update

OAIZIB-CM.py

@@ -14,8 +14,6 @@ from datasets import (
 
 logger = datasets.logging.get_logger(__name__)
 
-EXPECTED_SHAPE = (160, 384, 384)
-
 _CITATION = """\
 @article{YAO2024103035,
   title = {CartiMorph: A framework for automated knee articular cartilage morphometrics},
@@ -61,16 +59,27 @@ _HOMEPAGE_URL = "https://github.com/YongchengYAO/CartiMorph"
 class OAIZIBCMDataset(GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
 
+    @staticmethod
+    def load_nifti(example):
+        """Map function to load NIFTI images on demand."""
+        img_nib = nib.load(example["image_path"])
+        image = img_nib.get_fdata().astype("float32")
+        mask_nib = nib.load(example["mask_path"])
+        mask = mask_nib.get_fdata().astype("float32")
+        example["image"] = image
+        example["mask"] = mask
+        return example
+
     def _info(self):
         # Define dataset information including feature schema
         return DatasetInfo(
             description=_DESCRIPTION,
-            features=Features({
-                "image": Array3D(shape=EXPECTED_SHAPE, dtype="float32"),
-                "mask": Array3D(shape=EXPECTED_SHAPE, dtype="float32"),
-                "image_path": Value("string"),
-                "mask_path": Value("string"),
-            }),
+            features=Features(
+                {
+                    "image_path": Value("string"),
+                    "mask_path": Value("string"),
+                }
+            ),
             homepage=_HOMEPAGE_URL,
             citation=_CITATION,
         )
@@ -82,14 +91,14 @@ class OAIZIBCMDataset(GeneratorBasedBuilder):
         test_csv_url = f"{base_url}/test.csv"
         csv_paths = dl_manager.download({"train": train_csv_url, "test": test_csv_url})
         logger.info(f"Downloaded CSV paths: {csv_paths}")
-        
+
         # Extract main dataset archive
         data_root_dir = dl_manager.download_and_extract(
             "https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM/resolve/load_dataset-support/data/OAIZIB-CM.zip"
         )
         data_dir = os.path.join(data_root_dir, "OAIZIB-CM")
         logger.info(f"Data directory set to {data_dir}")
-        
+
         # Load split metadata
         train_df = pd.read_csv(csv_paths["train"])
         test_df = pd.read_csv(csv_paths["test"])
@@ -116,48 +125,24 @@ class OAIZIBCMDataset(GeneratorBasedBuilder):
             mask_dir = os.path.join(data_dir, "labelsTs")
         else:
             raise ValueError(f"Unknown split: {split}")
-        
+
         # Log directories and ensure they exist
         logger.info(f"Looking for {split} images in: {img_dir}")
         logger.info(f"Looking for {split} masks in: {mask_dir}")
         os.makedirs(img_dir, exist_ok=True)
         os.makedirs(mask_dir, exist_ok=True)
-        
+
         # Process and yield examples
         count = 0
-        skipped = 0
         for idx, row in df.iterrows():
             img_file = row["image"]
             mask_file = row["mask"]
             img_path = os.path.join(img_dir, img_file)
             mask_path = os.path.join(mask_dir, mask_file)
+            # Only yield paths, don't load data into memory
+            yield idx, {
+                "image_path": img_path,
+                "mask_path": mask_path,
+            }
 
-            # Skip files that don't exist
-            if not os.path.exists(img_path):
-                logger.warning(f"Image not found: {img_path}")
-                skipped += 1
-                continue
-            if not os.path.exists(mask_path):
-                logger.warning(f"Mask not found: {mask_path}")
-                skipped += 1
-                continue
-
-            try:
-                # Load and process image data
-                img_nib = nib.load(img_path)
-                image = img_nib.get_fdata().astype("float32")
-                mask_nib = nib.load(mask_path)
-                mask = mask_nib.get_fdata().astype("float32")
-
-                yield idx, {
-                    "image": image,
-                    "mask": mask,
-                    "image_path": img_path,
-                    "mask_path": mask_path,
-                }
-                count += 1
-            except Exception as e:
-                logger.error(f"Error processing {img_path} and {mask_path}: {e}")
-                skipped += 1
-                
-        logger.info(f"Successfully yielded {count} examples for {split} split, skipped {skipped}")
+        logger.info(f"Successfully yielded {count} examples for {split} split")