460 lines
18 KiB
C++
460 lines
18 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2021 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/activations.h"
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#include "gtest/gtest.h"
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#include "test_utils.h"
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TEST(Linear, shape_5_1_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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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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 = {-2.5, -0.1, 0, 0.55,
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std::numeric_limits<float>::infinity()};
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std::vector<float> golden = {-0.5, 1.9, 2, 2.55,
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std::numeric_limits<float>::infinity()};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Linear>(1, 2);
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Linear, shape_5_1_fp32_omit_b) {
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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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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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 = {-2.5, -0.1, 0, 0.55,
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std::numeric_limits<float>::infinity()};
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std::vector<float> golden = {-5.0, -0.2, 0, 1.1,
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std::numeric_limits<float>::infinity()};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Linear>(2);
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Gelu, shape_5_1_fp32_approximate) {
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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({5, 1});
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tim::vx::ShapeType out_shape({5, 1});
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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 = {-3, -1, 0, 1, 3};
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std::vector<float> golden = {-0.00363752, -0.15880796, 0, 0.841192,
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2.9963627};
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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::Gelu>(true);
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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-5f));
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}
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TEST(Gelu, shape_5_1_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({5, 1});
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tim::vx::ShapeType out_shape({5, 1});
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const float InputMin = -127, InputMax = 128, OutputMin = -127,
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OutputMax = 128;
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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<u_int8_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_float_data = {-3, -1, 0, 1, 3};
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std::vector<float> golden_float = {-0.00404951, -0.15865529, 0, 0.8413447,
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2.9959507};
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std::vector<uint8_t> input_data =
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Quantize<uint8_t>(in_float_data, 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::Gelu>(false);
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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_TRUE(ArraysMatch(golden, output, (uint8_t)1));
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}
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TEST(HardSigmoid, shape_5_1_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({20, 1});
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tim::vx::ShapeType out_shape({20, 1});
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std::vector<float> scalesInput = {0.00228914}; //scale
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std::vector<int32_t> zeroPointsInput = {128}; //zero point
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std::vector<float> scalesOutput = {0.005};
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std::vector<int32_t> zeroPointsOutput = {128};
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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<uint8_t> in_data = {65, 255, 140, 92, 142, 122, 117,
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167, 132, 117, 44, 99, 109, 96,
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216, 222, 135, 126, 113, 100};
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std::vector<uint8_t> golden_data = {222, 240, 229, 225, 229, 227, 227,
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232, 228, 227, 220, 225, 226, 225,
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236, 237, 229, 228, 227, 225};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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auto op = graph->CreateOperation<tim::vx::ops::HardSigmoid>(0.2, 0.5);
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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_data.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden_data, output, (uint8_t)1));
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}
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TEST(HardSigmoid, a_b_) {
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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({20, 1});
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tim::vx::ShapeType out_shape({20, 1});
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std::vector<float> scalesInput = {0.00228914}; //scale
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std::vector<int32_t> zeroPointsInput = {128}; //zero point
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std::vector<float> scalesOutput = {0.005};
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std::vector<int32_t> zeroPointsOutput = {128};
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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<uint8_t> in_data = {65, 255, 140, 92, 142, 122, 117,
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167, 132, 117, 44, 99, 109, 96,
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216, 222, 135, 126, 113, 100};
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std::vector<uint8_t> golden_data = {239, 255, 250, 243, 250, 247, 246,
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253, 249, 246, 236, 244, 245, 244,
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255, 255, 249, 248, 246, 244};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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auto op = graph->CreateOperation<tim::vx::ops::HardSigmoid>(0.3, 0.6);
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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_data.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden_data, output);
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}
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TEST(Elu, shape_5_1_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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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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 = {-2.5, -0.1, 0, 0.55, 99};
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std::vector<float> golden = {-0.917915, -0.0951626, 0, 0.55, 99};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Elu>();
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(Elu, shape_5_1_fp32_a) {
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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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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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 = {-2.5, -0.1, 0, 0.55, 99};
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std::vector<float> golden = {-0.458957, -0.0475813, 0, 0.55, 99};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Elu>(0.5);
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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#ifdef _VSI_NN_OP_SELU_H
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TEST(Selu, shape_2_2) {
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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({2, 2});
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tim::vx::ShapeType out_shape({2, 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 = {2, 1, 3, 10};
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std::vector<float> golden = {2.1014, 1.0507, 3.1521, 10.507};
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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::Selu>();
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(*op).BindInputs({in_tensor}).BindOutputs({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-5f));
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}
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#endif
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#ifdef _VSI_NN_OP_CELU_H
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TEST(Celu, shape_2_2) {
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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({2, 2});
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tim::vx::ShapeType out_shape({2, 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 = {-1, 0.71, 3, 10};
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std::vector<float> golden = {-0.69762, 0.71, 3, 10};
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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::Celu>(1.3);
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(*op).BindInputs({in_tensor}).BindOutputs({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-5f));
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}
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#endif
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TEST(Sign, shape_5_1_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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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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 = {-3.7, -1, 0, 0.5, 12};
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std::vector<float> golden = {-1, -1, 0, 1, 1};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Sign>();
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(SoftSign, shape_5_1_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 io_shape({5, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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|
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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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|
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std::vector<float> in_data = {-3.7, -1, 0, 0.5, 12};
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std::vector<float> golden = {-0.78723, -0.5, 0, 0.33333, 0.92308};
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|
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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|
|
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auto op = graph->CreateOperation<tim::vx::ops::SoftSign>();
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(*op).BindInputs({input_tensor}).BindOutputs({output_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(5, 0);
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|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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|
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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|
} |