COMPMID-1074: Rename WinograLayer.cpp to WinogradConvolutionLayer.cpp

Change-Id: Iccac7cd6cb458469568d0cd6fb36b262353f4188
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129261
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>

1 files changed, 0 insertions, 389 deletions

diff --git a/tests/validation/fixtures/WinogradLayerFixture.h b/tests/validation/fixtures/WinogradLayerFixture.h
deleted file mode 100644
index 173444ccd8..0000000000
--- a/tests/validation/fixtures/WinogradLayerFixture.h
+++ /dev/null
@@ -1,389 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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_WINOGRAD_LAYER_FIXTURE
-#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE
-
-#include "arm_compute/core/TensorShape.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/IAccessor.h"
-#include "tests/framework/Asserts.h"
-#include "tests/framework/Fixture.h"
-#include "tests/validation/Helpers.h"
-#include "tests/validation/reference/ActivationLayer.h"
-#include "tests/validation/reference/ConvolutionLayer.h"
-#include "tests/validation/reference/Utils.h"
-#include "tests/validation/reference/Winograd.h"
-
-#include <random>
-
-namespace arm_compute
-{
-namespace test
-{
-namespace validation
-{
-using namespace arm_compute::misc::shape_calculator;
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool use_bias = true>
-class WinogradConvolutionLayerValidationFixture : public framework::Fixture
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type, ActivationLayerInfo act_info)
-    {
-        ARM_COMPUTE_UNUSED(dilation);
-
-        _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
-        _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
-    }
-
-protected:
-    template <typename U>
-    void fill(U &&tensor, int i, float min, float max)
-    {
-        switch(tensor.data_type())
-        {
-            case DataType::F32:
-            {
-                std::uniform_real_distribution<> distribution(min, max);
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            default:
-            {
-                ARM_COMPUTE_ERROR("Not supported");
-                library->fill_tensor_uniform(tensor, i);
-                break;
-            }
-        }
-    }
-
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
-                              DataType data_type, ActivationLayerInfo act_info)
-    {
-        // Create tensors
-        TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1);
-        TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1);
-        TensorType bias    = create_tensor<TensorType>(bias_shape, data_type, 1);
-        TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1);
-
-        // Create and configure function
-        FunctionType conv;
-        ARM_COMPUTE_EXPECT(static_cast<bool>(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info)), framework::LogLevel::ERRORS);
-        conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info);
-
-        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();
-        dst.allocator()->allocate();
-        bias.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, -1.f, 1.f);
-        fill(AccessorType(weights), 1, -1.f, 1.f);
-        fill(AccessorType(bias), 2, -1.f, 1.f);
-
-        // Compute Winograd Convolution function
-        conv.run();
-
-        return dst;
-    }
-
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
-                                      DataType data_type, ActivationLayerInfo act_info)
-    {
-        // Create reference
-        SimpleTensor<T> src{ input_shape, data_type, 1 };
-        SimpleTensor<T> weights{ weights_shape, data_type, 1 };
-        SimpleTensor<T> bias{ bias_shape, data_type, 1 };
-
-        // Fill reference
-        fill(src, 0, -1.f, 1.f);
-        fill(weights, 1, -1.f, 1.f);
-        if(use_bias)
-        {
-            fill(bias, 2, -1.f, 1.f);
-        }
-        else
-        {
-            fill(bias, 2, 0.f, 0.f);
-        }
-
-        SimpleTensor<T> conv_out = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
-
-        return (act_info.enabled()) ? reference::activation_layer<T>(conv_out, act_info) : conv_out;
-    }
-
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class WinogradInputTransformValidationFixture : public framework::Fixture
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type)
-    {
-        TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
-
-        _target    = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
-        _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type);
-    }
-
-protected:
-    template <typename U>
-    void fill(U &&tensor, int i, float min, float max)
-    {
-        switch(tensor.data_type())
-        {
-            case DataType::F32:
-            {
-                std::uniform_real_distribution<> distribution(min, max);
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            default:
-            {
-                ARM_COMPUTE_ERROR("Not supported");
-                library->fill_tensor_uniform(tensor, i);
-                break;
-            }
-        }
-    }
-
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
-    {
-        TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
-        TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
-
-        // Create and configure function
-        FunctionType transf;
-        transf.configure(&src, &dst, winograd_info);
-
-        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Allocate tensors
-        src.allocator()->allocate();
-        dst.allocator()->allocate();
-
-        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Fill tensors
-        fill(AccessorType(src), 0, -1.f, 1.f);
-
-        // Compute Winograd input transform function
-        transf.run();
-
-        return dst;
-    }
-
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
-    {
-        // Create reference
-        SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout };
-
-        // Fill reference
-        fill(src, 0, -1.f, 1.f);
-
-        return reference::winograd_input_transform<T>(src, output_shape, winograd_info);
-    }
-
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class WinogradFilterTransformValidationFixture : public framework::Fixture
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, Size2D output_tile, DataLayout data_layout, DataType data_type)
-    {
-        WinogradInfo winograd_info(output_tile, Size2D(input_shape[0], input_shape[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */);
-        TensorShape  output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
-
-        _target    = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type);
-        _reference = compute_reference(input_shape, output_shape, winograd_info, data_layout, data_type);
-    }
-
-protected:
-    template <typename U>
-    void fill(U &&tensor, int i, float min, float max)
-    {
-        switch(tensor.data_type())
-        {
-            case DataType::F32:
-            {
-                std::uniform_real_distribution<> distribution(min, max);
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            default:
-            {
-                ARM_COMPUTE_ERROR("Not supported");
-                library->fill_tensor_uniform(tensor, i);
-                break;
-            }
-        }
-    }
-
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
-    {
-        // Create tensors
-        TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
-        TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
-
-        // Create and configure function
-        FunctionType filter_transform;
-        filter_transform.configure(&src, &dst, winograd_info);
-
-        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Allocate tensors
-        src.allocator()->allocate();
-        dst.allocator()->allocate();
-
-        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Fill tensors
-        fill(AccessorType(src), 0, -1.f, 1.f);
-
-        filter_transform.run();
-
-        return dst;
-    }
-
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataLayout data_layout, DataType data_type)
-    {
-        // Create reference
-        SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout };
-
-        // Fill reference
-        fill(src, 0, -1.f, 1.f);
-
-        return reference::winograd_filter_transform<T>(src, output_shape, winograd_info);
-    }
-
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-};
-
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
-class WinogradOutputTransformValidationFixture : public framework::Fixture
-{
-public:
-    template <typename...>
-    void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type)
-    {
-        TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
-
-        _target    = compute_target(input_shape, output_shape, winograd_info, data_type);
-        _reference = compute_reference(input_shape, output_shape, winograd_info, data_type);
-    }
-
-protected:
-    template <typename U>
-    void fill(U &&tensor, int i, float min, float max)
-    {
-        switch(tensor.data_type())
-        {
-            case DataType::F32:
-            {
-                std::uniform_real_distribution<> distribution(min, max);
-                library->fill(tensor, distribution, i);
-                break;
-            }
-            default:
-            {
-                ARM_COMPUTE_ERROR("Not supported");
-                library->fill_tensor_uniform(tensor, i);
-                break;
-            }
-        }
-    }
-
-    TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type)
-    {
-        // Create tensors
-        TensorType src = create_tensor<TensorType>(input_shape, data_type);
-        TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), winograd_info.output_data_layout);
-
-        // Create and configure function
-        FunctionType output_transform;
-        output_transform.configure(&src, nullptr, &dst, winograd_info);
-
-        ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Allocate tensors
-        src.allocator()->allocate();
-        dst.allocator()->allocate();
-
-        ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
-
-        // Fill tensors
-        fill(AccessorType(src), 0, -1.f, 1.f);
-
-        output_transform.run();
-
-        return dst;
-    }
-
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const WinogradInfo &winograd_info, DataType data_type)
-    {
-        // Create reference
-        SimpleTensor<T> src{ input_shape, data_type };
-
-        // Fill reference
-        fill(src, 0, -1.f, 1.f);
-
-        return reference::winograd_output_transform<T>(src, output_shape, winograd_info);
-    }
-
-    TensorType      _target{};
-    SimpleTensor<T> _reference{};
-};
-} // namespace validation
-} // namespace test
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */