264 lines
11 KiB
C++
264 lines
11 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/simple_operations.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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#include <cstdlib>
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TEST(Floor, 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,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, 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, std::numeric_limits<float>::infinity() };
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std::vector<float> golden = {-3, -1, 0, 0, std::numeric_limits<float>::infinity() };
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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auto add = graph->CreateOperation<tim::vx::ops::Floor>();
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(*add).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(Round, shape_15_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({15, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, 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 = { 0.1, 0.5, 0.9, 1.2, 1.5,
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1.8, 2.3, 2.5, 2.7, -1.1,
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-1.5, -1.9, -2.2, -2.5, -2.8 };
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std::vector<float> golden = {0., 0., 1., 1., 2.,
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2., 2., 2., 3., -1.,
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-2., -2., -2., -2., -3. };
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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auto add = graph->CreateOperation<tim::vx::ops::Round>();
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(*add).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(15, 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(Ceil, 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,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, 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, std::numeric_limits<float>::infinity() };
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std::vector<float> golden = {-2, 0, 0, 1, std::numeric_limits<float>::infinity() };
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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auto add = graph->CreateOperation<tim::vx::ops::Ceil>();
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(*add).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(Cast, shape_5_1_fp32_to_int32) {
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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,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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io_shape, 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, std::numeric_limits<float>::infinity() };
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std::vector<int> golden = {-2, 0, 0, 0, std::numeric_limits<int>::max()};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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auto add = graph->CreateOperation<tim::vx::ops::Cast>();
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(*add).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<int> 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(DataConvert, quantize_shape_2_3_fp32_to_asym_u8) {
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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({2, 3});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 0);
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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::UINT8, io_shape,
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tim::vx::TensorAttribute::OUTPUT, quant);
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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 = {0.8458, 0.6214, 0.4666, 0.6065, 0.8895, 0.1535};
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std::vector<uint8_t> golden = {235, 173, 130, 168, 247, 43};
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auto quantize = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*quantize).BindInput(input_tensor).BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output(6, 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(DataConvert, dequantize_shape_2_3_asym_u8_to_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({2, 3});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 0);
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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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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, io_shape,
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tim::vx::TensorAttribute::INPUT, quant);
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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 = {235, 173, 130, 168, 247, 43};
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std::vector<float> golden = {0.8458, 0.6214, 0.4666, 0.6065, 0.8895, 0.1535};
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auto dequantize = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*dequantize).BindInput(input_tensor).BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(6, 0);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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for (uint32_t idx = 0; idx < output.size(); idx++)
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EXPECT_NEAR(golden[idx], output[idx], 0.01f);
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}
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TEST(DataConvert, requantize_shape_2_3_asym_u8) {
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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({2, 3});
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tim::vx::Quantization in_quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 0);
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tim::vx::Quantization out_quant(tim::vx::QuantType::ASYMMETRIC, 0.0036, 10);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, io_shape,
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tim::vx::TensorAttribute::INPUT, in_quant);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, io_shape,
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tim::vx::TensorAttribute::OUTPUT, out_quant);
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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 = {235, 173, 130, 168, 247, 43};
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std::vector<uint8_t> golden = {245, 183, 140, 178, 255, 53};
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auto requantize = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*requantize).BindInput(input_tensor).BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output(6, 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(Rcp, 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,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, 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, std::numeric_limits<float>::infinity() };
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std::vector<float> golden = {-0.4, -10, std::numeric_limits<float>::infinity(), 1.81818, 0.};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
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auto add = graph->CreateOperation<tim::vx::ops::Rcp>();
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(*add).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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} |