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diff --git a/runtimes/neurun/core/src/util/ShapeInference.cc b/runtimes/neurun/core/src/util/ShapeInference.cc
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+++ b/runtimes/neurun/core/src/util/ShapeInference.cc
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+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/Utils.h"
+#include "model/InternalType.h"
+#include "model/Shape.h"
+#include "model/operation/AvgPool2DNode.h"
+#include "model/operation/MaxPool2DNode.h"
+#include "util/ShapeInference.h"
+
+namespace neurun
+{
+namespace shape_inference
+{
+
+//
+// Helper functions
+//
+
+namespace
+{
+
+template <typename T, typename U>
+typename std::enable_if<std::is_integral<T>::value && std::is_integral<U>::value,
+                        typename std::common_type<T, U>::type>::type
+ceil_div(T dividend, U divisor)
+{
+  assert(dividend > 0 && divisor > 0 && "this implementations is for positive numbers only");
+  return (dividend + divisor - 1) / divisor;
+}
+
+// Calculate the result of broadcast of two shapes
+model::Shape broadcastShapes(const model::Shape &lhs_shape, const model::Shape &rhs_shape)
+{
+  model::Shape out_shape;
+  auto max_rank = std::max(lhs_shape.rank(), rhs_shape.rank());
+
+  for (int idx = 0; idx < max_rank; ++idx)
+  {
+    // Go over operands dimensions from right to left
+    int lhs_idx = lhs_shape.rank() - idx - 1;
+    int rhs_idx = rhs_shape.rank() - idx - 1;
+
+    int32_t lhs_dim = lhs_idx >= 0 ? lhs_shape.dim(lhs_idx) : 1;
+    int32_t rhs_dim = rhs_idx >= 0 ? rhs_shape.dim(rhs_idx) : 1;
+
+    if (lhs_dim != 1 && rhs_dim != 1 && lhs_dim != rhs_dim)
+      throw std::runtime_error("Incompatible shapes for broadcast");
+
+    out_shape.prepend(std::max(lhs_dim, rhs_dim));
+  }
+
+  return out_shape;
+}
+
+// Calculate output height and width of convolution-like operation
+std::pair<int, int> calcConvLikeHeightAndWidth(const int in_h, const int in_w, const int ker_h,
+                                               const int ker_w, const model::Padding pad,
+                                               const model::Stride stride)
+{
+  int32_t out_h = 0, out_w = 0;
+
+  switch (pad.type)
+  {
+    case model::PaddingType::SAME:
+      out_h = ceil_div(in_h, stride.vertical);
+      out_w = ceil_div(in_w, stride.horizontal);
+      break;
+    case model::PaddingType::VALID:
+      out_h = ceil_div(in_h - ker_h + 1, stride.vertical);
+      out_w = ceil_div(in_w - ker_w + 1, stride.horizontal);
+      break;
+    case model::PaddingType::EXPLICIT:
+      out_h = (in_h + pad.param.top + pad.param.bottom - ker_h) / stride.vertical + 1;
+      out_w = (in_w + pad.param.left + pad.param.right - ker_w) / stride.horizontal + 1;
+      break;
+    default:
+      assert(false);
+  }
+
+  return {out_h, out_w};
+}
+
+} // namespace
+
+//
+// Shape inference
+//
+
+Shapes inferEltwiseShape(const model::Shape &lhs_shape, const model::Shape &rhs_shape)
+{
+  return {broadcastShapes(lhs_shape, rhs_shape)};
+}
+
+Shapes inferAvgPoolShape(const model::Shape &in_shape,
+                         const model::operation::AvgPool2DNode::Param &param,
+                         const model::Layout layout)
+{
+  assert(layout == model::Layout::NHWC);
+  auto ifm_shape = in_shape.asFeature(layout);
+  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, param.kh, param.kw,
+                                                  param.padding, param.stride);
+  // Pooling don't change number of channels and batch size
+  return {model::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, ifm_shape.C}};
+}
+
+Shapes inferConcatShape(const Shapes &in_shapes, const model::operation::ConcatNode::Param &param)
+{
+  const int32_t concat_axis = param.axis;
+  const auto &first_in_shape = in_shapes[0];
+
+  // Check that all shapes are equal except for concat axis dimension
+  for (const auto &in_shape : in_shapes)
+  {
+    assert(in_shape.rank() == first_in_shape.rank());
+    for (int64_t dim_idx = 0; dim_idx < in_shape.rank(); ++dim_idx)
+      assert(dim_idx == concat_axis || in_shape.dim(dim_idx) == first_in_shape.dim(dim_idx));
+  }
+
+  // Calculate output shape
+  model::Shape out_shape(first_in_shape);
+  out_shape.dim(concat_axis) = 0;
+  for (const auto &in_shape : in_shapes)
+    out_shape.dim(concat_axis) += in_shape.dim(concat_axis);
+  return {out_shape};
+}
+
+Shapes inferMaxPoolShape(const model::Shape &in_shape,
+                         const model::operation::MaxPool2DNode::Param &param,
+                         const model::Layout layout)
+{
+  assert(layout == model::Layout::NHWC);
+  auto ifm_shape = in_shape.asFeature(layout);
+  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, param.kh, param.kw,
+                                                  param.padding, param.stride);
+  // Pooling don't change number of channels and batch size
+  return {model::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, ifm_shape.C}};
+}
+
+Shapes inferConv2DShape(const model::Shape &in_shape, const model::Shape &ker_shape,
+                        const model::operation::Conv2DNode::Param &param, model::Layout layout)
+{
+  assert(layout == model::Layout::NHWC);
+  auto ifm_shape = in_shape.asFeature(layout);
+
+  // Kernel format is [depth_out, kernel_height, kernel_width, depth_in]
+  auto kf_shape = ker_shape.asFeature(layout);
+  assert(ifm_shape.C == kf_shape.C);
+
+  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W,
+                                                  param.padding, param.stride);
+
+  return {model::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.N}};
+}
+
+Shapes inferDepthwiseConv2DShape(const model::Shape &in_shape, const model::Shape &ker_shape,
+                                 const model::operation::DepthwiseConv2DNode::Param &param,
+                                 model::Layout layout)
+{
+  assert(layout == model::Layout::NHWC);
+  auto ifm_shape = in_shape.asFeature(layout);
+
+  // Kernel format is [1, kernel_height, kernel_width, depth_out]
+  auto kf_shape = ker_shape.asFeature(layout);
+  assert(kf_shape.C == static_cast<int32_t>(ifm_shape.C * param.multiplier));
+  assert(kf_shape.N == 1);
+
+  const auto out_h_w = calcConvLikeHeightAndWidth(ifm_shape.H, ifm_shape.W, kf_shape.H, kf_shape.W,
+                                                  param.padding, param.stride);
+
+  return {model::Shape{ifm_shape.N, out_h_w.first, out_h_w.second, kf_shape.C}};
+}
+
+Shapes inferFullyConnectedShape(const model::Shape &in_shape, const model::Shape &ker_shape)
+{
+  assert(in_shape.rank() >= 2);
+  assert(ker_shape.rank() == 2);
+
+  const auto input_size_with_batch = in_shape.num_elements();
+  const auto num_units = ker_shape.dim(0);
+  const auto input_size = ker_shape.dim(1);
+  const auto batch_size = input_size_with_batch / input_size;
+  assert(input_size_with_batch % input_size == 0);
+
+  return {{model::Shape({static_cast<int32_t>(batch_size), num_units})}};
+}
+
+} // namespace shape_inference
+} // namespace neurun