/**************************************************************************** * * Copyright (c) 2021 Vivante Corporation * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the "Software"), * to deal in the Software without restriction, including without limitation * the rights to use, copy, modify, merge, publish, distribute, sublicense, * and/or sell copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER * DEALINGS IN THE SOFTWARE. * *****************************************************************************/ #include "tim/vx/context.h" #include "tim/vx/graph.h" #include "tim/vx/ops/relational_operations.h" #include "gtest/gtest.h" TEST(Equal, shape_1_uint8) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({1}); tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 1, 0); tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, io_shape, tim::vx::TensorAttribute::INPUT, quant); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { 255 }; std::vector in_data2 = { 0 }; std::vector golden = {0}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size())); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size())); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(1, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); } TEST(NotEqual, shape_5_fp32) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({5}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity() }; std::vector in_data2 = { -2, -1, 0.2, 0.55, std::numeric_limits::infinity() }; std::vector golden = {1, 1, 1, 0, 0}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size()*4)); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size()*4)); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(5, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); } TEST(Less, shape_5_1_fp32) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({1,5}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { 0.1, 0.1, 0, 0.55, std::numeric_limits::infinity() }; std::vector in_data2 = { -1, -1, 0.2, 0.55, std::numeric_limits::infinity() }; std::vector golden = {0, 0, 1, 0, 0}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size()*4)); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size()*4)); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(5, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); } TEST(GreaterOrEqual, shape_5_2_1_fp32) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({5,2,1}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity(), -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity() }; std::vector in_data2 = { -2, -1, 0.2, 0.55, std::numeric_limits::infinity(), -2, -1, 0.2, 0.55, std::numeric_limits::infinity() }; std::vector golden = {0, 1, 0, 1, 1, 0, 1, 0, 1, 1}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size()*4)); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size()*4)); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(10, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); } TEST(Greater, shape_5_2_1_1_fp32) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({5,2,1,1}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity(), -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity() }; std::vector in_data2 = { -2, -1, 0.2, 0.55, std::numeric_limits::infinity(), -2, -1, 0.2, 0.55, std::numeric_limits::infinity() }; std::vector golden = {0, 1, 0, 0, 0, 0, 1, 0, 0, 0}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size()*4)); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size()*4)); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(10, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); } TEST(LessOrEqual, shape_1_5_2_1_1_fp32) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType io_shape({1,5,2,1,1}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec output_spec(tim::vx::DataType::BOOL8, io_shape, tim::vx::TensorAttribute::OUTPUT); auto input_tensor1 = graph->CreateTensor(input_spec); auto input_tensor2 = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data1 = { -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity(), -2.5, -0.1, 0, 0.55, std::numeric_limits::infinity() }; std::vector in_data2 = { -2, -1, 0.2, 0.55, std::numeric_limits::infinity(), -2, -1, 0.2, 0.55, std::numeric_limits::infinity() }; std::vector golden = {1, 0, 1, 1, 1, 1, 0, 1, 1, 1}; EXPECT_TRUE(input_tensor1->CopyDataToTensor(in_data1.data(), in_data1.size()*4)); EXPECT_TRUE(input_tensor2->CopyDataToTensor(in_data2.data(), in_data2.size()*4)); auto op = graph->CreateOperation(); (*op).BindInputs({input_tensor1, input_tensor2}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); //Not using vector because it uses a bitfield representation internally //and it's cumbersome to copy tensor data to it. std::vector output(10, 0); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); EXPECT_EQ(golden, output); }