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addn unit test 

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
Chen Xin 2021-07-21 15:52:19 +08:00 committed by Kainan Cha
parent 3a0bc515a1
commit a09ffe8b98
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src/tim/vx/ops/addn_test.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/addn.h"
#include "tim/vx/types.h"
#include "src/tim/vx/test_utils.h"
#include "gtest/gtest.h"
TEST(AddN, shape_2_2_int32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({2, 2});
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor_x = graph->CreateTensor(input_spec);
auto input_tensor_y = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<int32_t> in_data_x = {
3, 5,
4, 8 };
std::vector<int32_t> in_data_y = {
1, 6,
2, 9 };
std::vector<int32_t> golden = {
4, 11,
6, 17 }; //correct answer
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::AddN>(2); //To refer to the AddN function definition to give the parameters
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<int32_t> output(4);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(AddN, shape_3_1_float32) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({3, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor_x = graph->CreateTensor(input_spec);
auto input_tensor_y = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data_x = {
3, 5, 7 };
std::vector<float> in_data_y = {
1, 6, 2 };
std::vector<float> golden = {
4, 11, 9 };
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(in_data_x.data(), in_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(in_data_y.data(), in_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::AddN>(2);
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(3);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(AddN, shape_2_2_uint8_QuantizedTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({2, 2});
tim::vx::ShapeType out_shape({2, 2});
float InputMin = -127, InputMax = 128, OutputMin = -127, OutputMax = 128;
std::pair<float, int32_t> scalesAndZp;
scalesAndZp = QuantizationParams<u_int8_t>(InputMin, InputMax);
std::vector<float> scalesInput = {scalesAndZp.first}; //scale
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
std::vector<float> scalesOutput = {scalesAndZp.first};
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
scalesInput, zeroPointsInput);
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
scalesOutput, zeroPointsOutput);
tim::vx::TensorSpec input_spec_x(tim::vx::DataType::UINT8, in_shape,
tim::vx::TensorAttribute::INPUT, quantInput);
tim::vx::TensorSpec input_spec_y(tim::vx::DataType::UINT8, in_shape,
tim::vx::TensorAttribute::INPUT, quantInput);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
tim::vx::TensorAttribute::OUTPUT, quantOutput);
auto input_tensor_x = graph->CreateTensor(input_spec_x);
auto input_tensor_y = graph->CreateTensor(input_spec_y);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_float_data_x = {
3.1, 5.1,
4.1, 8 };
std::vector<float> in_float_data_y = {
1.1, 6.1,
2.1, 9 };
std::vector<float> golden_float = {
4.2, 11.2,
6.2, 17 };
std::vector<u_int8_t> input_data_x =
Quantize<uint8_t>(in_float_data_x, scalesInput[0], zeroPointsInput[0]);//Quantification process
std::vector<u_int8_t> input_data_y =
Quantize<uint8_t>(in_float_data_y, scalesInput[0], zeroPointsInput[0]);
std::vector<u_int8_t> golden =
Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
EXPECT_TRUE(input_tensor_x->CopyDataToTensor(input_data_x.data(), input_data_x.size()*4));
EXPECT_TRUE(input_tensor_y->CopyDataToTensor(input_data_y.data(), input_data_y.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::AddN>(2);
(*op).BindInputs({input_tensor_x, input_tensor_y}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(4);
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}