Add map for InstanceNormalization
Currently instance normalization only support float32 data type. Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia
Kainan Cha
parent
e3b127df50
commit
b4b6a369a7
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/****************************************************************************
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*
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* Copyright (c) 2020 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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#ifndef TIM_VX_OPS_INSTANCENOMALIZATION_H_
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#define TIM_VX_OPS_INSTANCENOMALIZATION_H_
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#include "tim/vx/operation.h"
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namespace tim {
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namespace vx {
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namespace ops {
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class InstanceNormalization : public Operation {
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public:
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InstanceNormalization(Graph* graph, float eps = 1e-5f);
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protected:
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float eps_;
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};
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} // namespace ops
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} // namespace vx
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} // namespace tim
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#endif
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@ -60,7 +60,7 @@ ReduceMax|REDUCE_MAX|Mapped|[tf.math.reduce_max](https://tensorflow.google.cn/ap
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ReduceAny|REDUCE_ANY|Mapped|[tf.math.reduce_any](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_any)
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ReduceProd|REDUCE_PROD|Mapped|[tf.math.reduce_prod](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_prod)
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ReduceMean|REDUCE_MEAN|Mapped|[tf.math.reduce_mean](https://tensorflow.google.cn/api_docs/python/tf/math/reduce_mean)
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||INSTANCE_NORM|Unmapped|[tfa.layers.InstanceNormalization](https://tensorflow.google.cn/addons/api_docs/python/tfa/layers/InstanceNormalization)
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|InstanceNormalization|INSTANCE_NORM|Mapped|[tfa.layers.InstanceNormalization](https://tensorflow.google.cn/addons/api_docs/python/tfa/layers/InstanceNormalization)
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||TENSORSTACKCONCAT|Unmapped|
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StridedSlice|STRIDED_SLICE|Mapped|[tf.strided_slice](https://tensorflow.google.cn/api_docs/python/tf/strided_slice)
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||SIGNAL_FRAME|Unmapped
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@ -0,0 +1,39 @@
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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/ops/instancenormalization.h"
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#include "operation_private.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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InstanceNormalization::InstanceNormalization(Graph* graph, float eps)
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: Operation(graph, VSI_NN_OP_INSTANCE_NORM), eps_(eps) {
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this->impl()->node()->nn_param.instancenorm.eps = eps_;
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}
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} // namespace ops
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} // namespace vx
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} // namespace tim
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@ -0,0 +1,218 @@
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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/instancenormalization.h"
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#include "gtest/gtest.h"
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namespace {
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template<typename T>
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::testing::AssertionResult ArraysMatch(const std::vector<T>& expected,
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const std::vector<T>& actual,
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T abs_error){
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for (size_t i = 0; i < expected.size(); ++i){
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EXPECT_NEAR(expected[i], actual[i], abs_error) << "at index:" << i;
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}
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return ::testing::AssertionSuccess();
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}
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}
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TEST(OP, instance_norm_shape_3_6_1_float) {
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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({3, 6, 1});
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tim::vx::ShapeType param_shape({6});
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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 param_spec(tim::vx::DataType::FLOAT32,
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param_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 gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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-2, 0, 2,
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-3, 0, 3,
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-4, 0, 4,
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-5, 0, 5,
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-6, 0, 6,
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-7, 0, 7 };
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(18);
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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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}
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TEST(OP, instance_norm_shape_3_3_6_1_float) {
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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, 6, 1});
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tim::vx::ShapeType param_shape({6});
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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 param_spec(tim::vx::DataType::FLOAT32,
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param_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 gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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-2, 0, 2, -2, 0, 2,
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-3, 0, 3, -3, 0, 3,
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-4, 0, 4, -4, 0, 4,
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-5, 0, 5, -5, 0, 5,
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-6, 0, 6, -6, 0, 6,
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-7, 0, 7, -7, 0, 7,
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};
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(36);
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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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}
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#if 0
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// Fail case
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TEST(OP, instance_norm_shape_3_6_1_uint8) {
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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({3, 6, 1});
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tim::vx::ShapeType param_shape({6});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 7);
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tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1.22474f, 1);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
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io_shape, tim::vx::TensorAttribute::INPUT, input_quant);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
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io_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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5, 7, 9,
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4, 7, 10,
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3, 7, 11,
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2, 7, 12,
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1, 7, 13,
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0, 7, 14 };
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(18);
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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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#endif
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