diff --git a/include/tim/vx/ops/instancenormalization.h b/include/tim/vx/ops/instancenormalization.h new file mode 100644 index 0000000..9f20eb6 --- /dev/null +++ b/include/tim/vx/ops/instancenormalization.h @@ -0,0 +1,42 @@ +/**************************************************************************** +* +* Copyright (c) 2020 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. +* +*****************************************************************************/ +#ifndef TIM_VX_OPS_INSTANCENOMALIZATION_H_ +#define TIM_VX_OPS_INSTANCENOMALIZATION_H_ +#include "tim/vx/operation.h" + +namespace tim { +namespace vx { +namespace ops { +class InstanceNormalization : public Operation { + public: + InstanceNormalization(Graph* graph, float eps = 1e-5f); + + protected: + float eps_; +}; + +} // namespace ops +} // namespace vx +} // namespace tim +#endif diff --git a/src/tim/vx/ops/README.md b/src/tim/vx/ops/README.md index 8d088b0..42b54d4 100644 --- a/src/tim/vx/ops/README.md +++ b/src/tim/vx/ops/README.md @@ -60,7 +60,7 @@ ReduceMax|REDUCE_MAX|Mapped|[tf.math.reduce_max](https://tensorflow.google.cn/ap ReduceAny|REDUCE_ANY|Mapped|[tf.math.reduce_any](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_any) ReduceProd|REDUCE_PROD|Mapped|[tf.math.reduce_prod](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_prod) ReduceMean|REDUCE_MEAN|Mapped|[tf.math.reduce_mean](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_mean) -||INSTANCE_NORM|Unmapped|[tfa.layers.InstanceNormalization](https://tensorflow.google.cn/addons/api_docs/python/tfa/layers/InstanceNormalization) +|InstanceNormalization|INSTANCE_NORM|Mapped|[tfa.layers.InstanceNormalization](https://tensorflow.google.cn/addons/api_docs/python/tfa/layers/InstanceNormalization) ||TENSORSTACKCONCAT|Unmapped| StridedSlice|STRIDED_SLICE|Mapped|[tf.strided_slice](https://tensorflow.google.cn/api_docs/python/tf/strided_slice) ||SIGNAL_FRAME|Unmapped diff --git a/src/tim/vx/ops/instancenormalization.cc b/src/tim/vx/ops/instancenormalization.cc new file mode 100644 index 0000000..45b0d36 --- /dev/null +++ b/src/tim/vx/ops/instancenormalization.cc @@ -0,0 +1,39 @@ +/**************************************************************************** +* +* 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/ops/instancenormalization.h" + +#include "operation_private.h" +#include "vsi_nn_pub.h" + +namespace tim { +namespace vx { +namespace ops { +InstanceNormalization::InstanceNormalization(Graph* graph, float eps) + : Operation(graph, VSI_NN_OP_INSTANCE_NORM), eps_(eps) { + this->impl()->node()->nn_param.instancenorm.eps = eps_; +} + +} // namespace ops +} // namespace vx +} // namespace tim \ No newline at end of file diff --git a/src/tim/vx/ops/instancenormalization_test.cc b/src/tim/vx/ops/instancenormalization_test.cc new file mode 100644 index 0000000..5b102f8 --- /dev/null +++ b/src/tim/vx/ops/instancenormalization_test.cc @@ -0,0 +1,218 @@ +/**************************************************************************** +* +* 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/instancenormalization.h" + +#include "gtest/gtest.h" + +namespace { +template +::testing::AssertionResult ArraysMatch(const std::vector& expected, + const std::vector& actual, + T abs_error){ + for (size_t i = 0; i < expected.size(); ++i){ + EXPECT_NEAR(expected[i], actual[i], abs_error) << "at index:" << i; + } + + return ::testing::AssertionSuccess(); +} +} + +TEST(OP, instance_norm_shape_3_6_1_float) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 6, 1}); + tim::vx::ShapeType param_shape({6}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32, + param_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto gamma_tensor = graph->CreateTensor(param_spec); + auto beta_tensor = graph->CreateTensor(param_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + -2, 0, 2, + -3, 0, 3, + -4, 0, 4, + -5, 0, 5, + -6, 0, 6, + -7, 0, 7 }; + std::vector gamma = { + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f + }; + std::vector beta = { + .0f, .0f, .0f, + .0f, .0f, .0f + }; + std::vector golden = { + -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float))); + EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float))); + + auto op = graph->CreateOperation(2e-5f); + (*op).BindInputs({input_tensor, beta_tensor, gamma_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(18); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(OP, instance_norm_shape_3_3_6_1_float) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({2, 3, 6, 1}); + tim::vx::ShapeType param_shape({6}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32, + param_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto gamma_tensor = graph->CreateTensor(param_spec); + auto beta_tensor = graph->CreateTensor(param_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + -2, 0, 2, -2, 0, 2, + -3, 0, 3, -3, 0, 3, + -4, 0, 4, -4, 0, 4, + -5, 0, 5, -5, 0, 5, + -6, 0, 6, -6, 0, 6, + -7, 0, 7, -7, 0, 7, + }; + std::vector gamma = { + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f + }; + std::vector beta = { + .0f, .0f, .0f, + .0f, .0f, .0f + }; + std::vector golden = { + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + -1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float))); + EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float))); + + auto op = graph->CreateOperation(2e-5f); + (*op).BindInputs({input_tensor, beta_tensor, gamma_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(36); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +#if 0 +// Fail case +TEST(OP, instance_norm_shape_3_6_1_uint8) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 6, 1}); + tim::vx::ShapeType param_shape({6}); + tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 7); + tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1.22474f, 1); + tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, + io_shape, tim::vx::TensorAttribute::INPUT, input_quant); + tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32, + param_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, + io_shape, tim::vx::TensorAttribute::OUTPUT, output_quant); + + auto input_tensor = graph->CreateTensor(input_spec); + auto gamma_tensor = graph->CreateTensor(param_spec); + auto beta_tensor = graph->CreateTensor(param_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 5, 7, 9, + 4, 7, 10, + 3, 7, 11, + 2, 7, 12, + 1, 7, 13, + 0, 7, 14 }; + std::vector gamma = { + 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f + }; + std::vector beta = { + .0f, .0f, .0f, + .0f, .0f, .0f + }; + std::vector golden = { + 0, 1, 2, + 0, 1, 2, + 0, 1, 2, + 0, 1, 2, + 0, 1, 2, + 0, 1, 2, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size())); + EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float))); + EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float))); + + auto op = graph->CreateOperation(2e-5f); + (*op).BindInputs({input_tensor, beta_tensor, gamma_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(18); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); +} +#endif