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/*
* Copyright (c) 2017-2023 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 "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTensorAllocator.h"
#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
#include "tests/CL/CLAccessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/DirectConvolutionLayerDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"
/** Synced with tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp
* Please check there for any differences in the coverage
*/
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
RelativeTolerance<half> tolerance_fp16(half(0.2)); /**< Tolerance for floating point tests */
RelativeTolerance<float> tolerance_fp32(0.05f); /**< Tolerance for floating point tests */
constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/
constexpr float tolerance_num = 0.07f; /**< Tolerance number */
constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1); /**< Tolerance for quantized tests */
const auto data_strides = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3));
const auto data_strides_small = combine(framework::dataset::make("StrideX", 1), framework::dataset::make("StrideY", 1));
const auto data_ksize_one = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1)));
const auto data_ksize_one_small = combine(framework::dataset::make("PadX", 0), combine(framework::dataset::make("PadY", 0), framework::dataset::make("KernelSize", 1)));
const auto data_ksize_three = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3)));
const auto data_ksize_five = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5)));
const auto data_ksize_nine = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 9)));
const auto data_ksize_nine_small = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 9)));
const auto data_all_kernels = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five);
const auto data = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels));
const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_ksize_nine));
const auto data_small = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_one_small));
const auto data_small9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_nine_small));
/** Direct convolution nightly data set. */
const auto data_nightly = combine(data, framework::dataset::make("NumKernels", { 1, 4 }));
const auto data_nightly_9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 1, 4 }));
const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }),
combine(framework::dataset::make("StrideX", { 1 }),
combine(framework::dataset::make("StrideY", { 1 }),
combine(framework::dataset::make("PadX", { 4 }),
combine(framework::dataset::make("PadY", { 4 }),
combine(framework::dataset::make("KernelSize", 9),
framework::dataset::make("NumKernels", { 16 })))))));
/** Direct convolution precommit data set. */
const auto data_precommit = combine(data_small, framework::dataset::make("NumKernels", { 1 }));
const auto data_precommit_9x9 = combine(data_small9x9, framework::dataset::make("NumKernels", { 1 }));
/** Activation function Dataset*/
const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) });
} // namespace
TEST_SUITE(CL)
TEST_SUITE(DirectConvolutionLayer)
/** Check whether the configuration of a Direct Convolution layer with no
* bias leads to a successful execution.
*/
TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT)
{
const auto src_shape = TensorShape(27U, 13U, 2U);
const auto weights_shape = TensorShape(3U, 3U, 2U, 4U);
const auto bias_shape = TensorShape(4U);
const auto dst_shape = TensorShape(25U, 11U, 4U);
constexpr auto dt = DataType::F32;
auto src = create_tensor<CLTensor>(src_shape, dt);
auto weights = create_tensor<CLTensor>(weights_shape, dt);
auto dst = create_tensor<CLTensor>(dst_shape, dt);
const auto conv_info = PadStrideInfo(1, 1, 0, 0);
// Create Direct Convolution function
CLDirectConvolutionLayer conv{};
conv.configure(&src, &weights, nullptr, &dst, conv_info);
src.allocator()->allocate();
weights.allocator()->allocate();
dst.allocator()->allocate();
library->fill_tensor_value(CLAccessor(src), 1.f);
library->fill_tensor_value(CLAccessor(weights), 1.f);
conv.run();
// Compute reference to compare
SimpleTensor<float> ref_src{ src_shape, dt };
SimpleTensor<float> ref_weights{ weights_shape, dt };
SimpleTensor<float> ref_bias{ bias_shape, dt };
library->fill_tensor_value(ref_src, 1.f);
library->fill_tensor_value(ref_weights, 1.f);
// No bias
library->fill_tensor_value(ref_bias, 0.f);
auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info);
validate(CLAccessor(dst), ref_dst);
}
/** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal
* would lead to successful run
*/
TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT)
{
auto src_shape = TensorShape(33U, 27U, 3U);
auto weights_shape = TensorShape(5U, 7U, 3U, 4U); // non-square kernel
const auto bias_shape = TensorShape(4U);
auto dst_shape = TensorShape(11U, 12U, 4U);
constexpr auto dt = DataType::F32;
TensorShape src_shape_nhwc(src_shape);
TensorShape weights_shape_nhwc(weights_shape);
TensorShape dst_shape_nhwc(dst_shape);
// Non-square shapes are only allowed for NHWC
permute(src_shape_nhwc, PermutationVector(2U, 0U, 1U));
permute(weights_shape_nhwc, PermutationVector(2U, 0U, 1U));
permute(dst_shape_nhwc, PermutationVector(2U, 0U, 1U));
auto src = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
auto weights = create_tensor<CLTensor>(weights_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
auto dst = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), DataLayout::NHWC);
const auto conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR);
// Create direct convolution function
CLDirectConvolutionLayer conv{};
conv.configure(&src, &weights, nullptr, &dst, conv_info);
src.allocator()->allocate();
weights.allocator()->allocate();
dst.allocator()->allocate();
library->fill_tensor_value(CLAccessor(src), 1.f);
library->fill_tensor_value(CLAccessor(weights), 1.f);
conv.run();
// Compute reference to compare
SimpleTensor<float> ref_src{ src_shape, dt };
SimpleTensor<float> ref_weights{ weights_shape, dt };
SimpleTensor<float> ref_bias{ bias_shape, dt };
library->fill_tensor_value(ref_src, 1.f);
library->fill_tensor_value(ref_weights, 1.f);
// No bias
library->fill_tensor_value(ref_bias, 0.f);
auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info);
validate(CLAccessor(dst), ref_dst);
}
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching data type input/weights
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid: Mismatching input feature maps
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases size
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported biases dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
}),
framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
})),
framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(3U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
})),
framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
})),
framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
})),
framework::dataset::make("Expected", { false, false, false, false, false, false, true })),
input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
{
bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
template <typename T>
using CLDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
template <typename T>
using CLDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>;
template <typename T>
using CLDirectConvolutionValidationWithTensorShapesFixture = DirectConvolutionValidationWithTensorShapesFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
template <typename T>
using CLDirectConvolutionLayerQuantizedFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
template <typename T>
using CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture = DirectConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T, true>;
template <typename T>
using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
TEST_SUITE(NHWC)
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", {
TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported
TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported
TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported
}),
framework::dataset::make("WeightsInfo",{
TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
})),
framework::dataset::make("BiasesInfo",{
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
})),
framework::dataset::make("OutputInfo",{
TensorInfo(TensorShape(4U, 15U, 1U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(4U, 23U, 11U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(4U, 9U, 4U), 1, DataType::F32, DataLayout::NHWC),
})),
framework::dataset::make("ConvInfo", {
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(3, 3, 0, 0),
})),
framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
})),
framework::dataset::make("Expected", { true, true, true })),
input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
{
bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
framework::dataset::make("PadX", { 1, 3, 0, 4 })),
framework::dataset::make("PadY", { 1, 3, 0, 4 })),
framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
framework::dataset::make("DataType", DataType::F16)),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 1 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 9 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::F16)),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
TEST_SUITE_END() // FP16
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
framework::dataset::make("PadX", { 1, 3, 0, 4 })),
framework::dataset::make("PadY", { 1, 3, 0, 4 })),
framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 2 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 3 })),
framework::dataset::make("KernelSize", { 3 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 1 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 9 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
TEST_SUITE_END() // FP32
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
framework::dataset::make("PadX", { 1, 3, 0, 4 })),
framework::dataset::make("PadY", { 1, 3, 0, 4 })),
framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
framework::dataset::make("NumKernels", { 7, 3, 1, 3 })),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 2 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 3 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(1.1f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 1 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 9 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8
TEST_SUITE(QASYMM8_SIGNED)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
framework::dataset::make("PadX", { 1, 3, 0, 4 })),
framework::dataset::make("PadY", { 1, 3, 0, 4 })),
framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
framework::dataset::make("NumKernels", { 7, 3, 1, 3 })),
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
TensorShape(19U, 5U, 16U, 4U),
TensorShape(13U, 5U, 17U, 2U),
TensorShape(32U, 37U, 13U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 1 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 3 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
framework::dataset::make("StrideX", { 1 })),
framework::dataset::make("StrideY", { 1 })),
framework::dataset::make("PadX", { 1 })),
framework::dataset::make("PadY", { 1 })),
framework::dataset::make("KernelSize", { 9 })),
framework::dataset::make("NumKernels", { 3 })),
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
framework::dataset::make("QuantizationInfo", QuantizationInfo(2.f / 255, 10))),
framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
framework::dataset::make("DataLayout", DataLayout::NHWC)))
{
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8_SIGNED
TEST_SUITE_END() // Quantized
TEST_SUITE_END() // NHWC
TEST_SUITE(NCHW)
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", {
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported kernel width
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Non-rectangular weights dimensions are unsupported
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW) // Unsupported stride
}),
framework::dataset::make("WeightsInfo",{
TensorInfo(TensorShape(11U, 11U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW)
})),
framework::dataset::make("BiasesInfo",{
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NCHW)
})),
framework::dataset::make("OutputInfo",{
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(23U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, DataLayout::NCHW)
})),
framework::dataset::make("ConvInfo", {
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(3, 3, 0, 0)
})),
framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
})),
framework::dataset::make("Expected", { false, false, false})),
input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
{
bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
TEST_SUITE(Float)
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", DataType::F16)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F16)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
}
TEST_SUITE_END() // FP16
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
DataType::F32)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit,
framework::dataset::make("DataType",
DataType::F32)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType", DataType::F32)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
TEST_SUITE_END() // FP32
TEST_SUITE(FP32_CustomDataset)
FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::F32)),
ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
}
TEST_SUITE_END() // FP32_CustomDataset
TEST_SUITE_END() // Float
/// @note: Every quantized test has a version with or without activation because the quantization info given is
/// ignored when there is no activation. Instead of using the same quantization information for all the tensors, the
/// fixture generates separate quantization info for each input and the output tensor.
/// When we can also support dynamic quantization with the presence of activation, these two versions should be merged
/// again, with the explicitly specified quantization info removed
const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
});
const auto NoActivation = framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo()
});
const auto IgnoredQuantizationInfo = framework::dataset::make("IgnoredQuantizationInfo",
{
QuantizationInfo()
});
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit,
framework::dataset::make("DataType", DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit,
framework::dataset::make("DataType", DataType::QASYMM8),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit,
framework::dataset::make("DataType", DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit,
framework::dataset::make("DataType", DataType::QASYMM8),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmall9x9WithActivation, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(data_precommit_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(data_nightly, framework::dataset::make("DataType",
DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLargeWithActivation, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(data_nightly, framework::dataset::make("DataType",
DataType::QASYMM8),
framework::dataset::make("QuantizationInfoIf", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(data_nightly_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunLarge9x9WithActivation, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(data_nightly_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(3.f / 255, 10), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(CustomDataset, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::QASYMM8),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(CustomDatasetWithActivation, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::QASYMM8),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8
TEST_SUITE(QASYMM8_SIGNED)
FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit, framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmallWithActivation, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit, framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, -10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit,
framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunMixedDataLayoutWithActivation, CLDirectConvolutionLayerQuantizedMixedDataLayoutFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit,
framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.1f, -10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunSmall9x9WithActivation, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(data_precommit_9x9,
framework::dataset::make("DataType",
DataType::QASYMM8_SIGNED),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunCustomDataset, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED),
IgnoredQuantizationInfo,
NoActivation,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
FIXTURE_DATA_TEST_CASE(RunCustomDatasetWithActivation, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) }),
QuantizedActivationFunctionsDataset,
framework::dataset::make("DataLayout", { DataLayout::NCHW })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
TEST_SUITE_END() // QASYMM8_SIGNED
TEST_SUITE_END() // Quantized
TEST_SUITE_END() // NCHW
TEST_SUITE_END() // DirectConvolutionLayer
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
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