Tfmini requires positive values for the dimensions. Therefore, we need to test...

Tfmini requires positive values for the dimensions. Therefore, we need to test for no paddings in both width and height direction.

PiperOrigin-RevId: 190520529

research/object_detection/utils/ops.py

@@ -180,26 +180,24 @@ def pad_to_multiple(tensor, multiple):
     padded_tensor_width = int(
         math.ceil(float(tensor_width) / multiple) * multiple)
 
-  if (padded_tensor_height == tensor_height and
-      padded_tensor_width == tensor_width):
-    return tensor
-
   if tensor_depth is None:
     tensor_depth = tf.shape(tensor)[3]
 
   # Use tf.concat instead of tf.pad to preserve static shape
-  height_pad = tf.zeros([
-      batch_size, padded_tensor_height - tensor_height, tensor_width,
-      tensor_depth
-  ])
-  padded_tensor = tf.concat([tensor, height_pad], 1)
-  width_pad = tf.zeros([
-      batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
-      tensor_depth
-  ])
-  padded_tensor = tf.concat([padded_tensor, width_pad], 2)
-
-  return padded_tensor
+  if padded_tensor_height != tensor_height:
+    height_pad = tf.zeros([
+        batch_size, padded_tensor_height - tensor_height, tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, height_pad], 1)
+  if padded_tensor_width != tensor_width:
+    width_pad = tf.zeros([
+        batch_size, padded_tensor_height, padded_tensor_width - tensor_width,
+        tensor_depth
+    ])
+    tensor = tf.concat([tensor, width_pad], 2)
+
+  return tensor
 
 
 def padded_one_hot_encoding(indices, depth, left_pad):

research/object_detection/utils/ops_test.py

@@ -136,6 +136,13 @@ class OpsTestPadToMultiple(tf.test.TestCase):
       padded_tensor_out = sess.run(padded_tensor)
     self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
 
+  def test_non_square_padding(self):
+    tensor = tf.constant([[[[0.], [0.]]]])
+    padded_tensor = ops.pad_to_multiple(tensor, 2)
+    with self.test_session() as sess:
+      padded_tensor_out = sess.run(padded_tensor)
+    self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
+
   def test_padding(self):
     tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
     padded_tensor = ops.pad_to_multiple(tensor, 4)