forked from mindspore-Ecosystem/mindspore
126 lines
4.9 KiB
C++
126 lines
4.9 KiB
C++
/**
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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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#include <vector>
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#include <memory>
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#include "common/common_test.h"
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#include "ops/fusion/full_connection.h"
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#include "ir/dtype/type.h"
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#include "ir/value.h"
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#include "abstract/dshape.h"
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#include "utils/tensor_construct_utils.h"
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namespace mindspore {
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namespace ops {
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class TestFullConnection : public UT::Common {
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public:
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TestFullConnection() {}
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void SetUp() {}
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void TearDown() {}
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};
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TEST_F(TestFullConnection, test_full_connection_1) {
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auto op = std::make_shared<FullConnection>();
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bool has_bias = false;
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bool use_axis = false;
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int64_t axis = 3;
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op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
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auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract()});
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MS_EXCEPTION_IF_NULL(abstract);
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EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
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auto shape_ptr = abstract->BuildShape();
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MS_EXCEPTION_IF_NULL(shape_ptr);
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EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
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auto shape = shape_ptr->cast<abstract::ShapePtr>();
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MS_EXCEPTION_IF_NULL(shape);
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auto shape_vec = shape->shape();
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auto type = abstract->BuildType();
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MS_EXCEPTION_IF_NULL(type);
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EXPECT_EQ(type->isa<TensorType>(), true);
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auto tensor_type = type->cast<TensorTypePtr>();
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MS_EXCEPTION_IF_NULL(tensor_type);
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auto data_type = tensor_type->element();
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MS_EXCEPTION_IF_NULL(data_type);
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EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
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EXPECT_EQ(shape_vec.size(), 2);
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EXPECT_EQ(shape_vec[0], 2);
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EXPECT_EQ(shape_vec[1], 2);
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}
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TEST_F(TestFullConnection, test_full_connection_2) {
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auto op = std::make_shared<FullConnection>();
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bool has_bias = true;
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bool use_axis = false;
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int64_t axis = 1;
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op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
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auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto tensor_3 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 2});
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auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract(), tensor_3->ToAbstract()});
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MS_EXCEPTION_IF_NULL(abstract);
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EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
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auto shape_ptr = abstract->BuildShape();
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MS_EXCEPTION_IF_NULL(shape_ptr);
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EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
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auto shape = shape_ptr->cast<abstract::ShapePtr>();
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MS_EXCEPTION_IF_NULL(shape);
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auto shape_vec = shape->shape();
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auto type = abstract->BuildType();
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MS_EXCEPTION_IF_NULL(type);
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EXPECT_EQ(type->isa<TensorType>(), true);
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auto tensor_type = type->cast<TensorTypePtr>();
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MS_EXCEPTION_IF_NULL(tensor_type);
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auto data_type = tensor_type->element();
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MS_EXCEPTION_IF_NULL(data_type);
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EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
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EXPECT_EQ(shape_vec.size(), 2);
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EXPECT_EQ(shape_vec[0], 2);
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EXPECT_EQ(shape_vec[1], 2);
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}
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TEST_F(TestFullConnection, test_full_connection_3) {
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auto op = std::make_shared<FullConnection>();
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bool has_bias = false;
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bool use_axis = true;
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int64_t axis = 1;
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op->Init(has_bias, axis, use_axis, NO_ACTIVATION);
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auto tensor_1 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto tensor_2 = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat16, std::vector<int64_t>{2, 3});
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auto abstract = op->Infer({tensor_1->ToAbstract(), tensor_2->ToAbstract()});
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MS_EXCEPTION_IF_NULL(abstract);
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EXPECT_EQ(abstract->isa<abstract::AbstractTensor>(), true);
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auto shape_ptr = abstract->BuildShape();
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MS_EXCEPTION_IF_NULL(shape_ptr);
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EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
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auto shape = shape_ptr->cast<abstract::ShapePtr>();
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MS_EXCEPTION_IF_NULL(shape);
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auto shape_vec = shape->shape();
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auto type = abstract->BuildType();
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MS_EXCEPTION_IF_NULL(type);
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EXPECT_EQ(type->isa<TensorType>(), true);
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auto tensor_type = type->cast<TensorTypePtr>();
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MS_EXCEPTION_IF_NULL(tensor_type);
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auto data_type = tensor_type->element();
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MS_EXCEPTION_IF_NULL(data_type);
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EXPECT_EQ(data_type->type_id(), kNumberTypeFloat16);
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EXPECT_EQ(shape_vec.size(), 2);
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EXPECT_EQ(shape_vec[0], 2);
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EXPECT_EQ(shape_vec[1], 2);
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}
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} // namespace ops
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} // namespace mindspore
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