zhaozhenlong
5 years ago
parent
7913f8361c
commit
9b4cce8208
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/**
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* Copyright 2020 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 "mindspore/core/utils/log_adapter.h"
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#include "common/common_test.h"
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#include "mindspore/lite/src/runtime/kernel/arm/fp32/l2_norm.h"
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#include "src/kernel_registry.h"
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#include "src/lite_kernel.h"
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using mindspore::schema::Format_NHWC;
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namespace mindspore {
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class TestL2NormFp32 : public mindspore::CommonTest {
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public:
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TestL2NormFp32() = default;
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void Init(const std::vector<int> &input_shape, const std::vector<int> &output_shape, float *input_data,
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float *output_data, const int axis_num, ActType activation_type, const int thread_num);
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void TearDown() override;
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public:
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float err_tol_ = 1e-5;
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lite::Tensor in_tensor_;
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lite::Tensor out_tensor_;
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std::vector<lite::Tensor *> inputs_{&in_tensor_};
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std::vector<lite::Tensor *> outputs_{&out_tensor_};
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L2NormParameter param_;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Resize};
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lite::Context ctx_ = lite::Context();
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kernel::KernelCreator creator_ = nullptr;
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kernel::LiteKernel *kernel_ = nullptr;
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};
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void TestL2NormFp32::TearDown() {
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in_tensor_.SetData(nullptr);
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out_tensor_.SetData(nullptr);
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}
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void TestL2NormFp32::Init(const std::vector<int> &input_shape, const std::vector<int> &output_shape, float *input_data,
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float *output_data, const int axis_num, ActType activation_type, const int thread_num) {
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in_tensor_.set_data_type(kNumberTypeFloat32);
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in_tensor_.SetFormat(Format_NHWC);
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in_tensor_.set_shape(input_shape);
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out_tensor_.set_data_type(kNumberTypeFloat32);
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out_tensor_.set_shape(output_shape);
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in_tensor_.SetData(input_data);
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out_tensor_.SetData(output_data);
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param_.axis_num_ = axis_num;
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if (axis_num == 1) {
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param_.axis_ = reinterpret_cast<int *>(malloc(sizeof(int)));
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param_.axis_[0] = -1;
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}
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param_.epsilon_ = 1e-6;
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param_.act_type_ = activation_type;
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desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_L2Norm};
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ctx_ = lite::Context();
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ctx_.thread_num_ = thread_num;
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creator_ = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator_, nullptr);
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kernel_ = creator_(inputs_, outputs_, reinterpret_cast<OpParameter *>(¶m_), &ctx_, desc, nullptr);
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ASSERT_NE(kernel_, nullptr);
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}
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// 2thread all axis no_activation
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TEST_F(TestL2NormFp32, Test1) {
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float input_data[18] = {-9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0,
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0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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float output_data[18] = {0};
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std::vector<int> input_shape = {1, 3, 2, 3};
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std::vector<int> output_shape = {1, 3, 2, 3};
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std::vector<float> expect = {-0.40699407, -0.3617725, -0.31655094, -0.27132937, -0.22610782, -0.18088625,
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-0.13566469, -0.09044313, -0.045221563, 0.0, 0.045221563, 0.09044313,
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0.13566469, 0.18088625, 0.22610782, 0.27132937, 0.31655094, 0.3617725};
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auto output_size = 18;
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int axis_num = 0;
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ActType act_type = ActType_No;
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int thread_num = 2;
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Init(input_shape, output_shape, input_data, output_data, axis_num, act_type, thread_num);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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CompareOutputData(output_data, expect.data(), output_size, err_tol_);
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}
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// 2thread all axis relu
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TEST_F(TestL2NormFp32, Test2) {
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float input_data[18] = {-9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0,
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0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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float output_data[18] = {0};
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std::vector<int> input_shape = {1, 3, 2, 3};
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std::vector<int> output_shape = {1, 3, 2, 3};
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std::vector<float> expect = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.045221563, 0.09044313,
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0.13566469, 0.18088625, 0.22610782, 0.27132937, 0.31655094, 0.3617725};
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auto output_size = 18;
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int axis_num = 0;
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ActType act_type = ActType_Relu;
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int thread_num = 2;
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Init(input_shape, output_shape, input_data, output_data, axis_num, act_type, thread_num);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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CompareOutputData(output_data, expect.data(), output_size, err_tol_);
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}
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// 4 thread trailing axis no activation
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TEST_F(TestL2NormFp32, Test3) {
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float input_data[18] = {-9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0,
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0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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float output_data[18] = {0};
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std::vector<int> input_shape = {1, 3, 2, 3};
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std::vector<int> output_shape = {1, 3, 2, 3};
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std::vector<float> expect = {-0.6461623, -0.57436645, -0.5025706, -0.6837635, -0.5698029, -0.45584232,
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-0.8017837, -0.5345225, -0.26726124, 0.0, 0.4472136, 0.8944272,
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0.42426407, 0.56568545, 0.7071068, 0.49153918, 0.57346237, 0.65538555};
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auto output_size = 18;
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int axis_num = 1;
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ActType act_type = ActType_No;
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int thread_num = 4;
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Init(input_shape, output_shape, input_data, output_data, axis_num, act_type, thread_num);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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CompareOutputData(output_data, expect.data(), output_size, err_tol_);
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}
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// 1 thread trailing axis no activation
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TEST_F(TestL2NormFp32, Test4) {
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float input_data[18] = {-9.0, -8.0, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0,
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0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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float output_data[18] = {0};
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std::vector<int> input_shape = {1, 3, 2, 3};
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std::vector<int> output_shape = {1, 3, 2, 3};
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std::vector<float> expect = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.4472136, 0.8944272,
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0.42426407, 0.56568545, 0.7071068, 0.49153918, 0.57346237, 0.65538555};
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auto output_size = 18;
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int axis_num = 1;
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ActType act_type = ActType_Relu6;
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int thread_num = 1;
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Init(input_shape, output_shape, input_data, output_data, axis_num, act_type, thread_num);
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auto ret = kernel_->Run();
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EXPECT_EQ(0, ret);
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CompareOutputData(output_data, expect.data(), output_size, err_tol_);
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}
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} // namespace mindspore
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