Update new_dataset_script.py

new_dataset_script.py

@@ -60,6 +60,13 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
 
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="full", version=VERSION, description="The full dataset"),
+        datasets.BuilderConfig(name="small", version=VERSION, description="A small sample of the dataset for quicker loading"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "full"
+
     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
     # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
@@ -108,88 +115,103 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
 
     def _split_generators(self, dl_manager):
-    # Download and extract the dataset using Hugging Face's datasets library
-    data_files = dl_manager.download_and_extract({
-        "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
-        "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
-    })
+        # Download and extract the dataset using Hugging Face's datasets library
+        data_files = dl_manager.download_and_extract({
+            "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
+            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
+        })
+        
+        # Load the CSV file containing species and scientific names
+        species_info = pd.read_csv(data_files["csv"])
+        
+        # The directory 'Labeled Stomatal Images' is where the images and labels are stored after extraction
+        extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
     
-    # Load the CSV file containing species and scientific names
-    species_info = pd.read_csv(data_files["csv"])
+        # Get the list of image filenames from the CSV
+        all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
     
-    # The directory 'Labeled Stomatal Images' is where the images and labels are stored after extraction
-    extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")
-
-    # Get the list of image filenames from the CSV
-    all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
-
-    # Shuffle the list for random split
-    random.seed(42)  # Set a random seed for reproducibility
-    random.shuffle(all_image_filenames)
+        # Shuffle the list for random split
+        random.seed(42)  # Set a random seed for reproducibility
+        random.shuffle(all_image_filenames)
     
-    # Split the files into train/validation/test
-    num_files = len(all_image_filenames)
-    train_split_end = int(num_files * 0.7)
-    val_split_end = train_split_end + int(num_files * 0.15)
+        if self.config.name == "full":
+                # Use all the data for the full configuration
+                train_files, val_files, test_files = self._split_files(all_image_filenames)
+        elif self.config.name == "small":
+                # Use only a subset of the data for the small configuration
+                small_subset = all_image_filenames[:1000]  # For example, use only the first 100 images
+                train_files, val_files, test_files = self._split_files(small_subset)
+            
+        
     
-    train_files = all_image_filenames[:train_split_end]
-    val_files = all_image_filenames[train_split_end:val_split_end]
-    test_files = all_image_filenames[val_split_end:]
-
-    return [
-        datasets.SplitGenerator(
-            name=datasets.Split.TRAIN,
-            gen_kwargs={
-                "filepaths": train_files,
-                "species_info": species_info,
-                "data_dir": extracted_images_path,
-                "split": "train",
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.VALIDATION,
-            gen_kwargs={
-                "filepaths": val_files,
-                "species_info": species_info,
-                "data_dir": extracted_images_path,
-                "split": "validation",
-            },
-        ),
-        datasets.SplitGenerator(
-            name=datasets.Split.TEST,
-            gen_kwargs={
-                "filepaths": test_files,
-                "species_info": species_info,
-                "data_dir": extracted_images_path,
-                "split": "test",
-            },
-        ),
-    ]
+    
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": train_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_images_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": val_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_images_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": test_files,
+                    "species_info": species_info,
+                    "data_dir": extracted_images_path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _split_files(self, file_list):
+        num_files = len(file_list)
+        train_split_end = int(num_files * 0.7)
+        val_split_end = train_split_end + int(num_files * 0.15)
+        
+        train_files = file_list[:train_split_end]
+        val_files = file_list[train_split_end:val_split_end]
+        test_files = file_list[val_split_end:]
+    
+        return train_files, val_files, test_files
+
+
 
- 
+    
 
 # ... other necessary imports and class definitions
     def _parse_yolo_labels(self, label_path, width, height):
-    annotations = []
-    with open(label_path, 'r') as file:
-        yolo_data = file.readlines()
-
-    for line in yolo_data:
-        class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
-        x_min = (x_center_rel - width_rel / 2) * width
-        y_min = (y_center_rel - height_rel / 2) * height
-        x_max = (x_center_rel + width_rel / 2) * width
-        y_max = (y_center_rel + height_rel / 2) * height
-        annotations.append({
-            "category_id": int(class_id),
-            "bounding_box": {
-                "x_min": x_min,
-                "y_min": y_min,
-                "x_max": x_max,
-                "y_max": y_max
-            }
-        })
-    return annotations
+        annotations = []
+        with open(label_path, 'r') as file:
+            yolo_data = file.readlines()
+    
+        for line in yolo_data:
+            class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
+            x_min = (x_center_rel - width_rel / 2) * width
+            y_min = (y_center_rel - height_rel / 2) * height
+            x_max = (x_center_rel + width_rel / 2) * width
+            y_max = (y_center_rel + height_rel / 2) * height
+            annotations.append({
+                "category_id": int(class_id),
+                "bounding_box": {
+                    "x_min": x_min,
+                    "y_min": y_min,
+                    "x_max": x_max,
+                    "y_max": y_max
+                }
+            })
+        return annotations
 
     def _generate_examples(self, filepaths, species_info, data_dir, split):
         """Yields examples as (key, example) tuples."""