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, 250 insertions, 0 deletions

diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h
new file mode 100644
index 0000000000..d4d68f1af8
--- /dev/null
+++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h
@@ -0,0 +1,250 @@
+/*
+ * 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 "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/RawTensor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/CPP/FullyConnectedLayer.h"
+#include "tests/validation/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 */