Add "dummy" option to VideoLoader.

Returns dummy frames if set; used when we don't need frame images
in the prediction pipeline.

sleap/nn/utils.py

@@ -309,6 +309,7 @@ class VideoLoader:
     dataset: str = None
     input_format: str = None
     grayscale: bool = False
+    dummy: bool = False
     chunk_size: int = 32
     prefetch_chunks: int = 1
     frame_inds: Optional[List[int]] = None
@@ -367,8 +368,12 @@ def _load_video(self, filename) -> "Video":
         )
 
     def load_frames(self, frame_inds):
-        local_vid = self._load_video(self.video.filename)
-        imgs = local_vid[np.array(frame_inds).astype("int64")]
+        if self.dummy:
+            dummy_shape = (len(frame_inds), *self._shape[1:])
+            imgs = np.zeros(dummy_shape, dtype="int8")
+        else:
+            local_vid = self._load_video(self.video.filename)
+            imgs = local_vid[np.array(frame_inds).astype("int64")]
         return imgs
 
     def tf_load_frames(self, frame_inds):

tests/nn/test_utils.py

@@ -1,4 +1,5 @@
 from sleap.nn.utils import VideoLoader
+import numpy as np
 
 
 def test_grayscale_video():
@@ -7,3 +8,11 @@ def test_grayscale_video():
 
     vid = VideoLoader(filename="tests/data/videos/small_robot.mp4", grayscale=True)
     assert vid.shape[-1] == 1
+
+
+def test_dummy_video():
+    vid = VideoLoader(filename="tests/data/videos/small_robot.mp4", dummy=True)
+
+    x = vid.load_frames([1, 3, 5])
+    assert x.shape == (3, 320, 560, 3)
+    assert np.all(x == 0)