Update unit tests for #2663

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

tests/estimators/object_detection/test_pytorch_yolo.py

@@ -85,76 +85,76 @@ def test_loss_gradient(art_warning, get_pytorch_yolo):
 
         expected_gradients1 = np.asarray(
             [
-                -0.00033619,
-                0.00458546,
-                -0.00084969,
-                -0.00095304,
-                -0.00403843,
-                0.00225406,
-                -0.00369539,
-                -0.0099816,
-                -0.01046214,
-                -0.00290693,
-                0.00075546,
-                -0.0002135,
-                -0.00659937,
-                -0.00380152,
-                -0.00593928,
-                -0.00179838,
-                -0.00213012,
-                0.00526429,
-                0.00332446,
-                0.00543861,
-                0.00284291,
-                0.00426832,
-                -0.00586808,
-                -0.0017767,
-                -0.00231807,
-                -0.01142277,
-                -0.00021731,
-                0.00076714,
-                0.00289533,
-                0.00993828,
-                0.00472939,
-                0.00232432,
+                -7.8263599e-04,
+                -3.2761338e-04,
+                -1.7732104e-04,
+                5.0963718e-07,
+                -1.2021367e-04,
+                1.5550642e-05,
+                -1.2371356e-04,
+                6.0041926e-05,
+                7.2321229e-05,
+                2.8970995e-04,
+                3.2069255e-04,
+                -9.7214943e-06,
+                4.1050217e-04,
+                3.4139317e-04,
+                3.2144223e-04,
+                8.0305658e-04,
+                1.0029323e-03,
+                5.4904580e-04,
+                3.4701737e-04,
+                9.2334412e-05,
+                4.5694585e-05,
+                -4.1882982e-04,
+                -1.1162873e-03,
+                -1.2383220e-03,
+                -1.2119032e-03,
+                -1.3792568e-03,
+                -1.0219158e-03,
+                -1.7796915e-04,
+                1.6578102e-04,
+                -4.0390861e-04,
+                5.0578610e-04,
+                3.2289932e-05,
             ]
         )
         np.testing.assert_array_almost_equal(grads[0, 0, 208, 192:224], expected_gradients1, decimal=2)
 
         expected_gradients2 = np.asarray(
             [
-                0.00079487,
-                0.00426403,
-                -0.00151893,
-                0.00798506,
-                0.00937666,
-                0.01206836,
-                -0.00319753,
-                0.00506421,
-                0.00291614,
-                -0.00053876,
-                0.00281978,
-                -0.0027451,
-                0.00319698,
-                0.00287863,
-                0.00370754,
-                0.004611,
-                -0.00213012,
-                0.00440465,
-                -0.00077857,
-                0.00023536,
-                0.0035248,
-                -0.00810297,
-                0.00698602,
-                0.00877033,
-                0.01452724,
-                0.00161957,
-                0.02649526,
-                -0.0071549,
-                0.02670361,
-                -0.00759722,
-                -0.02353876,
-                0.00860081,
+                4.02656849e-04,
+                1.32368109e-03,
+                1.06753211e-03,
+                1.02746498e-03,
+                4.34952060e-04,
+                1.30278734e-03,
+                1.65620341e-03,
+                8.48031021e-04,
+                2.80185544e-04,
+                2.04326061e-04,
+                -9.31014947e-05,
+                -4.90375911e-04,
+                -3.42604442e-04,
+                1.36689676e-04,
+                3.08552640e-04,
+                3.88148270e-04,
+                1.00293232e-03,
+                -1.08163455e-04,
+                -1.41605944e-03,
+                -1.96112506e-03,
+                -6.27453031e-04,
+                -9.53144976e-04,
+                -6.66696171e-04,
+                -5.78872336e-04,
+                -1.52492896e-04,
+                -1.06580940e-03,
+                1.04899483e-03,
+                5.83183893e-04,
+                8.98627564e-04,
+                3.37607635e-04,
+                8.34865321e-04,
+                5.12865488e-04,
             ]
         )
         np.testing.assert_array_almost_equal(grads[0, 0, 192:224, 208], expected_gradients2, decimal=2)
@@ -306,21 +306,19 @@ def test_patch(art_warning, get_pytorch_yolo):
         assert result[0]["scores"].shape == (10647,)
         expected_detection_scores = np.asarray(
             [
-                2.0061936e-08,
-                8.2958641e-06,
-                1.5368976e-05,
-                8.5753290e-06,
-                1.5901747e-05,
-                3.8245958e-05,
-                4.6325898e-05,
-                7.1730128e-06,
-                4.3095843e-06,
-                1.0766385e-06,
+                2.0058684e-08,
+                8.2879878e-06,
+                1.5323505e-05,
+                8.5337388e-06,
+                1.5668766e-05,
+                3.7196922e-05,
+                4.5348370e-05,
+                6.9575308e-06,
+                4.2298670e-06,
+                1.0316832e-06,
             ]
         )
-        np.testing.assert_raises(
-            AssertionError, np.testing.assert_array_almost_equal, result[0]["scores"][:10], expected_detection_scores, 6
-        )
+        np.testing.assert_allclose(result[0]["scores"][:10], expected_detection_scores, rtol=1e-5, atol=1e-8)
 
     except ARTTestException as e:
         art_warning(e)