Refactor physicsgen.py to update dataset descriptions, versioning, and implement hf_hub_download for parquet file retrieval

physicsgen.py

@@ -1,9 +1,7 @@
 import os
-import csv
-from PIL import Image
 import datasets
-import pyarrow as pa
 import pyarrow.parquet as pq
+from huggingface_hub import hf_hub_download
 
 # Define configurations for each flavor.
 BUILDER_CONFIGS = [
@@ -19,12 +17,12 @@ BUILDER_CONFIGS = [
     ),
     datasets.BuilderConfig(
         name="sound_diffraction",
-        description="Physical dataset: reflection variant",
+        description="Physical dataset: diffraction variant",
         data_dir="sound_diffraction"
     ),
     datasets.BuilderConfig(
         name="sound_combined",
-        description="Physical dataset: reflection variant",
+        description="Physical dataset: combined variant",
         data_dir="sound_combined"
     ),
     datasets.BuilderConfig(
@@ -51,7 +49,7 @@ BUILDER_CONFIGS = [
 
 class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = BUILDER_CONFIGS
-    VERSION = datasets.Version("1.0.8")
+    VERSION = datasets.Version("1.0.2")
 
     def _info(self):
         if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
@@ -59,7 +57,7 @@ class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
                 "lat": datasets.Value("float"),
                 "long": datasets.Value("float"),
                 "db": datasets.Value("string"),
-                "soundmap": datasets.Image(),
+                "soundmap": datasets.Image(),    # Expects a dict: {"bytes": ...}
                 "osm": datasets.Image(),
                 "temperature": datasets.Value("int32"),
                 "humidity": datasets.Value("int32"),
@@ -77,7 +75,6 @@ class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
                 "p1": datasets.Value("float"),
                 "p2": datasets.Value("float"),
                 "cx": datasets.Value("float"),
-                "distortion_path": datasets.Value("string"),
             })
         elif self.config.name in ["ball_roll", "ball_bounce"]:
             features = datasets.Features({
@@ -86,39 +83,42 @@ class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
                 "GroundIncli": datasets.Value("float"),
                 "InputTime": datasets.Value("int32"),
                 "TargetTime": datasets.Value("int32"),
-                "input_image": datasets.Image(),
+                "input_image": datasets.Image(),   # Expects {"bytes": ...}
                 "target_image": datasets.Image(),
             })
         else:
             raise ValueError(f"Unknown config name: {self.config.name}")
         return datasets.DatasetInfo(
-            description="Multiple variant physical tasks dataset.",
+            description="Multiple variant physical tasks dataset stored as parquet files.",
             features=features,
         )
 
     def _split_generators(self, dl_manager):
-        base_dir = os.path.join(os.path.dirname(__file__), self.config.data_dir)
-        #print("Base directory:", base_dir)  # Debug print; remove in production.
+        # Use hf_hub_download to fetch the parquet files directly from the Hub.
+        repo_id = "mspitzna/physicsgen"  # Replace with your repo id if different.
+        train_file = hf_hub_download(repo_id=repo_id, filename=f"{self.config.data_dir}/train.parquet")
+        test_file = hf_hub_download(repo_id=repo_id, filename=f"{self.config.data_dir}/test.parquet")
+        eval_file = hf_hub_download(repo_id=repo_id, filename=f"{self.config.data_dir}/eval.parquet")
         return [
             datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={"parquet_file": os.path.join(base_dir, "train.parquet")},
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.TEST,
-            gen_kwargs={"parquet_file": os.path.join(base_dir, "test.parquet")},
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.VALIDATION,
-            gen_kwargs={"parquet_file": os.path.join(base_dir, "eval.parquet")},
-        ),
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"parquet_file": train_file},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"parquet_file": test_file},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"parquet_file": eval_file},
+            ),
         ]
 
     def _generate_examples(self, parquet_file):
         table = pq.read_table(parquet_file)
         examples = table.to_pylist()
 
-        # Wrap image bytes into the format expected by datasets.Image
+        # Wrap image bytes into the format expected by datasets.Image.
         if self.config.name in ["sound_baseline", "sound_reflection", "sound_diffraction", "sound_combined"]:
             for example in examples:
                 for key in ["soundmap", "osm", "soundmap_512"]:
@@ -129,6 +129,5 @@ class MyPhysicalDataset(datasets.GeneratorBasedBuilder):
                 for key in ["input_image", "target_image"]:
                     if example.get(key) is not None and isinstance(example[key], bytes):
                         example[key] = {"bytes": example[key]}
-
         for idx, row in enumerate(examples):
             yield idx, row