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lschmidt/
rcan-dsc

Name: rcan-dsc
Availability: InStock

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RCAN-DSC (4× Downscaling of Wind Velocities)

This model is a custom-trained version of the RCAN model from the super-image library.
It is adapted for downscaling of 2-channel ERA5 data (e.g., wind u and v components), by a factor of 4× (trained using COSMO-REA6 as high-resolution data).

🧠 Model Description

Based on the original RCAN architecture from super-image.
sub_mean and add_mean normalization layers have been removed
Supports multi-channel inputs, currently set up for 2-channel wind velocity fields.

🧪 Example

python

from super_image import RcanModel, RcanConfig
from huggingface_hub import hf_hub_download
import torch

# load model
path = hf_hub_download(repo_id="lschmidt/rcan-dsc", filename="rcan_model.py")
exec(open(path).read())
model = load_rcan()

# load config
config, _ = RcanConfig.from_pretrained("lschmidt/rcan-dsc")

# load pretrained weights
state_dict_path = hf_hub_download(repo_id="lschmidt/rcan-dsc", filename="pytorch_model_4x.pt")
state_dict = torch.load(state_dict_path, map_location="cpu")
model.load_state_dict(state_dict, strict=False)

# generate sample data (B, C, W, H) 
inputs = torch.randn(1, 2, 10, 10)

# or use test data
data_path = hf_hub_download(
    repo_id="lschmidt/rcan-dsc",
    filename="test_wind_velocities.nc",
    subfolder="test_data"  
)
ds = xr.open_dataset(data_path)
u = ds["u100"].values[0]
v = ds["v100"].values[0]
inputs = torch.from_numpy(np.stack([u, v], axis=0)).unsqueeze(0).float()  

# prediction
output = model(inputs)