis = reanalysis.sel(time=slice('2020-01-01', '2021-01-01'))
+    reanalysis = reanalysis.isel(time=slice(100,107), longitude=slice(
         0, 1440, grid_step), latitude=slice(0, 721, grid_step))
+
+    reanalysis = reanalysis[variables]
     print(f'size: {reanalysis.nbytes / (1024 ** 3)} GiB')
 
     lat_lons = np.array(
@@ -155,31 +168,26 @@ def __getitem__(self, index):
     ).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]
+        dirpath=ckpt_path, save_top_k=1, monitor="loss")
 
     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)
+    model = LitFengWuGHR(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,
+        max_epochs=100,
         precision="16-mixed",
         callbacks=[checkpoint_callback],
         log_every_n_steps=3
 
     )
 
     trainer.fit(model, dset)
+
+    torch.save(model.state_dict(), ckpt_path / "best.pt")