Add test for multilevel_native_crop_and_resize

research/object_detection/utils/spatial_transform_ops_test.py

@@ -515,6 +515,28 @@ class MatMulCropAndResizeTest(test_case.TestCase):
 
 class NativeCropAndResizeTest(test_case.TestCase):
 
+  # def testBatchCropAndResize3x3To2x2_2Channels(self):
+
+  #   def graph_fn(image, boxes):
+  #     return spatial_ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
+
+  #   image = np.array([[[[1, 0], [2, 1], [3, 2]],
+  #                      [[4, 3], [5, 4], [6, 5]],
+  #                      [[7, 6], [8, 7], [9, 8]]],
+  #                     [[[1, 0], [2, 1], [3, 2]],
+  #                      [[4, 3], [5, 4], [6, 5]],
+  #                      [[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
+  #   boxes = np.array([[[0, 0, 1, 1],
+  #                      [0, 0, .5, .5]],
+  #                     [[1, 1, 0, 0],
+  #                      [.5, .5, 0, 0]]], dtype=np.float32)
+  #   expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
+  #                       [[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
+  #                      [[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
+  #                       [[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
+  #   crop_output = self.execute_cpu(graph_fn, [image, boxes])
+  #   self.assertAllClose(crop_output, expected_output)
+
   def testBatchCropAndResize3x3To2x2_2Channels(self):
 
     def graph_fn(image, boxes):
@@ -536,6 +558,35 @@ class NativeCropAndResizeTest(test_case.TestCase):
                         [[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
     crop_output = self.execute_cpu(graph_fn, [image, boxes])
     self.assertAllClose(crop_output, expected_output)
+  
+  def testMultilevelBatchCropAndResize3x3To2x2_2Channels(self):
+
+    def graph_fn(image1, image2, boxes, box_levels):
+      return spatial_ops.multilevel_native_crop_and_resize([image1, image2],
+                                                           boxes,
+                                                           box_levels,
+                                                          crop_size=[2, 2])
+    image = [np.array([[[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]],
+                      [[[1, 0], [2, 1], [3, 2]],
+                       [[4, 3], [5, 4], [6, 5]],
+                       [[7, 6], [8, 7], [9, 8]]]], dtype=np.float32),
+             np.array([[[[1, 0], [2, 1]],
+                       [[4, 3], [5, 4]]],
+                      [[[1, 0], [2, 1]],
+                       [[4, 3], [5, 4]]]], dtype=np.float32)]
+    boxes = np.array([[[0, 0, 1, 1],
+                       [0, 0, .5, .5]],
+                      [[1, 1, 0, 0],
+                       [.5, .5, 0, 0]]], dtype=np.float32)
+    box_levels = np.array([[0, 1], [0, 0]], dtype=np.float32)
+    expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
+                        [[[1, 0], [1.5, 0.5]], [[2.5, 1.5], [3, 2]]]],
+                       [[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
+                        [[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
+    crop_output = self.execute_cpu(graph_fn, [*image, boxes, box_levels])
+    self.assertAllClose(crop_output, expected_output)
 
 
 if __name__ == '__main__':