455 lines
21 KiB
C++
455 lines
21 KiB
C++
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/****************************************************************************
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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/svdf.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(Svdf, shape_3_2_10_1_4_float) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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uint32_t input_size = 3, batches = 2, memory_size = 10, rank = 1, num_units = 4, spectrogram_length = num_units;
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tim::vx::ShapeType input_shape({input_size, batches});
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tim::vx::ShapeType state_in_shape({rank * num_units * memory_size, batches});
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tim::vx::ShapeType weights_feature_shape({input_size, rank * num_units});
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tim::vx::ShapeType weights_time_shape({memory_size, rank * num_units});
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tim::vx::ShapeType output_shape({num_units, batches});
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tim::vx::ShapeType state_out_shape({rank * num_units * memory_size, batches});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec state_in_spec(tim::vx::DataType::FLOAT32,
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state_in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_feature_spec(tim::vx::DataType::FLOAT32,
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weights_feature_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_time_spec(tim::vx::DataType::FLOAT32,
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weights_time_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec state_out_spec(tim::vx::DataType::FLOAT32,
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state_out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto state_in_tensor = graph->CreateTensor(state_in_spec);
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auto weights_feature_tensor = graph->CreateTensor(weights_feature_spec);
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auto weights_time_tensor = graph->CreateTensor(weights_time_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto state_out_tensor = graph->CreateTensor(state_out_spec);
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std::vector<float> in_data = {
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0.12609188, -0.46347019, -0.89598465,
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0.35867718, 0.36897406, 0.73463392,
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};
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std::vector<float> state_in;
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for(uint32_t m = 0; m < rank * num_units * memory_size * batches; m++)
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state_in.push_back(0);
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std::vector<float> weights_feature = {
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-0.31930989, -0.36118156, 0.0079667,
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0.37613347, 0.22197971, 0.12416199,
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0.27901134, 0.27557442, 0.3905206,
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-0.36137494, -0.06634006, -0.10640851};
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std::vector<float> weights_time = {
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-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
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0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
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0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
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-0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
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-0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
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0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
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-0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
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-0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657};
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std::vector<float> golden = {
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0.014899, -0.0517661, -0.143725, -0.00271883,
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-0.03004015, 0.09565311, 0.1587342, 0.00784263,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(state_in_tensor->CopyDataToTensor(
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state_in.data(), state_in.size() * sizeof(float)));
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EXPECT_TRUE(weights_feature_tensor->CopyDataToTensor(
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weights_feature.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(weights_time_tensor->CopyDataToTensor(
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weights_time.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Svdf>(rank, num_units, spectrogram_length);
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(*op).BindInputs({input_tensor, state_in_tensor, weights_feature_tensor, weights_time_tensor})
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.BindOutputs({output_tensor, state_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(output_tensor->CopyDataFromTensor(output.data()));
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// EXPECT_EQ(golden, output);
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EXPECT_TRUE(ArraysMatch(golden, output,1e-5f));
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}
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TEST(Svdf, shape_3_2_10_2_4_float) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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uint32_t input_size = 3, batches = 2, memory_size = 10, rank = 2, num_units = 4, spectrogram_length = num_units;
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tim::vx::ShapeType input_shape({input_size, batches});
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tim::vx::ShapeType state_in_shape({rank * num_units * memory_size, batches});
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tim::vx::ShapeType weights_feature_shape({input_size, rank * num_units});
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tim::vx::ShapeType weights_time_shape({memory_size, rank * num_units});
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tim::vx::ShapeType output_shape({num_units, batches});
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tim::vx::ShapeType state_out_shape({rank * num_units * memory_size, batches});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec state_in_spec(tim::vx::DataType::FLOAT32,
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state_in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_feature_spec(tim::vx::DataType::FLOAT32,
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weights_feature_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_time_spec(tim::vx::DataType::FLOAT32,
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weights_time_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec state_out_spec(tim::vx::DataType::FLOAT32,
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state_out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto state_in_tensor = graph->CreateTensor(state_in_spec);
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auto weights_feature_tensor = graph->CreateTensor(weights_feature_spec);
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auto weights_time_tensor = graph->CreateTensor(weights_time_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto state_out_tensor = graph->CreateTensor(state_out_spec);
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std::vector<float> in_data = {
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0.12609188, -0.46347019, -0.89598465,
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0.35867718, 0.36897406, 0.73463392,};
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std::vector<float> state_in;
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for(uint32_t m = 0; m < rank * num_units * memory_size * batches; m++)
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state_in.push_back(0);
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std::vector<float> weights_feature = {
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-0.31930989, 0.0079667, 0.39296314,
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0.37613347, 0.12416199, 0.15785322,
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0.27901134, 0.3905206, 0.21931258,
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-0.36137494, -0.10640851, 0.31053296,
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-0.36118156, -0.0976817, -0.36916667,
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0.22197971, 0.15294972, 0.38031587,
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0.27557442, 0.39635518, -0.21580373,
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-0.06634006, -0.02702999, 0.27072677
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};
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std::vector<float> weights_time = {
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-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
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0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
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0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
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-0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
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-0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
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0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
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-0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
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-0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657,
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-0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486,
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0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187,
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-0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589,
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0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836,
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-0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277,
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-0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214,
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0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326,
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0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763,
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};
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std::vector<float> golden = {
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-0.09623547, -0.10193135, 0.11083051, -0.0347917,
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0.1141196, 0.12965347, -0.12652366, 0.01007236,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(state_in_tensor->CopyDataToTensor(
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state_in.data(), state_in.size() * sizeof(float)));
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EXPECT_TRUE(weights_feature_tensor->CopyDataToTensor(
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weights_feature.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(weights_time_tensor->CopyDataToTensor(
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weights_time.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Svdf>(rank, num_units, spectrogram_length);
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(*op).BindInputs({input_tensor, state_in_tensor, weights_feature_tensor, weights_time_tensor})
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.BindOutputs({output_tensor, state_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(output_tensor->CopyDataFromTensor(output.data()));
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// EXPECT_EQ(golden, output);
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EXPECT_TRUE(ArraysMatch(golden, output,1e-5f));
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}
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TEST(Svdf, shape_3_2_10_3_4_float) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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uint32_t input_size = 3, batches = 2, memory_size = 10, rank = 3, num_units = 4, spectrogram_length = num_units;
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tim::vx::ShapeType input_shape({input_size, batches});
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tim::vx::ShapeType state_in_shape({rank * num_units * memory_size, batches});
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tim::vx::ShapeType weights_feature_shape({input_size, rank * num_units});
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tim::vx::ShapeType weights_time_shape({memory_size, rank * num_units});
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tim::vx::ShapeType output_shape({num_units, batches});
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tim::vx::ShapeType state_out_shape({rank * num_units * memory_size, batches});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec state_in_spec(tim::vx::DataType::FLOAT32,
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state_in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_feature_spec(tim::vx::DataType::FLOAT32,
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weights_feature_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weights_time_spec(tim::vx::DataType::FLOAT32,
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weights_time_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec state_out_spec(tim::vx::DataType::FLOAT32,
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state_out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto state_in_tensor = graph->CreateTensor(state_in_spec);
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auto weights_feature_tensor = graph->CreateTensor(weights_feature_spec);
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auto weights_time_tensor = graph->CreateTensor(weights_time_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto state_out_tensor = graph->CreateTensor(state_out_spec);
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std::vector<float> in_data = {
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0.12609188, -0.46347019, -0.89598465,
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0.35867718, 0.36897406, 0.73463392,};
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std::vector<float> state_in;
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for(uint32_t m = 0; m < rank * num_units * memory_size * batches; m++)
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state_in.push_back(0);
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std::vector<float> weights_feature = {
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-0.31930989, 0.0079667, 0.39296314,
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0.37613347, 0.12416199, 0.15785322,
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0.27901134, 0.3905206, 0.21931258,
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-0.36137494, -0.10640851, 0.31053296,
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-0.36118156, -0.0976817, -0.36916667,
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0.22197971, 0.15294972, 0.38031587,
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0.27557442, 0.39635518, -0.21580373,
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-0.06634006, -0.02702999, 0.27072677,
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-0.31930989, -0.36118156, 0.0079667,
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0.37613347, 0.22197971, 0.12416199,
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0.27901134, 0.27557442, 0.3905206,
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-0.36137494, -0.06634006, -0.10640851
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};
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std::vector<float> weights_time = {
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-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
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0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
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0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
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-0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
|
||
|
|
|
||
|
|
-0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
|
||
|
|
0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
|
||
|
|
|
||
|
|
-0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
|
||
|
|
-0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657,
|
||
|
|
|
||
|
|
-0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486,
|
||
|
|
0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187,
|
||
|
|
|
||
|
|
-0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589,
|
||
|
|
0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836,
|
||
|
|
|
||
|
|
-0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277,
|
||
|
|
-0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214,
|
||
|
|
|
||
|
|
0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326,
|
||
|
|
0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763,
|
||
|
|
|
||
|
|
0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
|
||
|
|
-0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
|
||
|
|
|
||
|
|
-0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
|
||
|
|
0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
|
||
|
|
|
||
|
|
-0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
|
||
|
|
-0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657,
|
||
|
|
|
||
|
|
-0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486,
|
||
|
|
0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187,
|
||
|
|
};
|
||
|
|
|
||
|
|
std::vector<float> golden = {
|
||
|
|
-0.207435, 0.120099, 0.00247115, -0.0325705,
|
||
|
|
0.245774, -0.128524, -0.065059, 0.0644025,
|
||
|
|
};
|
||
|
|
|
||
|
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(
|
||
|
|
in_data.data(), in_data.size() * sizeof(float)));
|
||
|
|
EXPECT_TRUE(state_in_tensor->CopyDataToTensor(
|
||
|
|
state_in.data(), state_in.size() * sizeof(float)));
|
||
|
|
EXPECT_TRUE(weights_feature_tensor->CopyDataToTensor(
|
||
|
|
weights_feature.data(), in_data.size() * sizeof(float)));
|
||
|
|
EXPECT_TRUE(weights_time_tensor->CopyDataToTensor(
|
||
|
|
weights_time.data(), in_data.size() * sizeof(float)));
|
||
|
|
|
||
|
|
auto op = graph->CreateOperation<tim::vx::ops::Svdf>(rank, num_units, spectrogram_length);
|
||
|
|
(*op).BindInputs({input_tensor, state_in_tensor, weights_feature_tensor, weights_time_tensor})
|
||
|
|
.BindOutputs({output_tensor, state_out_tensor});
|
||
|
|
|
||
|
|
EXPECT_TRUE(graph->Compile());
|
||
|
|
EXPECT_TRUE(graph->Run());
|
||
|
|
|
||
|
|
std::vector<float> output(golden.size());
|
||
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||
|
|
// EXPECT_EQ(golden, output);
|
||
|
|
EXPECT_TRUE(ArraysMatch(golden, output,1e-5f));
|
||
|
|
}
|
||
|
|
|
||
|
|
// TEST(Svdf, shape_3_2_10_1_4_uint8) {
|
||
|
|
// auto ctx = tim::vx::Context::Create();
|
||
|
|
// auto graph = ctx->CreateGraph();
|
||
|
|
|
||
|
|
// const float InputMin = -127, InputMax = 128, OutputMin = -127, OutputMax = 128;
|
||
|
|
// uint32_t input_size = 3, batches = 2, memory_size = 10, rank = 1, num_units = 4, spectrogram_length = num_units;
|
||
|
|
|
||
|
|
// std::pair<float, int32_t> scalesAndZp;
|
||
|
|
// scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
|
||
|
|
// std::vector<float> scalesInput = {scalesAndZp.first}; //scale
|
||
|
|
// std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
|
||
|
|
// scalesAndZp = QuantizationParams<uint8_t>(OutputMin, OutputMax);
|
||
|
|
// std::vector<float> scalesOutput = {scalesAndZp.first};
|
||
|
|
// std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||
|
|
|
||
|
|
// tim::vx::ShapeType input_shape({input_size, batches});
|
||
|
|
// tim::vx::ShapeType state_in_shape({rank * num_units * memory_size, batches});
|
||
|
|
// tim::vx::ShapeType weights_feature_shape({input_size, rank * num_units});
|
||
|
|
// tim::vx::ShapeType weights_time_shape({memory_size, rank * num_units});
|
||
|
|
// tim::vx::ShapeType output_shape({num_units, batches});
|
||
|
|
// tim::vx::ShapeType state_out_shape({rank * num_units * memory_size, batches});
|
||
|
|
|
||
|
|
// 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(tim::vx::DataType::UINT8,
|
||
|
|
// input_shape, tim::vx::TensorAttribute::INPUT, quantInput);
|
||
|
|
// tim::vx::TensorSpec state_in_spec(tim::vx::DataType::UINT8,
|
||
|
|
// state_in_shape, tim::vx::TensorAttribute::INPUT, quantInput);
|
||
|
|
// tim::vx::TensorSpec weights_feature_spec(tim::vx::DataType::UINT8,
|
||
|
|
// weights_feature_shape, tim::vx::TensorAttribute::INPUT, quantInput);
|
||
|
|
// tim::vx::TensorSpec weights_time_spec(tim::vx::DataType::UINT8,
|
||
|
|
// weights_time_shape, tim::vx::TensorAttribute::INPUT, quantInput);
|
||
|
|
// tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
|
||
|
|
// output_shape, tim::vx::TensorAttribute::OUTPUT, quantOutput);
|
||
|
|
// tim::vx::TensorSpec state_out_spec(tim::vx::DataType::UINT8,
|
||
|
|
// state_out_shape, tim::vx::TensorAttribute::OUTPUT, quantOutput);
|
||
|
|
|
||
|
|
// auto input_tensor = graph->CreateTensor(input_spec);
|
||
|
|
// auto state_in_tensor = graph->CreateTensor(state_in_spec);
|
||
|
|
// auto weights_feature_tensor = graph->CreateTensor(weights_feature_spec);
|
||
|
|
// auto weights_time_tensor = graph->CreateTensor(weights_time_spec);
|
||
|
|
// auto output_tensor = graph->CreateTensor(output_spec);
|
||
|
|
// auto state_out_tensor = graph->CreateTensor(state_out_spec);
|
||
|
|
|
||
|
|
// std::vector<float> in_data_float = {
|
||
|
|
// 0.12609188, -0.46347019, -0.89598465,
|
||
|
|
// 0.35867718, 0.36897406, 0.73463392,
|
||
|
|
// };
|
||
|
|
|
||
|
|
// std::vector<uint8_t> state_in_uint8;
|
||
|
|
// for(uint32_t m = 0; m < rank * num_units * memory_size * batches; m++)
|
||
|
|
// state_in_uint8.push_back(0);
|
||
|
|
|
||
|
|
// std::vector<float> weights_feature_float = {
|
||
|
|
// -0.31930989, -0.36118156, 0.0079667,
|
||
|
|
// 0.37613347, 0.22197971, 0.12416199,
|
||
|
|
// 0.27901134, 0.27557442, 0.3905206,
|
||
|
|
// -0.36137494, -0.06634006, -0.10640851};
|
||
|
|
|
||
|
|
// std::vector<float> weights_time_float = {
|
||
|
|
// -0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156,
|
||
|
|
// 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199,
|
||
|
|
|
||
|
|
// 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518,
|
||
|
|
// -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296,
|
||
|
|
|
||
|
|
// -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236,
|
||
|
|
// 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846,
|
||
|
|
|
||
|
|
// -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166,
|
||
|
|
// -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657};
|
||
|
|
|
||
|
|
// std::vector<float> golden_float = {
|
||
|
|
// 0.014899, -0.0517661, -0.143725, -0.00271883,
|
||
|
|
// -0.03004015, 0.09565311, 0.1587342, 0.00784263,
|
||
|
|
// };
|
||
|
|
|
||
|
|
// std::vector<uint8_t> in_data_uint8 =
|
||
|
|
// Quantize<uint8_t>(in_data_float, scalesInput[0], zeroPointsInput[0]); //Quantification process
|
||
|
|
// std::vector<uint8_t> weights_feature_uint8 =
|
||
|
|
// Quantize<uint8_t>(weights_feature_float, scalesInput[0], zeroPointsInput[0]);
|
||
|
|
// std::vector<uint8_t> weights_time_uint8 =
|
||
|
|
// Quantize<uint8_t>(weights_time_float, scalesInput[0], zeroPointsInput[0]);
|
||
|
|
// std::vector<uint8_t> golden_uint8 =
|
||
|
|
// Quantize<uint8_t>(golden_float, scalesInput[0], zeroPointsInput[0]);
|
||
|
|
|
||
|
|
// EXPECT_TRUE(input_tensor->CopyDataToTensor(
|
||
|
|
// in_data_uint8.data(), in_data_uint8.size() * sizeof(uint8_t)));
|
||
|
|
// EXPECT_TRUE(state_in_tensor->CopyDataToTensor(
|
||
|
|
// state_in_uint8.data(), state_in_uint8.size() * sizeof(uint8_t)));
|
||
|
|
// EXPECT_TRUE(weights_feature_tensor->CopyDataToTensor(
|
||
|
|
// weights_feature_uint8.data(), weights_feature_uint8.size() * sizeof(uint8_t)));
|
||
|
|
// EXPECT_TRUE(weights_time_tensor->CopyDataToTensor(
|
||
|
|
// weights_time_uint8.data(), weights_time_uint8.size() * sizeof(uint8_t)));
|
||
|
|
|
||
|
|
// auto op = graph->CreateOperation<tim::vx::ops::Svdf>(rank, num_units, spectrogram_length);
|
||
|
|
// (*op).BindInputs({input_tensor, state_in_tensor, weights_feature_tensor, weights_time_tensor})
|
||
|
|
// .BindOutputs({output_tensor, state_out_tensor});
|
||
|
|
|
||
|
|
// EXPECT_TRUE(graph->Compile());
|
||
|
|
// EXPECT_TRUE(graph->Run());
|
||
|
|
|
||
|
|
// std::vector<uint8_t> output_uint8(golden_uint8.size());
|
||
|
|
// EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_uint8.data()));
|
||
|
|
// EXPECT_EQ(golden_uint8, output_uint8);
|
||
|
|
// // EXPECT_TRUE(ArraysMatch(golden, output,1e-5f));
|
||
|
|
// }
|