COMPMID-415: Move ConvolutionLayer to new validation

Change-Id: I1f40dff43142c4e2c096122bfa1ca08241ff80ff
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81952
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 0 insertions, 197 deletions

diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
deleted file mode 100644
index d9dd34b9ec..0000000000
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ /dev/null
@@ -1,197 +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.
- */
-#include "AssetsLibrary.h"
-#include "CL/CLAccessor.h"
-#include "Globals.h"
-#include "TypePrinter.h"
-#include "Utils.h"
-#include "validation/Datasets.h"
-#include "validation/Reference.h"
-#include "validation/Validation.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
-#include "arm_compute/runtime/Tensor.h"
-#include "arm_compute/runtime/TensorAllocator.h"
-
-#include "boost_wrapper.h"
-
-#include <random>
-#include <string>
-#include <tuple>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-using namespace arm_compute::test::validation;
-
-namespace
-{
-/** Define tolerance of the direct convolution layer
- *
- * @param[in] dt DataType of the tensor.
- *
- * @return Tolerance depending on the data type.
- */
-float direct_convolution_layer_tolerance(DataType dt)
-{
-    switch(dt)
-    {
-        case DataType::F16:
-            return 0.1f;
-        case DataType::F32:
-            return 1e-3f;
-        default:
-            return 0.f;
-    }
-}
-
-/** Compute CL direct convolution layer function.
- *
- * @param[in] src_shape            Shape of the input tensor.
- * @param[in] weights_shape        Shape of the weights.
- * @param[in] bias_shape           Shape of the bias tensor.
- * @param[in] dst_shape            Shape of the output tensor.
- * @param[in] dt                   Data type of input, convolution matrix and output tensors.
- * @param[in] conv_info            Padding and stride information.
- * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers
- *
- * @return Computed output tensor.
-*/
-CLTensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape,
-                                   DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0)
-{
-    // Create tensors
-    CLTensor src     = create_tensor<CLTensor>(src_shape, dt, 1, fixed_point_position);
-    CLTensor weights = create_tensor<CLTensor>(weights_shape, dt, 1, fixed_point_position);
-
-    CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position);
-    CLTensor dst  = create_tensor<CLTensor>(dst_shape, dt, 1, fixed_point_position);
-
-    // Create and configure function
-    CLDirectConvolutionLayer conv_layer;
-    conv_layer.configure(&src, &weights, &bias, &dst, conv_info);
-
-    // Allocate tensors
-    src.allocator()->allocate();
-    weights.allocator()->allocate();
-    dst.allocator()->allocate();
-    bias.allocator()->allocate();
-
-    BOOST_TEST(!src.info()->is_resizable());
-    BOOST_TEST(!weights.info()->is_resizable());
-    BOOST_TEST(!dst.info()->is_resizable());
-    BOOST_TEST(!bias.info()->is_resizable());
-
-    // Fill tensors
-    switch(dt)
-    {
-        case DataType::F16:
-        case DataType::F32:
-        {
-            std::uniform_real_distribution<> distribution(-1.f, 1.f);
-            library->fill(CLAccessor(src), distribution, 0);
-            library->fill(CLAccessor(weights), distribution, 1);
-            library->fill(CLAccessor(bias), distribution, 2);
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("Not supported");
-        }
-    }
-
-    // Compute function
-    conv_layer.run();
-
-    return dst;
-}
-
-TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info)
-{
-    TensorShape out_shape(in_shape);
-    const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
-                                                                                             in_shape.y(),
-                                                                                             kernel_shape.x(),
-                                                                                             kernel_shape.y(),
-                                                                                             conv_info);
-    out_shape.set(0, scaled_dims.first);
-    out_shape.set(1, scaled_dims.second);
-    out_shape.set(2, kernel_shape[3]);
-    return out_shape;
-}
-
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(CL)
-BOOST_AUTO_TEST_SUITE(DirectConvolutionLayer)
-
-BOOST_AUTO_TEST_SUITE(Float)
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(W1x1, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 4, 1) * boost::unit_test::data::xrange(1, 4,
-                     1)
-                     * boost::unit_test::data::make({ 1, 4, 8, 16 }),
-                     input_shape, dt, sx, sy, num_kernels)
-{
-    const unsigned int  kernel_size = 1;
-    const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
-    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
-    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
-    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
-
-    CLTensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
-
-    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
-
-    // Validate output
-    validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt));
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make({ DataType::F16, DataType::F32 }) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3,
-                     1)
-                     * boost::unit_test::data::xrange(0, 2, 1)
-                     * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
-                     input_shape, dt, sx, sy, px, py, num_kernels)
-{
-    const unsigned int  kernel_size = 3;
-    const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
-    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
-    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
-    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
-
-    CLTensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
-
-    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
-
-    // Validate output
-    validate(CLAccessor(dst), ref, direct_convolution_layer_tolerance(dt));
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* DOXYGEN_SKIP_THIS */