pull/4140/head
liuwenhao4
5 years ago
parent
57b3052c1e
commit
e6109bb759
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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 <iostream>
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#include "utils/log_adapter.h"
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#include "common/common_test.h"
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#include "mindspore/lite/src/runtime/kernel/arm/nnacl/concat_parameter.h"
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#include "mindspore/lite/src/kernel_registry.h"
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#include "mindspore/lite/src/lite_kernel.h"
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#include "mindspore/lite/src/ir/tensor.h"
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namespace mindspore {
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class TestConcatInt8 : public mindspore::Common {
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public:
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TestConcatInt8() {}
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};
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TEST_F(TestConcatInt8, Concat1_axis0) {
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std::vector<int8_t> input1 = {1, 2, 3, 4, 5, 6};
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std::vector<int> shape1 = {3, 2};
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std::vector<int8_t> input2 = {7, 8, 9, 10, 11, 12};
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std::vector<int> shape2 = {3, 2};
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std::vector<int8_t *> input(2, nullptr);
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input[0] = input1.data();
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input[1] = input2.data();
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int8_t output[12];
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std::vector<int> output_shape = {6, 2};
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lite::tensor::QuantArg input_quant_arg;
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input_quant_arg.scale = 1.0;
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input_quant_arg.zeroPoint = 0;
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lite::tensor::QuantArg output_quant_arg;
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output_quant_arg.scale = 1.0;
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output_quant_arg.zeroPoint = 0;
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lite::tensor::Tensor *input_tensor1 = new lite::tensor::Tensor;
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TypeId tid_int8 = kNumberTypeInt8;
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input_tensor1->SetData(input1.data());
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input_tensor1->set_shape(shape1);
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input_tensor1->AddQuantParam(input_quant_arg);
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input_tensor1->set_data_type(tid_int8);
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lite::tensor::Tensor *input_tensor2 = new lite::tensor::Tensor;
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input_tensor2->SetData(input2.data());
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input_tensor2->set_shape(shape2);
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input_tensor2->AddQuantParam(input_quant_arg);
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input_tensor2->set_data_type(tid_int8);
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std::vector<lite::tensor::Tensor *> inputs_tensor(2);
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inputs_tensor[0] = input_tensor1;
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inputs_tensor[1] = input_tensor2;
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std::vector<lite::tensor::Tensor *> outputs_tensor(1);
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lite::tensor::Tensor *output0_tensor = new lite::tensor::Tensor;
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output0_tensor->SetData(output);
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output0_tensor->set_shape(output_shape);
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output0_tensor->AddQuantParam(output_quant_arg);
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output0_tensor->set_data_type(tid_int8);
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outputs_tensor[0] = output0_tensor;
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ConcatParameter op_param;
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op_param.op_parameter_.type_ = schema::PrimitiveType_Concat;
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op_param.axis_ = 0;
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lite::Context *ctx = new lite::Context;
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ctx->thread_num_ = 1;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Concat};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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kernel::LiteKernel *kernel =
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creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), ctx, desc);
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ASSERT_NE(kernel, nullptr);
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auto output_tensor_shape = output0_tensor->shape();
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ASSERT_EQ(output_tensor_shape, output_shape);
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kernel->Run();
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std::vector<int8_t> except_result = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
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PrintData("output data", output, input1.size() + input2.size());
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CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001);
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input_tensor1->SetData(nullptr);
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input_tensor2->SetData(nullptr);
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output0_tensor->SetData(nullptr);
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delete input_tensor1;
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delete input_tensor2;
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delete output0_tensor;
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delete ctx;
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}
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TEST_F(TestConcatInt8, Concat1_axis1_thread2) {
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std::vector<int8_t> input1 = {10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25};
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std::vector<int> shape1 = {2, 3, 2};
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std::vector<int8_t> input2 = {30, 31, 32, 33};
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std::vector<int> shape2 = {2, 1, 2};
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std::vector<int8_t *> input(2, nullptr);
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input[0] = input1.data();
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input[1] = input2.data();
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int8_t output[16];
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std::vector<int> output_shape = {2, 4, 2};
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lite::tensor::QuantArg input_quant_arg;
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input_quant_arg.scale = 1.0;
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input_quant_arg.zeroPoint = 0;
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lite::tensor::QuantArg output_quant_arg;
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output_quant_arg.scale = 1.0;
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output_quant_arg.zeroPoint = 0;
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lite::tensor::Tensor *input_tensor1 = new lite::tensor::Tensor;
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TypeId tid_int8 = kNumberTypeInt8;
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input_tensor1->SetData(input1.data());
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input_tensor1->set_shape(shape1);
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input_tensor1->AddQuantParam(input_quant_arg);
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input_tensor1->set_data_type(tid_int8);
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lite::tensor::Tensor *input_tensor2 = new lite::tensor::Tensor;
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input_tensor2->SetData(input2.data());
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input_tensor2->set_shape(shape2);
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input_tensor2->AddQuantParam(input_quant_arg);
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input_tensor2->set_data_type(tid_int8);
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std::vector<lite::tensor::Tensor *> inputs_tensor(2);
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inputs_tensor[0] = input_tensor1;
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inputs_tensor[1] = input_tensor2;
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std::vector<lite::tensor::Tensor *> outputs_tensor(1);
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lite::tensor::Tensor *output0_tensor = new lite::tensor::Tensor;
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output0_tensor->SetData(output);
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output0_tensor->set_shape(output_shape);
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output0_tensor->AddQuantParam(output_quant_arg);
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output0_tensor->set_data_type(tid_int8);
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outputs_tensor[0] = output0_tensor;
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ConcatParameter op_param;
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op_param.op_parameter_.type_ = schema::PrimitiveType_Concat;
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op_param.axis_ = 1;
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lite::Context *ctx = new lite::Context;
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ctx->thread_num_ = 2;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Concat};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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kernel::LiteKernel *kernel =
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creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), ctx, desc);
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ASSERT_NE(kernel, nullptr);
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auto output_tensor_shape = output0_tensor->shape();
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ASSERT_EQ(output_tensor_shape, output_shape);
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kernel->Run();
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std::vector<int8_t> except_result = {10, 11, 12, 13, 14, 15, 30, 31, 20, 21, 22, 23, 24, 25, 32, 33};
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PrintData("output data", output, input1.size() + input2.size());
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CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001);
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input_tensor1->SetData(nullptr);
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input_tensor2->SetData(nullptr);
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output0_tensor->SetData(nullptr);
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delete input_tensor1;
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delete input_tensor2;
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delete output0_tensor;
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delete ctx;
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}
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TEST_F(TestConcatInt8, Concat1_axis1_thread2_quant1) {
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std::vector<int8_t> input1 = {10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25};
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std::vector<int> shape1 = {2, 3, 2};
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std::vector<int8_t> input2 = {30, 31, 32, 33};
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std::vector<int> shape2 = {2, 1, 2};
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std::vector<int8_t *> input(2, nullptr);
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input[0] = input1.data();
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input[1] = input2.data();
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int8_t output[16];
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std::vector<int> output_shape = {2, 4, 2};
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lite::tensor::QuantArg input_quant_arg;
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input_quant_arg.scale = 1.0;
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input_quant_arg.zeroPoint = 0;
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lite::tensor::QuantArg output_quant_arg;
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output_quant_arg.scale = 2.0;
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output_quant_arg.zeroPoint = 0;
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lite::tensor::Tensor *input_tensor1 = new lite::tensor::Tensor;
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TypeId tid_int8 = kNumberTypeInt8;
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input_tensor1->SetData(input1.data());
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input_tensor1->set_shape(shape1);
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input_tensor1->AddQuantParam(input_quant_arg);
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input_tensor1->set_data_type(tid_int8);
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lite::tensor::Tensor *input_tensor2 = new lite::tensor::Tensor;
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input_tensor2->SetData(input2.data());
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input_tensor2->set_shape(shape2);
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input_tensor2->AddQuantParam(input_quant_arg);
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input_tensor2->set_data_type(tid_int8);
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std::vector<lite::tensor::Tensor *> inputs_tensor(2);
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inputs_tensor[0] = input_tensor1;
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inputs_tensor[1] = input_tensor2;
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std::vector<lite::tensor::Tensor *> outputs_tensor(1);
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lite::tensor::Tensor *output0_tensor = new lite::tensor::Tensor;
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output0_tensor->SetData(output);
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output0_tensor->set_shape(output_shape);
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output0_tensor->AddQuantParam(output_quant_arg);
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output0_tensor->set_data_type(tid_int8);
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outputs_tensor[0] = output0_tensor;
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ConcatParameter op_param;
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op_param.op_parameter_.type_ = schema::PrimitiveType_Concat;
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op_param.axis_ = 1;
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lite::Context *ctx = new lite::Context;
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ctx->thread_num_ = 2;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Concat};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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kernel::LiteKernel *kernel =
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creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), ctx, desc);
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ASSERT_NE(kernel, nullptr);
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auto output_tensor_shape = output0_tensor->shape();
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ASSERT_EQ(output_tensor_shape, output_shape);
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kernel->Run();
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std::vector<int8_t> except_result = {5, 6, 6, 7, 7, 8, 15, 16, 10, 11, 11, 12, 12, 13, 16, 17};
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PrintData("output data", output, input1.size() + input2.size());
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CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001);
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input_tensor1->SetData(nullptr);
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input_tensor2->SetData(nullptr);
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output0_tensor->SetData(nullptr);
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delete input_tensor1;
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delete input_tensor2;
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delete output0_tensor;
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delete ctx;
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}
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} // namespace mindspore
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,150 @@
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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 <iostream>
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#include "utils/log_adapter.h"
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#include "common/common_test.h"
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#include "mindspore/lite/src/runtime/kernel/arm/nnacl/reshape_parameter.h"
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#include "mindspore/lite/src/kernel_registry.h"
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#include "mindspore/lite/src/lite_kernel.h"
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#include "mindspore/lite/src/ir/tensor.h"
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namespace mindspore {
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class TestReshapeInt8 : public mindspore::Common {
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public:
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TestReshapeInt8() {}
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};
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TEST_F(TestReshapeInt8, reshape_quant0) {
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std::vector<int8_t> input1 = {10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25};
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std::vector<int> shape1 = {2, 3, 2};
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std::vector<int8_t *> input(1, nullptr);
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input[0] = input1.data();
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int8_t output[12];
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std::vector<int> output_shape = {2, 6};
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lite::tensor::QuantArg input_quant_arg;
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input_quant_arg.scale = 1.0;
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input_quant_arg.zeroPoint = 0;
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lite::tensor::QuantArg output_quant_arg;
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output_quant_arg.scale = 1.0;
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output_quant_arg.zeroPoint = 0;
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lite::tensor::Tensor *input_tensor1 = new lite::tensor::Tensor;
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TypeId tid_int8 = kNumberTypeInt8;
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input_tensor1->SetData(input1.data());
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input_tensor1->set_shape(shape1);
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input_tensor1->AddQuantParam(input_quant_arg);
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input_tensor1->set_data_type(tid_int8);
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std::vector<lite::tensor::Tensor *> inputs_tensor(1);
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inputs_tensor[0] = input_tensor1;
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std::vector<lite::tensor::Tensor *> outputs_tensor(1);
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lite::tensor::Tensor *output0_tensor = new lite::tensor::Tensor;
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output0_tensor->SetData(output);
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output0_tensor->set_shape(output_shape);
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output0_tensor->AddQuantParam(output_quant_arg);
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output0_tensor->set_data_type(tid_int8);
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outputs_tensor[0] = output0_tensor;
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ReshapeParameter op_param;
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op_param.op_parameter_.type_ = schema::PrimitiveType_Reshape;
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lite::Context *ctx = new lite::Context;
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ctx->thread_num_ = 1;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Reshape};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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kernel::LiteKernel *kernel =
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creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), ctx, desc);
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ASSERT_NE(kernel, nullptr);
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auto output_tensor_shape = output0_tensor->shape();
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ASSERT_EQ(output_tensor_shape, output_shape);
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kernel->Run();
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std::vector<int8_t> except_result = {10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25};
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PrintData("output data", output, input1.size());
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PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size());
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CompareOutputData(output, except_result.data(), input1.size(), 0.000001);
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input_tensor1->SetData(nullptr);
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output0_tensor->SetData(nullptr);
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delete input_tensor1;
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delete output0_tensor;
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delete ctx;
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}
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TEST_F(TestReshapeInt8, reshape_quant1_thread2) {
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std::vector<int8_t> input1 = {10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25};
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std::vector<int> shape1 = {2, 3, 2};
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std::vector<int8_t *> input(1, nullptr);
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input[0] = input1.data();
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int8_t output[12];
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std::vector<int> output_shape = {2, 6};
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lite::tensor::QuantArg input_quant_arg;
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input_quant_arg.scale = 1.0;
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input_quant_arg.zeroPoint = 0;
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lite::tensor::QuantArg output_quant_arg;
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output_quant_arg.scale = 2.0;
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output_quant_arg.zeroPoint = 1;
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lite::tensor::Tensor *input_tensor1 = new lite::tensor::Tensor;
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TypeId tid_int8 = kNumberTypeInt8;
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input_tensor1->SetData(input1.data());
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input_tensor1->set_shape(shape1);
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input_tensor1->AddQuantParam(input_quant_arg);
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input_tensor1->set_data_type(tid_int8);
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std::vector<lite::tensor::Tensor *> inputs_tensor(1);
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inputs_tensor[0] = input_tensor1;
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std::vector<lite::tensor::Tensor *> outputs_tensor(1);
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lite::tensor::Tensor *output0_tensor = new lite::tensor::Tensor;
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output0_tensor->SetData(output);
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output0_tensor->set_shape(output_shape);
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output0_tensor->AddQuantParam(output_quant_arg);
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output0_tensor->set_data_type(tid_int8);
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outputs_tensor[0] = output0_tensor;
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ReshapeParameter op_param;
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op_param.op_parameter_.type_ = schema::PrimitiveType_Reshape;
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lite::Context *ctx = new lite::Context;
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ctx->thread_num_ = 2;
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kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_Reshape};
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auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
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ASSERT_NE(creator, nullptr);
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kernel::LiteKernel *kernel =
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creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), ctx, desc);
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ASSERT_NE(kernel, nullptr);
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auto output_tensor_shape = output0_tensor->shape();
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ASSERT_EQ(output_tensor_shape, output_shape);
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kernel->Run();
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std::vector<int8_t> except_result = {6, 7, 7, 8, 8, 9, 11, 12, 12, 13, 13, 14};
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PrintData("output data", output, input1.size());
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PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size());
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CompareOutputData(output, except_result.data(), input1.size(), 0.000001);
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input_tensor1->SetData(nullptr);
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output0_tensor->SetData(nullptr);
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delete input_tensor1;
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delete output0_tensor;
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delete ctx;
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}
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} // namespace mindspore
|
||||
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Reference in new issue