forked from mindspore-Ecosystem/mindspore
!26398 [feat] [assistant] [I48OBC] add new LogicalNot
Merge pull request !26398 from 桂胜楠/logicalnot
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commit
c13f31861c
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@ -1,5 +1,5 @@
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/**
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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* Copyright 2020-2021 Huawei Technologies Co., Ltd
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*
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* you may not use this file except in compliance with the License.
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@ -14,19 +14,26 @@
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* limitations under the License.
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* limitations under the License.
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*/
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*/
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#include "ops/logical_not.h"
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#include <map>
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#include <string>
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#include <set>
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#include <set>
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#include <vector>
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#include <memory>
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#include "ops/logical_not.h"
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#include "ops/op_utils.h"
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#include "ops/op_utils.h"
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#include "utils/check_convert_utils.h"
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namespace mindspore {
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namespace mindspore {
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namespace ops {
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namespace ops {
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namespace {
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namespace {
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abstract::ShapePtr LogicalNotInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
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abstract::ShapePtr LogicalNotInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
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MS_EXCEPTION_IF_NULL(primitive);
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MS_EXCEPTION_IF_NULL(primitive);
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auto in_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kShape];
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auto shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape());
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return std::make_shared<abstract::Shape>(in_shape);
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auto in_shape = shape_map[kShape];
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auto min_shape = shape_map[kMinShape];
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auto max_shape = shape_map[kMaxShape];
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return std::make_shared<abstract::Shape>(in_shape, min_shape, max_shape);
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}
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}
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TypePtr LogicalNotInferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
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TypePtr LogicalNotInferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
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@ -35,14 +42,21 @@ TypePtr LogicalNotInferType(const PrimitivePtr &prim, const std::vector<Abstract
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MS_EXCEPTION_IF_NULL(input_args[0]);
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MS_EXCEPTION_IF_NULL(input_args[0]);
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auto infer_dtype = input_args[0]->BuildType();
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auto infer_dtype = input_args[0]->BuildType();
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std::set<TypePtr> local_bool = {kBool};
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std::set<TypePtr> local_bool = {kBool};
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return CheckAndConvertUtils::CheckTensorTypeValid("x", infer_dtype, local_bool, op_name);
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(void)CheckAndConvertUtils::CheckTensorTypeValid("x", infer_dtype, local_bool, op_name);
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return infer_dtype;
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}
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}
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} // namespace
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} // namespace
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AbstractBasePtr LogicalNotInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
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AbstractBasePtr LogicalNotInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
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const std::vector<AbstractBasePtr> &input_args) {
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const std::vector<AbstractBasePtr> &input_args) {
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return std::make_shared<abstract::AbstractTensor>(LogicalNotInferType(primitive, input_args),
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MS_EXCEPTION_IF_NULL(primitive);
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LogicalNotInferShape(primitive, input_args)->shape());
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const int64_t input_num = 1;
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CheckAndConvertUtils::CheckInputArgs(input_args, kGreaterEqual, input_num, primitive->name());
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auto infer_type = LogicalNotInferType(primitive, input_args);
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auto infer_shape = LogicalNotInferShape(primitive, input_args);
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return abstract::MakeAbstract(infer_shape, infer_type);
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}
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}
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REGISTER_PRIMITIVE_C(kNameLogicalNot, LogicalNot);
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REGISTER_PRIMITIVE_EVAL_IMPL(LogicalNot, prim::kPrimLogicalNot, LogicalNotInfer, nullptr, true);
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} // namespace ops
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} // namespace ops
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} // namespace mindspore
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} // namespace mindspore
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@ -1,5 +1,5 @@
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/**
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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* Copyright 2020-2021 Huawei Technologies Co., Ltd
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*
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* you may not use this file except in compliance with the License.
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@ -37,9 +37,9 @@ class MS_CORE_API LogicalNot : public PrimitiveC {
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/// \brief Init. Refer to the parameters of Python API @ref mindspore.ops.LogicalNot for the inputs.
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/// \brief Init. Refer to the parameters of Python API @ref mindspore.ops.LogicalNot for the inputs.
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void Init() {}
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void Init() {}
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};
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};
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AbstractBasePtr LogicalNotInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
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AbstractBasePtr LogicalNotInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
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const std::vector<AbstractBasePtr> &input_args);
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const std::vector<AbstractBasePtr> &input_args);
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using kPrimLogicalNotPtr = std::shared_ptr<LogicalNot>;
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} // namespace ops
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} // namespace ops
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} // namespace mindspore
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} // namespace mindspore
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@ -203,6 +203,7 @@ from .less_equal import _less_equal_tbe
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from .less_equal_ds import _less_equal_ds_tbe
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from .less_equal_ds import _less_equal_ds_tbe
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from .logical_and import _logical_and_tbe
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from .logical_and import _logical_and_tbe
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from .logical_not import _logical_not_tbe
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from .logical_not import _logical_not_tbe
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from .logical_not_ds import _logical_not_ds_tbe
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from .logical_or import _logical_or_tbe
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from .logical_or import _logical_or_tbe
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from .logical_or_ds import _logical_or_ds_tbe
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from .logical_or_ds import _logical_or_ds_tbe
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from .reduce_max import _reduce_max_tbe
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from .reduce_max import _reduce_max_tbe
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@ -0,0 +1,37 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""LogicalNot op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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logical_not_op_info = TBERegOp("LogicalNot") \
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.fusion_type("ELEMWISE") \
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.async_flag(False) \
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.binfile_name("logical_not.so") \
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.compute_cost(10) \
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.kernel_name("logical_not") \
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.dynamic_shape(True) \
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.partial_flag(True) \
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.input(0, "x", False, "required", "all") \
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.output(0, "y", True, "required", "all") \
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.op_pattern("formatAgnostic") \
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.dtype_format(DataType.BOOL_None, DataType.BOOL_None) \
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.get_op_info()
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@op_info_register(logical_not_op_info)
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def _logical_not_ds_tbe():
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"""LogicalNot TBE register"""
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return
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"""
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"""
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class LogicalNot(PrimitiveWithInfer):
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class LogicalNot(Primitive):
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"""
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"""
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Computes the "logical NOT" of a tensor element-wise.
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Computes the "logical NOT" of a tensor element-wise.
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@ -3889,18 +3889,6 @@ class LogicalNot(PrimitiveWithInfer):
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"""Initialize LogicalNot"""
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"""Initialize LogicalNot"""
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self.init_prim_io_names(inputs=['x'], outputs=['output'])
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self.init_prim_io_names(inputs=['x'], outputs=['output'])
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def infer_shape(self, x_shape):
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return x_shape
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def infer_dtype(self, x_dtype):
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validator.check_tensor_dtype_valid("x", x_dtype, [mstype.bool_], self.name + " or '~' operator")
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return mstype.tensor_type(mstype.bool_)
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def infer_value(self, x):
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if x is not None:
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x = x.asnumpy()
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return Tensor(np.logical_not(x))
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return None
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class LogicalAnd(_LogicBinaryOp):
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class LogicalAnd(_LogicBinaryOp):
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context.set_context(mode=context.PYNATIVE_MODE)
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context.set_context(mode=context.PYNATIVE_MODE)
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with pytest.raises(TypeError) as err:
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with pytest.raises(TypeError) as err:
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net(input_x)
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net(input_x)
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assert "For 'LogicalNot or '~' operator', the type of 'x' should be Tensor[Bool], " \
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assert "For primitive[LogicalNot], the input argument[x] must be a type of { Tensor[Bool],}, " \
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"but got Tensor[Int32]" in str(err.value)
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"but got Int32." in str(err.value)
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context.set_context(mode=context.GRAPH_MODE)
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context.set_context(mode=context.GRAPH_MODE)
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with pytest.raises(TypeError) as err:
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with pytest.raises(TypeError) as err:
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net(input_x)
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net(input_x)
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assert "For 'LogicalNot or '~' operator', the type of 'x' should be Tensor[Bool], " \
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assert "For primitive[LogicalNot], the input argument[x] must be a type of { Tensor[Bool],}, " \
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"but got Tensor[Int32]" in str(err.value)
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"but got Int32." in str(err.value)
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