COMPMID-415: Rename and move tests

The boost validation is now "standalone" in validation_old and builds as
arm_compute_validation_old. The new validation builds now as
arm_compute_validation.

Change-Id: Ib93ba848a25680ac60afb92b461d574a0757150d
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86187
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 0 insertions, 250 deletions

diff --git a/tests/validation_new/fixtures/FullyConnectedLayerFixture.h b/tests/validation_new/fixtures/FullyConnectedLayerFixture.h
deleted file mode 100644
index 0953b0b67e..0000000000
--- a/tests/validation_new/fixtures/FullyConnectedLayerFixture.h
+++ /dev/null
@@ -1,250 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * 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 ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
-#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
-
-#include "arm_compute/core/TensorShape.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Utils.h"
-#include "framework/Asserts.h"
-#include "framework/Fixture.h"
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/IAccessor.h"
-#include "tests/RawTensor.h"
-#include "tests/validation_new/CPP/FullyConnectedLayer.h"
-#include "tests/validation_new/Helpers.h"
-
-#include <random>
-
-namespace arm_compute
-{
-namespace test
-{
-namespace validation
-{
-namespace
-{
-RawTensor transpose(const RawTensor &src, int interleave = 1)
-{
-    // Create reference
-    TensorShape dst_shape(src.shape());
-    dst_shape.set(0, src.shape().y() * interleave);
-    dst_shape.set(1, std::ceil(src.shape().x() / static_cast<float>(interleave)));
-
-    RawTensor dst{ dst_shape, src.data_type() };
-
-    // Compute reference
-    uint8_t *out_ptr = dst.data();
-
-    for(int i = 0; i < dst.num_elements(); i += interleave)
-    {
-        Coordinates coord   = index2coord(dst.shape(), i);
-        size_t      coord_x = coord.x();
-        coord.set(0, coord.y() * interleave);
-        coord.set(1, coord_x / interleave);
-
-        const int num_elements = std::min<int>(interleave, src.shape().x() - coord.x());
-
-        std::copy_n(static_cast<const uint8_t *>(src(coord)), num_elements * src.element_size(), out_ptr);
-
-        out_ptr += interleave * dst.element_size();
-    }
-
-    return dst;
-}
-} // namespace
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
-class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits)
-    {
-        ARM_COMPUTE_UNUSED(weights_shape);
-        ARM_COMPUTE_UNUSED(bias_shape);
-
-        _fractional_bits = fractional_bits;
-        _data_type       = data_type;
-
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
-    }
-
-protected:
-    template <typename U>
-    void fill(U &&tensor, int i)
-    {
-        if(is_data_type_float(_data_type))
-        {
-            std::uniform_real_distribution<> distribution(0.5f, 1.f);
-            library->fill(tensor, distribution, i);
-        }
-        else
-        {
-            library->fill_tensor_uniform(tensor, i);
-        }
-    }
-
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
-                              bool reshape_weights, DataType data_type, int fixed_point_position)
-    {
-        TensorShape reshaped_weights_shape(weights_shape);
-
-        // Test actions depending on the target settings
-        //
-        //            | reshape   | !reshape
-        // -----------+-----------+---------------------------
-        //  transpose |           | ***
-        // -----------+-----------+---------------------------
-        // !transpose | transpose | transpose &
-        //            |           | transpose1xW (if required)
-        //
-        // ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
-        if(!reshape_weights || !transpose_weights)
-        {
-            const size_t shape_x = reshaped_weights_shape.x();
-            reshaped_weights_shape.set(0, reshaped_weights_shape.y());
-            reshaped_weights_shape.set(1, shape_x);
-
-            // Weights have to be passed reshaped
-            // Transpose 1xW for batched version
-            if(!reshape_weights && output_shape.y() > 1 && run_interleave)
-            {
-                const int   transpose_width = 16 / data_size_from_type(data_type);
-                const float shape_x         = reshaped_weights_shape.x();
-                reshaped_weights_shape.set(0, reshaped_weights_shape.y() * transpose_width);
-                reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
-            }
-        }
-
-        // Create tensors
-        TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
-        TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
-        TensorType bias    = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
-        TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
-
-        // Create and configure function.
-        FunctionType fc;
-        fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
-
-        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Allocate tensors
-        src.allocator()->allocate();
-        weights.allocator()->allocate();
-        bias.allocator()->allocate();
-        dst.allocator()->allocate();
-
-        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Fill tensors
-        fill(AccessorType(src), 0);
-        fill(AccessorType(bias), 2);
-
-        if(!reshape_weights || !transpose_weights)
-        {
-            TensorShape tmp_shape(weights_shape);
-            RawTensor   tmp(tmp_shape, data_type, 1, fixed_point_position);
-
-            // Fill with original shape
-            fill(tmp, 1);
-
-            // Transpose elementwise
-            tmp = transpose(tmp);
-
-            // Reshape weights for batched runs
-            if(!reshape_weights && output_shape.y() > 1 && run_interleave)
-            {
-                // Transpose with interleave
-                const int interleave_size = 16 / tmp.element_size();
-                tmp                       = transpose(tmp, interleave_size);
-            }
-
-            AccessorType weights_accessor(weights);
-
-            for(int i = 0; i < tmp.num_elements(); ++i)
-            {
-                Coordinates coord = index2coord(tmp.shape(), i);
-                std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
-                            tmp.element_size(),
-                            static_cast<RawTensor::value_type *>(weights_accessor(coord)));
-            }
-        }
-        else
-        {
-            fill(AccessorType(weights), 1);
-        }
-
-        // Compute NEFullyConnectedLayer function
-        fc.run();
-
-        return dst;
-    }
-
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
-                                      bool reshape_weights, DataType data_type, int fixed_point_position = 0)
-    {
-        // Create reference
-        SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
-        SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
-        SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
-
-        // Fill reference
-        fill(src, 0);
-        fill(weights, 1);
-        fill(bias, 2);
-
-        return reference::fully_connected_layer<T>(src, weights, bias, output_shape);
-    }
-
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-    int             _fractional_bits{};
-    DataType        _data_type{};
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
-class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
-    {
-        FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
-                                                                                                                         reshape_weights, data_type,
-                                                                                                                         0);
-    }
-};
-} // namespace validation
-} // namespace test
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */