Fixed Unexpectedly huge RAM consumption of parallel datamanager (nerf…

…studio-project#3060)

* Reduced the memory usage by caching image after the datamanager process starts

nerfstudio/data/datamanagers/parallel_datamanager.py

@@ -93,10 +93,10 @@ def __init__(
         self.exclude_batch_keys_from_device = self.dataset.exclude_batch_keys_from_device
         self.pixel_sampler = pixel_sampler
         self.ray_generator = RayGenerator(self.dataset.cameras)
-        self.cache_images()
 
     def run(self):
         """Append out queue in parallel with ray bundles and batches."""
+        self.cache_images()
         while True:
             batch = self.pixel_sampler.sample(self.img_data)
             ray_indices = batch["indices"]