mapped signal frame & unit test (#234)
Signed-off-by: Chen Xin <jack.chen@verisilicon.com> Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
chxin66
GitHub
parent
dc31091db5
commit
1f85d21558
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@ -38,9 +38,9 @@ namespace ops {
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* ```
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*/
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class shuffle_channel : public Operation {
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class ShuffleChannel : public Operation {
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public:
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explicit shuffle_channel(Graph* graph, int32_t num_groups, int32_t index_axis);
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explicit ShuffleChannel(Graph* graph, int32_t num_groups, int32_t index_axis);
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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};
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@ -0,0 +1,60 @@
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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_SIGNALFRAME_H_
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#define TIM_VX_OPS_SIGNALFRAME_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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* ## Signalframe
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*
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* ```
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* tf.signal.frame(
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signal, frame_length, frame_step, pad_end=False, pad_value=0, axis=0, name=None
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) : Expands signal's axis dimension into frames of frame_length.
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* ```
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*/
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class SignalFrame : public Operation {
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public:
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SignalFrame(Graph* graph, uint32_t window_length, uint32_t step, uint32_t pad_end=0,
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uint32_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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const uint32_t window_length_;
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const uint32_t step_;
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const uint32_t pad_end_;
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const uint32_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 /* TIM_VX_OPS_SIGNALFRAME_H_ */
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@ -97,15 +97,15 @@ Unstack|UNSTACK|Mapped|[tf.unstack](https://tensorflow.google.cn/api_docs/python
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Tile|TILE|Mapped|[tf.tile](https://tensorflow.google.cn/api_docs/python/tf/tile)
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GroupedConv2d|GROUPED_CONV2D|Mapped|[ANEURALNETWORKS_GROUPED_CONV_2D](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a847acf8d9f3d2343328c3dbe6d447c50)
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SpatialTransformer|SPATIAL_TRANSFORMER|Mapped|[SpatialTransformer](https://github.com/daerduoCarey/SpatialTransformerLayer)
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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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ShuffleChannel|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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GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)
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GroupedConv1d|GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Conv1D?hl=en)
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|SignalFrame|SIGNAL_FRAME|Mapped|[tf.signal.frame](https://tensorflow.google.cn/api_docs/python/tf/signal/frame)
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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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||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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|UnidirectionalSequenceGRU|GRU_OVXLIB|Planned 21Q4|[tf.keras.layers.GRU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
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@ -119,7 +119,6 @@ GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://www.tensorflow.org/api_do
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||HASHTABLE_LOOKUP|Planned 21Q4|[ANEURALNETWORKS_HASHTABLE_LOOKUP](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0aca92716c8c73c1f0fa7f0757916fee26)
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||EMBEDDING_LOOKUP|Planned 21Q4|[ANEURALNETWORKS_EMBEDDING_LOOKUP](developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a8d2ada77adb74357fc0770405bca0e3)
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||LSH_PROJECTION|Planned 21Q4|[ANEURALNETWORKS_LSH_PROJECTION](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a800cdcec5d7ba776789cb2d1ef669965)
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||SVDF|Mapped |[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
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||HEATMAP_MAX_KEYPOINT|Planned 21Q4|[ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a5ffccf92d127766a741225ff7ad6f743)
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||AXIS_ALIGNED_BBOX_TRANSFORM|Planned 21Q4|[ANEURALNETWORKS_AXIS_ALIGNED_BBOX_TRANSFORM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0afd7603dd54060e6a52f5861674448528)
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||BOX_WITH_NMS_LIMIT|Planned 21Q4|[ANEURALNETWORKS_BOX_WITH_NMX_LIMIT](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2d81e878c19e15700dad111ba6c0be89)
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@ -132,10 +131,8 @@ GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://www.tensorflow.org/api_do
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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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||ERF|Planned 21Q4|[tf.math.erf](https://tensorflow.google.cn/api_docs/python/tf/math/erf)
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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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||NMS|Planned 21Q4|[tf.image.non_max_suppression](https://tensorflow.google.cn/api_docs/python/tf/image/non_max_suppression)
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||GROUPED_CONV1D|Planned 21Q4|
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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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||CONV_RELU|Deprecated
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@ -28,16 +28,16 @@ namespace tim {
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namespace vx {
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namespace ops {
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shuffle_channel::shuffle_channel(Graph* graph, int32_t num_groups,
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ShuffleChannel::ShuffleChannel(Graph* graph, int32_t num_groups,
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int32_t index_axis)
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: Operation(graph, VSI_NN_OP_SHUFFLECHANNEL, 1, 1) {
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this->impl()->node()->nn_param.shufflechannel.group_number = num_groups;
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this->impl()->node()->nn_param.shufflechannel.axis = index_axis;
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}
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std::shared_ptr<Operation> shuffle_channel::Clone(
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std::shared_ptr<Operation> ShuffleChannel::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<shuffle_channel>(
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return graph->CreateOperation<ShuffleChannel>(
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this->impl()->node()->nn_param.shufflechannel.group_number,
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this->impl()->node()->nn_param.shufflechannel.axis);
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}
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@ -29,7 +29,7 @@
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#include "gtest/gtest.h"
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TEST(shuffle_channel, shape_3_6_groupnum2_dim1_float32) {
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TEST(ShuffleChannel, shape_3_6_groupnum2_dim1_float32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -61,7 +61,7 @@ TEST(shuffle_channel, shape_3_6_groupnum2_dim1_float32) {
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};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::shuffle_channel>(2, 1);
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auto op = graph->CreateOperation<tim::vx::ops::ShuffleChannel>(2, 1);
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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@ -72,7 +72,7 @@ TEST(shuffle_channel, shape_3_6_groupnum2_dim1_float32) {
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EXPECT_EQ(golden, output);
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}
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TEST(shuffle_channel, shape_4_2_2_groupnum2_dim0_float32) {
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TEST(ShuffleChannel, shape_4_2_2_groupnum2_dim0_float32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -94,7 +94,7 @@ TEST(shuffle_channel, shape_4_2_2_groupnum2_dim0_float32) {
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};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::shuffle_channel>(2, 0);
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auto op = graph->CreateOperation<tim::vx::ops::ShuffleChannel>(2, 0);
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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@ -105,7 +105,7 @@ TEST(shuffle_channel, shape_4_2_2_groupnum2_dim0_float32) {
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EXPECT_EQ(golden, output);
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}
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TEST(shuffle_channel, shape_1_4_2_2_groupnum2_dim1_float32) {
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TEST(ShuffleChannel, shape_1_4_2_2_groupnum2_dim1_float32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -127,7 +127,7 @@ TEST(shuffle_channel, shape_1_4_2_2_groupnum2_dim1_float32) {
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};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::shuffle_channel>(2, 1);
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auto op = graph->CreateOperation<tim::vx::ops::ShuffleChannel>(2, 1);
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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@ -138,7 +138,7 @@ TEST(shuffle_channel, shape_1_4_2_2_groupnum2_dim1_float32) {
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EXPECT_EQ(golden, output);
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}
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TEST(shuffle_channel, shape_4_1_2_2_groupnum4_dim0_float32) {
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TEST(ShuffleChannel, shape_4_1_2_2_groupnum4_dim0_float32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -160,7 +160,7 @@ TEST(shuffle_channel, shape_4_1_2_2_groupnum4_dim0_float32) {
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};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::shuffle_channel>(4, 0);
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auto op = graph->CreateOperation<tim::vx::ops::ShuffleChannel>(4, 0);
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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@ -171,7 +171,7 @@ TEST(shuffle_channel, shape_4_1_2_2_groupnum4_dim0_float32) {
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EXPECT_EQ(golden, output);
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}
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TEST(shuffle_channel, shape_4_1_2_2_groupnum1_dim3_float32) {
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TEST(ShuffleChannel, shape_4_1_2_2_groupnum1_dim3_float32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -193,7 +193,7 @@ TEST(shuffle_channel, shape_4_1_2_2_groupnum1_dim3_float32) {
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};
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EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::shuffle_channel>(1, 3);
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auto op = graph->CreateOperation<tim::vx::ops::ShuffleChannel>(1, 3);
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(*op).BindInput(in_tensor).BindOutput(out_tensor);
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EXPECT_TRUE(graph->Compile());
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@ -0,0 +1,52 @@
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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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*
|
||||
* 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 "operation_private.h"
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#include "tim/vx/ops/signal_frame.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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SignalFrame::SignalFrame(Graph* graph, uint32_t window_length, uint32_t step, uint32_t pad_end,
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uint32_t axis)
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: Operation(graph, VSI_NN_OP_SIGNAL_FRAME),
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window_length_(window_length),
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step_(step),
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pad_end_(pad_end),
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axis_(axis) {
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this->impl()->node()->nn_param.signalframe.window_length = window_length_;
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this->impl()->node()->nn_param.signalframe.step = step_;
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this->impl()->node()->nn_param.signalframe.pad_end = pad_end_;
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this->impl()->node()->nn_param.signalframe.axis = axis_;
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}
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std::shared_ptr<Operation> SignalFrame::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<SignalFrame>(
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this->window_length_, this->step_, this->pad_end_, 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 +1,81 @@
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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
|
||||
* copy of this software and associated documentation files (the "Software"),
|
||||
* to deal in the Software without restriction, including without limitation
|
||||
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
||||
* and/or sell copies of the Software, and to permit persons to whom the
|
||||
* Software is furnished to do so, subject to the following conditions:
|
||||
*
|
||||
* The above copyright notice and this permission notice shall be included in
|
||||
* all copies or substantial portions of the Software.
|
||||
*
|
||||
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
* 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/signal_frame.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(SignalFrame, shape_10_3_float_step_2_windows_4) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType in_shape({10, 3});
|
||||
tim::vx::ShapeType out_shape({4, 4, 3});
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
||||
in_shape, tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
||||
out_shape, tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
std::vector<float> in_data = {
|
||||
0.9854245 , 1.3478903 , 2.079034 , 0.5336022 , -0.8521084 ,
|
||||
1.4714626 , -1.6673858 , 1.1760164 , 0.58944523, -0.38136077,
|
||||
0.4713266 , -0.54476035, 0.17260066, 0.4458921 , 0.07180826,
|
||||
-0.5209453 , 0.67287415, -0.40036386, 1.819254 , -0.83165807,
|
||||
0.7842376 , -0.51183605, 0.5516365 , -0.3449794 , -0.4545289 ,
|
||||
1.4418068 , 2.6290808 , 0.26231438, -0.50589 , -1.903558 ,
|
||||
};
|
||||
|
||||
std::vector<float> golden = {
|
||||
0.9854245 , 1.3478903 , 2.079034 , 0.5336022 ,
|
||||
2.079034 , 0.5336022 , -0.8521084 , 1.4714626 ,
|
||||
-0.8521084 , 1.4714626 , -1.6673858 , 1.1760164 ,
|
||||
-1.6673858 , 1.1760164 , 0.58944523, -0.38136077,
|
||||
|
||||
0.4713266 , -0.54476035, 0.17260066, 0.4458921 ,
|
||||
0.17260066, 0.4458921 , 0.07180826, -0.5209453 ,
|
||||
0.07180826, -0.5209453 , 0.67287415, -0.40036386,
|
||||
0.67287415, -0.40036386, 1.819254 , -0.83165807,
|
||||
|
||||
0.7842376 , -0.51183605, 0.5516365 , -0.3449794 ,
|
||||
0.5516365 , -0.3449794 , -0.4545289 , 1.4418068 ,
|
||||
-0.4545289 , 1.4418068 , 2.6290808 , 0.26231438,
|
||||
2.6290808 , 0.26231438, -0.50589 , -1.903558 ,
|
||||
};
|
||||
|
||||
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
||||
|
||||
auto op = graph->CreateOperation<tim::vx::ops::SignalFrame>(4, 2, 0, 0);
|
||||
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
std::vector<float> output(golden.size() * sizeof(float));
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
|
||||
}
|
||||
Loading…
Reference in New Issue