Update v-lol-trains.py

v-lol-trains.py

@@ -31,7 +31,6 @@ _HOMEPAGE = "https://huggingface.co/datasets/LukasHug/v-lol-trains/"
 
 _LICENSE = "cc-by-4.0"
 _IMAGES_URL = "https://huggingface.co/datasets/LukasHug/v-lol-trains/resolve/main/data"
-# _DIR = './data'
 _DIR = _IMAGES_URL
 # _URL_DATA = {
 #     "V-LoL-Trains-TheoryX": f"{_DIR}/V-LoL-Trains-TheoryX.zip",
@@ -53,28 +52,43 @@ _URL_DATA = {
     "V-LoL-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip",
     "V-LoL-Blocks-Numerical": f"{_DIR}/SimpleObjects_numerical_MichalskiTrains_base_scene_len_2-4.zip",
     "V-LoL-Blocks-Complex": f"{_DIR}/SimpleObjects_complex_MichalskiTrains_base_scene_len_2-4.zip",
-    "V-LoL-Trains-TheoryX-len7": f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7.zip",
-    "V-LoL-Trains-Numerical-len7": f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7.zip",
-    "V-LoL-Trains-Complex-len7": f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7.zip",
-    "V-LoL-Random-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_2-4.zip",
-    "V-LoL-Random-Trains-TheoryX": f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_2-4.zip",
+    "V-LoL-Trains-TheoryX-len7":
+        {'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip",
+         'test': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7.zip"},
+    "V-LoL-Trains-Numerical-len7":
+        {'train': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_2-4.zip",
+         'test': f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7.zip"},
+    "V-LoL-Trains-Complex-len7":
+        {'train': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_2-4.zip",
+         'test': f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7.zip"},
+    "V-LoL-Random-Blocks-TheoryX":
+        {'train': f"{_DIR}/SimpleObjects_theoryx_MichalskiTrains_base_scene_len_2-4.zip",
+         'test': f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_7.zip"},
+    "V-LoL-Random-Trains-TheoryX":
+        {'train': f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_2-4.zip",
+         'test': f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_7.zip"},
+    #     "V-LoL-Trains-TheoryX-len7": f"{_DIR}/Trains_theoryx_MichalskiTrains_base_scene_len_7.zip",
+    #     "V-LoL-Trains-Numerical-len7": f"{_DIR}/Trains_numerical_MichalskiTrains_base_scene_len_7.zip",
+    #     "V-LoL-Trains-Complex-len7": f"{_DIR}/Trains_complex_MichalskiTrains_base_scene_len_7.zip",
+    #     "V-LoL-Random-Blocks-TheoryX": f"{_DIR}/SimpleObjects_theoryx_RandomTrains_base_scene_len_2-4.zip",
+    #     "V-LoL-Random-Trains-TheoryX": f"{_DIR}/Trains_theoryx_RandomTrains_base_scene_len_2-4.zip",
 }
 
 _NAMES = ["westbound", "eastbound"]
-
-
 class VLoLConfig(datasets.BuilderConfig):
     """Builder Config for Food-101"""
 
     def __init__(self, data_url, **kwargs):
         """BuilderConfig for Food-101.
         Args:
-          data_url: `string`, url to download the zip file from.
           metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
           **kwargs: keyword arguments forwarded to super.
         """
         super(VLoLConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.data_url = data_url
+        if isinstance(data_url, dict):
+            self.metadata_urls = data_url
+        else:
+            self.metadata_urls = {'train': data_url, 'test': None}
 
 
 class vloltrains(datasets.GeneratorBasedBuilder):
@@ -104,24 +118,22 @@ class vloltrains(datasets.GeneratorBasedBuilder):
             task_templates=ImageClassification(image_column="image", label_column="label"),
         )
 
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archive_path = os.path.join(dl_manager.download_and_extract(self.config.data_url), self.config.data_url.split('/')[-1].split('.')[0])
+    def get_data(self, dl_manager, url):
+        archive_path = os.path.join(dl_manager.download_and_extract(url), url.split('/')[-1].split('.')[0])
         # print containg folders
         print(os.listdir(archive_path))
         image_dir = os.path.join(archive_path, "images")
         metadata_pth = os.path.join(archive_path, "all_scenes", "all_scenes.json")
-        self.images, self.y, self.trains, self.masks = [], [], [], []
+        images, y, trains, masks = [], [], [], []
         # ds settings
         # load data
         with open(metadata_pth, 'r') as f:
             all_scenes = json.load(f)
             for scene in all_scenes['scenes']:
-                self.images.append(scene['image_filename'])
+                images.append(scene['image_filename'])
                 train = scene['train']
-                y = int(train.split(' ')[0] == 'east')
-                self.y.append(y)
-                # self.depths.append(scene['depth_map_filename'])
+                y.append(int(train.split(' ')[0] == 'east'))
+                # depths.append(scene['depth_map_filename'])
                 # if 'train' in scene:
                 #     # new json data format
                 #     train = scene['train']
@@ -133,24 +145,32 @@ class vloltrains(datasets.GeneratorBasedBuilder):
                 #     # old json data format
                 #     train = scene['m_train']
                 #     train = jsonpickle.decode(train)
-                #     # self.trains.append(train.replace('michalski_trains.m_train.', 'm_train.'))
+                #     # trains.append(train.replace('michalski_trains.m_train.', 'm_train.'))
                 #     # text = train.to_txt()
                 #     # t1 = MichalskiTrain.from_text(text, train_vis)
                 # lab = int(train.get_label() == 'east')
-                # self.y.append(lab)
-                # self.trains.append(train)
-                # self.masks.append(scene['car_masks'])
-        # split y and images into train and test
-        self.y_train, self.y_test, self.images_train, self.images_test = train_test_split(self.y, self.images,
-                                                                                          test_size=0.2, random_state=0)
+                # y.append(lab)
+                # trains.append(train)
+                # masks.append(scene['car_masks'])
+        return image_dir, y, images
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        if self.config.metadata_urls['test'] is None:
+            image_dir, y, images = self.get_data(dl_manager, self.config.metadata_urls['train'])
+            image_dir_train, image_dir_test = image_dir, image_dir
+            y_train, y_test, images_train, images_test = train_test_split(y, images, test_size=0.2, random_state=0)
+        else:
+            image_dir_train, y_train, images_train = self.get_data(dl_manager, self.config.metadata_urls['train'])
+            image_dir_test, y_test, images_test = self.get_data(dl_manager, self.config.metadata_urls['test'])
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"image_dir": image_dir, "labels": self.y_train, "images": self.images_train}
+                gen_kwargs={"image_dir": image_dir_train, "labels": y_train, "images": images_train}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"image_dir": image_dir, "labels": self.y_test, "images": self.images_test}
+                gen_kwargs={"image_dir": image_dir_test, "labels": y_test, "images": images_test}
             ),
         ]