/**************************************************************************** * * 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/spatial_transformer.h" #include "gtest/gtest.h" TEST(SpatialTransformer, shape_1_3_3_1_u8) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType in_shape({1, 3, 3, 1}); tim::vx::ShapeType theta_shape({6}); tim::vx::ShapeType out_shape({1, 3, 3, 1}); tim::vx::Quantization io_quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 0); tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape, tim::vx::TensorAttribute::INPUT, io_quant); tim::vx::TensorSpec theta_spec(tim::vx::DataType::UINT8, theta_shape, tim::vx::TensorAttribute::INPUT, io_quant); tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape, tim::vx::TensorAttribute::OUTPUT, io_quant); auto input_tensor = graph->CreateTensor(input_spec); auto theta_tensor = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); std::vector in_data = { 2, 4, 6, 2, 4, 6, 2, 4, 6 }; std::vector theta_data = { 2, 2, 2, 2, 2, 2 }; std::vector values_golden = { 2,3,2, 2,3,2, 2,3,2 }; EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size())); EXPECT_TRUE(theta_tensor->CopyDataToTensor(theta_data.data(), theta_data.size())); auto op = graph->CreateOperation( 3, 3, true, true, true, true, true, true, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ); (*op).BindInputs({input_tensor, theta_tensor}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); EXPECT_TRUE(graph->Run()); std::vector output_values(values_golden.size()); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data())); EXPECT_EQ(values_golden, output_values); }