| @@ -139,7 +139,7 @@ class Validator: | |||||
| def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name): | def check_int_range(arg_name, arg_value, lower_limit, upper_limit, rel, prim_name): | ||||
| """Method for checking whether an int value is in some range.""" | """Method for checking whether an int value is in some range.""" | ||||
| rel_fn = Rel.get_fns(rel) | rel_fn = Rel.get_fns(rel) | ||||
| type_mismatch = not isinstance(arg_value, int) | |||||
| type_mismatch = not isinstance(arg_value, int) or isinstance(arg_value, bool) | |||||
| excp_cls = TypeError if type_mismatch else ValueError | excp_cls = TypeError if type_mismatch else ValueError | ||||
| if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit): | if type_mismatch or not rel_fn(arg_value, lower_limit, upper_limit): | ||||
| rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit) | rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit) | ||||
| @@ -461,7 +461,8 @@ class Conv2dTranspose(_Conv): | |||||
| width of the kernel. | width of the kernel. | ||||
| stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents | stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents | ||||
| the height and width of movement are both strides, or a tuple of two int numbers that | the height and width of movement are both strides, or a tuple of two int numbers that | ||||
| represent height and width of movement respectively. Default: 1. | |||||
| represent height and width of movement respectively. Its value should be equal to or greater than 1. | |||||
| Default: 1. | |||||
| pad_mode (str): Select the mode of the pad. The optional values are | pad_mode (str): Select the mode of the pad. The optional values are | ||||
| "pad", "same", "valid". Default: "same". | "pad", "same", "valid". Default: "same". | ||||
| @@ -115,6 +115,8 @@ class ExpandDims(PrimitiveWithInfer): | |||||
| >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) | >>> input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) | ||||
| >>> expand_dims = P.ExpandDims() | >>> expand_dims = P.ExpandDims() | ||||
| >>> output = expand_dims(input_tensor, 0) | >>> output = expand_dims(input_tensor, 0) | ||||
| [[[2.0, 2.0], | |||||
| [2.0, 2.0]]] | |||||
| """ | """ | ||||
| @prim_attr_register | @prim_attr_register | ||||
| @@ -887,6 +889,8 @@ class Fill(PrimitiveWithInfer): | |||||
| Examples: | Examples: | ||||
| >>> fill = P.Fill() | >>> fill = P.Fill() | ||||
| >>> fill(mindspore.float32, (2, 2), 1) | >>> fill(mindspore.float32, (2, 2), 1) | ||||
| [[1.0, 1.0], | |||||
| [1.0, 1.0]] | |||||
| """ | """ | ||||
| @prim_attr_register | @prim_attr_register | ||||
| @@ -2364,6 +2368,8 @@ class Eye(PrimitiveWithInfer): | |||||
| Examples: | Examples: | ||||
| >>> eye = P.Eye() | >>> eye = P.Eye() | ||||
| >>> out_tensor = eye(2, 2, mindspore.int32) | >>> out_tensor = eye(2, 2, mindspore.int32) | ||||
| [[1, 0], | |||||
| [0, 1]] | |||||
| """ | """ | ||||
| @prim_attr_register | @prim_attr_register | ||||
| @@ -2244,10 +2244,10 @@ class Equal(_LogicBinaryOp): | |||||
| When the inputs are one tensor and one scalar, the scalar only could be a constant. | When the inputs are one tensor and one scalar, the scalar only could be a constant. | ||||
| Inputs: | Inputs: | ||||
| - **input_x** (Union[Tensor, Number, bool]) - The first input is a number or | |||||
| a bool or a tensor whose data type is number or bool. | |||||
| - **input_y** (Union[Tensor, Number, bool]) - The second input is a number or | |||||
| a bool when the first input is a tensor or a tensor whose data type is number or bool. | |||||
| - **input_x** (Union[Tensor, Number]) - The first input is a number or | |||||
| a tensor whose data type is number. | |||||
| - **input_y** (Union[Tensor, Number]) - The second input is a number | |||||
| when the first input is a tensor or a tensor whose data type is number. | |||||
| Outputs: | Outputs: | ||||
| Tensor, the shape is the same as the one after broadcasting,and the data type is bool. | Tensor, the shape is the same as the one after broadcasting,and the data type is bool. | ||||