From ec77704198688c8d9ff621ee8538967cb365ce77 Mon Sep 17 00:00:00 2001
From: liuxiao93 <liuxiao93@huawei.com>
Date: Wed, 4 Nov 2020 16:37:28 +0800
Subject: [PATCH] fix BNTrainingUpdate shape check about inputs.

---
 mindspore/ops/operations/nn_ops.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index fb34e62dbf6..38e6bc8a18c 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -836,12 +836,12 @@ class BNTrainingUpdate(PrimitiveWithInfer):
         validator.check_equal_int(len(b), 1, "b rank", self.name)
         validator.check_equal_int(len(mean), 1, "mean rank", self.name)
         validator.check_equal_int(len(variance), 1, "variance rank", self.name)
-        validator.check("sum shape", sum, "x_shape[1]", x[1], Rel.EQ, self.name)
+        validator.check("sum shape", sum[0], "x_shape[1]", x[1], Rel.EQ, self.name)
         validator.check("square_sum shape", square_sum, "sum", sum, Rel.EQ, self.name)
-        validator.check("scale shape", scale, "x_shape[1]", x[1], Rel.EQ, self.name)
-        validator.check("offset shape", b, "x_shape[1]", x[1], Rel.EQ, self.name)
-        validator.check("mean shape", mean, "x_shape[1]", x[1], Rel.EQ, self.name)
-        validator.check("variance shape", variance, "x_shape[1]", x[1], Rel.EQ, self.name)
+        validator.check("scale shape", scale[0], "x_shape[1]", x[1], Rel.EQ, self.name)
+        validator.check("offset shape", b[0], "x_shape[1]", x[1], Rel.EQ, self.name)
+        validator.check("mean shape", mean[0], "x_shape[1]", x[1], Rel.EQ, self.name)
+        validator.check("variance shape", variance[0], "x_shape[1]", x[1], Rel.EQ, self.name)
         return (x, variance, variance, variance, variance)
 
     def infer_dtype(self, x, sum, square_sum, scale, b, mean, variance):
@@ -5436,7 +5436,7 @@ class CTCGreedyDecoder(PrimitiveWithInfer):
           `num_labels` indicates the number of actual labels. Blank labels are reserved.
           Default blank label is `num_classes - 1`. Data type must be float32 or float64.
         - **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of (`batch_size`).
-          The type must be int32. Each value in the tensor must not greater than `max_time`.
+          The type must be int32. Each value in the tensor must be equal to or less than `max_time`.
 
     Outputs:
         - **decoded_indices** (Tensor) - A tensor with shape of (`total_decoded_outputs`, 2).