Mapped Erf operation & unit tests (#211)
Signed-off-by: Chen Xin <jack.chen@verisilicon.com> Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
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chxin66
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516a914c73
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@ -38,6 +38,7 @@
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#include "tim/vx/ops/depth2space.h"
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#include "tim/vx/ops/dropout.h"
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#include "tim/vx/ops/elementwise.h"
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#include "tim/vx/ops/erf.h"
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#include "tim/vx/ops/fullyconnected.h"
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#include "tim/vx/ops/gather.h"
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#include "tim/vx/ops/gathernd.h"
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@ -75,6 +76,7 @@
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#include "tim/vx/ops/squeeze.h"
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#include "tim/vx/ops/stack.h"
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#include "tim/vx/ops/stridedslice.h"
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#include "tim/vx/ops/svdf.h"
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#include "tim/vx/ops/tile.h"
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#include "tim/vx/ops/transpose.h"
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#include "tim/vx/ops/unidirectional_sequence_lstm.h"
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@ -0,0 +1,53 @@
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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_ERF_H_
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#define TIM_VX_OPS_ERF_H_
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#include "tim/vx/operation.h"
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#include "tim/vx/types.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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* ## Erf
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*
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* Computes the Gauss error function of x element-wise.
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*
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* - no parameters
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*/
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class Erf : public Operation {
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public:
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Erf(Graph* graph);
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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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_ERF_H_ */
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@ -100,10 +100,10 @@ SpatialTransformer|SPATIAL_TRANSFORMER|Mapped|[SpatialTransformer](https://githu
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shuffle_channel|SHUFFLECHANNEL|Mapped|[ANEURALNETWORKS_CHANNEL_SHUFFLE](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a5b993c1211c4b1bc52fb595a3025251d)
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Gelu|GELU|Mapped|[tf.nn.gelu](https://tensorflow.google.cn/api_docs/python/tf/nn/gelu)
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Svdf|SVDF|Mapped|[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
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Erf|ERF|Mapped|[tf.math.erf](https://tensorflow.google.cn/api_docs/python/tf/math/erf)
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||PROPOSAL| TBD |[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)
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||ROI_POOL|Planned 22Q1 |[ANEURALNETWORKS_ROI_POOLING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6736198af337b2efbdb0b6b64dee7fe4)
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||ROI_ALIGN| TBD |[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)
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||SHUFFLECHANNEL|Mapped|[ANEURALNETWORKS_CHANNEL_SHUFFLE](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a5b993c1211c4b1bc52fb595a3025251d)
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||SIGNAL_FRAME|Planned 21Q3|[tf.signal.frame](https://tensorflow.google.cn/api_docs/python/tf/signal/frame)
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||TOPK|Planned 21Q4|[tf.math.top_k](https://tensorflow.google.cn/api_docs/python/tf/math/top_k)
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|GRUCell|GRUCELL_OVXLIB|Planned 21Q3|[tf.keras.layers.GRUCell](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
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@ -0,0 +1,42 @@
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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/erf.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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Erf::Erf(Graph* graph) : Operation(graph, VSI_NN_OP_ERF) {}
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std::shared_ptr<Operation> Erf::Clone(std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<Erf>();
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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 +1,124 @@
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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/erf.h"
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#include "gtest/gtest.h"
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#include "test_utils.h"
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TEST(Erf, shape_3_2_fp32) {
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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 in_shape({3, 2});
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tim::vx::ShapeType out_shape({3, 2});
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tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32, in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32, out_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto in_tensor = graph->CreateTensor(in_spec);
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auto out_tensor = graph->CreateTensor(out_spec);
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std::vector<float> in_data = {
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1, 2, 3,
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0,-1,-2};
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std::vector<float> golden = {
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0.8427007, 0.9953223, 0.999978,
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0 ,-0.8427007,-0.9953223};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Erf>();
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(*op).BindInput(in_tensor).BindOutput(out_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(golden.size());
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EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-2f));
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}
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TEST(Erf, shape_3_2_uint8_Quantized) {
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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 in_shape({3, 2});
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tim::vx::ShapeType out_shape({3, 2});
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const float InputMin = -128, InputMax = 127, OutputMin = -128,
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OutputMax = 127;
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std::pair<float, int32_t> scalesAndZp;
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scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
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std::vector<float> scalesInput = {scalesAndZp.first}; //scale
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std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point
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scalesAndZp = QuantizationParams<uint8_t>(OutputMin, OutputMax);
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std::vector<float> scalesOutput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
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tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
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scalesInput, zeroPointsInput);
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tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
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scalesOutput, zeroPointsOutput);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape,
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tim::vx::TensorAttribute::INPUT, quantInput);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
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tim::vx::TensorAttribute::OUTPUT,
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quantOutput);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data_float = {
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1, 2, 3,
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0,-1,-2};
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std::vector<float> golden_float = {
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0.8427007, 0.9953223, 0.999978,
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0 ,-0.8427007,-0.9953223};
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std::vector<uint8_t> input_data =
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Quantize<uint8_t>(in_data_float, scalesInput[0],
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zeroPointsInput[0]); //Quantification process
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std::vector<uint8_t> golden =
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Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::Erf>();
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(*op).BindInput(input_tensor).BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output(golden.size());
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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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