more dataset loading adjustments

dataset.py

@@ -1,8 +1,14 @@
 
 import datasets
+import logging
+
+# Configure logging
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO)
 
 class ForestSegmentationDataset(datasets.GeneratorBasedBuilder):
     def _info(self):
+        logger.info("Defining dataset schema...")
         return datasets.DatasetInfo(
             features=datasets.Features({
                 "sample_id": datasets.Value("string"),
@@ -12,8 +18,9 @@ class ForestSegmentationDataset(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        #sample_stream = dl_manager.iter_jsonl("index.jsonl")
-        sample_stream = dl_manager.iter_jsonl("index.parquet")
+        logger.info("Loading sample stream from index.parquet...")
+        sample_stream = dl_manager.iter_parquet("index.parquet")
+        logger.info("Sample stream loaded successfully.")
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -22,9 +29,15 @@ class ForestSegmentationDataset(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, samples):
-        for sample in samples:
-            yield sample["sample_id"], {
-                "sample_id": sample["sample_id"],
-                "image_paths": sample["image_paths"],
-                "mask": sample["mask_path"],
-            }
+        logger.info("Starting to generate examples...")
+        for i, sample in enumerate(samples):
+            if i % 1000 == 0:
+                logger.info(f"Processed {i} samples...")
+            try:
+                yield sample["sample_id"], {
+                    "sample_id": sample["sample_id"],
+                    "image_paths": sample["image_paths"],
+                    "mask": sample["mask_path"],
+                }
+            except Exception as e:
+                logger.error(f"Error processing sample {sample.get('sample_id', 'unknown')}: {e}")