Add map for moments
Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia
Kainan Cha
parent
3c59694025
commit
3fa2bf519a
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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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#ifndef TIM_VX_OPS_MOMENTS_H_
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#define TIM_VX_OPS_MOMENTS_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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/**
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* ## Moments
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*
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* The mean and variance are calculated by aggregating the contents of x across axes.
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* If x is 1-D and axes = [0] this is just the mean and variance of a vector.
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*
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* - axes : Axes along which to compute mean and variance.
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* - keep_dims : Produce moments with the same dimensionality as input.
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*/
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class Moments : public Operation {
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public:
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Moments(Graph* graph, const std::vector<int32_t>& axes,
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bool keep_dims = false);
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protected:
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const std::vector<int32_t> axes_;
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const bool keep_dims_;
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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 /* TIM_VX_OPS_MEMONTS_H_ */
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@ -83,6 +83,7 @@ LogSoftmax|LOG_SOFTMAX|Mapped|[tf.nn.log_softmax](https://tensorflow.google.cn/a
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HardSwish|SWISH|Mapped|[tf.keras.activations.swish](https://tensorflow.google.cn/api_docs/python/tf/keras/activations/swish)
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HardSwish|SWISH|Mapped|[tf.keras.activations.swish](https://tensorflow.google.cn/api_docs/python/tf/keras/activations/swish)
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GatherNd|GATHER_ND|Mapped|[tf.gather_nd](https://tensorflow.google.cn/api_docs/python/tf/gather_nd)
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GatherNd|GATHER_ND|Mapped|[tf.gather_nd](https://tensorflow.google.cn/api_docs/python/tf/gather_nd)
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Cast|CAST|Mapped|[tf.cast](https://tensorflow.google.cn/api_docs/python/tf/cast)
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Cast|CAST|Mapped|[tf.cast](https://tensorflow.google.cn/api_docs/python/tf/cast)
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Moments|MOMENTS|Mapped|[tf.moments](https://tensorflow.google.cn/api_docs/python/tf/nn/moments)
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Squeeze|SQUEEZE|Mapped|[tf.squeeze](https://tensorflow.google.cn/api_docs/python/tf/squeeze)
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Squeeze|SQUEEZE|Mapped|[tf.squeeze](https://tensorflow.google.cn/api_docs/python/tf/squeeze)
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HardSigmoid|HARD_SIGMOID|Mapped|[tf.keras.activations.hard_sigmoid](https://tensorflow.google.cn/api_docs/python/tf/keras/activations/hard_sigmoid)
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HardSigmoid|HARD_SIGMOID|Mapped|[tf.keras.activations.hard_sigmoid](https://tensorflow.google.cn/api_docs/python/tf/keras/activations/hard_sigmoid)
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Mish|MISH|Mapped|[tfa.activations.mish](https://tensorflow.google.cn/addons/api_docs/python/tfa/activations/mish)
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Mish|MISH|Mapped|[tfa.activations.mish](https://tensorflow.google.cn/addons/api_docs/python/tfa/activations/mish)
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@ -0,0 +1,41 @@
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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/moments.h"
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#include "operation_private.h"
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#include "type_utils.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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Moments::Moments(Graph* graph, const std::vector<int32_t>& axes, bool keep_dims)
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: Operation(graph, VSI_NN_OP_MOMENTS), axes_(axes), keep_dims_(keep_dims) {
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this->impl()->node()->nn_param.moments.axis = axes_.data();
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this->impl()->node()->nn_param.moments.axis_num = axes_.size();
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this->impl()->node()->nn_param.moments.keep_dim = ToVxBool(keep_dims_);
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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 +1,200 @@
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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 <string>
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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/moments.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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const std::string& msg){
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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) << msg << " 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(Moments, shape_6_3_1_float_axes_0_1) {
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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 input_shape({6, 3, 1});
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tim::vx::ShapeType output_shape({1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto mean = graph->CreateTensor(output_spec);
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auto variance = 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> mean_golden = {
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0
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};
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std::vector<float> variance_golden = {
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15.444444
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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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std::vector<int32_t> axes = { 0, 1 };
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auto op = graph->CreateOperation<tim::vx::ops::Moments>(axes);
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(*op).BindInputs({input_tensor}).BindOutputs({mean, variance});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> mean_output(mean_golden);
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std::vector<float> variance_output(variance_golden);
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EXPECT_TRUE(mean->CopyDataFromTensor(mean_output.data()));
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EXPECT_TRUE(variance->CopyDataFromTensor(variance_output.data()));
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EXPECT_TRUE(ArraysMatch(mean_golden, mean_output, 1e-5f, "mean output"));
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EXPECT_TRUE(ArraysMatch(variance_golden, variance_output, 1e-5f, "variance output"));
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}
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TEST(Moments, shape_3_6_1_float_axes_1_keepdims) {
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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 input_shape({3, 6, 1});
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tim::vx::ShapeType output_shape({3,1,1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec variance_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto mean = graph->CreateTensor(mean_spec);
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auto variance = graph->CreateTensor(variance_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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};
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std::vector<float> mean_golden = {
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-4.5, 0, 4.5
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};
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std::vector<float> variance_golden = {
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2.916666, 0, 2.916666
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float)));
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std::vector<int32_t> axes = { 1 };
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auto op = graph->CreateOperation<tim::vx::ops::Moments>(axes, true);
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(*op).BindInputs({input_tensor}).BindOutputs({mean, variance});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> mean_output(mean_golden);
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std::vector<float> variance_output(variance_golden);
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EXPECT_TRUE(mean->CopyDataFromTensor(mean_output.data()));
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EXPECT_TRUE(variance->CopyDataFromTensor(variance_output.data()));
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EXPECT_TRUE(ArraysMatch(mean_golden, mean_output, 1e-5f, "mean output"));
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EXPECT_TRUE(ArraysMatch(variance_golden, variance_output, 1e-5f, "variance output"));
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}
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#if 0
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// TODO: Support uint8
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TEST(Moments, shape_3_6_1_uint8_axes_1_keepdims) {
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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 input_shape({3, 6, 1});
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tim::vx::ShapeType output_shape({3,1,1});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 7);
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tim::vx::Quantization mean_quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 9);
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tim::vx::Quantization variance_quant(tim::vx::QuantType::ASYMMETRIC, 2.916666, 0);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
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input_shape, tim::vx::TensorAttribute::INPUT, input_quant);
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tim::vx::TensorSpec mean_spec(tim::vx::DataType::UINT8,
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output_shape, tim::vx::TensorAttribute::OUTPUT, mean_quant);
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tim::vx::TensorSpec variance_spec(tim::vx::DataType::UINT8,
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output_shape, tim::vx::TensorAttribute::OUTPUT, variance_quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto mean = graph->CreateTensor(mean_spec);
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auto variance = graph->CreateTensor(variance_spec);
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std::vector<uint8_t> 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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};
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std::vector<uint8_t> mean_golden = {
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0, 9, 18
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};
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std::vector<uint8_t> variance_golden = {
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1, 0, 1
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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std::vector<int32_t> axes = { 1 };
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auto op = graph->CreateOperation<tim::vx::ops::Moments>(axes, true);
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(*op).BindInputs({input_tensor}).BindOutputs({mean, variance});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> mean_output(mean_golden);
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std::vector<uint8_t> variance_output(variance_golden);
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EXPECT_TRUE(mean->CopyDataFromTensor(mean_output.data()));
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EXPECT_TRUE(variance->CopyDataFromTensor(variance_output.data()));
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EXPECT_EQ(mean_golden, mean_output);
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EXPECT_EQ(variance_golden, variance_output);
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}
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#endif
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