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--- |
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license: mit |
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tags: |
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- downscaling |
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- edsr |
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- ERA5 - COSMO-REA6 |
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- wind |
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- super-image |
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library_name: super-image |
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model_type: edsr |
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datasets: |
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- your-dataset-name |
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--- |
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# EDSR-DSC (4× Downscaling of Wind Velocities) |
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This model is a custom-trained version of the Enhanced Deep Super-Resolution (EDSR) model from the [`super-image`](https://github.com/eugenesiow/super-image) library. |
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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). |
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--- |
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## 🧠 Model Architecture |
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- **Base**: EDSR ([Lim et al. 2017](https://arxiv.org/abs/1707.02921)) |
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- **Input channels**: 2 (U & V components of wind speed) |
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- **Output channels**: 2 |
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- **Feature channels (`n_feats`)**: 64 |
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- **Residual blocks**: 32 |
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- **Mean-shift normalization**: Removed |
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- **Upsampling**: Enabled |
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- **Scale factor**: 4× |
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--- |
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## 📦 Files in this Repository |
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| File | Description | |
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|------------------------|-----------------------------------------------| |
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| `config.json` | Configuration for the modified EDSR model | |
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| `pytorch_model_4x.pt` | Pretrained weights for 4× upscaling | |
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--- |
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## 🚀 How to Use |
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```python |
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from super_image import EdsrModel, EdsrConfig |
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from huggingface_hub import hf_hub_download |
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import torch |
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import xarray as xr |
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import numpy as np |
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# load config |
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config, _ = EdsrConfig.from_pretrained("lschmidt/edsr-dsc") |
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# load model & remove normalization |
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model = EdsrModel(config) |
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del model.sub_mean |
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del model.add_mean |
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# load pre-trained weights |
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state_dict_path = hf_hub_download(repo_id="lschmidt/edsr-dsc", filename="pytorch_model_4x.pt") |
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state_dict = torch.load(state_dict_path, map_location="cpu") |
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model.load_state_dict(state_dict, strict=False) |
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# create random input: must be a 4D tensor (B, C=2, H, W) |
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inputs = torch.randn(1, 2, 40, 40) # replace with coarse wind velocity fields |
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# or use sample data |
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data_path = hf_hub_download( |
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repo_id="lschmidt/edsr-dsc", |
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filename="test_wind_velocities.nc", |
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subfolder="test_data" |
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) |
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ds = xr.open_dataset(data_path) |
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u = ds["u100"].values[0] |
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v = ds["v100"].values[0] |
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inputs = torch.from_numpy(np.stack([u, v], axis=0)).unsqueeze(0).float() |
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# prediction |
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outputs = model(inputs) |
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