Upload check_dataset.py

check_dataset.py

@@ -0,0 +1,49 @@
+from datasets import load_dataset
+from tqdm import tqdm
+import torch
+from torch.utils.data import DataLoader
+from scripts.forward_model import LidarForwardImagingModel
+
+forward_model = LidarForwardImagingModel()
+
+BATCH_SIZE = 64
+
+def make_loader(split: str):
+    ds = load_dataset("anfera236/HHDC", split=split)
+    # return PyTorch tensors for the "cube" column
+    ds.set_format(type="torch", columns=["cube"])
+    # wrap in a DataLoader to get batches
+    loader = DataLoader(
+        ds,
+        batch_size=BATCH_SIZE,
+        shuffle=False,   # no need to shuffle for shape checking
+    )
+    return ds, loader
+
+
+def check_split(split_name: str):
+    print(f"Checking {split_name} dataset batches (batch_size={BATCH_SIZE})...")
+    ds, loader = make_loader(split_name)
+
+    for batch in tqdm(loader):
+        cubes = batch["cube"]           # shape: (B, 128, 48, 48)
+        # sanity check on input shape
+        assert cubes.ndim == 4, f"Expected 4D input (B, 128, 48, 48), got {cubes.shape}"
+        assert cubes.shape[1:] == (128, 48, 48), f"Bad input sample shape: {cubes.shape}"
+
+        # forward pass (expects model to support batched input)
+        output = forward_model(cubes)   # expected shape: (B, 128, 32, 16)
+
+        # sanity checks on output shape
+        assert output.ndim == 4, f"Expected 4D output (B, 128, 32, 16), got {output.shape}"
+        assert output.shape[0] == cubes.shape[0], (
+            f"Batch size mismatch: input B={cubes.shape[0]}, output B={output.shape[0]}"
+        )
+        assert output.shape[1:] == (128, 32, 16), f"Bad output sample shape: {output.shape}"
+
+
+if __name__ == "__main__":
+    check_split("train")
+    check_split("validation")
+    check_split("test")
+    print("All splits passed shape checks ✅")