COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/tests/dataset/ActivationFunctionDataset.h b/tests/dataset/ActivationFunctionDataset.h
new file mode 100644
index 0000000..11e4baa
--- /dev/null
+++ b/tests/dataset/ActivationFunctionDataset.h
@@ -0,0 +1,66 @@
+/*
+ * 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_DATASET_ACTIVATION_FUNCTION_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_ACTIVATION_FUNCTION_DATASET_H__
+
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible activation functions.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on all activation functions.
+ */
+class ActivationFunctions final : public GenericDataset<ActivationLayerInfo::ActivationFunction, 9>
+{
+public:
+ ActivationFunctions()
+ : GenericDataset
+ {
+ ActivationLayerInfo::ActivationFunction::ABS,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LINEAR,
+ ActivationLayerInfo::ActivationFunction::LOGISTIC,
+ ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::SOFT_RELU,
+ ActivationLayerInfo::ActivationFunction::SQRT,
+ ActivationLayerInfo::ActivationFunction::SQUARE,
+ ActivationLayerInfo::ActivationFunction::TANH
+ }
+ {
+ }
+
+ ~ActivationFunctions() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_ACTIVATION_FUNCTION_DATASET_H__
diff --git a/tests/dataset/ActivationLayerDataset.h b/tests/dataset/ActivationLayerDataset.h
new file mode 100644
index 0000000..9d7cffb
--- /dev/null
+++ b/tests/dataset/ActivationLayerDataset.h
@@ -0,0 +1,177 @@
+/*
+ * 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_DATASET_ACTIVATION_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_ACTIVATION_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <sstream>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class ActivationLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "ActivationLayer";
+ ss << "_I" << shape;
+ ss << "_F_" << info.activation();
+ return ss.str();
+ }
+
+public:
+ TensorShape shape;
+ ActivationLayerInfo info;
+};
+
+template <unsigned int Size>
+using ActivationLayerDataset = GenericDataset<ActivationLayerDataObject, Size>;
+
+class AlexNetActivationLayerDataset final : public ActivationLayerDataset<5>
+{
+public:
+ AlexNetActivationLayerDataset()
+ : GenericDataset
+ {
+ ActivationLayerDataObject{ TensorShape(55U, 55U, 96U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ ActivationLayerDataObject{ TensorShape(27U, 27U, 256U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ ActivationLayerDataObject{ TensorShape(13U, 13U, 384U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ ActivationLayerDataObject{ TensorShape(13U, 13U, 256U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ ActivationLayerDataObject{ TensorShape(4096U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ }
+ {
+ }
+
+ ~AlexNetActivationLayerDataset() = default;
+};
+
+class LeNet5ActivationLayerDataset final : public ActivationLayerDataset<1>
+{
+public:
+ LeNet5ActivationLayerDataset()
+ : GenericDataset
+ {
+ ActivationLayerDataObject{ TensorShape(500U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ }
+ {
+ }
+
+ ~LeNet5ActivationLayerDataset() = default;
+};
+
+class GoogLeNetActivationLayerDataset final : public ActivationLayerDataset<33>
+{
+public:
+ GoogLeNetActivationLayerDataset()
+ : GenericDataset
+ {
+ // conv1/relu_7x7
+ ActivationLayerDataObject{ TensorShape(112U, 112U, 64U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // conv2/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(56U, 56U, 64U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // conv2/relu_3x3
+ ActivationLayerDataObject{ TensorShape(56U, 56U, 192U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3a/relu_1x1, inception_3b/relu_pool_proj
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 64U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3a/relu_3x3_reduce, inception_3b/relu_5x5
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 96U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3a/relu_3x3, inception_3b/relu_1x1, inception_3b/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 128U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3a/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 16U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3a/relu_5x5, inception_3a/relu_pool_proj, inception_3b/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 32U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_3b/relu_3x3
+ ActivationLayerDataObject{ TensorShape(28U, 28U, 192U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_1x1
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 192U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 96U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_3x3
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 208U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 16U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_5x5
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 48U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4a/relu_pool_proj, inception_4b/relu_5x5, inception_4b/relu_pool_proj, inception_4c/relu_5x5, inception_4c/relu_pool_proj, inception_4d/relu_5x5, inception_4d/relu_pool_proj
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 64U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4b/relu_1x1, inception_4e/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 160U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4b/relu_3x3_reduce, inception_4d/relu_1x1
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 112U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4b/relu_3x3
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 224U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4b/relu_5x5_reduce, inception_4c/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 24U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4c/relu_1x1, inception_4c/relu_3x3_reduce, inception_4e/relu_5x5, inception_4e/relu_pool_proj
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 128U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4c/relu_3x3, inception_4e/relu_1x1
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 256U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4d/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 144U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4d/relu_3x3
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 288U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4d/relu_5x5_reduce, inception_4e/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 32U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_4e/relu_3x3
+ ActivationLayerDataObject{ TensorShape(14U, 14U, 320U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5a/relu_1x1
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 256U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5a/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 160U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5a/relu_3x3
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 320U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5a/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 32U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5a/relu_5x5, inception_5a/relu_pool_proj, inception_5b/relu_5x5, inception_5b/relu_pool_proj
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 128U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5b/relu_1x1, inception_5b/relu_3x3
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 384U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5b/relu_3x3_reduce
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 192U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) },
+ // inception_5b/relu_5x5_reduce
+ ActivationLayerDataObject{ TensorShape(7U, 7U, 48U), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) }
+ }
+ {
+ }
+
+ ~GoogLeNetActivationLayerDataset() = default;
+};
+
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_ACTIVATION_LAYER_DATASET_H__
diff --git a/tests/dataset/BatchNormalizationLayerDataset.h b/tests/dataset/BatchNormalizationLayerDataset.h
new file mode 100644
index 0000000..4323b8f
--- /dev/null
+++ b/tests/dataset/BatchNormalizationLayerDataset.h
@@ -0,0 +1,90 @@
+/*
+ * 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_DATASET_BATCH_NORMALIZATION_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_BATCH_NORMALIZATION_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <ostream>
+#include <sstream>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class BatchNormalizationLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "BatchNormalizationLayer";
+ ss << "_I" << shape0;
+ ss << "_I" << shape1;
+ ss << "_I" << epsilon;
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &s, const BatchNormalizationLayerDataObject &obj)
+ {
+ s << static_cast<std::string>(obj);
+ return s;
+ }
+
+public:
+ TensorShape shape0;
+ TensorShape shape1;
+ float epsilon;
+};
+
+template <unsigned int Size>
+using BatchNormalizationLayerDataset = GenericDataset<BatchNormalizationLayerDataObject, Size>;
+
+class RandomBatchNormalizationLayerDataset final : public BatchNormalizationLayerDataset<3>
+{
+public:
+ RandomBatchNormalizationLayerDataset()
+ : GenericDataset
+ {
+ BatchNormalizationLayerDataObject{ TensorShape(15U, 16U, 2U, 12U), TensorShape(2U), 0.1f },
+ BatchNormalizationLayerDataObject{ TensorShape(21U, 11U, 12U, 7U), TensorShape(12U), 0.1f },
+ BatchNormalizationLayerDataObject{ TensorShape(7U, 3U, 6U, 11U), TensorShape(6U), 0.1f },
+ }
+ {
+ }
+
+ ~RandomBatchNormalizationLayerDataset() = default;
+};
+
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_BATCH_NORMALIZATION_LAYER_DATASET_H__
diff --git a/tests/dataset/BorderModeDataset.h b/tests/dataset/BorderModeDataset.h
new file mode 100644
index 0000000..37c7a5b
--- /dev/null
+++ b/tests/dataset/BorderModeDataset.h
@@ -0,0 +1,82 @@
+/*
+ * 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_BORDER_MODE_DATASET_H__
+#define __ARM_COMPUTE_TEST_BORDER_MODE_DATASET_H__
+
+#include "arm_compute/core/Types.h"
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+#include <array>
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible border modes.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on all border modes.
+ */
+class BorderModes
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = BorderMode;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _modes.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const BorderMode *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _modes.data();
+ }
+
+private:
+ std::array<BorderMode, 3> _modes{ { BorderMode::UNDEFINED, BorderMode::CONSTANT, BorderMode::REPLICATE } };
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/ConvertPolicyDataset.h b/tests/dataset/ConvertPolicyDataset.h
new file mode 100644
index 0000000..697dba6
--- /dev/null
+++ b/tests/dataset/ConvertPolicyDataset.h
@@ -0,0 +1,82 @@
+/*
+ * 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_CONVERT_POLICY_DATASETS_H__
+#define __ARM_COMPUTE_TEST_CONVERT_POLICY_DATASETS_H__
+
+#include "arm_compute/core/Types.h"
+
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible convert/overflow policies.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different convert policies.
+ */
+class ConvertPolicies
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = ConvertPolicy;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _policies.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const ConvertPolicy *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _policies.data();
+ }
+
+private:
+ std::array<ConvertPolicy, 2> _policies{ { ConvertPolicy::WRAP, ConvertPolicy::SATURATE } };
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/ConvolutionLayerDataset.h b/tests/dataset/ConvolutionLayerDataset.h
new file mode 100644
index 0000000..e66117e
--- /dev/null
+++ b/tests/dataset/ConvolutionLayerDataset.h
@@ -0,0 +1,269 @@
+/*
+ * 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_DATASET_CONVOLUTION_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_CONVOLUTION_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "dataset/GenericDataset.h"
+#include "dataset/ShapeDatasets.h"
+
+#include <sstream>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Convolution Layer data object */
+class ConvolutionLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "ConvolutionLayer";
+ ss << "_I" << src_shape;
+ ss << "_K" << weights_shape;
+ ss << "_PS" << info;
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &os, const ConvolutionLayerDataObject &obj)
+ {
+ os << static_cast<std::string>(obj);
+ return os;
+ }
+
+public:
+ TensorShape src_shape;
+ TensorShape weights_shape;
+ TensorShape bias_shape;
+ TensorShape dst_shape;
+ PadStrideInfo info;
+};
+
+template <unsigned int Size>
+using ConvolutionLayerDataset = GenericDataset<ConvolutionLayerDataObject, Size>;
+
+/** Data set containing small convolution layer shapes */
+class SmallConvolutionLayerDataset final : public ConvolutionLayerDataset<3>
+{
+public:
+ SmallConvolutionLayerDataset()
+ : GenericDataset
+ {
+ ConvolutionLayerDataObject{ TensorShape(23U, 27U, 5U), TensorShape(3U, 3U, 5U, 21U), TensorShape(21U), TensorShape(11U, 25U, 21U), PadStrideInfo(2, 1, 0, 0) },
+ ConvolutionLayerDataObject{ TensorShape(33U, 27U, 7U), TensorShape(5U, 5U, 7U, 11U), TensorShape(11U), TensorShape(11U, 12U, 11U), PadStrideInfo(3, 2, 1, 0) },
+ ConvolutionLayerDataObject{ TensorShape(17U, 31U, 2U, 7U), TensorShape(5U, 5U, 2U, 5U), TensorShape(5U), TensorShape(15U, 15U, 5U, 7U), PadStrideInfo(1, 2, 1, 1) }
+ }
+ {
+ }
+
+ ~SmallConvolutionLayerDataset() = default;
+};
+
+/** Data set containing direct convolution tensor shapes. */
+class DirectConvolutionShapes final : public ShapeDataset<3>
+{
+public:
+ DirectConvolutionShapes()
+ : ShapeDataset(TensorShape(3U, 3U, 3U, 2U, 4U, 5U),
+ TensorShape(32U, 37U, 3U),
+ TensorShape(13U, 15U, 8U, 3U))
+ {
+ }
+};
+
+/** AlexNet's convolution layers tensor shapes. */
+class AlexNetConvolutionLayerDataset final : public ConvolutionLayerDataset<5>
+{
+public:
+ AlexNetConvolutionLayerDataset()
+ : GenericDataset
+ {
+ ConvolutionLayerDataObject{ TensorShape(227U, 227U, 3U), TensorShape(11U, 11U, 3U, 96U), TensorShape(96U), TensorShape(55U, 55U, 96U), PadStrideInfo(4, 4, 0, 0) },
+ ConvolutionLayerDataObject{ TensorShape(27U, 27U, 96U), TensorShape(5U, 5U, 96U, 256U), TensorShape(256U), TensorShape(27U, 27U, 256U), PadStrideInfo(1, 1, 2, 2) },
+ ConvolutionLayerDataObject{ TensorShape(13U, 13U, 256U), TensorShape(3U, 3U, 256U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1) },
+ ConvolutionLayerDataObject{ TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 384U), TensorShape(384U), TensorShape(13U, 13U, 384U), PadStrideInfo(1, 1, 1, 1) },
+ ConvolutionLayerDataObject{ TensorShape(13U, 13U, 384U), TensorShape(3U, 3U, 384U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 1, 1) }
+ }
+ {
+ }
+
+ ~AlexNetConvolutionLayerDataset() = default;
+};
+
+/** LeNet5's convolution layers tensor shapes. */
+class LeNet5ConvolutionLayerDataset final : public ConvolutionLayerDataset<2>
+{
+public:
+ LeNet5ConvolutionLayerDataset()
+ : GenericDataset
+ {
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 1U), TensorShape(5U, 5U, 1U, 20U), TensorShape(20U), TensorShape(24U, 24U, 20U), PadStrideInfo(1, 1, 0, 0) },
+ ConvolutionLayerDataObject{ TensorShape(12U, 12U, 20U), TensorShape(5U, 5U, 20U, 50U), TensorShape(50U), TensorShape(8U, 8U, 50U), PadStrideInfo(1, 1, 0, 0) },
+ }
+ {
+ }
+
+ ~LeNet5ConvolutionLayerDataset() = default;
+};
+
+/** GoogleLeNet v1 convolution layers tensor shapes (Part 1).
+ *
+ * @note Dataset is split into two to avoid a register allocation failure produced by clang in Android debug builds.
+ */
+class GoogLeNetConvolutionLayerDataset1 final : public ConvolutionLayerDataset<32>
+{
+public:
+ GoogLeNetConvolutionLayerDataset1()
+ : GenericDataset
+ {
+ // conv1/7x7_s2
+ ConvolutionLayerDataObject{ TensorShape(224U, 224U, 3U), TensorShape(7U, 7U, 3U, 64U), TensorShape(64U), TensorShape(112U, 112U, 64U), PadStrideInfo(2, 2, 3, 3) },
+ // conv2/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(56U, 56U, 64U), TensorShape(1U, 1U, 64U, 64U), TensorShape(64U), TensorShape(56U, 56U, 64U), PadStrideInfo(1, 1, 0, 0) },
+ // conv2/3x3
+ ConvolutionLayerDataObject{ TensorShape(56U, 56U, 64U), TensorShape(3U, 3U, 64U, 192U), TensorShape(192U), TensorShape(56U, 56U, 192U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_3a/1x1
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 64U), TensorShape(64U), TensorShape(28U, 28U, 64U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3a/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 96U), TensorShape(96U), TensorShape(28U, 28U, 96U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3a/3x3
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 96U), TensorShape(3U, 3U, 96U, 128U), TensorShape(128U), TensorShape(28U, 28U, 128U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_3a/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 16U), TensorShape(16U), TensorShape(28U, 28U, 16U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3a/5x5
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 16U), TensorShape(5U, 5U, 16U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_3a/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(1U, 1U, 192U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3b/1x1, inception_3b/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 128U), TensorShape(128U), TensorShape(28U, 28U, 128U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3b/3x3
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 128U), TensorShape(3U, 3U, 128U, 192U), TensorShape(192U), TensorShape(28U, 28U, 192U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_3b/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 32U), TensorShape(32U), TensorShape(28U, 28U, 32U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_3b/5x5
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 32U), TensorShape(5U, 5U, 32U, 96U), TensorShape(96U), TensorShape(28U, 28U, 96U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_3b/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(1U, 1U, 256U, 64U), TensorShape(64U), TensorShape(28U, 28U, 64U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4a/1x1
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 192U), TensorShape(192U), TensorShape(14U, 14U, 192U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4a/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 96U), TensorShape(96U), TensorShape(14U, 14U, 96U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4a/3x3
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 96U), TensorShape(3U, 3U, 96U, 208U), TensorShape(208U), TensorShape(14U, 14U, 208U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_4a/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 16U), TensorShape(16U), TensorShape(14U, 14U, 16U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4a/5x5
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 16U), TensorShape(5U, 5U, 16U, 48U), TensorShape(48U), TensorShape(14U, 14U, 48U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_4a/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(1U, 1U, 480U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4b/1x1
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 160U), TensorShape(160U), TensorShape(14U, 14U, 160U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4b/3x3_reduce, inception_4d/1x1
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 112U), TensorShape(112U), TensorShape(14U, 14U, 112U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4b/3x3
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 112U), TensorShape(3U, 3U, 112U, 224U), TensorShape(224U), TensorShape(14U, 14U, 224U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_4b/5x5_reduce, inception_4c/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 24U), TensorShape(24U), TensorShape(14U, 14U, 24U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4b/5x5, inception_4c/5x5
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 24U), TensorShape(5U, 5U, 24U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_4b/pool_proj, inception_4c/pool_proj, inception_4d/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4c/1x1, inception_4c/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4c/3x3
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 128U), TensorShape(3U, 3U, 128U, 256U), TensorShape(256U), TensorShape(14U, 14U, 256U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_4d/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 144U), TensorShape(144U), TensorShape(14U, 14U, 144U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4d/3x3
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 144U), TensorShape(3U, 3U, 144U, 288U), TensorShape(288U), TensorShape(14U, 14U, 288U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_4d/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(1U, 1U, 512U, 32U), TensorShape(32U), TensorShape(14U, 14U, 32U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4d/5x5
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 32U), TensorShape(5U, 5U, 32U, 64U), TensorShape(64U), TensorShape(14U, 14U, 64U), PadStrideInfo(1, 1, 2, 2) },
+ }
+ {
+ }
+
+ ~GoogLeNetConvolutionLayerDataset1() = default;
+};
+
+/** GoogleLeNet v1 convolution layers tensor shapes (Part 2). */
+class GoogLeNetConvolutionLayerDataset2 final : public ConvolutionLayerDataset<17>
+{
+public:
+ GoogLeNetConvolutionLayerDataset2()
+ : GenericDataset
+ {
+ // inception_4e/1x1
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 256U), TensorShape(256U), TensorShape(14U, 14U, 256U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4e/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 160U), TensorShape(160U), TensorShape(14U, 14U, 160U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4e/3x3
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 160U), TensorShape(3U, 3U, 160U, 320U), TensorShape(320U), TensorShape(14U, 14U, 320U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_4e/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 32U), TensorShape(32U), TensorShape(14U, 14U, 32U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_4e/5x5
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 32U), TensorShape(5U, 5U, 32U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_4e/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(1U, 1U, 528U, 128U), TensorShape(128U), TensorShape(14U, 14U, 128U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5a/1x1
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 256U), TensorShape(256U), TensorShape(7U, 7U, 256U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5a/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 160U), TensorShape(160U), TensorShape(7U, 7U, 160U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5a/3x3
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 160U), TensorShape(3U, 3U, 160U, 320U), TensorShape(320U), TensorShape(7U, 7U, 320U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_5a/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 32U), TensorShape(32U), TensorShape(7U, 7U, 32U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5a/5x5
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 32U), TensorShape(5U, 5U, 32U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 2, 2) },
+ // inception_5a/pool_proj, inception_5b/pool_proj
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5b/1x1
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 384U), TensorShape(384U), TensorShape(7U, 7U, 384U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5b/3x3_reduce
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 192U), TensorShape(192U), TensorShape(7U, 7U, 192U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5b/3x3
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 192U), TensorShape(3U, 3U, 192U, 384U), TensorShape(384U), TensorShape(7U, 7U, 384U), PadStrideInfo(1, 1, 1, 1) },
+ // inception_5b/5x5_reduce
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(1U, 1U, 832U, 48U), TensorShape(48U), TensorShape(7U, 7U, 48U), PadStrideInfo(1, 1, 0, 0) },
+ // inception_5b/5x5
+ ConvolutionLayerDataObject{ TensorShape(7U, 7U, 48U), TensorShape(5U, 5U, 48U, 128U), TensorShape(128U), TensorShape(7U, 7U, 128U), PadStrideInfo(1, 1, 2, 2) }
+ }
+ {
+ }
+
+ ~GoogLeNetConvolutionLayerDataset2() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_CONVOLUTION_LAYER_DATASET_H__
diff --git a/tests/dataset/DataTypeDatasets.h b/tests/dataset/DataTypeDatasets.h
new file mode 100644
index 0000000..8c63857
--- /dev/null
+++ b/tests/dataset/DataTypeDatasets.h
@@ -0,0 +1,193 @@
+/*
+ * 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_DATA_TYPE_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATA_TYPE_DATASET_H__
+
+#include "arm_compute/core/Types.h"
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Abstract data set containing data types.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different data types.
+ */
+template <unsigned int Size>
+class DataTypes
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = DataType;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _types.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const DataType *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _types.data();
+ }
+
+protected:
+ /** Protected constructor to make the class abstract. */
+ template <typename... Ts>
+ DataTypes(Ts &&... types)
+ : _types{ { types... } }
+ {
+ }
+
+ /** Protected destructor to prevent deletion of derived classes through a
+ * pointer to the base class.
+ */
+ ~DataTypes() = default;
+
+private:
+ std::array<DataType, Size> _types;
+};
+
+/** Data set containing all data types. */
+class AllDataTypes final : public DataTypes<14>
+{
+public:
+ AllDataTypes()
+ : DataTypes{ DataType::U8, DataType::S8, DataType::U16, DataType::S16,
+ DataType::U32, DataType::S32, DataType::U64, DataType::S64,
+ DataType::F16, DataType::F32, DataType::F64, DataType::SIZET,
+ DataType::QS8, DataType::QS16 }
+ {
+ }
+
+ ~AllDataTypes() = default;
+};
+
+/** Data set containing all unsigned data types. */
+class UnsignedDataTypes final : public DataTypes<4>
+{
+public:
+ UnsignedDataTypes()
+ : DataTypes{ DataType::U8, DataType::U16, DataType::U32, DataType::U64 }
+ {
+ }
+
+ ~UnsignedDataTypes() = default;
+};
+
+/** Data set containing all signed data types. */
+class SignedDataTypes final : public DataTypes<4>
+{
+public:
+ SignedDataTypes()
+ : DataTypes{ DataType::S8, DataType::S16, DataType::S32, DataType::S64 }
+ {
+ }
+
+ ~SignedDataTypes() = default;
+};
+
+/** Data set containing all floating point data types. */
+class FloatDataTypes final : public DataTypes<3>
+{
+public:
+ FloatDataTypes()
+ : DataTypes{ DataType::F16, DataType::F32, DataType::F64 }
+ {
+ }
+
+ ~FloatDataTypes() = default;
+};
+
+/** Data set containing all fixed point data types. */
+class FixedPointDataTypes final : public DataTypes<2>
+{
+public:
+ FixedPointDataTypes()
+ : DataTypes{ DataType::QS8, DataType::QS16 }
+ {
+ }
+
+ ~FixedPointDataTypes() = default;
+};
+
+/** Supported CNN float types. */
+class CNNFloatDataTypes final : public DataTypes<1>
+{
+public:
+ CNNFloatDataTypes()
+ : DataTypes{ DataType::F32 }
+ {
+ }
+
+ ~CNNFloatDataTypes() = default;
+};
+
+/** Supported CNN fixed point types. */
+class CNNFixedPointDataTypes final : public DataTypes<1>
+{
+public:
+ CNNFixedPointDataTypes()
+ : DataTypes{ DataType::QS8 }
+ {
+ }
+
+ ~CNNFixedPointDataTypes() = default;
+};
+
+/** Supported CNN types. */
+class CNNDataTypes final : public DataTypes<2>
+{
+public:
+ CNNDataTypes()
+ : DataTypes{ DataType::F32, DataType::QS8 }
+ {
+ }
+
+ ~CNNDataTypes() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/FullyConnectedLayerDataset.h b/tests/dataset/FullyConnectedLayerDataset.h
new file mode 100644
index 0000000..53b7d02
--- /dev/null
+++ b/tests/dataset/FullyConnectedLayerDataset.h
@@ -0,0 +1,155 @@
+/*
+ * 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_DATASET_FULLY_CONNECTED_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_FULLY_CONNECTED_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "dataset/GenericDataset.h"
+
+#include <sstream>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class FullyConnectedLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "FullyConnectedLayer";
+ ss << "_I" << src_shape;
+ ss << "_K" << weights_shape;
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &os, const FullyConnectedLayerDataObject &obj)
+ {
+ os << static_cast<std::string>(obj);
+ return os;
+ }
+
+public:
+ TensorShape src_shape;
+ TensorShape weights_shape;
+ TensorShape bias_shape;
+ TensorShape dst_shape;
+ bool transpose_weights;
+ bool are_weights_reshaped;
+};
+
+template <unsigned int Size>
+using FullyConnectedLayerDataset = GenericDataset<FullyConnectedLayerDataObject, Size>;
+
+class SmallFullyConnectedLayerDataset final : public FullyConnectedLayerDataset<5>
+{
+public:
+ SmallFullyConnectedLayerDataset()
+ : GenericDataset
+ {
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 7U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 7U, 3U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U, 3U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(201U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 7U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U), true, true },
+ FullyConnectedLayerDataObject{ TensorShape(201U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U), true, true },
+ }
+ {
+ }
+
+ ~SmallFullyConnectedLayerDataset() = default;
+};
+
+class LargeFullyConnectedLayerDataset final : public FullyConnectedLayerDataset<5>
+{
+public:
+ LargeFullyConnectedLayerDataset()
+ : GenericDataset
+ {
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 257U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 257U, 2U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U, 2U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(3127U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U), true, false },
+ FullyConnectedLayerDataObject{ TensorShape(9U, 5U, 257U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U), true, true },
+ FullyConnectedLayerDataObject{ TensorShape(3127U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U), true, true },
+ }
+ {
+ }
+
+ ~LargeFullyConnectedLayerDataset() = default;
+};
+
+class AlexNetFullyConnectedLayerDataset final : public FullyConnectedLayerDataset<3>
+{
+public:
+ AlexNetFullyConnectedLayerDataset()
+ : GenericDataset
+ {
+ FullyConnectedLayerDataObject{ TensorShape(6U, 6U, 256U), TensorShape(9216U, 4096U), TensorShape(4096U), TensorShape(4096U), true },
+ FullyConnectedLayerDataObject{ TensorShape(4096U), TensorShape(4096U, 4096U), TensorShape(4096U), TensorShape(4096U), true },
+ FullyConnectedLayerDataObject{ TensorShape(4096U), TensorShape(4096U, 1000U), TensorShape(1000U), TensorShape(1000U), true },
+ }
+ {
+ }
+
+ ~AlexNetFullyConnectedLayerDataset() = default;
+};
+
+class LeNet5FullyConnectedLayerDataset final : public FullyConnectedLayerDataset<2>
+{
+public:
+ LeNet5FullyConnectedLayerDataset()
+ : GenericDataset
+ {
+ FullyConnectedLayerDataObject{ TensorShape(4U, 4U, 50U), TensorShape(800U, 500U), TensorShape(500U), TensorShape(500U) },
+ FullyConnectedLayerDataObject{ TensorShape(500U), TensorShape(500U, 10U), TensorShape(10U), TensorShape(10U) },
+ }
+ {
+ }
+
+ ~LeNet5FullyConnectedLayerDataset() = default;
+};
+
+class GoogLeNetFullyConnectedLayerDataset final : public FullyConnectedLayerDataset<1>
+{
+public:
+ GoogLeNetFullyConnectedLayerDataset()
+ : GenericDataset
+ {
+ FullyConnectedLayerDataObject{ TensorShape(1024U), TensorShape(1024U, 1000U), TensorShape(1000U), TensorShape(1000U), true },
+ }
+ {
+ }
+
+ ~GoogLeNetFullyConnectedLayerDataset() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_FULLY_CONNECTED_LAYER_DATASET_H__
diff --git a/tests/dataset/GEMMDataset.h b/tests/dataset/GEMMDataset.h
new file mode 100644
index 0000000..f45bc3e
--- /dev/null
+++ b/tests/dataset/GEMMDataset.h
@@ -0,0 +1,204 @@
+/*
+ * 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_DATASET_GEMM_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_GEMM_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <ostream>
+#include <sstream>
+
+#include <tuple>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class GEMMDataObject
+{
+public:
+ //Data object used for matrix multiple
+ //D = alpha * A * B + beta * C;
+ TensorShape shape_a;
+ TensorShape shape_b;
+ TensorShape shape_c;
+ TensorShape shape_d;
+ float alpha;
+ float beta;
+
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "GEMM";
+ ss << "_A" << shape_a;
+ ss << "_B" << shape_b;
+ ss << "_C" << shape_c;
+ ss << "_D" << shape_d;
+ ss << "_alpha" << alpha;
+ ss << "_beta" << beta;
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &os, const GEMMDataObject &obj)
+ {
+ os << static_cast<std::string>(obj);
+ return os;
+ }
+};
+
+class SmallGEMMDataset : public GenericDataset<GEMMDataObject, 4>
+{
+public:
+ SmallGEMMDataset()
+ : GenericDataset
+ {
+ GEMMDataObject{ TensorShape(21u, 13u), TensorShape(33u, 21u), TensorShape(33u, 13u), TensorShape(33u, 13u), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(31u, 1u), TensorShape(23u, 31u), TensorShape(23u, 1u), TensorShape(23u, 1u), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(38u, 12u), TensorShape(21u, 38u), TensorShape(21u, 12u), TensorShape(21u, 12u), 0.2f, 1.2f },
+ GEMMDataObject{ TensorShape(32u, 1u), TensorShape(17u, 32u), TensorShape(17u, 1u), TensorShape(17u, 1u), 0.4f, 0.7f },
+ }
+ {
+ }
+
+ ~SmallGEMMDataset() = default;
+};
+
+class LargeGEMMDataset : public GenericDataset<GEMMDataObject, 4>
+{
+public:
+ LargeGEMMDataset()
+ : GenericDataset
+ {
+ GEMMDataObject{ TensorShape(923u, 429u), TensorShape(871u, 923u), TensorShape(871u, 429u), TensorShape(871u, 429u), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1021u, 1u), TensorShape(783u, 1021u), TensorShape(783u, 1u), TensorShape(783u, 1u), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(681u, 1023u), TensorShape(213u, 681u), TensorShape(213u, 1023u), TensorShape(213u, 1023u), 0.2f, 1.2f },
+ GEMMDataObject{ TensorShape(941u, 1u), TensorShape(623u, 941u), TensorShape(623u, 1u), TensorShape(623u, 1u), 0.4f, 0.7f },
+ }
+ {
+ }
+
+ ~LargeGEMMDataset() = default;
+};
+
+class GoogLeNetGEMMDataset1 : public GenericDataset<GEMMDataObject, 32>
+{
+public:
+ GoogLeNetGEMMDataset1()
+ : GenericDataset
+ {
+ GEMMDataObject{ TensorShape(147U, 12544U), TensorShape(64U, 147U), TensorShape(64U, 12544U), TensorShape(64U, 12544U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(64U, 3136U), TensorShape(64U, 64U), TensorShape(64U, 3136U), TensorShape(64U, 3136U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(576U, 3136U), TensorShape(192U, 576U), TensorShape(192U, 3136U), TensorShape(192U, 3136U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(192U, 784U), TensorShape(64U, 192U), TensorShape(64U, 784U), TensorShape(64U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(192U, 784U), TensorShape(96U, 192U), TensorShape(96U, 784U), TensorShape(96U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(864U, 784U), TensorShape(128U, 864U), TensorShape(128U, 784U), TensorShape(128U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(192U, 784U), TensorShape(16U, 192U), TensorShape(16U, 784U), TensorShape(16U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(400U, 784U), TensorShape(32U, 400U), TensorShape(32U, 784U), TensorShape(32U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(192U, 784U), TensorShape(32U, 192U), TensorShape(32U, 784U), TensorShape(32U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(256U, 784U), TensorShape(128U, 256U), TensorShape(128U, 784U), TensorShape(128U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(256U, 784U), TensorShape(128U, 256U), TensorShape(128U, 784U), TensorShape(128U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1152U, 784U), TensorShape(192U, 1152U), TensorShape(192U, 784U), TensorShape(192U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(256U, 784U), TensorShape(32U, 256U), TensorShape(32U, 784U), TensorShape(32U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(800U, 784U), TensorShape(96U, 800U), TensorShape(96U, 784U), TensorShape(96U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(256U, 784U), TensorShape(64U, 256U), TensorShape(64U, 784U), TensorShape(64U, 784U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(480U, 196U), TensorShape(192U, 480U), TensorShape(192U, 196U), TensorShape(192U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(480U, 196U), TensorShape(96U, 480U), TensorShape(96U, 196U), TensorShape(96U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(864U, 196U), TensorShape(204U, 864U), TensorShape(204U, 196U), TensorShape(204U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(480U, 196U), TensorShape(16U, 480U), TensorShape(16U, 196U), TensorShape(16U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(400U, 196U), TensorShape(48U, 400U), TensorShape(48U, 196U), TensorShape(48U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(480U, 196U), TensorShape(64U, 480U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(508U, 196U), TensorShape(160U, 508U), TensorShape(160U, 196U), TensorShape(160U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(508U, 196U), TensorShape(112U, 508U), TensorShape(112U, 196U), TensorShape(112U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1008U, 196U), TensorShape(224U, 1008U), TensorShape(224U, 196U), TensorShape(224U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(508U, 196U), TensorShape(24U, 508U), TensorShape(24U, 196U), TensorShape(24U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(600U, 196U), TensorShape(64U, 600U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(508U, 196U), TensorShape(64U, 508U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(128U, 512U), TensorShape(128U, 196U), TensorShape(128U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(128U, 512U), TensorShape(128U, 196U), TensorShape(128U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1152U, 196U), TensorShape(256U, 1152U), TensorShape(256U, 196U), TensorShape(256U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(24U, 512U), TensorShape(24U, 196U), TensorShape(24U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(600U, 196U), TensorShape(64U, 600U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f }
+ }
+ {
+ }
+
+ ~GoogLeNetGEMMDataset1() = default;
+};
+
+class GoogLeNetGEMMDataset2 : public GenericDataset<GEMMDataObject, 32>
+{
+public:
+ GoogLeNetGEMMDataset2()
+ : GenericDataset
+ {
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(64U, 512U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(112U, 512U), TensorShape(112U, 196U), TensorShape(112U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(144U, 512U), TensorShape(144U, 196U), TensorShape(144U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1296U, 196U), TensorShape(288U, 1296U), TensorShape(288U, 196U), TensorShape(288U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(32U, 512U), TensorShape(32U, 196U), TensorShape(32U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(800U, 196U), TensorShape(64U, 800U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(512U, 196U), TensorShape(64U, 512U), TensorShape(64U, 196U), TensorShape(64U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(528U, 196U), TensorShape(256U, 528U), TensorShape(256U, 196U), TensorShape(256U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(528U, 196U), TensorShape(160U, 528U), TensorShape(160U, 196U), TensorShape(160U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1440U, 196U), TensorShape(320U, 1440U), TensorShape(320U, 196U), TensorShape(320U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(528U, 196U), TensorShape(32U, 528U), TensorShape(32U, 196U), TensorShape(32U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(800U, 196U), TensorShape(128U, 800U), TensorShape(128U, 196U), TensorShape(128U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(528U, 196U), TensorShape(128U, 528U), TensorShape(128U, 196U), TensorShape(128U, 196U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(256U, 832U), TensorShape(256U, 49U), TensorShape(256U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(160U, 832U), TensorShape(160U, 49U), TensorShape(160U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1440U, 49U), TensorShape(320U, 1440U), TensorShape(320U, 49U), TensorShape(320U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(48U, 832U), TensorShape(48U, 49U), TensorShape(48U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1200U, 49U), TensorShape(128U, 1200U), TensorShape(128U, 49U), TensorShape(128U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(128U, 832U), TensorShape(128U, 49U), TensorShape(128U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(384U, 832U), TensorShape(384U, 49U), TensorShape(384U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(192U, 832U), TensorShape(192U, 49U), TensorShape(192U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1728U, 49U), TensorShape(384U, 1728U), TensorShape(384U, 49U), TensorShape(384U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(48U, 832U), TensorShape(48U, 49U), TensorShape(48U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1200U, 49U), TensorShape(128U, 1200U), TensorShape(128U, 49U), TensorShape(128U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(832U, 49U), TensorShape(128U, 832U), TensorShape(128U, 49U), TensorShape(128U, 49U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(508U, 16U), TensorShape(128U, 508U), TensorShape(128U, 16U), TensorShape(128U, 16U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(2048U, 1U), TensorShape(1024U, 2048U), TensorShape(1024U, 1U), TensorShape(1024U, 1U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1024U, 1U), TensorShape(1008U, 1024U), TensorShape(1008U, 1U), TensorShape(1008U, 1U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(528U, 16U), TensorShape(128U, 528U), TensorShape(128U, 16U), TensorShape(128U, 16U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(2048U, 1U), TensorShape(1024U, 2048U), TensorShape(1024U, 1U), TensorShape(1024U, 1U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1024U, 1U), TensorShape(1008U, 1024U), TensorShape(1008U, 1U), TensorShape(1008U, 1U), 1.0f, 0.0f },
+ GEMMDataObject{ TensorShape(1024U, 1U), TensorShape(1008U, 1024U), TensorShape(1008U, 1U), TensorShape(1008U, 1U), 1.0f, 0.0f }
+ }
+ {
+ }
+
+ ~GoogLeNetGEMMDataset2() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_GEMM_DATASET_H__
diff --git a/tests/dataset/GenericDataset.h b/tests/dataset/GenericDataset.h
new file mode 100644
index 0000000..4ca489b
--- /dev/null
+++ b/tests/dataset/GenericDataset.h
@@ -0,0 +1,97 @@
+/*
+ * 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_DATASET_GENERIC_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_GENERIC_DATASET_H__
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Abstract data set containing multiple objects T.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different configurations.
+ */
+template <class T, unsigned int Size>
+class GenericDataset
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = T;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _data.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const T *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _data.data();
+ }
+
+protected:
+ /** Protected constructor to make the class abstract. */
+ template <typename... Ts>
+ GenericDataset(Ts... objs)
+ : _data{ { objs... } }
+ {
+ }
+
+ /** Protected destructor to prevent deletion of derived class through a
+ * pointer to the base class.
+ */
+ ~GenericDataset() = default;
+
+private:
+ std::array<T, Size> _data;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_GENERIC_DATASET_H__
diff --git a/tests/dataset/ImageDatasets.h b/tests/dataset/ImageDatasets.h
new file mode 100644
index 0000000..555227e
--- /dev/null
+++ b/tests/dataset/ImageDatasets.h
@@ -0,0 +1,120 @@
+/*
+ * 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_IMAGE_DATASETS_H__
+#define __ARM_COMPUTE_TEST_IMAGE_DATASETS_H__
+
+#include <string>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Abstract data set containing image names.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different images.
+ */
+template <unsigned int Size>
+class ImageDataset
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = const std::string;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _images.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const std::string *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _images.data();
+ }
+
+protected:
+ /** Protected constructor to make the class abstract. */
+ template <typename... Ts>
+ ImageDataset(Ts... images)
+ : _images{ { images... } }
+ {
+ }
+
+ /** Protected destructor to prevent deletion of derived class through a
+ * pointer to the base class.
+ */
+ ~ImageDataset() = default;
+
+private:
+ std::array<std::string, Size> _images;
+};
+
+/** Data set containing names of small images. */
+class SmallImages final : public ImageDataset<2>
+{
+public:
+ SmallImages()
+ : ImageDataset("128x128.ppm", "640x480.ppm")
+ {
+ }
+};
+
+/** Data set containing names of large images. */
+class LargeImages final : public ImageDataset<3>
+{
+public:
+ LargeImages()
+#ifdef INTERNAL_ONLY
+ : ImageDataset("1280x720.ppm", "1920x1080.ppm", "4160x3120.ppm")
+ // The 4k image is too large to distribute
+#else
+ : ImageDataset("1280x720.ppm", "1920x1080.ppm")
+#endif /* INTERNAL_ONLY */
+ {
+ }
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/InterpolationPolicyDataset.h b/tests/dataset/InterpolationPolicyDataset.h
new file mode 100644
index 0000000..e6062ea
--- /dev/null
+++ b/tests/dataset/InterpolationPolicyDataset.h
@@ -0,0 +1,80 @@
+/*
+ * 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_INTERPOLATION_POLICY_DATASET_H__
+#define __ARM_COMPUTE_TEST_INTERPOLATION_POLICY_DATASET_H__
+
+#include "arm_compute/core/Types.h"
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible interpolation policies.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on all interpolation policies.
+ */
+class InterpolationPolicies
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = InterpolationPolicy;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _policies.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const InterpolationPolicy *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _policies.data();
+ }
+
+private:
+ std::array<InterpolationPolicy, 3> _policies{ { InterpolationPolicy::NEAREST_NEIGHBOR, InterpolationPolicy::BILINEAR, InterpolationPolicy::AREA } };
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/NormalizationLayerDataset.h b/tests/dataset/NormalizationLayerDataset.h
new file mode 100644
index 0000000..7234f41
--- /dev/null
+++ b/tests/dataset/NormalizationLayerDataset.h
@@ -0,0 +1,99 @@
+/*
+ * 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_DATASET_NORMALIZATION_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_NORMALIZATION_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <sstream>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class NormalizationLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "NormalizationLayer";
+ ss << "_I" << shape;
+ ss << "_F_" << info.type();
+ ss << "_S_" << info.norm_size();
+ return ss.str();
+ }
+
+public:
+ TensorShape shape;
+ NormalizationLayerInfo info;
+};
+
+template <unsigned int Size>
+using NormalizationLayerDataset = GenericDataset<NormalizationLayerDataObject, Size>;
+
+class GoogLeNetNormalizationLayerDataset final : public NormalizationLayerDataset<2>
+{
+public:
+ GoogLeNetNormalizationLayerDataset()
+ : GenericDataset
+ {
+ // conv2/norm2
+ NormalizationLayerDataObject{ TensorShape(56U, 56U, 192U), NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f) },
+ // pool1/norm1
+ NormalizationLayerDataObject{ TensorShape(56U, 56U, 64U), NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f) }
+ }
+ {
+ }
+
+ ~GoogLeNetNormalizationLayerDataset() = default;
+};
+
+class AlexNetNormalizationLayerDataset final : public NormalizationLayerDataset<2>
+{
+public:
+ AlexNetNormalizationLayerDataset()
+ : GenericDataset
+ {
+ NormalizationLayerDataObject{ TensorShape(55U, 55U, 96U), NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f) },
+ NormalizationLayerDataObject{ TensorShape(27U, 27U, 256U), NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f) },
+ }
+ {
+ }
+
+ ~AlexNetNormalizationLayerDataset() = default;
+};
+
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_NORMALIZATION_LAYER_DATASET_H__
diff --git a/tests/dataset/NormalizationTypeDataset.h b/tests/dataset/NormalizationTypeDataset.h
new file mode 100644
index 0000000..bb1975c
--- /dev/null
+++ b/tests/dataset/NormalizationTypeDataset.h
@@ -0,0 +1,80 @@
+/*
+ * 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_NORMALIZATION_TYPE_DATASET_H__
+#define __ARM_COMPUTE_TEST_NORMALIZATION_TYPE_DATASET_H__
+
+#include "arm_compute/core/Types.h"
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible normalization types.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on all normalization types.
+ */
+class NormalizationTypes
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = NormType;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _types.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const NormType *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _types.data();
+ }
+
+private:
+ std::array<NormType, 3> _types{ { NormType::IN_MAP_1D, NormType::IN_MAP_2D, NormType::CROSS_MAP } };
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/PoolingLayerDataset.h b/tests/dataset/PoolingLayerDataset.h
new file mode 100644
index 0000000..0a50a76
--- /dev/null
+++ b/tests/dataset/PoolingLayerDataset.h
@@ -0,0 +1,159 @@
+/*
+ * 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_DATASET_POOLING_LAYER_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_POOLING_LAYER_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class PoolingLayerDataObject
+{
+public:
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "PoolingLayer";
+ ss << "_I" << src_shape;
+ ss << "_S_" << info.pool_size();
+ ss << "_F_" << info.pool_type();
+ ss << "_PS" << info.pad_stride_info();
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &s, const PoolingLayerDataObject &obj)
+ {
+ s << static_cast<std::string>(obj);
+ return s;
+ }
+
+public:
+ TensorShape src_shape;
+ TensorShape dst_shape;
+ PoolingLayerInfo info;
+};
+
+template <unsigned int Size>
+using PoolingLayerDataset = GenericDataset<PoolingLayerDataObject, Size>;
+
+class AlexNetPoolingLayerDataset final : public PoolingLayerDataset<3>
+{
+public:
+ AlexNetPoolingLayerDataset()
+ : GenericDataset
+ {
+ PoolingLayerDataObject{ TensorShape(55U, 55U, 96U), TensorShape(27U, 27U, 96U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(27U, 27U, 256U), TensorShape(13U, 13U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(13U, 13U, 256U), TensorShape(6U, 6U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) },
+ }
+ {
+ }
+
+ ~AlexNetPoolingLayerDataset() = default;
+};
+
+class LeNet5PoolingLayerDataset final : public PoolingLayerDataset<2>
+{
+public:
+ LeNet5PoolingLayerDataset()
+ : GenericDataset
+ {
+ PoolingLayerDataObject{ TensorShape(24U, 24U, 20U), TensorShape(12U, 12U, 20U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(8U, 8U, 50U), TensorShape(4U, 4U, 50U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) },
+ }
+ {
+ }
+
+ ~LeNet5PoolingLayerDataset() = default;
+};
+
+class GoogLeNetPoolingLayerDataset final : public PoolingLayerDataset<10>
+{
+public:
+ GoogLeNetPoolingLayerDataset()
+ : GenericDataset
+ {
+ // FIXME: Add support for 7x7 pooling layer pool5/7x7_s1
+ // pool1/3x3_s2
+ PoolingLayerDataObject{ TensorShape(112U, 112U, 64U), TensorShape(56U, 56U, 64U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) },
+ // pool2/3x3_s2
+ PoolingLayerDataObject{ TensorShape(56U, 56U, 192U), TensorShape(28U, 28U, 192U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) },
+ // inception_3a/pool
+ PoolingLayerDataObject{ TensorShape(28U, 28U, 192U), TensorShape(28U, 28U, 192U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ // inception_3b/pool
+ PoolingLayerDataObject{ TensorShape(28U, 28U, 256U), TensorShape(28U, 28U, 256U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ // pool3/3x3_s2
+ PoolingLayerDataObject{ TensorShape(28U, 28U, 480U), TensorShape(14U, 14U, 480U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) },
+ // inception_4a/pool
+ PoolingLayerDataObject{ TensorShape(14U, 14U, 480U), TensorShape(14U, 14U, 480U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ // inception_4b/pool, inception_4c/pool, inception_4d/pool
+ PoolingLayerDataObject{ TensorShape(14U, 14U, 512U), TensorShape(14U, 14U, 512U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ // inception_4e/pool
+ PoolingLayerDataObject{ TensorShape(14U, 14U, 528U), TensorShape(14U, 14U, 528U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ // pool4/3x3_s2
+ PoolingLayerDataObject{ TensorShape(14U, 14U, 832U), TensorShape(7U, 7U, 832U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)) },
+ // inception_5a/pool, inception_5b/pool
+ PoolingLayerDataObject{ TensorShape(7U, 7U, 832U), TensorShape(7U, 7U, 832U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)) },
+ }
+ {
+ }
+
+ ~GoogLeNetPoolingLayerDataset() = default;
+};
+
+class RandomPoolingLayerDataset final : public PoolingLayerDataset<8>
+{
+public:
+ RandomPoolingLayerDataset()
+ : GenericDataset
+ {
+ PoolingLayerDataObject{ TensorShape(27U, 27U, 16U), TensorShape(13U, 13U, 16U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(13U, 13U, 32U), TensorShape(6U, 6U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(24U, 24U, 10U), TensorShape(12U, 12U, 10U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(8U, 8U, 30U), TensorShape(4U, 4U, 30U), PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(27U, 27U, 16U), TensorShape(13U, 13U, 16U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(13U, 13U, 32U), TensorShape(6U, 6U, 32U), PoolingLayerInfo(PoolingType::AVG, 3, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(24U, 24U, 10U), TensorShape(12U, 12U, 10U), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2, 2, 0, 0)) },
+ PoolingLayerDataObject{ TensorShape(8U, 8U, 30U), TensorShape(4U, 4U, 30U), PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2, 2, 0, 0)) },
+ }
+ {
+ }
+
+ ~RandomPoolingLayerDataset() = default;
+};
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_POOLING_LAYER_DATASET_H__
diff --git a/tests/dataset/RoundingPolicyDataset.h b/tests/dataset/RoundingPolicyDataset.h
new file mode 100644
index 0000000..c708720
--- /dev/null
+++ b/tests/dataset/RoundingPolicyDataset.h
@@ -0,0 +1,82 @@
+/*
+ * 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_ROUNDING_POLICY_DATASETS_H__
+#define __ARM_COMPUTE_TEST_ROUNDING_POLICY_DATASETS_H__
+
+#include "arm_compute/core/Types.h"
+
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Data set containing all possible rounding policies.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different rounding policies.
+ */
+class RoundingPolicies
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = RoundingPolicy;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _policies.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const RoundingPolicy *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _policies.data();
+ }
+
+private:
+ std::array<RoundingPolicy, 3> _policies{ { RoundingPolicy::TO_ZERO, RoundingPolicy::TO_NEAREST_UP, RoundingPolicy::TO_NEAREST_EVEN } };
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/ShapeDatasets.h b/tests/dataset/ShapeDatasets.h
new file mode 100644
index 0000000..73bdb8e
--- /dev/null
+++ b/tests/dataset/ShapeDatasets.h
@@ -0,0 +1,130 @@
+/*
+ * 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_SHAPE_DATASETS_H__
+#define __ARM_COMPUTE_TEST_SHAPE_DATASETS_H__
+
+#include "arm_compute/core/TensorShape.h"
+
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+/** Abstract data set containing tensor shapes.
+ *
+ * Can be used as input for Boost data test cases to automatically run a test
+ * case on different tensor shapes.
+ */
+template <unsigned int Size>
+class ShapeDataset
+{
+public:
+ /** Type of the samples in the data set. */
+ using sample = TensorShape;
+
+ /** Dimensionality of the data set. */
+ enum
+ {
+ arity = 1
+ };
+
+ /** Number of samples in the data set. */
+#ifdef BOOST
+ boost::unit_test::data::size_t size() const
+#else
+ unsigned int size() const
+#endif
+ {
+ return _shapes.size();
+ }
+
+ /** Type of the iterator used to step through all samples in the data set.
+ * Needs to support operator*() and operator++() which a pointer does.
+ */
+ using iterator = const TensorShape *;
+
+ /** Iterator to the first sample in the data set. */
+ iterator begin() const
+ {
+ return _shapes.data();
+ }
+
+protected:
+ /** Protected constructor to make the class abstract. */
+ template <typename... Ts>
+ ShapeDataset(Ts... shapes)
+ : _shapes{ { shapes... } }
+ {
+ }
+
+ /** Protected destructor to prevent deletion of derived class through a
+ * pointer to the base class.
+ */
+ ~ShapeDataset() = default;
+
+private:
+ std::array<TensorShape, Size> _shapes;
+};
+
+/** Data set containing one 1D tensor shape. */
+class Small1DShape final : public ShapeDataset<1>
+{
+public:
+ Small1DShape()
+ : ShapeDataset(TensorShape(128U))
+ {
+ }
+};
+
+/** Data set containing small tensor shapes. */
+class SmallShapes final : public ShapeDataset<3>
+{
+public:
+ SmallShapes()
+ : ShapeDataset(TensorShape(5U, 5U),
+ TensorShape(27U, 13U, 2U),
+ TensorShape(128U, 64U, 1U, 3U))
+ {
+ }
+};
+
+/** Data set containing large tensor shapes. */
+class LargeShapes final : public ShapeDataset<3>
+{
+public:
+ LargeShapes()
+ : ShapeDataset(TensorShape(1920U, 1080U),
+ TensorShape(1245U, 652U, 1U, 3U),
+ TensorShape(4160U, 3120U))
+ {
+ }
+};
+} // namespace test
+} // namespace arm_compute
+#endif
diff --git a/tests/dataset/ThresholdDataset.h b/tests/dataset/ThresholdDataset.h
new file mode 100644
index 0000000..956cf3d
--- /dev/null
+++ b/tests/dataset/ThresholdDataset.h
@@ -0,0 +1,95 @@
+/*
+ * 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_DATASET_THRESHOLD_DATASET_H__
+#define __ARM_COMPUTE_TEST_DATASET_THRESHOLD_DATASET_H__
+
+#include "TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "dataset/GenericDataset.h"
+
+#include <ostream>
+#include <sstream>
+
+#include <tuple>
+#include <type_traits>
+
+#ifdef BOOST
+#include "boost_wrapper.h"
+#endif
+
+namespace arm_compute
+{
+namespace test
+{
+class ThresholdDataObject
+{
+public:
+ uint8_t threshold;
+ uint8_t false_value;
+ uint8_t true_value;
+ ThresholdType type;
+ uint8_t upper;
+
+ operator std::string() const
+ {
+ std::stringstream ss;
+ ss << "Threshold";
+ ss << "_threshold_value" << threshold;
+ ss << "_false_value" << false_value;
+ ss << "_true_value" << true_value;
+ ss << "_type";
+ ss << ((type == ThresholdType::BINARY) ? "binary" : "range");
+ ss << "_upper" << upper;
+ return ss.str();
+ }
+
+ friend std::ostream &operator<<(std::ostream &os, const ThresholdDataObject &obj)
+ {
+ os << static_cast<std::string>(obj);
+ return os;
+ }
+};
+
+class ThresholdDataset : public GenericDataset<ThresholdDataObject, 4>
+{
+public:
+ ThresholdDataset()
+ : GenericDataset
+ {
+ ThresholdDataObject{ 10U, 25U, 3U, ThresholdType::BINARY, 0U },
+ ThresholdDataObject{ 20U, 1U, 0U, ThresholdType::BINARY, 0U },
+ ThresholdDataObject{ 30U, 1U, 0U, ThresholdType::RANGE, 100U },
+ ThresholdDataObject{ 100U, 1U, 0U, ThresholdType::RANGE, 200U },
+ }
+ {
+ }
+
+ ~ThresholdDataset() = default;
+};
+
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_DATASET_THRESHOLD_DATASET_H__