[WIP] kwargs flow through for Image Similarity -> Nearest Neighbors

src/python/turicreate/test/test_image_similarity.py

@@ -251,21 +251,25 @@ def get_psnr(x, y):
         )
 
         # Get model distances for comparison
-        img = data[0:1][self.feature][0]
-        img_fixed = tc.image_analysis.resize(img, *reversed(self.input_image_shape))
-        tc_ret = self.model.query(img_fixed, k=data.num_rows())
-
-        if _mac_ver() >= (10, 13):
-            from PIL import Image as _PIL_Image
-
-            pil_img = _PIL_Image.fromarray(img_fixed.pixel_data)
-            coreml_ret = coreml_model.predict({"awesome_image": pil_img})
-
-            # Compare distances
-            coreml_distances = np.array(coreml_ret["distance"])
-            tc_distances = tc_ret.sort("reference_label")["distance"].to_numpy()
-            psnr_value = get_psnr(coreml_distances, tc_distances)
-            self.assertTrue(psnr_value > 50)
+        if self.feature == "awesome_image":
+            img = data[0:1][self.feature][0]
+            img_fixed = tc.image_analysis.resize(img, *reversed(self.input_image_shape))
+            tc_ret = self.model.query(img_fixed, k=data.num_rows())
+
+            if _mac_ver() >= (10, 13):
+                from PIL import Image as _PIL_Image
+
+                pil_img = _PIL_Image.fromarray(img_fixed.pixel_data)
+                coreml_ret = coreml_model.predict({"awesome_image": pil_img})
+
+                # Compare distances
+                coreml_distances = np.array(coreml_ret["distance"])
+                tc_distances = tc_ret.sort("reference_label")["distance"].to_numpy()
+                psnr_value = get_psnr(coreml_distances, tc_distances)
+                self.assertTrue(psnr_value > 50)
+        else:
+            # Broad else clause to ignore features not supported in coreml
+            pass
 
     def test_save_and_load(self):
         with test_util.TempDirectory() as filename:
@@ -287,6 +291,60 @@ def test_save_and_load(self):
             print("Export coreml passed")
 
 
+class ImageSimilarityTestWithKwargs(unittest.TestCase):
+    @classmethod
+    def setUpClass(self, input_image_shape=(3, 224, 224), model="resnet-50"):
+        """
+        The setup class method for the basic test case with all default values.
+        """
+        self.feature = "awesome_image"
+        self.label = None
+        self.input_image_shape = input_image_shape
+        self.pre_trained_model = model
+
+        # Create the model
+        self.def_opts = {
+            "model": "resnet-50",
+            "verbose": True,
+        }
+
+        # Model
+        self.model = tc.image_similarity.create(
+            data, feature=self.feature, label=None, model=self.pre_trained_model,
+            method='lsh', distance='squared_euclidean'
+        )
+        self.nn_model = self.model.feature_extractor
+        self.lm_model = self.model.similarity_model
+        self.opts = self.def_opts.copy()
+
+        # Answers
+        self.get_ans = {
+            "similarity_model": lambda x: type(x)
+                                          == tc.nearest_neighbors.NearestNeighborsModel,
+            "feature": lambda x: x == self.feature,
+            "training_time": lambda x: x > 0,
+            "input_image_shape": lambda x: x == self.input_image_shape,
+            "label": lambda x: x == self.label,
+            "feature_extractor": lambda x: callable(x.extract_features),
+            "num_features": lambda x: x == self.lm_model.num_features,
+            "num_examples": lambda x: x == self.lm_model.num_examples,
+            "model": lambda x: (
+                    x == self.pre_trained_model
+                    or (
+                            self.pre_trained_model == "VisionFeaturePrint_Screen"
+                            and x == "VisionFeaturePrint_Scene"
+                    )
+            ),
+        }
+        self.fields_ans = self.get_ans.keys()
+
+    def assertModelWorks(self):
+        self.assertEqual(self.model.similarity_model.distance[0][1],
+                         'squared_euclidean'
+                         )
+
+
+
 class ImageSimilaritySqueezeNetTest(ImageSimilarityTest):
     @classmethod
     def setUpClass(self):
@@ -306,7 +364,7 @@ def setUpClass(self):
         )
 
 
-# A test to gaurantee that old code using the incorrect name still works.
+# A test to guarantee that old code using the incorrect name still works.
 @unittest.skipIf(
     _mac_ver() < (10, 14), "VisionFeaturePrint_Scene only supported on macOS 10.14+"
 )
@@ -316,3 +374,12 @@ def setUpClass(self):
         super(ImageSimilarityVisionFeaturePrintSceneTest_bad_name, self).setUpClass(
             model="VisionFeaturePrint_Screen", input_image_shape=(3, 299, 299)
         )
+
+
+# A test to ensure kwargs are still accepted in create()
+class ImageSimilarityCreateKwargsTest(ImageSimilarityTest):
+    @classmethod
+    def setUpClass(self):
+        super(ImageSimilarityCreateKwargsTest, self).setUpClass(
+            model="resnet-50", input_image_shape=(3, 300, 300)
+        )

src/python/turicreate/toolkits/image_similarity/image_similarity.py

@@ -22,7 +22,13 @@
 
 
 def create(
-    dataset, label=None, feature=None, model="resnet-50", verbose=True, batch_size=64
+    dataset,
+    label=None,
+    feature=None,
+    model="resnet-50",
+    verbose=True,
+    batch_size=64,
+    **kwargs
 ):
     """
     Create a :class:`ImageSimilarityModel` model.
@@ -63,6 +69,9 @@ def create(
     batch_size : int, optional
         If you are getting memory errors, try decreasing this value. If you
         have a powerful computer, increasing this value may improve performance.
+
+    **kwargs : optional
+        Options for downstream methods like :py:func:`tc.nearest_neighbors.create()`.
 
     Returns
     -------
@@ -148,6 +157,7 @@ def create(
         label=label,
         features=["__image_features__"],
         verbose=verbose,
+        **kwargs
     )
 
     # set input image shape