update pytorch detr unit tests

Signed-off-by: Farhan Ahmed <[email protected]>

art/estimators/object_detection/pytorch_object_detector.py

@@ -544,7 +544,8 @@ def compute_losses(
         loss_components, _ = self._get_losses(x=x, y=y)
         output = {}
         for key, value in loss_components.items():
-            output[key] = value.detach().cpu().numpy()
+            if key in self.attack_losses:
+                output[key] = value.detach().cpu().numpy()
         return output
 
     def compute_loss(  # type: ignore

tests/estimators/object_detection/conftest.py

@@ -246,3 +246,48 @@ def forward(self, x, targets=None):
     ]
 
     yield object_detector, x_test, y_test
+
+
+@pytest.fixture()
+def get_pytorch_detr(get_default_cifar10_subset):
+    import cv2
+
+    from art.estimators.object_detection.pytorch_detection_transformer import PyTorchDetectionTransformer
+
+    MEAN = [0.485, 0.456, 0.406]
+    STD = [0.229, 0.224, 0.225]
+
+    object_detector = PyTorchDetectionTransformer(
+        input_shape=(3, 800, 800),
+        clip_values=(0, 1),
+        preprocessing=(MEAN, STD),
+        channels_first=True,
+        attack_losses=("loss_ce", "loss_bbox", "loss_giou"),
+    )
+
+    (_, _), (x_test_cifar10, _) = get_default_cifar10_subset
+
+    x_test = cv2.resize(
+        x_test_cifar10[0].transpose((1, 2, 0)), dsize=(800, 800), interpolation=cv2.INTER_CUBIC
+    ).transpose((2, 0, 1))
+    x_test = np.expand_dims(x_test, axis=0)
+    x_test = np.repeat(x_test, repeats=2, axis=0)
+
+    # Create labels
+
+    result = object_detector.predict(x=x_test)
+
+    y_test = [
+        {
+            "boxes": result[0]["boxes"],
+            "labels": result[0]["labels"],
+            "scores": np.ones_like(result[0]["labels"]),
+        },
+        {
+            "boxes": result[1]["boxes"],
+            "labels": result[1]["labels"],
+            "scores": np.ones_like(result[1]["labels"]),
+        },
+    ]
+
+    yield object_detector, x_test, y_test