Support specifying pad_mode in pad (#355)

https://github.com/VeriSilicon/TIM-VX/issues/307

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

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

include/tim/vx/ops/pad.h

@@ -35,19 +35,35 @@ namespace ops {
  * Pads a tensor.
  *
  * - const_val : the value to pad.
+ * - pad_mode : the mode of pad.
+ * - front_size : Add pad values to the left and top.
+ * - back_size : Add pad values to the right and bottom.
  */
 
 class Pad : public DirectMapOp {
  public:
-  Pad(Graph* graph, const std::vector<uint32_t>& front_size,
+  typedef enum {
-      const std::vector<uint32_t>& back_size, int32_t const_val);
+    // signature
+    PAD_MODE_CONSTANT,
+    PAD_MODE_EDGE,
+    PAD_MODE_SYMMETRIC,
+    PAD_MODE_REFLECT,
+  } pad_mode_type;
 
-  std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
+  Pad(Graph* graph, const std::vector<uint32_t>& front_size,
+           const std::vector<uint32_t>& back_size, int32_t const_val);
+  Pad(Graph* graph, const std::vector<uint32_t>& front_size,
+      const std::vector<uint32_t>& back_size, int32_t const_val,
+      pad_mode_type pad_mode);
+
+  std::shared_ptr<Operation> Clone(
+      std::shared_ptr<Graph>& graph) const override;
 
  protected:
   std::vector<uint32_t> front_size_;
   std::vector<uint32_t> back_size_;
   int32_t const_val_;
+  pad_mode_type pad_mode_;
 };
 }  // namespace ops
 }  // namespace vx

src/tim/transform/ops/pad_layout_inference.h

@@ -51,15 +51,13 @@ class PadLayoutInfer : public OpLayoutInfer {
            sizeof(uint32_t) * dim_num);
     memcpy(back_size.data(), op_->impl()->node()->nn_param.pad.back_size,
            sizeof(uint32_t) * dim_num);
-    int32_t pad_value = op_->impl()->node()->nn_param.pad.const_val;
 
     if (!input_pv->IsAligned()) {
       front_size = MapMultipleAxis(input_pv->AsStdVec(), front_size);
       back_size = MapMultipleAxis(input_pv->AsStdVec(), back_size);
     }
 
-    auto pad = context_->infer_graph_->CreateOperation<vx::ops::Pad>(
+    auto pad = op_->Clone(context_->infer_graph_);
-        front_size, back_size, pad_value);
     auto out_infer = CreateOutputsTensor(input_pv);
     (*pad).BindInput(context_->GetMapedTensor(i_src));
     (*pad).BindOutput(out_infer[0]);

src/tim/vx/ops/pad.cc

@@ -29,21 +29,31 @@
 namespace tim {
 namespace vx {
 namespace ops {
+
 Pad::Pad(Graph* graph, const std::vector<uint32_t>& front_size,
          const std::vector<uint32_t>& back_size, int32_t const_val)
+    : Pad(graph, front_size, back_size, const_val, PAD_MODE_CONSTANT) {}
+
+Pad::Pad(Graph* graph, const std::vector<uint32_t>& front_size,
+         const std::vector<uint32_t>& back_size, int32_t const_val,
+         pad_mode_type pad_mode)
     : DirectMapOp(graph, VSI_NN_OP_PAD),
       front_size_(front_size),
       back_size_(back_size),
-      const_val_(const_val) {
+      const_val_(const_val),
+      pad_mode_(pad_mode) {
   this->impl()->node()->nn_param.pad.front_size = front_size_.data();
   this->impl()->node()->nn_param.pad.back_size = back_size_.data();
   this->impl()->node()->nn_param.pad.dim_num = front_size_.size();
-  this->impl()->node()->nn_param.pad.const_val = const_val_;
+  if (pad_mode_ == PAD_MODE_CONSTANT) {
-  this->impl()->node()->nn_param.pad.mode = VSI_NN_PAD_MODE_CONSTANT;
+    this->impl()->node()->nn_param.pad.const_val = const_val_;
+  }
+  this->impl()->node()->nn_param.pad.mode = (vsi_nn_pad_mode_e)pad_mode_;
 }
 
 std::shared_ptr<Operation> Pad::Clone(std::shared_ptr<Graph>& graph) const {
-  return graph->CreateOperation<Pad>(this->front_size_, this->back_size_, this->const_val_);
+  return graph->CreateOperation<Pad>(this->front_size_, this->back_size_,
+                                     this->const_val_, this->pad_mode_);
 }
 
 }  // namespace ops

src/tim/vx/ops/pad_test.cc

@@ -0,0 +1,143 @@
+/****************************************************************************
+*
+*    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/pad.h"
+#include "tim/vx/types.h"
+#include "test_utils.h"
+
+#include "gtest/gtest.h"
+
+TEST(Pad, constant) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3, 2});
+  tim::vx::ShapeType output_shape({5, 4});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                  tim::vx::TensorAttribute::OUTPUT);
+
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto output_tensor = graph->CreateTensor(output_spec);
+
+  std::vector<float> input_data = {
+      0, 1, 2, 3, 4, 5, 6,
+  };
+
+  std::vector<float> golden = {
+      1, 1, 1, 1, 1, 1, 0, 1, 2, 1, 1, 3, 4, 5, 1, 1, 1, 1, 1, 1,
+  };
+
+  EXPECT_TRUE(
+      input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
+  std::vector<uint32_t> front = {1, 1};
+  std::vector<uint32_t> back = {1, 1};
+  auto op = graph->CreateOperation<tim::vx::ops::Pad>(
+      front, back, 1, tim::vx::ops::Pad::PAD_MODE_CONSTANT);
+  (*op).BindInput(input_tensor).BindOutput(output_tensor);
+
+  EXPECT_TRUE(graph->Compile());
+  EXPECT_TRUE(graph->Run());
+  std::vector<float> output(golden.size());
+
+  EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+  EXPECT_EQ(golden, output);
+}
+
+TEST(Pad, reflect) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3, 2});
+  tim::vx::ShapeType output_shape({5, 4});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                  tim::vx::TensorAttribute::OUTPUT);
+
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto output_tensor = graph->CreateTensor(output_spec);
+
+  std::vector<float> input_data = {
+      0, 1, 2, 3, 4, 5, 6,
+  };
+
+  std::vector<float> golden = {
+      0, 0, 1, 2, 2, 0, 0, 1, 2, 2, 3, 3, 4, 5, 5, 3, 3, 4, 5, 5,
+  };
+
+  EXPECT_TRUE(
+      input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
+  std::vector<uint32_t> front = {1, 1};
+  std::vector<uint32_t> back = {1, 1};
+  auto op = graph->CreateOperation<tim::vx::ops::Pad>(
+      front, back, 0, tim::vx::ops::Pad::PAD_MODE_EDGE);
+  (*op).BindInput(input_tensor).BindOutput(output_tensor);
+
+  EXPECT_TRUE(graph->Compile());
+  EXPECT_TRUE(graph->Run());
+  std::vector<float> output(golden.size());
+
+  EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+  EXPECT_EQ(golden, output);
+}
+
+TEST(Pad, edge) {
+  auto ctx = tim::vx::Context::Create();
+  auto graph = ctx->CreateGraph();
+
+  tim::vx::ShapeType input_shape({3, 2});
+  tim::vx::ShapeType output_shape({5, 4});
+  tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
+                                 tim::vx::TensorAttribute::INPUT);
+  tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
+                                  tim::vx::TensorAttribute::OUTPUT);
+
+  auto input_tensor = graph->CreateTensor(input_spec);
+  auto output_tensor = graph->CreateTensor(output_spec);
+
+  std::vector<float> input_data = {
+      0, 1, 2, 3, 4, 5, 6,
+  };
+
+  std::vector<float> golden = {0, 0, 1, 2, 2, 0, 0, 1, 2, 2,
+                               3, 3, 4, 5, 5, 3, 3, 4, 5, 5};
+
+  EXPECT_TRUE(
+      input_tensor->CopyDataToTensor(input_data.data(), input_data.size() * 4));
+  std::vector<uint32_t> front = {1, 1};
+  std::vector<uint32_t> back = {1, 1};
+  auto op = graph->CreateOperation<tim::vx::ops::Pad>(
+      front, back, 0, tim::vx::ops::Pad::PAD_MODE_EDGE);
+  (*op).BindInput(input_tensor).BindOutput(output_tensor);
+
+  EXPECT_TRUE(graph->Compile());
+  EXPECT_TRUE(graph->Run());
+  std::vector<float> output(golden.size());
+
+  EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
+  EXPECT_EQ(golden, output);
+}