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---
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: all_samples
path: data/all_samples-*
dataset_info:
features:
- name: Base_2_2/Zone/CellData/diffusion_coefficient
list: float32
- name: Base_2_2/Zone/CellData/flow
list: float32
- name: Global/forcing_magnitude
list: float32
splits:
- name: all_samples
num_bytes: 6554400000
num_examples: 50000
download_size: 3321884222
dataset_size: 6554400000
---
Example of usage:
```python
import torch
from plaid.bridges import huggingface_bridge as hfb
from torch.utils.data import DataLoader
def reshape_all(batch: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
"""Helper function that reshapes the flattened fields into images of sizes (128, 128)."""
batch["diffusion_coefficient"] = batch["diffusion_coefficient"].reshape(
-1, 128, 128
)
batch["flow"] = batch["flow"].reshape(-1, 128, 128)
return batch
# Load the dataset from the hub
ds = hfb.load_dataset_from_hub(
repo_id="Nionio/PDEBench_2D_DarcyFlow", split="all_samples"
)
# Rename the features
ds = ds.rename_columns(
{
"Base_2_2/Zone/CellData/diffusion_coefficient": "diffusion_coefficient",
"Base_2_2/Zone/CellData/flow": "flow",
"Global/forcing_magnitude": "forcing",
}
)
# Convert to torch
ds = ds.with_format("torch")
# Reshape fields
ds = ds.map(reshape_all, batched=True)
# Example of usage with a DataLoader
dl = DataLoader(ds, batch_size=32, shuffle=True)
for batch in dl:
for k, v in batch.items():
print(k, v.shape)
break
``` |