rename util-> utils

Author: (Ian Stenbit)

keras_cv/__init__.py

@@ -14,4 +14,4 @@
 
 from keras_cv import layers
 from keras_cv import metrics
-from keras_cv import util
+from keras_cv import utils

keras_cv/metrics/coco/base.py

@@ -16,8 +16,8 @@
 import tensorflow.keras.initializers as initializers
 
 from keras_cv.metrics.coco import iou as iou_lib
-from keras_cv.metrics.coco import util
-from keras_cv.util import bbox
+from keras_cv.metrics.coco import utils
+from keras_cv.utils import bbox
 
 
 class COCOBase(keras.metrics.Metric):
@@ -106,35 +106,35 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         num_categories = self.category_ids.shape[0]
 
         # Sort by bbox.CONFIDENCE to make maxDetections easy to compute.
-        y_pred = util.sort_bboxes(y_pred, axis=bbox.CONFIDENCE)
+        y_pred = utils.sort_bboxes(y_pred, axis=bbox.CONFIDENCE)
         true_positives_update = tf.zeros_like(self.true_positives)
         false_positives_update = tf.zeros_like(self.false_positives)
         ground_truth_boxes_update = tf.zeros_like(self.ground_truth_boxes)
 
         for img in tf.range(num_images):
-            sentinel_filtered_y_true = util.filter_out_sentinels(y_true[img])
-            sentinel_filtered_y_pred = util.filter_out_sentinels(y_pred[img])
+            sentinel_filtered_y_true = utils.filter_out_sentinels(y_true[img])
+            sentinel_filtered_y_pred = utils.filter_out_sentinels(y_pred[img])
 
-            area_filtered_y_true = util.filter_boxes_by_area_range(
+            area_filtered_y_true = utils.filter_boxes_by_area_range(
                 sentinel_filtered_y_true, self.area_range[0], self.area_range[1]
             )
             # TODO(lukewood): try filtering area after max dts.
-            area_filtered_y_pred = util.filter_boxes_by_area_range(
+            area_filtered_y_pred = utils.filter_boxes_by_area_range(
                 sentinel_filtered_y_pred, self.area_range[0], self.area_range[1]
             )
 
             for k_i in tf.range(num_categories):
                 category = self.category_ids[k_i]
 
-                category_filtered_y_pred = util.filter_boxes(
+                category_filtered_y_pred = utils.filter_boxes(
                     area_filtered_y_pred, value=category, axis=bbox.CLASS
                 )
 
                 detections = category_filtered_y_pred
                 if self.max_detections < tf.shape(category_filtered_y_pred)[0]:
                     detections = category_filtered_y_pred[: self.max_detections]
 
-                ground_truths = util.filter_boxes(
+                ground_truths = utils.filter_boxes(
                     area_filtered_y_true, value=category, axis=bbox.CLASS
                 )
 

keras_cv/metrics/coco/numerical_tests/recall_correctness_test.py

@@ -19,7 +19,7 @@
 import tensorflow as tf
 
 from keras_cv.metrics.coco.recall import COCORecall
-from keras_cv.util import bbox
+from keras_cv.utils import bbox
 
 SAMPLE_FILE = os.path.dirname(os.path.abspath(__file__)) + "/sample_boxes.npz"
 

keras_cv/metrics/coco/util.py renamed to keras_cv/metrics/coco/utils.py

@@ -14,7 +14,7 @@
 """Contains shared utilities for Keras COCO metrics."""
 import tensorflow as tf
 
-from keras_cv.util import bbox
+from keras_cv.utils import bbox
 
 
 def filter_boxes_by_area_range(boxes, min_area, max_area):

keras_cv/metrics/coco/util_test.py renamed to keras_cv/metrics/coco/utils_test.py

@@ -15,35 +15,35 @@
 
 import tensorflow as tf
 
-from keras_cv.metrics.coco import util
-from keras_cv.util import bbox
+from keras_cv.metrics.coco import utils
+from keras_cv.utils import bbox
 
 
 class UtilTest(tf.test.TestCase):
     def test_filter_bboxes_empty(self):
         # set of bboxes
         y_pred = tf.stack([_dummy_bbox(category=1)])
-        result = util.filter_boxes(y_pred, 2, axis=bbox.CLASS)
+        result = utils.filter_boxes(y_pred, 2, axis=bbox.CLASS)
 
         self.assertEqual(result.shape[0], 0)
 
     def test_bbox_area(self):
         boxes = tf.constant([[0, 0, 100, 100]], dtype=tf.float32)
-        areas = util.bbox_area(boxes)
+        areas = utils.bbox_area(boxes)
         self.assertAllClose(areas, tf.constant((10000.0,)))
 
     def test_filter_bboxes(self):
         # set of bboxes
         y_pred = tf.stack([_dummy_bbox(category=1), _dummy_bbox(category=2)])
-        result = util.filter_boxes(y_pred, 2, axis=bbox.CLASS)
+        result = utils.filter_boxes(y_pred, 2, axis=bbox.CLASS)
 
         self.assertAllClose(result, tf.stack([_dummy_bbox(category=2)]))
 
     def test_to_sentinel_padded_bbox_tensor(self):
         box_set1 = tf.stack([_dummy_bbox(), _dummy_bbox()])
         box_set2 = tf.stack([_dummy_bbox()])
         boxes = [box_set1, box_set2]
-        bbox_tensor = util.to_sentinel_padded_bbox_tensor(boxes)
+        bbox_tensor = utils.to_sentinel_padded_bbox_tensor(boxes)
         self.assertAllClose(
             bbox_tensor[1, 1],
             -tf.ones(
@@ -54,18 +54,18 @@ def test_to_sentinel_padded_bbox_tensor(self):
     def test_filter_out_sentinels(self):
         # set of bboxes
         y_pred = tf.stack([_dummy_bbox(category=1), _dummy_bbox(category=-1)])
-        result = util.filter_out_sentinels(y_pred)
+        result = utils.filter_out_sentinels(y_pred)
 
         self.assertAllClose(result, tf.stack([_dummy_bbox(category=1)]))
 
     def test_end_to_end_sentinel_filtering(self):
         box_set1 = tf.stack([_dummy_bbox(), _dummy_bbox()])
         box_set2 = tf.stack([_dummy_bbox()])
         boxes = [box_set1, box_set2]
-        bbox_tensor = util.to_sentinel_padded_bbox_tensor(boxes)
+        bbox_tensor = utils.to_sentinel_padded_bbox_tensor(boxes)
 
-        self.assertAllClose(util.filter_out_sentinels(bbox_tensor[0]), box_set1)
-        self.assertAllClose(util.filter_out_sentinels(bbox_tensor[1]), box_set2)
+        self.assertAllClose(utils.filter_out_sentinels(bbox_tensor[0]), box_set1)
+        self.assertAllClose(utils.filter_out_sentinels(bbox_tensor[1]), box_set2)
 
     def test_sort_bboxes_unsorted_list(self):
         y_pred = tf.expand_dims(
@@ -90,12 +90,12 @@ def test_sort_bboxes_unsorted_list(self):
             ),
             axis=0,
         )
-        y_sorted = util.sort_bboxes(y_pred, bbox.CONFIDENCE)
+        y_sorted = utils.sort_bboxes(y_pred, bbox.CONFIDENCE)
         self.assertAllClose(y_sorted, want)
 
     def test_sort_bboxes_empty_list(self):
         y_pred = tf.stack([])
-        y_sorted = util.sort_bboxes(y_pred)
+        y_sorted = utils.sort_bboxes(y_pred)
         self.assertAllClose(y_pred, y_sorted)
 
 

keras_cv/util/__init__.py renamed to keras_cv/utils/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from keras_cv.util import bbox
+from keras_cv.utils import bbox