Fix bug in handling of explicitly specified num parameter of split_v

and also add associated test case.
Change: 141469479

tensorflow/python/kernel_tests/split_op_test.py

@@ -24,6 +24,24 @@ import tensorflow as tf
 
 class SplitVOpTest(tf.test.TestCase):
 
+  def testExplicitNum(self):
+    size_splits = tf.placeholder(dtype=tf.int32, shape=[None])
+
+    value = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+
+    with self.test_session(use_gpu=False) as sess:
+      with self.assertRaises(ValueError) as context:
+        sess.run(tf.split_v(value, size_splits), {size_splits: [2, 2, 6]})
+
+      self.assertTrue("Cannot infer num from shape" in str(context.exception))
+
+      result = sess.run(tf.split_v(value, size_splits, num=3),
+                        {size_splits: [2, 2, 6]})
+
+    self.assertAllEqual(result[0], value[0:2])
+    self.assertAllEqual(result[1], value[2:4])
+    self.assertAllEqual(result[2], value[4:])
+
   def testListOfScalarTensors(self):
     a = tf.to_int32(5)
     b = tf.to_int32(6)

tensorflow/python/ops/array_ops.py

@@ -1294,7 +1294,7 @@ def split_v(value=None,
   ```python
   # 'value' is a tensor with shape [5, 30]
   # Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1
-  split0, split1, split2 = tf.split_v(1, [4, 15, 11], value)
+  split0, split1, split2 = tf.split_v(value, [4, 15, 11], 1)
   tf.shape(split0) ==> [5, 4]
   tf.shape(split1) ==> [5, 15]
   tf.shape(split2) ==> [5, 11]
@@ -1329,17 +1329,17 @@ def split_v(value=None,
     return gen_array_ops._split(
         split_dim=axis, num_split=num_or_size_splits, value=value, name=name)
   else:
+    size_splits = ops.convert_to_tensor(num_or_size_splits)
     if num is None:
-      size_splits = ops.convert_to_tensor(num_or_size_splits)
       size_splits_shape = size_splits.get_shape()
-      num = size_splits_shape.dims
-    if num is None:
-      raise ValueError("Cannot infer num from shape %s" % value_shape)
+      num = size_splits_shape.dims[0]
+      if num._value is None:
+        raise ValueError("Cannot infer num from shape %s" % num_or_size_splits)
     return gen_array_ops._split_v(
         value=value,
         size_splits=size_splits,
         split_dim=axis,
-        num_split=num[0],
+        num_split=num,
         name=name)