update: add run data generator script

Author: minkyu-choi07

ns_vfs/generator/data_generator.py

@@ -4,7 +4,7 @@
 import random
 
 from ns_vfs.data.frame import BenchmarkLTLFrame
-from ns_vfs.loader.benchmark_image import BenchmarkImageLoader, Cifar10ImageLoader
+from ns_vfs.loader.benchmark_image import BenchmarkImageLoader
 
 
 class DataGenerator(abc.ABC):
@@ -110,14 +110,3 @@ def ltl_function(
         )
 
         return ltl_frame
-
-
-if __name__ == "__main__":
-    cifar_dir = (
-        "/opt/Neuro-Symbolic-Video-Frame-Search/artifacts/data/benchmark_image_dataset/cifar-10-batches-py"
-    )
-    cifar_video_generator = BenchmarkVideoGenerator(
-        image_data_loader=Cifar10ImageLoader(cifar_dir_path=cifar_dir)
-    )
-    cifar_video_generator.generate(max_number_frame=50)
-    print("Hello World!")

ns_vfs/loader/benchmark_image.py

@@ -11,6 +11,8 @@
 
 
 class BenchmarkImageLoader(DataLoader):
+    """Benchmark image loader."""
+
     class_labels: list
     data: BenchmarkRawImage
 
@@ -81,6 +83,7 @@ def load_data(self, data_path, load_all_batch: bool = False) -> dict:
         return data
 
     def unpickle(self, file):
+        """Unpickle CIFAR image data."""
         import pickle
 
         with open(file, "rb") as fo:

run_diffusion_model.py

@@ -24,9 +24,7 @@
 
     pix_2_pix = PixToPix(config=config)
 
-    loaded_data = np.load(
-        os.path.join(artifact_path, "frame_window_automata.npy"), allow_pickle=True
-    ).item()
+    loaded_data = np.load(os.path.join(artifact_path, "frame_window_automata.npy"), allow_pickle=True).item()
     i = 0
     for key, value in loaded_data.items():
         if value.verification_result == "true":