Add map for Conv1D
Convolution 1D operation, support float32, int8, int16, uint8. Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia
Kainan Cha
parent
88f7141cfe
commit
be0a566042
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@ -0,0 +1,65 @@
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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_CONV1D_H_
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#define TIM_VX_OPS_CONV1D_H_
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#include <array>
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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 Conv1d : public Operation {
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public:
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Conv1d(Graph* graph, int32_t weights, PadType padding,
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uint32_t ksize, uint32_t stride,
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uint32_t dilation, int32_t multiplier = 0,
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DataLayout input_layout = DataLayout::WHCN,
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DataLayout kernel_layout = DataLayout::WHIcOc);
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Conv1d(Graph* graph, int32_t weights, PadType padding,
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uint32_t ksize, uint32_t stride, uint32_t dilation,
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const std::array<uint32_t, 2>& pad, int32_t multiplier = 0,
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DataLayout input_layout = DataLayout::WHCN,
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DataLayout kernel_layout = DataLayout::WHIcOc);
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DataLayout KernelDataLayout() { return kernel_layout_; }
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protected:
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const uint32_t weights_;
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const PadType padding_;
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const uint32_t ksize_;
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const uint32_t stride_;
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const uint32_t dilation_;
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const std::array<uint32_t, 2> pad_;
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const int32_t multiplier_;
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const DataLayout kernel_layout_;
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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_CONV2D_H_ */
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@ -67,7 +67,7 @@ StridedSlice|STRIDED_SLICE|Mapped|[tf.strided_slice](https://tensorflow.google.c
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||A_TIMES_B_PLUS_C|Unmapped|[tf.add(tf.mul(A, B), C)](https://github.com/hujie-frank/SENet/blob/master/include/caffe/layers/axpy_layer.hpp)
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||A_TIMES_B_PLUS_C|Unmapped|[tf.add(tf.mul(A, B), C)](https://github.com/hujie-frank/SENet/blob/master/include/caffe/layers/axpy_layer.hpp)
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||SVDF|Unmapped|[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
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||SVDF|Unmapped|[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
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Abs|ABS|Mapped|[tf.math.abs](https://tensorflow.google.cn/api_docs/python/tf/math/abs)
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Abs|ABS|Mapped|[tf.math.abs](https://tensorflow.google.cn/api_docs/python/tf/math/abs)
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||CONV1D|Unmapped|[tf.nn.conv1d](https://tensorflow.google.cn/api_docs/python/tf/nn/conv1d)
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|Conv1d|CONV1D|Mapped|[tf.nn.conv1d](https://tensorflow.google.cn/api_docs/python/tf/nn/conv1d)
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NBG|NBG|Mapped
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NBG|NBG|Mapped
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||CONCATSHIFT|Unmapped
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||CONCATSHIFT|Unmapped
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LocalResponseNormalization|LRN2|Mapped|[tf.nn.local_response_normalization](https://tensorflow.google.cn/api_docs/python/tf/nn/local_response_normalization)
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LocalResponseNormalization|LRN2|Mapped|[tf.nn.local_response_normalization](https://tensorflow.google.cn/api_docs/python/tf/nn/local_response_normalization)
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@ -0,0 +1,67 @@
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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/conv1d.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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Conv1d::Conv1d(Graph* graph, int32_t weights, PadType padding,
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uint32_t ksize, uint32_t stride,
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uint32_t dilation, int32_t multiplier,
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DataLayout input_layout, DataLayout kernel_layout)
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: Conv1d(graph, weights, padding, ksize, stride, dilation, {0, 0},
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multiplier, input_layout, kernel_layout) {}
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Conv1d::Conv1d(Graph* graph, int32_t weights, PadType padding,
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uint32_t ksize, uint32_t stride, uint32_t dilation,
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const std::array<uint32_t, 2>& pad, int32_t multiplier,
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DataLayout input_layout, DataLayout kernel_layout)
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: Operation(graph, VSI_NN_OP_CONV1D, 0, 0, input_layout),
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weights_(weights),
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padding_(padding),
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ksize_(ksize),
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stride_(stride),
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dilation_(dilation),
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pad_(pad),
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multiplier_(multiplier),
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kernel_layout_(kernel_layout) {
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this->impl()->node()->nn_param.conv1d.ksize = ksize_;
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this->impl()->node()->nn_param.conv1d.stride = stride_;
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this->impl()->node()->nn_param.conv1d.pad_type = TranslatePadType(padding_);
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this->impl()->node()->nn_param.conv1d.weights = weights;
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this->impl()->node()->nn_param.conv1d.group = 1;
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this->impl()->node()->nn_param.conv1d.dilation = dilation_;
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this->impl()->node()->nn_param.conv1d.pad[0] = pad_[0];
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this->impl()->node()->nn_param.conv1d.pad[1] = pad_[1];
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this->impl()->node()->nn_param.conv1d.multiplier = multiplier_;
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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 +1,272 @@
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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/conv1d.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(Conv1d, ksize_1_stride_1_weights_3_no_bias_whcn_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({1,6,3});
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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 weight_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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-1, 0, 1,
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-1.5, 0.5, 1.5,
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-2, -0.5, 2,
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-2.5, 0, 2.5,
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-3, 0.5, 3,
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-3.5, 0.5, 3.5,
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};
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std::vector<float> weight = {
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-3, -2, -1.5, 1.5, 2, 3,
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-2.5, -2, -1.5, 1.5, 2, 2.5,
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-2.5, -2, 0, 0, 2, 2.5,
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};
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std::vector<float> golden = {
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-11.25, 2.25, 11.25,
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-10, 2, 10,
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-9.25, 1.25, 9.25,
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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(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Conv1d>(3, tim::vx::PadType::VALID,
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1, 1, 1);
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(*op).BindInputs({input_tensor, weight_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(golden.size() * sizeof(float));
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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(Conv1d, ksize_6_stride_1_weights_2_whcn_shape_6_2_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 input_shape({6, 2, 1});
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tim::vx::ShapeType output_shape({1, 2, 1});
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tim::vx::ShapeType param_shape({6, 2, 2});
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tim::vx::ShapeType bias_shape({2});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 6);
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tim::vx::Quantization weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 22);
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tim::vx::Quantization bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0625, 0);
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tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 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 weight_spec(tim::vx::DataType::UINT8,
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param_shape, tim::vx::TensorAttribute::CONSTANT, weight_quant);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32,
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bias_shape, tim::vx::TensorAttribute::CONSTANT, bias_quant);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
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output_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec);
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auto bias_tensor = graph->CreateTensor(bias_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<uint8_t> in_data = {
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4, 5, 6, 6, 7, 8,
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0, 2, 4, 8, 10, 12,
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};
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std::vector<uint8_t> weight = {
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12, 14,
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16, 28,
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30, 32,
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8, 10,
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12, 32,
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34, 36,
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4, 6,
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8, 36,
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38, 40,
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0, 2,
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4, 40,
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42, 44,
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};
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std::vector<int32_t> bias = {
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-20, 100,
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};
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std::vector<uint8_t> golden = {
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85, 175,
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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(weight_tensor->CopyDataToTensor(weight.data(), weight.size()));
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EXPECT_TRUE(bias_tensor->CopyDataToTensor(bias.data(), bias.size() * sizeof(int32_t)));
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auto op = graph->CreateOperation<tim::vx::ops::Conv1d>(2, tim::vx::PadType::VALID, 6, 1, 1);
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(*op).BindInputs({input_tensor, weight_tensor, bias_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<uint8_t> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, static_cast<uint8_t>(0)));
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}
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TEST(Conv1d, ksize_3_stride_1_pad_1_weights_2_no_bias_whcn_shape_6_2_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 input_shape({6, 2, 1});
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tim::vx::ShapeType output_shape({3, 2, 1});
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tim::vx::ShapeType param_shape({3, 2, 2});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 6);
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tim::vx::Quantization weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 22);
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tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 69);
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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 weight_spec(tim::vx::DataType::UINT8,
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param_shape, tim::vx::TensorAttribute::CONSTANT, weight_quant);
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||||||
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
|
||||||
|
output_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
|
||||||
|
|
||||||
|
auto input_tensor = graph->CreateTensor(input_spec);
|
||||||
|
auto weight_tensor = graph->CreateTensor(weight_spec);
|
||||||
|
auto output_tensor = graph->CreateTensor(output_spec);
|
||||||
|
|
||||||
|
std::vector<uint8_t> in_data = {
|
||||||
|
4, 4, 6, 6, 8, 8,
|
||||||
|
0, 2, 4, 8, 10, 12,
|
||||||
|
};
|
||||||
|
std::vector<uint8_t> weight = {
|
||||||
|
12, 14, 16,
|
||||||
|
8, 10, 12,
|
||||||
|
4, 6, 8,
|
||||||
|
0, 2, 4,
|
||||||
|
};
|
||||||
|
std::vector<uint8_t> golden = {
|
||||||
|
116, 57, 28,
|
||||||
|
148, 45, 0,
|
||||||
|
};
|
||||||
|
|
||||||
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
|
||||||
|
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size()));
|
||||||
|
|
||||||
|
std::array<uint32_t, 2> pad = {0, 1};
|
||||||
|
auto op = graph->CreateOperation<tim::vx::ops::Conv1d>(
|
||||||
|
2, tim::vx::PadType::AUTO, 3, 2, 1, pad);
|
||||||
|
(*op).BindInputs({input_tensor, weight_tensor}).BindOutputs({output_tensor});
|
||||||
|
|
||||||
|
EXPECT_TRUE(graph->Compile());
|
||||||
|
EXPECT_TRUE(graph->Run());
|
||||||
|
|
||||||
|
std::vector<uint8_t> output(golden.size());
|
||||||
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||||
|
EXPECT_TRUE(ArraysMatch(golden, output, static_cast<uint8_t>(0)));
|
||||||
|
}
|
||||||
|
|
||||||
|
#if 0
|
||||||
|
// Fail case
|
||||||
|
// Internal impl conv1d don't support multiplier, need wait for the fix.
|
||||||
|
TEST(Conv1d, ksize_3_stride_2_multiplier_1_whcn_shape_7_2_1_uint8) {
|
||||||
|
auto ctx = tim::vx::Context::Create();
|
||||||
|
auto graph = ctx->CreateGraph();
|
||||||
|
|
||||||
|
tim::vx::ShapeType input_shape({7, 2, 1});
|
||||||
|
tim::vx::ShapeType output_shape({3, 2, 1});
|
||||||
|
tim::vx::ShapeType param_shape({3, 1, 2});
|
||||||
|
tim::vx::ShapeType bias_shape({2});
|
||||||
|
tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 6);
|
||||||
|
tim::vx::Quantization weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 22);
|
||||||
|
tim::vx::Quantization bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0625, 0);
|
||||||
|
tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 39);
|
||||||
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
|
||||||
|
input_shape, tim::vx::TensorAttribute::INPUT, input_quant);
|
||||||
|
tim::vx::TensorSpec weight_spec(tim::vx::DataType::UINT8,
|
||||||
|
param_shape, tim::vx::TensorAttribute::CONSTANT, weight_quant);
|
||||||
|
tim::vx::TensorSpec bias_spec(tim::vx::DataType::UINT8,
|
||||||
|
bias_shape, tim::vx::TensorAttribute::CONSTANT, bias_quant);
|
||||||
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
|
||||||
|
output_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
|
||||||
|
|
||||||
|
auto input_tensor = graph->CreateTensor(input_spec);
|
||||||
|
auto weight_tensor = graph->CreateTensor(weight_spec);
|
||||||
|
auto bias_tensor = graph->CreateTensor(bias_spec);
|
||||||
|
auto output_tensor = graph->CreateTensor(output_spec);
|
||||||
|
|
||||||
|
std::vector<uint8_t> in_data = {
|
||||||
|
4, 4, 6, 10, 6, 8, 8,
|
||||||
|
0, 2, 4, 10, 8, 10, 12,
|
||||||
|
};
|
||||||
|
std::vector<uint8_t> weight = {
|
||||||
|
12, 14, 16,
|
||||||
|
8, 10, 12,
|
||||||
|
};
|
||||||
|
std::vector<int32_t> bias = {
|
||||||
|
-20, 100,
|
||||||
|
};
|
||||||
|
std::vector<uint8_t> golden = {
|
||||||
|
43, 26, 27,
|
||||||
|
72, 24, 0,
|
||||||
|
};
|
||||||
|
|
||||||
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
|
||||||
|
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size()));
|
||||||
|
EXPECT_TRUE(bias_tensor->CopyDataToTensor(bias.data(), bias.size() * sizeof(int32_t)));
|
||||||
|
|
||||||
|
auto op = graph->CreateOperation<tim::vx::ops::Conv1d>(
|
||||||
|
2, tim::vx::PadType::AUTO, 3, 2, 1, 1);
|
||||||
|
(*op).BindInputs({input_tensor, weight_tensor, bias_tensor}).BindOutputs({output_tensor});
|
||||||
|
|
||||||
|
EXPECT_TRUE(graph->Compile());
|
||||||
|
EXPECT_TRUE(graph->Run());
|
||||||
|
|
||||||
|
std::vector<uint8_t> output(golden.size());
|
||||||
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||||
|
EXPECT_TRUE(ArraysMatch(golden, output, static_cast<uint8_t>(0)));
|
||||||
|
}
|
||||||
|
#endif
|
||||||
Loading…
Reference in New Issue