[Cleanup] remove print statements in tests (#1287)

* [Cleanup] remove print statements in tests

* Fix formatting

Author: ianstenbit

keras_cv/bounding_box/iou_test.py

@@ -26,7 +26,6 @@ def test_compute_single_iou(self):
         # area of bb1 and bb1_off_by_1 are each 10000.
         # intersection area is 99*99=9801
         # iou=9801/(2*10000 - 9801)=0.96097656633
-        print(iou_lib.compute_iou(bb1, bb1_off_by_1, "yxyx"))
         self.assertAlmostEqual(
             iou_lib.compute_iou(bb1, bb1_off_by_1, "yxyx")[0], 0.96097656633
         )

keras_cv/models/object_detection3d/center_pillar.py

@@ -49,7 +49,7 @@ def __init__(
             # 8:end outputs is for bin-based classification and regression
             size += 2 * num_head_bin[i]
             self._per_class_prediction_size.append(size)
-        print("per class prediction size {}".format(self._per_class_prediction_size))
+
         if not share_head:
             for i in range(num_class):
                 # 1x1 conv for each voxel/pixel.

keras_cv/models/object_detection3d/center_pillar_test.py

@@ -88,8 +88,6 @@ def test_center_pillar_call(self):
         outputs = model(point_xyz, point_feature, point_mask, training=True)
         self.assertEqual(outputs["class_1"].shape, [2, 400, 400, 12])
         self.assertEqual(outputs["class_2"].shape, [2, 400, 400, 12])
-        for k, v in outputs.items():
-            print(k, v.shape)
 
     def test_center_pillar_predict(self):
         voxel_net = DynamicVoxelization(

keras_cv/models/segmentation/deeplab_test.py

@@ -157,10 +157,9 @@ def keep_image_and_mask_only(data):
             metrics=["accuracy"],
         )
 
-        model_history = model.fit(
+        model.fit(
             training_dataset, epochs=epochs, steps_per_epoch=num_images // batch_size
         )
-        print(model_history)
 
 
 if __name__ == "__main__":

keras_cv/ops/within_box_3d_test.py

@@ -14,7 +14,6 @@
 # ==============================================================================
 
 import os
-from datetime import datetime
 
 import numpy as np
 import pytest
@@ -139,7 +138,6 @@ def test_batched_unrotated(self):
             ]
             * 2
         ).astype("float32")
-        print(points.shape)
         res = keras_cv.ops.within_box3d_index(points, boxes)
         self.assertAllEqual(
             [[0, 0, -1, 0, -1, -1, -1], [-1, -1, -1, -1, -1, 0, -1]], res
@@ -183,7 +181,6 @@ def test_many_points(self):
         points, boxes = get_points_boxes()
 
         for _ in range(5):
-            print(datetime.now())
             res = keras_cv.ops.within_box3d_index(points, boxes)
             self.assertAllClose(res.shape, points.shape[:1])