fix case where there are no good matches for the prompt

inference/models/owlv2/owlv2.py

@@ -171,7 +171,7 @@ def get_class_preds_from_embeds(
     survival_indices = torchvision.ops.nms(
         to_corners(pred_boxes), pred_scores, iou_threshold
     )
-    # put on numpy and filter to post-nms
+    # filter to post-nms
     pred_boxes = pred_boxes[survival_indices, :]
     pred_classes = pred_classes[survival_indices]
     pred_scores = pred_scores[survival_indices]
@@ -371,6 +371,9 @@ def infer_from_embed(
             all_predicted_classes.append(classes)
             all_predicted_scores.append(scores)
 
+        if not all_predicted_boxes:
+            return []
+
         all_predicted_boxes = torch.cat(all_predicted_boxes, dim=0)
         all_predicted_classes = torch.cat(all_predicted_classes, dim=0)
         all_predicted_scores = torch.cat(all_predicted_scores, dim=0)

tests/inference/models_predictions_tests/test_owlv2.py

@@ -1,7 +1,10 @@
+import pytest
+
 from inference.core.entities.requests.owlv2 import OwlV2InferenceRequest
 from inference.models.owlv2.owlv2 import OwlV2
 
 
+@pytest.mark.slow
 def test_owlv2():
     image = {
         "type": "url",
@@ -49,6 +52,14 @@ def test_owlv2():
     assert abs(532 - posts[3].x) < 1.5
     assert abs(572 - posts[4].x) < 1.5
 
+
+@pytest.mark.slow
+def test_owlv2_multiple_prompts():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+
     # test we can handle multiple (positive and negative) prompts for the same image
     request = OwlV2InferenceRequest(
         image=image,
@@ -96,7 +107,15 @@ def test_owlv2():
     assert abs(532 - posts[2].x) < 1.5
     assert abs(572 - posts[3].x) < 1.5
 
-    # test that we can handle no prompts for an image
+
+@pytest.mark.slow
+def test_owlv2_image_without_prompts():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+
+    # test that we can handle an image without any prompts
     request = OwlV2InferenceRequest(
         image=image,
         training_data=[
@@ -124,3 +143,147 @@ def test_owlv2():
 
     response = OwlV2().infer_from_request(request)
     assert len(response.predictions) == 5
+
+
+@pytest.mark.slow
+def test_owlv2_bad_prompt():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+
+    # test that we can handle a bad prompt
+    request = OwlV2InferenceRequest(
+        image=image,
+        training_data=[
+            {
+                "image": image,
+                "boxes": [
+                    {
+                        "x": 1,
+                        "y": 1,
+                        "w": 1,
+                        "h": 1,
+                        "cls": "post",
+                        "negative": False,
+                    }
+                ],
+            }
+        ],
+        visualize_predictions=True,
+        confidence=0.9,
+    )
+
+    response = OwlV2().infer_from_request(request)
+    assert len(response.predictions) == 0
+
+
+@pytest.mark.slow
+def test_owlv2_bad_prompt_hidden_among_good_prompts():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+
+    # test that we can handle a bad prompt
+    request = OwlV2InferenceRequest(
+        image=image,
+        training_data=[
+            {
+                "image": image,
+                "boxes": [
+                    {
+                        "x": 1,
+                        "y": 1,
+                        "w": 1,
+                        "h": 1,
+                        "cls": "post",
+                        "negative": False,
+                    },
+                    {
+                        "x": 223,
+                        "y": 306,
+                        "w": 40,
+                        "h": 226,
+                        "cls": "post",
+                        "negative": False,
+                    },
+                ],
+            }
+        ],
+        visualize_predictions=True,
+        confidence=0.9,
+    )
+
+    response = OwlV2().infer_from_request(request)
+    assert len(response.predictions) == 5
+
+
+@pytest.mark.slow
+def test_owlv2_no_training_data():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+
+    # test that we can handle no training data
+    request = OwlV2InferenceRequest(
+        image=image,
+        training_data=[],
+    )
+
+    response = OwlV2().infer_from_request(request)
+    assert len(response.predictions) == 0
+
+
+@pytest.mark.slow
+def test_owlv2_multiple_training_images():
+    image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/seawithdock.jpeg",
+    }
+    second_image = {
+        "type": "url",
+        "value": "https://media.roboflow.com/inference/dock2.jpg",
+    }
+
+    request = OwlV2InferenceRequest(
+        image=image,
+        training_data=[
+            {
+                "image": image,
+                "boxes": [
+                    {
+                        "x": 223,
+                        "y": 306,
+                        "w": 40,
+                        "h": 226,
+                        "cls": "post",
+                        "negative": False,
+                    }
+                ],
+            },
+            {
+                "image": second_image,
+                "boxes": [
+                    {
+                        "x": 3009,
+                        "y": 1873,
+                        "w": 289,
+                        "h": 811,
+                        "cls": "post",
+                        "negative": True,
+                    }
+                ],
+            },
+        ],
+        visualize_predictions=True,
+        confidence=0.9,
+    )
+
+    response = OwlV2().infer_from_request(request)
+    assert len(response.predictions) == 5
+
+
+if __name__ == "__main__":
+    test_owlv2()