Map OneHot & unit test (#258)
Signed-off-by: yuenan.li <yuenan.li@verisilicon.com> Co-authored-by: yuenan.li <yuenan.li@verisilicon.com>
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liyuenan
GitHub
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commit
7c63ba621e
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@ -54,6 +54,7 @@
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#include "tim/vx/ops/maxunpool2d.h"
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#include "tim/vx/ops/moments.h"
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#include "tim/vx/ops/nbg.h"
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#include "tim/vx/ops/onehot.h"
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#include "tim/vx/ops/pad.h"
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#include "tim/vx/ops/pool2d.h"
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#include "tim/vx/ops/reduce.h"
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@ -0,0 +1,59 @@
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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_OPERATION_ONE_HOT_H_
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#define TIM_VX_OPERATION_ONE_HOT_H_
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#include "tim/vx/direct_map_op.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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* ## OneHot
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*
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* Create a one-hot tensor.
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*
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* - depth : A scalar defining the depth of the one hot dimension.
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* - on_value : A scalar defining the value to fill in output.
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* - off_value : A scalar defining the value to fill in output.
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* - axis : The axis to fill.
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*/
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class OneHot : public DirectMapOp {
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public:
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OneHot(Graph* graph, int32_t depth, float on_value = 1, float off_value = 0,
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int32_t axis = 0);
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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protected:
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int32_t depth_;
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float on_value_;
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float off_value_;
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int32_t axis_;
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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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@ -131,7 +131,7 @@ GroupedConv1d|GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://tensorflow.
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||CEIL|Planned 21Q4|[tf.math.ceil](https://tensorflow.google.cn/api_docs/python/tf/math/ceil)
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||SEQUENCE_MASK|Planned 21Q4|[tf.math.ceil](https://tensorflow.google.cn/api_docs/python/tf/sequence_mask)
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||REPEAT|Planned 21Q4|[tf.repeat](https://tensorflow.google.cn/api_docs/python/tf/repeat)
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||ONE_HOT|Planned 21Q4|[tf.one_hot](https://tensorflow.google.cn/api_docs/python/tf/one_hot)
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OneHot|ONE_HOT|Mapped|[tf.one_hot](https://tensorflow.google.cn/api_docs/python/tf/one_hot)
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||NMS|Planned 21Q4|[tf.image.non_max_suppression](https://tensorflow.google.cn/api_docs/python/tf/image/non_max_suppression)
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||SCATTER_ND_UPDATE|Planned 21Q4|[tf.compat.v1.scatter_nd_update](https://tensorflow.google.cn/api_docs/python/tf/compat/v1/scatter_nd_update)
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||GELU|Planned 21Q4|[tf.nn.gelu](https://tensorflow.google.cn/api_docs/python/tf/nn/gelu)
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@ -0,0 +1,52 @@
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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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#include "tim/vx/ops/onehot.h"
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#include "direct_map_op_impl.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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OneHot::OneHot(Graph* graph, int32_t depth, float on_value, float off_value,
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int32_t axis)
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: DirectMapOp(graph, VSI_NN_OP_ONE_HOT),
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depth_(depth),
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on_value_(on_value),
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off_value_(off_value),
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axis_(axis) {
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this->impl()->node()->nn_param.one_hot.depth = depth_;
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this->impl()->node()->nn_param.one_hot.on_value = on_value_;
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this->impl()->node()->nn_param.one_hot.off_value = off_value_;
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this->impl()->node()->nn_param.one_hot.axis = axis_;
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}
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std::shared_ptr<Operation> OneHot::Clone(std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<OneHot>(this->depth_, this->on_value_,
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this->off_value_, this->axis_);
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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 +1,326 @@
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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/onehot.h"
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#include "tim/vx/types.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(OneHot, shape_3_out_flaot_depth_3) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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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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{3, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<float> golden = {1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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<float> output(9);
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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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TEST(OneHot, shape_3_out_int32_depth_3) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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{3, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<int32_t> golden = {1, 0, 0, 0, 1, 0, 0, 0, 1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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<int32_t> output(9);
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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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TEST(OneHot, shape_3_out_int8_depth_3) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8,
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{3, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<int8_t> golden = {1, 0, 0, 0, 1, 0, 0, 0, 1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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<int8_t> output(9);
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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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TEST(OneHot, shape_3_out_uint8_depth_3) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
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{3, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<uint8_t> golden = {1, 0, 0, 0, 1, 0, 0, 0, 1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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(9);
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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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TEST(OneHot, shape_3_out_int32_depth_1) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 1;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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{3, 1}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<int32_t> golden = {1, 0, 0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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<int32_t> output(3);
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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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TEST(OneHot, shape_3_out_int32_depth_4) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 4;
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tim::vx::ShapeType input_shape({3});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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{3, 4}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 1, 2};
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std::vector<int32_t> golden = {1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth);
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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<int32_t> output(12);
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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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TEST(OneHot, shape_3_out_int32_depth_3_on_6_off_N1) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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float on = 6;
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float off = -1;
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tim::vx::ShapeType input_shape({4});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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{4, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 2, -1, 1};
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std::vector<int32_t> golden = {6, -1, -1, -1, -1, 6, -1, -1, -1, -1, 6, -1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth, on, off);
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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<int32_t> output(12);
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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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TEST(OneHot, shape_3_out_int32_depth_3_on_5_off_0_axis_1) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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int32_t depth = 3;
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float on = 5;
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float off = 0;
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int32_t axis = 1;
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tim::vx::ShapeType input_shape({4});//AKA: indices
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
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{4, 3}, tim::vx::TensorAttribute::OUTPUT);
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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<int32_t> input_data = {0, 2, -1, 1};
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std::vector<int32_t> golden = {5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth, on, off, axis);
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(*op).BindInput(input_tensor).BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<int32_t> output(12);
|
||||
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(OneHot, shape_2_2_out_int32_depth_3_on_2_off_0) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
int32_t depth = 3;
|
||||
float on = 2;
|
||||
float off = 0;
|
||||
int32_t axis = 0;
|
||||
|
||||
tim::vx::ShapeType input_shape({2, 2});//AKA: indices
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32,
|
||||
input_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32,
|
||||
{2, 2, 3}, tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<int32_t> input_data = {0, 2, 1, -1};
|
||||
|
||||
std::vector<int32_t> golden = {2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::OneHot>(depth, on, off, axis);
|
||||
(*op).BindInput(input_tensor).BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
std::vector<int32_t> output(12);
|
||||
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
Loading…
Reference in New Issue