1 files changed, 0 insertions, 233 deletions
diff --git a/runtime/neurun/test/util/ShapeInference.cc b/runtime/neurun/test/util/ShapeInference.cc
deleted file mode 100644
index a8dea3a79..000000000
--- a/runtime/neurun/test/util/ShapeInference.cc
+++ /dev/null
@@ -1,233 +0,0 @@
-/*
- * 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 <gtest/gtest.h>
-
-#include "ir/Layout.h"
-#include "util/ShapeInference.h"
-
-using namespace neurun::ir;
-
-TEST(ShapeInference, Elementwise)
-{
-  Shape lhs_shape{1, 299, 299, 3};
-  Shape rhs_shape{3};
-  auto infered_shapes = neurun::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.dim(0), 1);
-  ASSERT_EQ(infered_out_shape.dim(1), 299);
-  ASSERT_EQ(infered_out_shape.dim(2), 299);
-  ASSERT_EQ(infered_out_shape.dim(3), 3);
-}
-
-TEST(ShapeInference, IncorrectElementwise)
-{
-  Shape lhs_shape{1, 299, 299, 3};
-  Shape rhs_shape{5, 3};
-  ASSERT_THROW(neurun::shape_inference::inferEltwiseShape(lhs_shape, rhs_shape),
-               std::runtime_error);
-}
-
-TEST(ShapeInference, Pool2DNodeSame)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Stride stride{3, 7};
-  Padding padding{PaddingType::SAME};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Pool2DNodeValid)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Stride stride{3, 7};
-  Padding padding{PaddingType::VALID};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Pool2DNodeExplicit)
-{
-  Shape in_shape{10, 3, 5, 20};
-
-  Stride stride{3, 7};
-  Padding padding{PaddingType::EXPLICIT, {4, 3, 2, 1}};
-
-  operation::AvgPool2D::Param avg_pool_param{3, 6, stride, padding, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferAvgPoolShape(in_shape, avg_pool_param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-
-  operation::MaxPool2D::Param max_pool_param{3, 6, stride, padding, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferMaxPoolShape(in_shape, max_pool_param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 20);
-}
-
-TEST(ShapeInference, Conv2D)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Shape ker_shape{30, 3, 6, 20};
-
-  operation::Conv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, Activation::NONE};
-  auto infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-
-  param = operation::Conv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-
-  param = operation::Conv2D::Param{Stride{3, 7}, Padding{PaddingType::EXPLICIT, {4, 3, 2, 1}},
-                                   Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 30);
-}
-
-TEST(ShapeInference, DepthwiseConv2D)
-{
-  Shape in_shape{10, 6, 12, 20};
-  Shape ker_shape{1, 3, 6, 60};
-
-  operation::DepthwiseConv2D::Param param{Stride{3, 7}, Padding{PaddingType::VALID}, 3,
-                                          Activation::NONE};
-  auto infered_shapes =
-      neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 1);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-
-  param = operation::DepthwiseConv2D::Param{Stride{3, 7}, Padding{PaddingType::SAME}, 3,
-                                            Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-
-  param = operation::DepthwiseConv2D::Param{
-      Stride{3, 7}, Padding{PaddingType::EXPLICIT, {4, 3, 2, 1}}, 3, Activation::NONE};
-  infered_shapes = neurun::shape_inference::inferDepthwiseConv2DShape(in_shape, ker_shape, param);
-  infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 4);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).N, 10);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).H, 3);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).W, 2);
-  ASSERT_EQ(infered_out_shape.asFeature(Layout::NHWC).C, 60);
-}
-
-TEST(ShapeInference, Concat)
-{
-  Shape in1{10, 20, 30, 3, 50};
-  Shape in2{10, 20, 30, 2, 50};
-  Shape in3{10, 20, 30, 2, 50};
-
-  operation::Concat::Param param{3};
-  auto infered_shapes = neurun::shape_inference::inferConcatShape({in1, in2, in3}, param);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 5);
-  ASSERT_EQ(infered_out_shape.dim(0), 10);
-  ASSERT_EQ(infered_out_shape.dim(1), 20);
-  ASSERT_EQ(infered_out_shape.dim(2), 30);
-  ASSERT_EQ(infered_out_shape.dim(3), 7);
-  ASSERT_EQ(infered_out_shape.dim(4), 50);
-}
-
-TEST(ShapeInference, FullyConnected)
-{
-  Shape in_shape{3, 4, 5, 6};
-  Shape ker_shape{3, 10};
-  auto infered_shapes = neurun::shape_inference::inferFullyConnectedShape(in_shape, ker_shape);
-  auto infered_out_shape = infered_shapes[0];
-
-  ASSERT_EQ(infered_out_shape.rank(), 2);
-  ASSERT_EQ(infered_out_shape.dim(0), 36);
-  ASSERT_EQ(infered_out_shape.dim(1), 3);
-}