mapped groupedconv1d & unit test (#233)

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>

Co-authored-by: Chen Xin <jack.chen@verisilicon.com>

include/tim/vx/ops/conv1d.h

@@ -36,22 +36,22 @@ class Conv1d : public Operation {
  public:
   Conv1d(Graph* graph, PadType padding, uint32_t stride,
          uint32_t dilation, int32_t multiplier = 0,
-         DataLayout input_layout = DataLayout::WHCN,
+         DataLayout input_layout = DataLayout::WCN,
-         DataLayout kernel_layout = DataLayout::WHIcOc);
+         DataLayout kernel_layout = DataLayout::WIcOc);
   Conv1d(Graph* graph, const std::array<uint32_t, 2>& pad,
          uint32_t stride, uint32_t dilation, int32_t multiplier = 0,
-         DataLayout input_layout = DataLayout::WHCN,
+         DataLayout input_layout = DataLayout::WCN,
-         DataLayout kernel_layout = DataLayout::WHIcOc);
+         DataLayout kernel_layout = DataLayout::WIcOc);
   Conv1d(Graph* graph, int32_t weights, PadType padding,
          uint32_t ksize, uint32_t stride,
          uint32_t dilation, int32_t multiplier = 0,
-         DataLayout input_layout = DataLayout::WHCN,
+         DataLayout input_layout = DataLayout::WCN,
-         DataLayout kernel_layout = DataLayout::WHIcOc);
+         DataLayout kernel_layout = DataLayout::WIcOc);
   Conv1d(Graph* graph, int32_t weights, PadType padding,
          uint32_t ksize, uint32_t stride, uint32_t dilation,
          const std::array<uint32_t, 2>& pad, int32_t multiplier = 0,
-         DataLayout input_layout = DataLayout::WHCN,
+         DataLayout input_layout = DataLayout::WCN,
-         DataLayout kernel_layout = DataLayout::WHIcOc);
+         DataLayout kernel_layout = DataLayout::WIcOc);
 
   DataLayout KernelDataLayout() { return kernel_layout_; }
 

include/tim/vx/ops/groupedconv1d.h

@@ -0,0 +1,82 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    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
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#ifndef TIM_VX_OPS_GROUPEDCONV1D_H_
+#define TIM_VX_OPS_GROUPEDCONV1D_H_
+
+#include <array>
+
+#include "tim/vx/operation.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+/**
+ * ## GroupedConv1d
+ *
+ * Performs a grouped 1-D convolution operation.
+ *
+ * Input:
+ * - input [WCN].
+ * - kernel [ WIcOc ] (Ic: Input Channels. Oc: Output Channels).Ic*group=C.
+ * - bias [ O ]. Optional.
+ *
+ * Attribute:
+ * - weights : the output channel number for weight tensor.
+ * - ksize : the height and width for weight tensor.
+ * - padding : AUTO, VALID or SAME.
+ * - pad : pad value for each spatial axis.
+ * - stride : stride along each spatial axis.
+ * - dilation : dilation value along each spatial axis of the filter.
+ * - group: Split conv to n group.
+ * - layout : WCN or CWN.
+ */
+
+class GroupedConv1d : public Operation {
+ public:
+  GroupedConv1d(Graph* graph, PadType padding,
+         uint32_t stride,
+         uint32_t dilation,
+         uint32_t group,
+         DataLayout input_layout = DataLayout::WCN,
+         DataLayout kernel_layout = DataLayout::WIcOc);
+
+  DataLayout KernelDataLayout() { return kernel_layout_; }
+
+  std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
+
+ protected:
+  const PadType padding_;
+  const uint32_t stride_;
+  const uint32_t dilation_;
+  const std::array<uint32_t, 2> pad_;
+  const uint32_t group_;
+  const DataLayout kernel_layout_;
+};
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim
+
+#endif /* TIM_VX_OPS_GROUPED_CONV1D_H_ */

include/tim/vx/types.h

@@ -69,7 +69,9 @@ enum class DataLayout {
   IcWHOc, /*TF*/
   OcIcWH, /*TVM for classic conv2d in tflite model*/
   IcOcWH, /*TVM for depthwise conv2d in tflite model*/
-  WHIcOc /*TIM-VX default*/
+  WHIcOc, /*TIM-VX default*/
+  WCN,   /*for conv1d*/
+  WIcOc, /*for conv1d*/
 };
 
 }  // namespace vx

src/tim/vx/ops/README.md

@@ -101,6 +101,7 @@ shuffle_channel|SHUFFLECHANNEL|Mapped|[ANEURALNETWORKS_CHANNEL_SHUFFLE](https://
 Gelu|GELU|Mapped|[tf.nn.gelu](https://tensorflow.google.cn/api_docs/python/tf/nn/gelu)
 Svdf|SVDF|Mapped|[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
 Erf|ERF|Mapped|[tf.math.erf](https://tensorflow.google.cn/api_docs/python/tf/math/erf)
+GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)
 ||PROPOSAL| TBD |[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)
 ||ROI_POOL|Planned 22Q1 |[ANEURALNETWORKS_ROI_POOLING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6736198af337b2efbdb0b6b64dee7fe4)
 ||ROI_ALIGN| TBD |[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)

src/tim/vx/ops/conv1d_test.cc

@@ -27,18 +27,19 @@
 #include "test_utils.h"
 #include "gtest/gtest.h"
 
-TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_whcn) {
+TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_wcn) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
-    tim::vx::ShapeType io_shape({3, 6, 1});
+    tim::vx::ShapeType in_shape({3, 6, 1});
     tim::vx::ShapeType param_shape({1,6,3});
+    tim::vx::ShapeType out_shape({3, 3, 1});
     tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
-                            io_shape, tim::vx::TensorAttribute::INPUT);
+                            in_shape, tim::vx::TensorAttribute::INPUT);
     tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
                             param_shape, tim::vx::TensorAttribute::INPUT);
     tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
-                            io_shape, tim::vx::TensorAttribute::OUTPUT);
+                            out_shape, tim::vx::TensorAttribute::OUTPUT);
 
     auto input_tensor = graph->CreateTensor(input_spec);
     auto weight_tensor = graph->CreateTensor(param_spec);
@@ -78,7 +79,7 @@ TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_whcn) {
     EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
 }
 
-TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_whcn) {
+TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_wcn) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
@@ -144,7 +145,7 @@ TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_whcn) {
     EXPECT_TRUE(ArraysMatch(golden, output, static_cast<uint8_t>(0)));
 }
 
-TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_whcn) {
+TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_wcn) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 
@@ -199,7 +200,7 @@ TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_whcn) {
 #if 0
 // Fail case
 // Internal impl conv1d don't support multiplier, need wait for the fix.
-TEST(Conv1d, shape_7_2_1_uint8_ksize_3_stride_2_multiplier_1_whcn) {
+TEST(Conv1d, shape_7_2_1_uint8_ksize_3_stride_2_multiplier_1_wcn) {
     auto ctx = tim::vx::Context::Create();
     auto graph = ctx->CreateGraph();
 

src/tim/vx/ops/groupedconv1d.cc

@@ -0,0 +1,59 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    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
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/ops/groupedconv1d.h"
+
+#include "operation_private.h"
+#include "type_utils.h"
+#include "vsi_nn_pub.h"
+
+namespace tim {
+namespace vx {
+namespace ops {
+
+GroupedConv1d::GroupedConv1d(Graph* graph,
+               PadType padding,
+               const uint32_t stride,
+               const uint32_t dilation,
+               uint32_t group,
+               DataLayout input_layout, DataLayout kernel_layout)
+    : Operation(graph, VSI_NN_OP_GROUPED_CONV1D, 3, 1, input_layout),
+      padding_(padding), stride_(stride), dilation_(dilation),
+      pad_({0,0}), group_(group),
+      kernel_layout_(kernel_layout) {
+  this->impl()->node()->nn_param.grouped_conv1d.pad_type = TranslatePadType(padding_);
+  this->impl()->node()->nn_param.grouped_conv1d.stride = stride_;
+  this->impl()->node()->nn_param.grouped_conv1d.group = group_;
+  this->impl()->node()->nn_param.grouped_conv1d.dilation = dilation_;
+  }
+
+std::shared_ptr<Operation> GroupedConv1d::Clone(
+    std::shared_ptr<Graph>& graph) const {
+  return graph->CreateOperation<GroupedConv1d>(
+      this->padding_, this->stride_, this->dilation_, this->group_, this->impl_->layout_,
+      this->kernel_layout_);
+}
+
+}  // namespace ops
+}  // namespace vx
+}  // namespace tim

src/tim/vx/ops/groupedconv1d_test.cc

@@ -0,0 +1,72 @@
+/****************************************************************************
+*
+*    Copyright (c) 2021 Vivante Corporation
+*
+*    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
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops/groupedconv1d.h"
+#include "test_utils.h"
+#include "gtest/gtest.h"
+
+TEST(GroupedConv1d, shape_6_2_1_float_ksize_6_stride_1_group_2_no_bias_wcn) {
+    auto ctx = tim::vx::Context::Create();
+    auto graph = ctx->CreateGraph();
+
+    tim::vx::ShapeType in_shape({6, 2, 1});
+    tim::vx::ShapeType param_shape({6, 1, 2});
+    tim::vx::ShapeType out_shape({1, 2, 1});
+    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
+                            in_shape, tim::vx::TensorAttribute::INPUT);
+    tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
+                            param_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 weight_tensor = graph->CreateTensor(param_spec);
+    auto output_tensor = graph->CreateTensor(output_spec);
+
+    std::vector<float> in_data = {
+        -1,    0, 1, -1.5, 0.5, 1.5,
+        -2, -0.5, 2, -2.5,   0, 2.5,
+        };
+    std::vector<float> weight = {
+        -3,   -2, -1.5, 1.5, 2,   3,
+        -2.5, -2, -1.5, 1.5, 2, 2.5,
+    };
+    std::vector<float> golden = {
+        4.75, 5.5,
+    };
+
+    EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
+    EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
+
+    auto op = graph->CreateOperation<tim::vx::ops::GroupedConv1d>(tim::vx::PadType::VALID, 1, 1, 2);
+    (*op).BindInputs({input_tensor, weight_tensor}).BindOutputs({output_tensor});
+
+    EXPECT_TRUE(graph->Compile());
+    EXPECT_TRUE(graph->Run());
+
+    std::vector<float> output(golden.size());
+    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+    EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
+}

src/tim/vx/ops/groupedconv2d.cc

@@ -58,15 +58,15 @@ GroupedConv2d::GroupedConv2d(Graph* graph,
     : Operation(graph, VSI_NN_OP_GROUPED_CONV2D, 3, 1, input_layout),
       padding_(PadType::AUTO), strides_(strides), dilation_(dilation), pad_(pad),
       group_number_(group_number), kernel_layout_(kernel_layout) {
-  this->impl()->node()->nn_param.conv2d.stride[0] = strides_[0];
+  this->impl()->node()->nn_param.grouped_conv2d.stride[0] = strides_[0];
-  this->impl()->node()->nn_param.conv2d.stride[1] = strides_[1];
+  this->impl()->node()->nn_param.grouped_conv2d.stride[1] = strides_[1];
-  this->impl()->node()->nn_param.conv2d.group = group_number_;
+  this->impl()->node()->nn_param.grouped_conv2d.group = group_number_;
-  this->impl()->node()->nn_param.conv2d.dilation[0] = dilation_[0];
+  this->impl()->node()->nn_param.grouped_conv2d.dilation[0] = dilation_[0];
-  this->impl()->node()->nn_param.conv2d.dilation[1] = dilation_[1];
+  this->impl()->node()->nn_param.grouped_conv2d.dilation[1] = dilation_[1];
-  this->impl()->node()->nn_param.conv2d.pad[0] = pad_[0];
+  this->impl()->node()->nn_param.grouped_conv2d.pad[0] = pad_[0];
-  this->impl()->node()->nn_param.conv2d.pad[1] = pad_[1];
+  this->impl()->node()->nn_param.grouped_conv2d.pad[1] = pad_[1];
-  this->impl()->node()->nn_param.conv2d.pad[2] = pad_[2];
+  this->impl()->node()->nn_param.grouped_conv2d.pad[2] = pad_[2];
-  this->impl()->node()->nn_param.conv2d.pad[3] = pad_[3];
+  this->impl()->node()->nn_param.grouped_conv2d.pad[3] = pad_[3];
 }
 
 std::shared_ptr<Operation> GroupedConv2d::Clone(