[nnc] Add convenient constructor to mir::Shape class (#2627)

Add a constructor to mir::Shape that accepts rank of the shape.

Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>

diff --git a/contrib/nnc/core/modelIR/Deserializer.cpp b/contrib/nnc/core/modelIR/Deserializer.cpp
index 82e1a6a..12e96ac 100644 (file)
--- a/contrib/nnc/core/modelIR/Deserializer.cpp
+++ b/contrib/nnc/core/modelIR/Deserializer.cpp
@@ -28,15 +28,11 @@ namespace mir
 // Shape Deserialization
 //
 
-static Shape deserializeFromMessage(const proto::TensorShapeProto& object_as_message)
-{
-  Shape res;
-  auto rank = (uint32_t) object_as_message.dims_size();
-  res.resize((uint32_t) rank);
-  for (uint32_t i = 0; i < rank; i++)
-  {
-    res.dim(i) = (uint32_t) object_as_message.dims(i);
-  }
+static Shape deserializeFromMessage(const proto::TensorShapeProto& object_as_message) {
+  int rank = object_as_message.dims_size();
+  Shape res(rank);
+  for (int i = 0; i < rank; ++i)
+    res.dim(i) = object_as_message.dims(i);
 
   return res;
 }

diff --git a/contrib/nnc/core/modelIR/Shape.cpp b/contrib/nnc/core/modelIR/Shape.cpp
index 358e3c0..99765ff 100644 (file)
--- a/contrib/nnc/core/modelIR/Shape.cpp
+++ b/contrib/nnc/core/modelIR/Shape.cpp
@@ -19,23 +19,13 @@
 #include <algorithm>
 #include <cassert>
 
-namespace nnc
-{
-namespace mir
-{
+namespace nnc {
+namespace mir {
 
 constexpr int32_t mir::Shape::autoDim;
 
-Shape::Shape(std::initializer_list<int32_t> &&l) : _dims{l}
-{
-  // DO NOTHING
-}
-
-int32_t Shape::rank() const { return _dims.size(); }
-Shape &Shape::resize(int32_t size)
-{
+void Shape::resize(int32_t size) {
   _dims.resize(size);
-  return *this;
 }
 
 int32_t Shape::dim(int32_t axis) const {
@@ -50,17 +40,14 @@ int32_t& Shape::dim(int32_t axis) {
   return _dims.at(dim);
 }
 
-int32_t Shape::numElements() const
-{
-  if (rank() == 0)
-  {
+int32_t Shape::numElements() const {
+  if (rank() == 0) {
     return 0;
   }
 
   int32_t res = 1;
 
-  for (int32_t axis = 0; axis < rank(); ++axis)
-  {
+  for (int32_t axis = 0; axis < rank(); ++axis) {
     assert(dim(axis) != Shape::autoDim);
     res *= dim(axis);
   }
@@ -68,32 +55,11 @@ int32_t Shape::numElements() const
   return res;
 }
 
-bool Shape::equal(const Shape &rhs) const
-{
-  if (rank() != rhs.rank())
-  {
-    return false;
-  }
-
-  int32_t r = rank();
-  for (int32_t axis = 0; axis < r; ++axis)
-  {
-    if (dim(axis) != rhs.dim(axis))
-    {
-      return false;
-    }
-  }
-
-  return true;
-}
-
-std::ostream &operator<<(std::ostream &s, const Shape &sh)
-{
+std::ostream& operator<<(std::ostream& s, const Shape& sh) {
   int32_t rank = sh.rank();
   s << "[";
 
-  for (int32_t axis = 0; axis < rank; ++axis)
-  {
+  for (int32_t axis = 0; axis < rank; ++axis) {
     if (axis != 0)
       s << ", ";
     if (sh.dim(axis) == Shape::autoDim)

diff --git a/contrib/nnc/core/modelIR/operations/Conv2DOp.cpp b/contrib/nnc/core/modelIR/operations/Conv2DOp.cpp
index 5f71eef..36a5435 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/Conv2DOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/Conv2DOp.cpp
@@ -30,8 +30,7 @@ void Conv2DOp::inferOutputShapes() {
   assert(_paddingBefore.size() == 2);
   assert(_paddingAfter.size() == 2);
 
-  Shape output_shape;
-  output_shape.resize(4);
+  Shape output_shape(4);
 
   // Batch size and number of channels.
   output_shape.dim(0) = input_shape.dim(0);

diff --git a/contrib/nnc/core/modelIR/operations/DeConv2DOp.cpp b/contrib/nnc/core/modelIR/operations/DeConv2DOp.cpp
index 4d429f3..b7d6dce 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/DeConv2DOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/DeConv2DOp.cpp
@@ -34,8 +34,7 @@ void DeConv2DOp::inferOutputShapes() {
   assert(kernel_shape.rank() == 4);
   assert(kernel_shape.dim(3) == input_shape.dim(3));
 
-  Shape output_shape;
-  output_shape.resize(4);
+  Shape output_shape(4);
 
   // Batch size and number of channels.
   output_shape.dim(0) = input_shape.dim(0);

diff --git a/contrib/nnc/core/modelIR/operations/DepthwiseConv2DOp.cpp b/contrib/nnc/core/modelIR/operations/DepthwiseConv2DOp.cpp
index 878a421..324260b 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/DepthwiseConv2DOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/DepthwiseConv2DOp.cpp
@@ -31,8 +31,7 @@ void DepthwiseConv2DOp::inferOutputShapes() {
   assert(_paddingBefore.size() == 2);
   assert(_paddingAfter.size() == 2);
 
-  Shape output_shape;
-  output_shape.resize(4);
+  Shape output_shape(4);
 
   // Batch size and number of channels.
   output_shape.dim(0) = input_shape.dim(0);

diff --git a/contrib/nnc/core/modelIR/operations/PadOp.cpp b/contrib/nnc/core/modelIR/operations/PadOp.cpp
index e99e815..c058bee 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/PadOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/PadOp.cpp
@@ -26,8 +26,7 @@ void PadOp::inferOutputShapes() {
 
   assert(num_dims == getNumDim());
 
-  Shape out_shape;
-  out_shape.resize(num_dims);
+  Shape out_shape(num_dims);
   for (int32_t dim = 0; dim < num_dims; ++dim) {
     std::pair<int32_t, int32_t> padding = getPaddingForDim(dim);
     out_shape.dim(dim) = padding.first + input_shape.dim(dim) + padding.second;

diff --git a/contrib/nnc/core/modelIR/operations/PoolOp.cpp b/contrib/nnc/core/modelIR/operations/PoolOp.cpp
index 122c4bc..742019b 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/PoolOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/PoolOp.cpp
@@ -29,8 +29,7 @@ void PoolOp::inferOutputShapes() {
   assert(_paddingBefore.size() == 2);
   assert(_paddingAfter.size() == 2);
 
-  Shape output_shape;
-  output_shape.resize(4);
+  Shape output_shape(4);
 
   // Batch size and number of channels.
   output_shape.dim(0) = input_shape.dim(0);

diff --git a/contrib/nnc/core/modelIR/operations/SqueezeOp.cpp b/contrib/nnc/core/modelIR/operations/SqueezeOp.cpp
index 7e7e70e..df1845d 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/SqueezeOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/SqueezeOp.cpp
@@ -25,8 +25,6 @@ void SqueezeOp::inferOutputShapes() {
 
   const auto& input_shape = getInputShape(0);
   int32_t input_rank = input_shape.rank();
-  Shape output_shape;
-  int32_t output_rank = 0;
 
   std::vector<int32_t> dims_to_squeeze;
 
@@ -51,8 +49,9 @@ void SqueezeOp::inferOutputShapes() {
     return;
   }
 
+  int32_t output_rank = 0;
   size_t squeezing_idx = 0;
-  output_shape.resize(input_rank - dims_to_squeeze.size());
+  Shape output_shape(input_rank - dims_to_squeeze.size());
   for (int32_t i = 0; i < input_rank; ++i) {
     if (squeezing_idx < dims_to_squeeze.size() && i == dims_to_squeeze[squeezing_idx]) {
       if (input_shape.dim(i) != 1)

diff --git a/contrib/nnc/core/modelIR/operations/TransposeOp.cpp b/contrib/nnc/core/modelIR/operations/TransposeOp.cpp
index 663b534..ff1420d 100644 (file)
--- a/contrib/nnc/core/modelIR/operations/TransposeOp.cpp
+++ b/contrib/nnc/core/modelIR/operations/TransposeOp.cpp
@@ -28,10 +28,9 @@ TransposeOp::TransposeOp(const IODescriptor& arg, const std::vector<std::size_t>
 
 void TransposeOp::inferOutputShapes() {
   auto& input_shape = getInputShape(0);
-  Shape output_shape;
-  output_shape.resize(input_shape.rank());
+  Shape output_shape(input_shape.rank());
   for (std::size_t i = 0; i < _axisOrder.size(); ++i)
-    output_shape.dim(i) = input_shape.dim(static_cast<int32_t>(_axisOrder.at(i)));
+    output_shape.dim(static_cast<int32_t>(i)) = input_shape.dim(static_cast<int32_t>(_axisOrder.at(i)));
   setOutputShape(0, output_shape);
 }
 

diff --git a/contrib/nnc/include/core/modelIR/Shape.h b/contrib/nnc/include/core/modelIR/Shape.h
index e9fcd5d..f495270 100644 (file)
--- a/contrib/nnc/include/core/modelIR/Shape.h
+++ b/contrib/nnc/include/core/modelIR/Shape.h
@@ -22,47 +22,40 @@
 #include <cstdint>
 #include <ostream>
 
-namespace nnc
-{
-namespace mir
-{
+namespace nnc {
+namespace mir {
 
-class Shape
-{
+class Shape {
 public:
   static constexpr int32_t autoDim = -1;
 
   Shape() = default;
-  Shape(std::initializer_list<int32_t> &&l);
-  explicit Shape(std::vector<int32_t> d) : _dims(d) {}
 
-  int32_t rank() const;
+  explicit Shape(int32_t rank) : _dims(static_cast<decltype(_dims)::size_type>(rank)) {}
 
-  Shape &resize(int32_t size);
+  Shape(std::initializer_list<int32_t> dims) : _dims(dims) {}
+
+  explicit Shape(const std::vector<int32_t>& dims) : _dims(dims) {}
+
+  int32_t rank() const { return static_cast<int32_t>(_dims.size()); }
+
+  void resize(int32_t size);
 
   int32_t& dim(int32_t axis);
-  int32_t  dim(int32_t axis) const;
+
+  int32_t dim(int32_t axis) const;
 
   int32_t numElements() const;
 
-  bool operator==(const Shape& rhs) const
-  {
-    return equal(rhs);
-  }
+  bool operator==(const Shape& rhs) const { return _dims == rhs._dims; }
 
-  bool operator!=(const Shape& rhs) const
-  {
-    return !equal(rhs);
-  }
+  bool operator!=(const Shape& rhs) const { return _dims != rhs._dims; }
 
 private:
-
-  bool equal(const Shape& rhs) const;
-
   std::vector<int32_t> _dims;
 };
 
-std::ostream &operator<<(std::ostream &s, const Shape &sh);
+std::ostream& operator<<(std::ostream& s, const Shape& sh);
 
 } // namespace mir
 } // namespace nnc

diff --git a/contrib/nnc/include/core/modelIR/operations/ResizeOp.h b/contrib/nnc/include/core/modelIR/operations/ResizeOp.h
index 2b1b1b5..639a2f4 100644 (file)
--- a/contrib/nnc/include/core/modelIR/operations/ResizeOp.h
+++ b/contrib/nnc/include/core/modelIR/operations/ResizeOp.h
@@ -38,8 +38,7 @@ public:
     // Infer output shape based on given scales.
     auto& input_shape = getInputShape(0);
     assert(input_shape.rank() == 4 && _scales.size() == 4);
-    Shape output_shape;
-    output_shape.resize(input_shape.rank());
+    Shape output_shape(input_shape.rank());
 
     for (int32_t i = 0; i < input_shape.rank(); ++i) {
       output_shape.dim(i) = (int32_t)lroundf(_scales.at(i) * input_shape.dim(i));

diff --git a/contrib/nnc/include/passes/common_frontend/shape_helper.h b/contrib/nnc/include/passes/common_frontend/shape_helper.h
index a0b9842..8dc6ecd 100644 (file)
--- a/contrib/nnc/include/passes/common_frontend/shape_helper.h
+++ b/contrib/nnc/include/passes/common_frontend/shape_helper.h
@@ -37,8 +37,7 @@ mir::Shape ShapeHelper::createShape(const Iterable &iter, std::size_t size)
     return mir::Shape{1};
   }
 
-  mir::Shape sh;
-  sh.resize(static_cast<int32_t>(size));
+  mir::Shape sh(static_cast<int32_t>(size));
 
   unsigned int i = 0;
   for (auto dim : iter)

diff --git a/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp b/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
index ec9661c..1b32ab2 100644 (file)
--- a/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
+++ b/contrib/nnc/passes/acl_soft_backend/AclCppOpGenerator.cpp
@@ -326,9 +326,8 @@ void AclCppOpGenerator::visit(ops::BiasAddOp& op) {
   string operation_name = out->name() + "_bias_add_layer";
 
   // Reshape the IR biases tensor and generate the corresponding DOM tensor.
-  Shape ir_biases_shape;
   const auto ir_input_shape = op.getInputShape(0);
-  ir_biases_shape.resize(ir_input_shape.rank());
+  Shape ir_biases_shape(ir_input_shape.rank());
 
   // TODO remove this if after batch axis is restored in all operations in Model IR
   if (op.getPrevNodes()[0].op->getType() == Operation::Type::fullyConnected) {
@@ -449,9 +448,8 @@ void AclCppOpGenerator::visit(ops::ScaleOp& op) {
   const auto& ir_scales = op.getWeights();
 
   // Reshape the IR scales tensor and generate the corresponding DOM tensor.
-  Shape ir_scales_shape;
   const auto ir_input_shape = transposeShape<2, 1, 3, 0>(op.getInputShape(0));
-  ir_scales_shape.resize(ir_input_shape.rank());
+  Shape ir_scales_shape(ir_input_shape.rank());
 
   // ACL CLArithmeticDivision supports input tensors broadcasting.
   for (int i = 0; i < ir_input_shape.rank(); ++i)
@@ -677,8 +675,7 @@ shared_ptr<ArtifactId> AclCppOpGenerator::genMultiplication(const string& prefix
   // Create a unit tensor with the rank = ir.shape.rank() and having all dimensions = 1. It is
   // possible to use such a tensor in the operation because of the broadcasting support for the
   // input tensors in the CLArithmeticDivision operation.
-  Shape ir_unit_shape;
-  ir_unit_shape.resize(ir_shape.rank());
+  Shape ir_unit_shape(ir_shape.rank());
 
   for (int i = 0; i < ir_unit_shape.rank(); ++i)
     ir_unit_shape.dim(i) = 1;

diff --git a/contrib/nnc/passes/tflite_frontend/tflite_op_creator.cpp b/contrib/nnc/passes/tflite_frontend/tflite_op_creator.cpp
index 757b241..4ed47df 100644 (file)
--- a/contrib/nnc/passes/tflite_frontend/tflite_op_creator.cpp
+++ b/contrib/nnc/passes/tflite_frontend/tflite_op_creator.cpp
@@ -212,8 +212,7 @@ TFLiteOpCreator::convertResizeNN(InputOps& inputs, InputParams& params,
   auto& input_shape = inputs[0]->getOutputShape(0);
   assert(input_shape.rank() == 4);
   mir::Tensor<int> out_shapes = mir::Tensor<int>(*params[0].get());
-  Shape res_shape;
-  res_shape.resize(4);
+  Shape res_shape(4);
   res_shape.dim(0) = input_shape.dim(0);
   res_shape.dim(1) = out_shapes.at(Index{0});
   res_shape.dim(2) = out_shapes.at(Index{1});