From 75c38a08a7602fe082c29b29e16598f80cc4c85a Mon Sep 17 00:00:00 2001 From: liuxiao93 Date: Tue, 21 Jul 2020 18:02:22 +0800 Subject: [PATCH] Fix bug about API of ops. --- mindspore/ops/operations/array_ops.py | 5 +++-- mindspore/ops/operations/nn_ops.py | 13 ++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 1e28a56db1..836f55d90a 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -435,7 +435,7 @@ class Squeeze(PrimitiveWithInfer): ValueError: If the corresponding dimension of the specified axis does not equal to 1. Args: - axis (int): Specifies the dimension indexes of shape to be removed, which will remove + axis (Union[int, tuple(int)]): Specifies the dimension indexes of shape to be removed, which will remove all the dimensions that are equal to 1. If specified, it must be int32 or int64. Default: (), an empty tuple. @@ -1427,7 +1427,8 @@ class UnsortedSegmentProd(PrimitiveWithInfer): Inputs: - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`. With float16, float32 or int32 data type. - - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`. Data type must be int32. + - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`, the value should be >= 0. + Data type must be int32. - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`, should be greater than 0. diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index cefac01442..8573c348e8 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -3760,12 +3760,12 @@ class ApplyAdagradV2(PrimitiveWithInfer): update_slots (bool): If `True`, `accum` will be updated. Default: True. Inputs: - - **var** (Parameter) - Variable to be updated. With float32 or float16 data type. + - **var** (Parameter) - Variable to be updated. With float32 data type. - **accum** (Parameter) - Accum to be updated. The shape and dtype should be the same as `var`. - With float32 or float16 data type. - - **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32 or float16 data type. + With float32 data type. + - **lr** (Union[Number, Tensor]) - The learning rate value, should be scalar. With float32 data type. - **grad** (Tensor) - A tensor for gradient. The shape and dtype should be the same as `var`. - With float32 or float16 data type. + With float32 data type. Outputs: Tuple of 2 Tensor, the updated parameters. @@ -3817,9 +3817,8 @@ class ApplyAdagradV2(PrimitiveWithInfer): def infer_dtype(self, var_dtype, accum_dtype, lr_dtype, grad_dtype): args = {'var': var_dtype, 'accum': accum_dtype, 'grad': grad_dtype} - valid_types = [mstype.float16, mstype.float32] - validator.check_tensor_type_same(args, valid_types, self.name) - validator.check_scalar_or_tensor_type_same({'lr': lr_dtype}, valid_types, self.name) + validator.check_tensor_type_same(args, [mstype.float32], self.name) + validator.check_scalar_or_tensor_type_same({'lr': lr_dtype}, [mstype.float32], self.name) return var_dtype, accum_dtype