Add check for instances with track assigned before training ID models

sleap/gui/learning/dialog.py

@@ -637,6 +637,20 @@ def get_items_for_inference(self, pipeline_form_data) -> runners.ItemsForInferen
             )
         return items_for_inference
 
+    def _validate_id_model(self) -> bool:
+        """Make sure we have instances with tracks set for ID models."""
+        if not self.labels.tracks:
+            message = "Cannot run ID model training without tracks."
+            return False
+
+        found_tracks = False
+        for inst in self.labels.instances():
+            if type(inst) == sleap.Instance and inst.track is not None:
+                found_tracks = True
+                break
+
+        return found_tracks
+
     def _validate_pipeline(self):
         can_run = True
         message = ""
@@ -655,6 +669,15 @@ def _validate_pipeline(self):
                     f"({', '.join(untrained)})."
                 )
 
+        # Make sure we have instances with tracks set for ID models.
+        if self.mode == "training" and self.current_pipeline in (
+            "top-down-id",
+            "bottom-up-id",
+        ):
+            can_run = self.validate_id_model()
+            if not can_run:
+                message = "Cannot run ID model training without tracks."
+
         # Make sure skeleton will be valid for bottom-up inference.
         if self.mode == "training" and self.current_pipeline == "bottom-up":
             skeleton = self.labels.skeletons[0]

tests/gui/learning/test_dialog.py

@@ -7,6 +7,7 @@
 import pytest
 from qtpy import QtWidgets
 
+import sleap
 from sleap.gui.learning.dialog import LearningDialog, TrainingEditorWidget
 from sleap.gui.learning.configs import (
     TrainingConfigFilesWidget,
@@ -429,3 +430,22 @@ def test_immutablilty_of_trained_config_info(
     # saving multiple configs from one config info.
     ld.save(output_dir=tmpdir)
     ld.save(output_dir=tmpdir)
+
+
+def test_validate_id_model(qtbot, min_labels_slp, min_labels_slp_path):
+    app = MainWindow(no_usage_data=True)
+    ld = LearningDialog(
+        mode="training",
+        labels_filename=Path(min_labels_slp_path),
+        labels=min_labels_slp,
+    )
+    assert not ld._validate_id_model()
+
+    # Add track but don't assign it to instances
+    new_track = sleap.Track(name="new_track")
+    min_labels_slp.tracks.append(new_track)
+    assert not ld._validate_id_model()
+
+    # Assign track to instances
+    min_labels_slp[0][0].track = new_track
+    assert ld._validate_id_model()