124 lines
4.9 KiB
C++
124 lines
4.9 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020-2023 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/erf.h"
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#include "gtest/gtest.h"
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#include "test_utils.h"
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TEST(Erf, shape_3_2_fp32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({3, 2});
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tim::vx::ShapeType out_shape({3, 2});
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tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32, in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32, out_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto in_tensor = graph->CreateTensor(in_spec);
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auto out_tensor = graph->CreateTensor(out_spec);
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std::vector<float> in_data = {
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1, 2, 3,
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0,-1,-2};
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std::vector<float> golden = {
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0.8427007, 0.9953223, 0.999978,
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0 ,-0.8427007,-0.9953223};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Erf>();
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-2f));
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}
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TEST(Erf, shape_3_2_uint8_Quantized) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({3, 2});
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tim::vx::ShapeType out_shape({3, 2});
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const float InputMin = -128, InputMax = 127, OutputMin = -128,
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OutputMax = 127;
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std::pair<float, int32_t> scalesAndZp;
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scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
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std::vector<float> scalesInput = {scalesAndZp.first}; //scale
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std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
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scalesAndZp = QuantizationParams<uint8_t>(OutputMin, OutputMax);
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std::vector<float> scalesOutput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
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tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
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scalesInput, zeroPointsInput);
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tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
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scalesOutput, zeroPointsOutput);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape,
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tim::vx::TensorAttribute::INPUT, quantInput);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
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tim::vx::TensorAttribute::OUTPUT,
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quantOutput);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data_float = {
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1, 2, 3,
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0,-1,-2};
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std::vector<float> golden_float = {
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0.8427007, 0.9953223, 0.999978,
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0 ,-0.8427007,-0.9953223};
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std::vector<uint8_t> input_data =
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Quantize<uint8_t>(in_data_float, scalesInput[0],
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zeroPointsInput[0]); //Quantification process
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std::vector<uint8_t> golden =
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Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Erf>();
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(*op).BindInput(input_tensor).BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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} |