Added "all" config.

README.md

@@ -133,7 +133,7 @@ from datasets import load_dataset
 import numpy as np
 
 # Load 128x128 resolution data
-dataset = load_dataset("your-username/goes16-satellite", name="128x128")
+dataset = load_dataset("Silicon23/ioai2025-athome-satellite-images", name="128x128")
 
 # Access a sample
 sample = dataset["train"][0]

ioai2025-athome-satellite-images.py

@@ -24,22 +24,40 @@ class Goes16Dataset(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
 
     BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="all",
+            version=VERSION,
+            description="All resolutions combined (128x128 and 256x256 images)",
+        ),
         datasets.BuilderConfig(
             name="128x128",
             version=VERSION,
-            description="128x128 resolution images",
+            description="128x128 resolution images only",
         ),
         datasets.BuilderConfig(
             name="256x256", 
             version=VERSION,
-            description="256x256 resolution images",
+            description="256x256 resolution images only",
         ),
     ]
 
-    DEFAULT_CONFIG_NAME = "128x128"
+    DEFAULT_CONFIG_NAME = "all"
 
     def _info(self):
-        if self.config.name == "128x128":
+        if self.config.name == "all":
+            # For "all" config, use flexible features that can handle both resolutions
+            features = datasets.Features({
+                "image": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),  # Variable size [16, H, W]
+                "label": datasets.Sequence(datasets.Sequence(datasets.Value("uint8"))),  # Variable size [H, W]
+                "i": datasets.Value("int32"),
+                "j": datasets.Value("int32"),
+                "start_time": datasets.Value("string"),
+                "end_time": datasets.Value("string"),
+                "ind": datasets.Value("int32"),
+                "size": datasets.Value("int32"),  # This field indicates the actual resolution
+                "resolution": datasets.Value("string"),  # Added field to explicitly show resolution
+            })
+        elif self.config.name == "128x128":
             # For 128x128, we know the exact dimensions
             features = datasets.Features({
                 "image": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),  # [16, 128, 128]
@@ -101,40 +119,77 @@ class Goes16Dataset(datasets.GeneratorBasedBuilder):
         # Filter metadata for the current split
         split_metadata = metadata[metadata['split'] == split]
         
-        if self.config.name == "128x128":
-            size = 128
-        else:  # 256x256
-            size = 256
-        
-        # Filter metadata for current resolution
-        size_metadata = split_metadata[split_metadata['size'] == size]
-        
-        # Get corresponding arrays
-        X_key = f"X_{split}_{size}"
-        Y_key = f"Y_{split}_{size}"
-        
-        X_data = data[X_key]  
-        Y_data = data[Y_key]
-        
         example_id = 0
         
-        # Generate examples using metadata
-        for _, row in size_metadata.iterrows():
-            ind = row['ind']
+        if self.config.name == "all":
+            # For "all" config, load both 128x128 and 256x256 data
+            for size in [128, 256]:
+                # Filter metadata for current resolution
+                size_metadata = split_metadata[split_metadata['size'] == size]
+                
+                # Get corresponding arrays
+                X_key = f"X_{split}_{size}"
+                Y_key = f"Y_{split}_{size}"
+                
+                X_data = data[X_key]  
+                Y_data = data[Y_key]
+                
+                # Generate examples using metadata
+                for _, row in size_metadata.iterrows():
+                    ind = row['ind']
+                    
+                    if ind < len(X_data):  # Safety check
+                        # Convert numpy arrays to lists for datasets compatibility
+                        image_array = X_data[ind].astype(np.float32)
+                        label_array = Y_data[ind].astype(np.uint8)
+                        
+                        yield example_id, {
+                            "image": image_array.tolist(),  # Convert to nested list
+                            "label": label_array.tolist(),  # Convert to nested list
+                            "i": int(row['i']),
+                            "j": int(row['j']),
+                            "start_time": str(row['start_time']),
+                            "end_time": str(row['end_time']),
+                            "ind": int(row['ind']),
+                            "size": int(row['size']),
+                            "resolution": f"{size}x{size}",  # Add explicit resolution field
+                        }
+                        example_id += 1
+        
+        else:
+            # For specific resolution configs (128x128 or 256x256)
+            if self.config.name == "128x128":
+                size = 128
+            else:  # 256x256
+                size = 256
+            
+            # Filter metadata for current resolution
+            size_metadata = split_metadata[split_metadata['size'] == size]
+            
+            # Get corresponding arrays
+            X_key = f"X_{split}_{size}"
+            Y_key = f"Y_{split}_{size}"
+            
+            X_data = data[X_key]  
+            Y_data = data[Y_key]
             
-            if ind < len(X_data):  # Safety check
-                # Convert numpy arrays to lists for datasets compatibility
-                image_array = X_data[ind].astype(np.float32)
-                label_array = Y_data[ind].astype(np.uint8)
+            # Generate examples using metadata
+            for _, row in size_metadata.iterrows():
+                ind = row['ind']
                 
-                yield example_id, {
-                    "image": image_array.tolist(),  # Convert to nested list
-                    "label": label_array.tolist(),  # Convert to nested list
-                    "i": int(row['i']),
-                    "j": int(row['j']),
-                    "start_time": str(row['start_time']),
-                    "end_time": str(row['end_time']),
-                    "ind": int(row['ind']),
-                    "size": int(row['size']),
-                }
-                example_id += 1 
+                if ind < len(X_data):  # Safety check
+                    # Convert numpy arrays to lists for datasets compatibility
+                    image_array = X_data[ind].astype(np.float32)
+                    label_array = Y_data[ind].astype(np.uint8)
+                    
+                    yield example_id, {
+                        "image": image_array.tolist(),  # Convert to nested list
+                        "label": label_array.tolist(),  # Convert to nested list
+                        "i": int(row['i']),
+                        "j": int(row['j']),
+                        "start_time": str(row['start_time']),
+                        "end_time": str(row['end_time']),
+                        "ind": int(row['ind']),
+                        "size": int(row['size']),
+                    }
+                    example_id += 1