COMPMID-415: Move FullyConnectedLayer to new validation
Change-Id: I7f60d6fb484d3962b88874e1531cec734c11e416
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/83556
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/datasets_new/FullyConnectedLayerDataset.h b/tests/datasets_new/FullyConnectedLayerDataset.h
index 562295f..8401e39 100644
--- a/tests/datasets_new/FullyConnectedLayerDataset.h
+++ b/tests/datasets_new/FullyConnectedLayerDataset.h
@@ -59,7 +59,7 @@
description << "In=" << *_src_it << ":";
description << "Weights=" << *_weights_it << ":";
description << "Biases=" << *_biases_it << ":";
- description << "Out=" << *_dst_it << ":";
+ description << "Out=" << *_dst_it;
return description.str();
}
@@ -113,6 +113,38 @@
std::vector<TensorShape> _bias_shapes{};
std::vector<TensorShape> _dst_shapes{};
};
+
+class SmallFullyConnectedLayerDataset final : public FullyConnectedLayerDataset
+{
+public:
+ SmallFullyConnectedLayerDataset()
+ {
+ // Conv -> FC
+ add_config(TensorShape(9U, 5U, 7U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U));
+ // Conv -> FC (batched)
+ add_config(TensorShape(9U, 5U, 7U, 3U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U, 3U));
+ // FC -> FC
+ add_config(TensorShape(201U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U));
+ // FC -> FC (batched)
+ add_config(TensorShape(201U, 3U), TensorShape(201U, 529U), TensorShape(529U), TensorShape(529U, 3U));
+
+ add_config(TensorShape(9U, 5U, 7U, 3U, 2U), TensorShape(315U, 271U), TensorShape(271U), TensorShape(271U, 3U, 2U));
+ }
+};
+
+class LargeFullyConnectedLayerDataset final : public FullyConnectedLayerDataset
+{
+public:
+ LargeFullyConnectedLayerDataset()
+ {
+ add_config(TensorShape(9U, 5U, 257U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U));
+ add_config(TensorShape(9U, 5U, 257U, 2U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U, 2U));
+ add_config(TensorShape(3127U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U));
+ add_config(TensorShape(3127U, 2U), TensorShape(3127U, 989U), TensorShape(989U), TensorShape(989U, 2U));
+
+ add_config(TensorShape(9U, 5U, 257U, 2U, 3U), TensorShape(11565U, 2123U), TensorShape(2123U), TensorShape(2123U, 2U, 3U));
+ }
+};
} // namespace datasets
} // namespace test
} // namespace arm_compute
diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp
deleted file mode 100644
index 21ea5a5..0000000
--- a/tests/validation/CL/FullyConnectedLayer.cpp
+++ /dev/null
@@ -1,221 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "CL/CLAccessor.h"
-#include "TypePrinter.h"
-#include "dataset/FullyConnectedLayerDataset.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-#include "validation/Datasets.h"
-#include "validation/Reference.h"
-#include "validation/Validation.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
-
-#include <random>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-using namespace arm_compute::test::validation;
-
-namespace
-{
-const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
-const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
-
-CLTensor compute_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
- bool transpose_weights, int fixed_point_position)
-{
- // Create tensors
- CLTensor src = create_tensor<CLTensor>(input_shape, dt, 1, fixed_point_position);
- CLTensor bias = create_tensor<CLTensor>(bias_shape, dt, 1, fixed_point_position);
- CLTensor dst = create_tensor<CLTensor>(output_shape, dt, 1, fixed_point_position);
-
- // Swap the first and second dimension of weights' shape if transpose_weights is true
- TensorShape ws = weights_shape;
- if(transpose_weights)
- {
- const size_t dimx = ws.x();
- ws.set(0, ws.y());
- ws.set(1, dimx);
- }
-
- CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position);
-
- // Create and configure function.
- // Note: We pass the weights already transposed
- CLFullyConnectedLayer fc;
- fc.configure(&src, &weights, &bias, &dst, false);
-
- // Allocate tensors
- src.allocator()->allocate();
- weights.allocator()->allocate();
- bias.allocator()->allocate();
- dst.allocator()->allocate();
-
- BOOST_TEST(!src.info()->is_resizable());
- BOOST_TEST(!weights.info()->is_resizable());
- BOOST_TEST(!bias.info()->is_resizable());
- BOOST_TEST(!dst.info()->is_resizable());
-
- // Fill tensors
- if(dt == DataType::F32)
- {
- std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
- library->fill(CLAccessor(src), distribution, 0);
- library->fill(CLAccessor(weights), distribution, 1);
- library->fill(CLAccessor(bias), distribution, 2);
- }
- else
- {
- library->fill_tensor_uniform(CLAccessor(src), 0);
- library->fill_tensor_uniform(CLAccessor(weights), 1);
- library->fill_tensor_uniform(CLAccessor(bias), 2);
- }
-
- // Compute NEFullyConnectedLayer function
- fc.run();
-
- return dst;
-}
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(CL)
-BOOST_AUTO_TEST_SUITE(FullyConnectedLayer)
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(Configuration,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }),
- fc_set, dt)
-{
- // Set fixed point position data type allowed
- int fixed_point_position = (dt == DataType::F32) ? 0 : 3;
-
- // Create tensors
- CLTensor src = create_tensor<CLTensor>(fc_set.src_shape, dt, 1, fixed_point_position);
- CLTensor bias = create_tensor<CLTensor>(fc_set.bias_shape, dt, 1, fixed_point_position);
- CLTensor dst = create_tensor<CLTensor>(fc_set.dst_shape, dt, 1, fixed_point_position);
-
- // Swap the first and second dimension of weights' shape if transpose_weights is true
- TensorShape ws = fc_set.weights_shape;
- if(fc_set.transpose_weights)
- {
- const size_t dimx = ws.x();
- ws.set(0, ws.y());
- ws.set(1, dimx);
- }
-
- CLTensor weights = create_tensor<CLTensor>(ws, dt, 1, fixed_point_position);
-
- BOOST_TEST(src.info()->is_resizable());
- BOOST_TEST(weights.info()->is_resizable());
- BOOST_TEST(bias.info()->is_resizable());
- BOOST_TEST(dst.info()->is_resizable());
-
- // Create and configure function.
- // Note: We pass the weights already transposed
- CLFullyConnectedLayer fc;
- fc.configure(&src, &weights, &bias, &dst, false);
-
- // Validate valid region
- const ValidRegion src_valid_region = shape_to_valid_region(fc_set.src_shape);
- const ValidRegion weights_valid_region = shape_to_valid_region(ws);
- const ValidRegion bias_valid_region = shape_to_valid_region(fc_set.bias_shape);
- const ValidRegion dst_valid_region = shape_to_valid_region(fc_set.dst_shape);
-
- validate(src.info()->valid_region(), src_valid_region);
- validate(weights.info()->valid_region(), weights_valid_region);
- validate(bias.info()->valid_region(), bias_valid_region);
- validate(dst.info()->valid_region(), dst_valid_region);
-}
-
-BOOST_AUTO_TEST_SUITE(Float)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
- fc_set, dt)
-{
- // Compute function
- CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Validate output
- validate(CLAccessor(dst), ref_dst, tolerance_f32);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
- LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
- fc_set, dt)
-{
- // Compute function
- CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Validate output
- validate(CLAccessor(dst), ref_dst, tolerance_f32);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE(Quantized)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
- fc_set, dt, fixed_point_position)
-{
- // Compute function
- CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Validate output
- validate(CLAccessor(dst), ref_dst, tolerance_q);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
- LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
- fc_set, dt, fixed_point_position)
-{
- // Compute function
- CLTensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Validate output
- validate(CLAccessor(dst), ref_dst, tolerance_q);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif // DOXYGEN_SKIP_THIS
diff --git a/tests/validation/NEON/FullyConnectedLayer.cpp b/tests/validation/NEON/FullyConnectedLayer.cpp
deleted file mode 100644
index 22572ec..0000000
--- a/tests/validation/NEON/FullyConnectedLayer.cpp
+++ /dev/null
@@ -1,243 +0,0 @@
-/*
- * Copyright (c) 2017 ARM Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "NEON/Accessor.h"
-#include "TypePrinter.h"
-#include "dataset/FullyConnectedLayerDataset.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-#include "validation/Datasets.h"
-#include "validation/Reference.h"
-#include "validation/Validation.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
-
-#include <random>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-using namespace arm_compute::test::validation;
-
-namespace
-{
-const float tolerance_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
-const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */
-#ifdef ARM_COMPUTE_ENABLE_FP16
-const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
-#endif /*ARM_COMPUTE_ENABLE_FP16*/
-
-Tensor compute_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
- bool transpose_weights, int fixed_point_position)
-{
- // Create tensors
- Tensor src = create_tensor<Tensor>(input_shape, dt, 1, fixed_point_position);
- Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position);
- Tensor dst = create_tensor<Tensor>(output_shape, dt, 1, fixed_point_position);
-
- // Swap the first and second dimension of weights' shape if transpose_weights is true
- TensorShape ws = weights_shape;
- if(transpose_weights)
- {
- const size_t dimx = ws.x();
- ws.set(0, ws.y());
- ws.set(1, dimx);
- }
-
- Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position);
-
- // Create and configure function.
- // Note: We pass the weights already transposed
- NEFullyConnectedLayer fc;
- fc.configure(&src, &weights, &bias, &dst, false);
-
- // Allocate tensors
- src.allocator()->allocate();
- weights.allocator()->allocate();
- bias.allocator()->allocate();
- dst.allocator()->allocate();
-
- BOOST_TEST(!src.info()->is_resizable());
- BOOST_TEST(!weights.info()->is_resizable());
- BOOST_TEST(!bias.info()->is_resizable());
- BOOST_TEST(!dst.info()->is_resizable());
-
- // Fill tensors
- if(dt == DataType::F16 || dt == DataType::F32)
- {
- std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
- library->fill(Accessor(src), distribution, 0);
- library->fill(Accessor(weights), distribution, 1);
- library->fill(Accessor(bias), distribution, 2);
- }
- else
- {
- library->fill_tensor_uniform(Accessor(src), 0);
- library->fill_tensor_uniform(Accessor(weights), 1);
- library->fill_tensor_uniform(Accessor(bias), 2);
- }
-
- // Compute NEFullyConnectedLayer function
- fc.run();
-
- return dst;
-}
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(NEON)
-BOOST_AUTO_TEST_SUITE(FullyConnectedLayer)
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(Configuration,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }),
- fc_set, dt)
-{
- // Set fixed point position data type allowed
- int fixed_point_position = (dt == DataType::F32) ? 0 : 3;
-
- // Create tensors
- Tensor src = create_tensor<Tensor>(fc_set.src_shape, dt, 1, fixed_point_position);
- Tensor bias = create_tensor<Tensor>(fc_set.bias_shape, dt, 1, fixed_point_position);
- Tensor dst = create_tensor<Tensor>(fc_set.dst_shape, dt, 1, fixed_point_position);
-
- // Swap the first and second dimension of weights' shape if transpose_weights is true
- TensorShape ws = fc_set.weights_shape;
- if(fc_set.transpose_weights)
- {
- const size_t dimx = ws.x();
- ws.set(0, ws.y());
- ws.set(1, dimx);
- }
-
- Tensor weights = create_tensor<Tensor>(ws, dt, 1, fixed_point_position);
-
- BOOST_TEST(src.info()->is_resizable());
- BOOST_TEST(weights.info()->is_resizable());
- BOOST_TEST(bias.info()->is_resizable());
- BOOST_TEST(dst.info()->is_resizable());
-
- // Create and configure function.
- // Note: We pass the weights already transposed
- NEFullyConnectedLayer fc;
- fc.configure(&src, &weights, &bias, &dst, false);
-
- // Validate valid region
- const ValidRegion src_valid_region = shape_to_valid_region(fc_set.src_shape);
- const ValidRegion weights_valid_region = shape_to_valid_region(ws);
- const ValidRegion bias_valid_region = shape_to_valid_region(fc_set.bias_shape);
- const ValidRegion dst_valid_region = shape_to_valid_region(fc_set.dst_shape);
-
- validate(src.info()->valid_region(), src_valid_region);
- validate(weights.info()->valid_region(), weights_valid_region);
- validate(bias.info()->valid_region(), bias_valid_region);
- validate(dst.info()->valid_region(), dst_valid_region);
-}
-
-#ifdef ARM_COMPUTE_ENABLE_FP16
-BOOST_AUTO_TEST_SUITE(Float16)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F16 }),
- fc_set, dt)
-{
- // Compute function
- Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Validate output
- validate(Accessor(dst), ref_dst, tolerance_f16);
-}
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* ARM_COMPUTE_ENABLE_FP16 */
-
-BOOST_AUTO_TEST_SUITE(Float)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
- fc_set, dt)
-{
- // Compute function
- Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Validate output
- validate(Accessor(dst), ref_dst, tolerance_f32);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
- LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::F32 }),
- fc_set, dt)
-{
- // Compute function
- Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, 0);
-
- // Validate output
- validate(Accessor(dst), ref_dst, tolerance_f32);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE(Quantized)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(RunSmall,
- SmallFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
- fc_set, dt, fixed_point_position)
-{
- // Compute function
- Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Validate output
- validate(Accessor(dst), ref_dst, tolerance_q);
-}
-
-BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly"))
-BOOST_DATA_TEST_CASE(RunLarge,
- LargeFullyConnectedLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7),
- fc_set, dt, fixed_point_position)
-{
- // Compute function
- Tensor dst = compute_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Compute reference
- RawTensor ref_dst = Reference::compute_reference_fully_connected_layer(fc_set.src_shape, fc_set.weights_shape, fc_set.bias_shape, fc_set.dst_shape, dt, fc_set.transpose_weights, fixed_point_position);
-
- // Validate output
- validate(Accessor(dst), ref_dst, tolerance_q);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* DOXYGEN_SKIP_THIS */
diff --git a/tests/validation/Reference.cpp b/tests/validation/Reference.cpp
index 16b4cb9..1ea017e 100644
--- a/tests/validation/Reference.cpp
+++ b/tests/validation/Reference.cpp
@@ -461,46 +461,6 @@
return ref_dst;
}
-RawTensor Reference::compute_reference_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
- DataType dt, bool transpose_weights, int fixed_point_position)
-{
- // Create reference
- RawTensor ref_src(input_shape, dt, 1, fixed_point_position);
- RawTensor ref_bias(bias_shape, dt, 1, fixed_point_position);
- RawTensor ref_dst(output_shape, dt, 1, fixed_point_position);
-
- // Swap the first and second dimension of weights' shape if transpose_weights is true
- TensorShape ws = weights_shape;
- if(transpose_weights)
- {
- const size_t dimx = ws.x();
- ws.set(0, ws.y());
- ws.set(1, dimx);
- }
-
- RawTensor ref_weights(ws, dt, 1, fixed_point_position);
-
- // Fill reference
- if(dt == DataType::F16 || dt == DataType::F32)
- {
- std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
- library->fill(ref_src, distribution, 0);
- library->fill(ref_weights, distribution, 1);
- library->fill(ref_bias, distribution, 2);
- }
- else
- {
- library->fill_tensor_uniform(ref_src, 0);
- library->fill_tensor_uniform(ref_weights, 1);
- library->fill_tensor_uniform(ref_bias, 2);
- }
-
- // Compute reference
- ReferenceCPP::fully_connected_layer(ref_src, ref_weights, ref_bias, ref_dst);
-
- return ref_dst;
-}
-
RawTensor Reference::compute_reference_pooling_layer(const TensorShape &shape_in, const TensorShape &shape_out, DataType dt, PoolingLayerInfo pool_info, int fixed_point_position)
{
// Create reference
diff --git a/tests/validation/Reference.h b/tests/validation/Reference.h
index 93e12ff..288dc0e 100644
--- a/tests/validation/Reference.h
+++ b/tests/validation/Reference.h
@@ -293,20 +293,6 @@
* @return Computed raw tensor.
*/
static RawTensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0);
- /** Compute reference for fully connected layer function
- *
- * @param[in] input_shape Shape for the input tensor
- * @param[in] weights_shape Shape for the weights tensor
- * @param[in] bias_shape Shape for the bias tensor
- * @param[in] output_shape Shape for the output tensor
- * @param[in] dt Data type to use
- * @param[in] transpose_weights Transpose the weights if true
- * @param[in] fixed_point_position Number of bits for the fractional part of the fixed point numbers
- *
- * @return Computed raw tensor.
- */
- static RawTensor compute_reference_fully_connected_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt,
- bool transpose_weights, int fixed_point_position);
/** Compute reference pooling layer.
*
* @param[in] shape_in Shape of the input tensor.
diff --git a/tests/validation/ReferenceCPP.cpp b/tests/validation/ReferenceCPP.cpp
index 3bf70a0..58b47f9 100644
--- a/tests/validation/ReferenceCPP.cpp
+++ b/tests/validation/ReferenceCPP.cpp
@@ -281,16 +281,6 @@
boost::apply_visitor(tensor_visitors::batch_normalization_layer_visitor(s, m, v, b, g, epsilon, fixed_point_position), d);
}
-// Fully connected layer
-void ReferenceCPP::fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst)
-{
- const TensorVariant s = TensorFactory::get_tensor(src);
- const TensorVariant w = TensorFactory::get_tensor(weights);
- const TensorVariant b = TensorFactory::get_tensor(bias);
- TensorVariant d = TensorFactory::get_tensor(dst);
- boost::apply_visitor(tensor_visitors::fully_connected_layer_visitor(s, w, b), d);
-}
-
// Pooling Layer
void ReferenceCPP::pooling_layer(const RawTensor &src, RawTensor &dst, PoolingLayerInfo pool_info)
{
diff --git a/tests/validation/ReferenceCPP.h b/tests/validation/ReferenceCPP.h
index ce424be..29612d1 100644
--- a/tests/validation/ReferenceCPP.h
+++ b/tests/validation/ReferenceCPP.h
@@ -259,14 +259,6 @@
*/
static void batch_normalization_layer(const RawTensor &src, RawTensor &dst, const RawTensor &mean, const RawTensor &var, const RawTensor &beta, const RawTensor &gamma, float epsilon,
int fixed_point_position = 0);
- /** Fully connected layer function
- *
- * @param[in] src Input tensor
- * @param[in] weights Weights tensor.
- * @param[in] bias Bias tensor.
- * @param[out] dst Result tensor.
- */
- static void fully_connected_layer(const RawTensor &src, const RawTensor &weights, const RawTensor &bias, RawTensor &dst);
/** Pooling layer of @p src based on the information from @p pool_info.
*
* @param[in] src Input tensor.
diff --git a/tests/validation/TensorOperations.h b/tests/validation/TensorOperations.h
index c4884be..f5be139 100644
--- a/tests/validation/TensorOperations.h
+++ b/tests/validation/TensorOperations.h
@@ -58,52 +58,6 @@
{
};
-template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
-void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position)
-{
- for(int x = 0; x < cols_weights; ++x)
- {
- T acc(0);
- for(int y = 0; y < rows_weights; ++y)
- {
- acc += in[y] * weights[x + y * cols_weights];
- }
- out[x] = acc + bias[x];
- }
-}
-
-// Vector matrix multiply for fixed point type
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr>
-void vector_matrix_multiply(const T *in, const T *weights, const T *bias, T *out, int cols_weights, int rows_weights, uint8_t fixed_point_position)
-{
- using namespace fixed_point_arithmetic;
- using promoted_type = typename fixed_point_arithmetic::traits::promote<T>::type;
-
- for(int x = 0; x < cols_weights; ++x)
- {
- // Reset accumulator
- fixed_point<promoted_type> acc(0, fixed_point_position);
-
- for(int y = 0; y < rows_weights; ++y)
- {
- const fixed_point<promoted_type> i_value(in[y], fixed_point_position, true);
- const fixed_point<promoted_type> w_value(weights[x + y * cols_weights], fixed_point_position, true);
- const fixed_point<promoted_type> iw = i_value * w_value;
- acc = iw + acc;
- }
-
- // Get the bias
- const fixed_point<T> b(bias[x], fixed_point_position, true);
-
- // Convert back and accumulate the bias
- fixed_point<T> res(acc);
- res = res + b;
-
- // Store the result
- out[x] = res.raw();
- }
-}
-
// Return a tensor element at a specified coordinate with different border modes
template <typename T>
T tensor_elem_at(const Tensor<T> &in, Coordinates coord, BorderMode border_mode, T constant_border_value)
@@ -1117,28 +1071,6 @@
}
}
-// Fully connected layer
-template <typename T>
-void fully_connected_layer(const Tensor<T> &in, const Tensor<T> &weights, const Tensor<T> &bias, Tensor<T> &out)
-{
- ARM_COMPUTE_ERROR_ON(weights.shape().x() != out.shape().x());
- ARM_COMPUTE_ERROR_ON(weights.shape().y() != in.shape().x() * in.shape().y() * in.shape().z());
- const int cols_weights = weights.shape().x();
- const int rows_weights = weights.shape().y();
- const int num_batches = in.shape().total_size() / rows_weights;
-
- for(int k = 0; k < num_batches; ++k)
- {
- vector_matrix_multiply<T>(in.data() + k * rows_weights,
- weights.data(),
- bias.data(),
- out.data() + k * cols_weights,
- cols_weights,
- rows_weights,
- in.fixed_point_position());
- }
-}
-
// Pooling layer
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
void pooling_layer(const Tensor<T> &in, Tensor<T> &out, PoolingLayerInfo pool_info)
diff --git a/tests/validation/TensorVisitors.h b/tests/validation/TensorVisitors.h
index 193697a..732cd0e 100644
--- a/tests/validation/TensorVisitors.h
+++ b/tests/validation/TensorVisitors.h
@@ -232,28 +232,6 @@
float _epsilon;
int _fixed_point_position;
};
-// Fully Connected Layer visitor
-struct fully_connected_layer_visitor : public boost::static_visitor<>
-{
-public:
- explicit fully_connected_layer_visitor(const TensorVariant &in, const TensorVariant &weights, const TensorVariant &bias)
- : _in(in), _weights(weights), _bias(bias)
- {
- }
- template <typename T>
- void operator()(Tensor<T> &out) const
- {
- const Tensor<T> &in = boost::get<Tensor<T>>(_in);
- const Tensor<T> &weights = boost::get<Tensor<T>>(_weights);
- const Tensor<T> &bias = boost::get<Tensor<T>>(_bias);
- tensor_operations::fully_connected_layer(in, weights, bias, out);
- }
-
-private:
- const TensorVariant &_in;
- const TensorVariant &_weights;
- const TensorVariant &_bias;
-};
// Pooling layer
struct pooling_layer_visitor : public boost::static_visitor<>
diff --git a/tests/validation_new/CL/FullyConnectedLayer.cpp b/tests/validation_new/CL/FullyConnectedLayer.cpp
new file mode 100644
index 0000000..9bf3a75
--- /dev/null
+++ b/tests/validation_new/CL/FullyConnectedLayer.cpp
@@ -0,0 +1,205 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "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/CLFullyConnectedLayer.h"
+#include "framework/Asserts.h"
+#include "framework/Macros.h"
+#include "framework/datasets/Datasets.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets_new/FullyConnectedLayerDataset.h"
+#include "tests/validation_new/Validation.h"
+#include "tests/validation_new/fixtures/FullyConnectedLayerFixture.h"
+#include "tests/validation_new/half.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+/** Tolerance for float operations */
+constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
+constexpr AbsoluteTolerance<float> tolerance_f16(0.4f);
+/** Tolerance for fixed point operations */
+constexpr AbsoluteTolerance<float> tolerance_fixed_point(1.f);
+
+/** CNN data types */
+const auto CNNDataTypes = framework::dataset::make("DataType",
+{
+ DataType::F16,
+ DataType::F32,
+ DataType::QS8,
+ DataType::QS16,
+});
+
+const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true }));
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(FullyConnectedLayer)
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallFullyConnectedLayerDataset(), datasets::LargeFullyConnectedLayerDataset()),
+ FullyConnectedParameters),
+ CNNDataTypes),
+ src_shape, weights_shape, bias_shape, dst_shape, transpose_weights, reshape_weights, data_type)
+{
+ // Set fixed point position data type allowed
+ int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
+
+ TensorShape ws(weights_shape);
+
+ // Transpose weights if not done in the function
+ if(!reshape_weights || !transpose_weights)
+ {
+ const size_t shape_x = ws.x();
+ ws.set(0, ws.y());
+ ws.set(1, shape_x);
+
+ // Weights have to be passed reshaped
+ // Transpose 1xW for batched version
+ if(!reshape_weights && dst_shape.y() > 1)
+ {
+ const float transpose_width = 16.0f / data_size_from_type(data_type);
+ const size_t shape_x = ws.x();
+ ws.set(0, ws.y() * static_cast<unsigned int>(transpose_width));
+ ws.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
+ }
+ }
+
+ // Create tensors
+ CLTensor src = create_tensor<CLTensor>(src_shape, data_type, 1, fixed_point_position);
+ CLTensor weights = create_tensor<CLTensor>(ws, data_type, 1, fixed_point_position);
+ CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type, 1, fixed_point_position);
+ CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type, 1, fixed_point_position);
+
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function.
+ CLFullyConnectedLayer fc;
+ fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
+
+ // Validate valid region
+ const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape);
+ validate(dst.info()->valid_region(), dst_valid_region);
+}
+
+template <typename T>
+using CLFullyConnectedLayerFixture = FullyConnectedLayerValidationFixture<CLTensor, CLAccessor, CLFullyConnectedLayer, T>;
+
+TEST_SUITE(Float)
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+template <typename T>
+using CLFullyConnectedLayerFixedPointFixture = FullyConnectedLayerValidationFixedPointFixture<CLTensor, CLAccessor, CLFullyConnectedLayer, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QS8)
+// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5
+FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 1, 6)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_fixed_point);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 1, 6)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_fixed_point);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(QS16)
+// Testing for fixed point position [1,14) as reciprocal limits the maximum fixed point position to 14
+FIXTURE_DATA_TEST_CASE(RunSmall, CLFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_fixed_point);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, CLFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_fixed_point);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation_new/CPP/FullyConnectedLayer.cpp b/tests/validation_new/CPP/FullyConnectedLayer.cpp
new file mode 100644
index 0000000..7852dab
--- /dev/null
+++ b/tests/validation_new/CPP/FullyConnectedLayer.cpp
@@ -0,0 +1,133 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "FullyConnectedLayer.h"
+
+#include "tests/validation_new/FixedPoint.h"
+#include "tests/validation_new/half.h"
+
+#include <numeric>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+// Vector matrix multiply for floating point
+template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
+void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ dst[y] = std::inner_product(src, src + cols_weights, weights, static_cast<T>(0)) + bias[y];
+ weights += cols_weights;
+ }
+}
+
+// Vector matrix multiply for fixed point type
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ using namespace fixed_point_arithmetic;
+ using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ // Reset accumulator
+ fixed_point<promoted_type> acc(0, fixed_point_position);
+
+ for(int x = 0; x < cols_weights; ++x)
+ {
+ const fixed_point<promoted_type> i_value(src[x], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(weights[x], fixed_point_position, true);
+ acc = acc + i_value * w_value;
+ }
+
+ // Get the bias
+ const fixed_point<T> b(bias[y], fixed_point_position, true);
+
+ // Convert back and accumulate the bias
+ fixed_point<T> res(acc);
+ res = res + b;
+
+ // Store the result
+ dst[y] = res.raw();
+
+ weights += cols_weights;
+ }
+}
+} // namespace
+
+template <typename T>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape)
+{
+ // Create reference
+ SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position() };
+
+ // Sanity checks
+ const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
+ const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions;
+ const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions);
+
+ ARM_COMPUTE_UNUSED(num_batch_dimensions);
+ ARM_COMPUTE_UNUSED(num_input_dimensions);
+ ARM_COMPUTE_UNUSED(linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x());
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x());
+
+ // Compute reference
+ const int cols_weights = weights.shape().x();
+ const int rows_weights = weights.shape().y();
+ const int num_batches = dst_shape.total_size_upper(1);
+
+ for(int k = 0; k < num_batches; ++k)
+ {
+ vector_matrix_multiply<T>(src.data() + k * cols_weights,
+ weights.data(),
+ bias.data(),
+ dst.data() + k * rows_weights,
+ cols_weights,
+ rows_weights,
+ src.fixed_point_position());
+ }
+
+ return dst;
+}
+
+template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape);
+template SimpleTensor<half_float::half> fully_connected_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::half> &bias,
+ const TensorShape &dst_shape);
+template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape);
+template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation_new/CPP/FullyConnectedLayer.h b/tests/validation_new/CPP/FullyConnectedLayer.h
new file mode 100644
index 0000000..5d62179
--- /dev/null
+++ b/tests/validation_new/CPP/FullyConnectedLayer.h
@@ -0,0 +1,44 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__
+#define __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__
+
+#include "tests/SimpleTensor.h"
+#include "tests/validation_new/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_H__ */
diff --git a/tests/validation_new/NEON/FullyConnectedLayer.cpp b/tests/validation_new/NEON/FullyConnectedLayer.cpp
new file mode 100644
index 0000000..6eb18eb
--- /dev/null
+++ b/tests/validation_new/NEON/FullyConnectedLayer.cpp
@@ -0,0 +1,211 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+#include "framework/Asserts.h"
+#include "framework/Macros.h"
+#include "framework/datasets/Datasets.h"
+#include "tests/NEON/Accessor.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets_new/FullyConnectedLayerDataset.h"
+#include "tests/validation_new/Validation.h"
+#include "tests/validation_new/fixtures/FullyConnectedLayerFixture.h"
+#include "tests/validation_new/half.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+/** Tolerance for float operations */
+constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
+#ifdef ARM_COMPUTE_ENABLE_FP16
+constexpr AbsoluteTolerance<float> tolerance_f16(0.01f);
+#endif /* ARM_COMPUTE_ENABLE_FP16*/
+/** Tolerance for fixed point operations */
+constexpr AbsoluteTolerance<float> tolerance_fixed_point(1.f);
+
+/** CNN data types */
+const auto CNNDataTypes = framework::dataset::make("DataType",
+{
+#ifdef ARM_COMPUTE_ENABLE_FP16
+ DataType::F16,
+#endif /* ARM_COMPUTE_ENABLE_FP16 */
+ DataType::F32,
+ DataType::QS8,
+ DataType::QS16,
+});
+
+const auto FullyConnectedParameters = combine(framework::dataset::make("TransposeWeights", { false, true }), framework::dataset::make("ReshapeWeights", { false, true }));
+} // namespace
+
+TEST_SUITE(NEON)
+TEST_SUITE(FullyConnectedLayer)
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallFullyConnectedLayerDataset(), datasets::LargeFullyConnectedLayerDataset()),
+ FullyConnectedParameters),
+ CNNDataTypes),
+ src_shape, weights_shape, bias_shape, dst_shape, transpose_weights, reshape_weights, data_type)
+{
+ // Set fixed point position data type allowed
+ int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
+
+ TensorShape ws(weights_shape);
+
+ // Transpose weights if not done in the function
+ if(!reshape_weights || !transpose_weights)
+ {
+ const size_t shape_x = ws.x();
+ ws.set(0, ws.y());
+ ws.set(1, shape_x);
+
+ // Weights have to be passed reshaped
+ // Transpose 1xW for batched version
+ if(!reshape_weights && dst_shape.y() > 1)
+ {
+ const float transpose_width = 16.0f / data_size_from_type(data_type);
+ const size_t shape_x = ws.x();
+ ws.set(0, ws.y() * static_cast<unsigned int>(transpose_width));
+ ws.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
+ }
+ }
+
+ // Create tensors
+ Tensor src = create_tensor<Tensor>(src_shape, data_type, 1, fixed_point_position);
+ Tensor weights = create_tensor<Tensor>(ws, data_type, 1, fixed_point_position);
+ Tensor bias = create_tensor<Tensor>(bias_shape, data_type, 1, fixed_point_position);
+ Tensor dst = create_tensor<Tensor>(dst_shape, data_type, 1, fixed_point_position);
+
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function.
+ NEFullyConnectedLayer fc;
+ fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
+
+ // Validate valid region
+ const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape);
+ validate(dst.info()->valid_region(), dst_valid_region);
+}
+
+template <typename T>
+using NEFullyConnectedLayerFixture = FullyConnectedLayerValidationFixture<Tensor, Accessor, NEFullyConnectedLayer, T>;
+
+TEST_SUITE(Float)
+#ifdef ARM_COMPUTE_ENABLE_FP16
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f16);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<half_float::half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f16);
+}
+TEST_SUITE_END()
+#endif /* ARM_COMPUTE_ENABLE_FP16 */
+
+TEST_SUITE(FP32)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallFullyConnectedLayerDataset(), FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeFullyConnectedLayerDataset(), FullyConnectedParameters),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+template <typename T>
+using NEFullyConnectedLayerFixedPointFixture = FullyConnectedLayerValidationFixedPointFixture<Tensor, Accessor, NEFullyConnectedLayer, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QS8)
+// Testing for fixed point position [1,6) as reciprocal limits the maximum fixed point position to 5
+FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 1, 6)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_fixed_point);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 1, 6)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_fixed_point);
+}
+TEST_SUITE_END()
+
+TEST_SUITE(QS16)
+// Testing for fixed point position [1,14) as reciprocal limits the maximum fixed point position to 14
+FIXTURE_DATA_TEST_CASE(RunSmall, NEFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_fixed_point);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEFullyConnectedLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeFullyConnectedLayerDataset(),
+ FullyConnectedParameters),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)))
+{
+ // Validate output
+ validate(Accessor(_target), _reference, tolerance_fixed_point);
+}
+TEST_SUITE_END()
+TEST_SUITE_END()
+
+TEST_SUITE_END()
+TEST_SUITE_END()
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation_new/fixtures/FullyConnectedLayerFixture.h b/tests/validation_new/fixtures/FullyConnectedLayerFixture.h
new file mode 100644
index 0000000..eb4aad8
--- /dev/null
+++ b/tests/validation_new/fixtures/FullyConnectedLayerFixture.h
@@ -0,0 +1,249 @@
+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "framework/Asserts.h"
+#include "framework/Fixture.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/RawTensor.h"
+#include "tests/validation_new/CPP/FullyConnectedLayer.h"
+#include "tests/validation_new/Helpers.h"
+
+#include <random>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RawTensor transpose(const RawTensor &src, int interleave = 1)
+{
+ // Create reference
+ TensorShape dst_shape(src.shape());
+ dst_shape.set(0, src.shape().y() * interleave);
+ dst_shape.set(1, std::ceil(src.shape().x() / static_cast<float>(interleave)));
+
+ RawTensor dst{ dst_shape, src.data_type() };
+
+ // Compute reference
+ uint8_t *out_ptr = dst.data();
+
+ for(int i = 0; i < dst.num_elements(); i += interleave)
+ {
+ Coordinates coord = index2coord(dst.shape(), i);
+ size_t coord_x = coord.x();
+ coord.set(0, coord.y() * interleave);
+ coord.set(1, coord_x / interleave);
+
+ const int num_elements = std::min<int>(interleave, src.shape().x() - coord.x());
+
+ std::copy_n(static_cast<const uint8_t *>(src(coord)), num_elements * src.element_size(), out_ptr);
+
+ out_ptr += interleave * dst.element_size();
+ }
+
+ return dst;
+}
+} // namespace
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits)
+ {
+ ARM_COMPUTE_UNUSED(weights_shape);
+ ARM_COMPUTE_UNUSED(bias_shape);
+
+ _fractional_bits = fractional_bits;
+ _data_type = data_type;
+
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ if(is_data_type_float(_data_type))
+ {
+ std::uniform_real_distribution<> distribution(0.5f, 1.f);
+ library->fill(tensor, distribution, i);
+ }
+ else
+ {
+ library->fill_tensor_uniform(tensor, i);
+ }
+ }
+
+ TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
+ bool reshape_weights, DataType data_type, int fixed_point_position)
+ {
+ TensorShape reshaped_weights_shape(weights_shape);
+
+ // Test actions depending on the target settings
+ //
+ // | reshape | !reshape
+ // -----------+-----------+---------------------------
+ // transpose | | ***
+ // -----------+-----------+---------------------------
+ // !transpose | transpose | transpose &
+ // | | transpose1xW (if required)
+ //
+ // ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
+ if(!reshape_weights || !transpose_weights)
+ {
+ const size_t shape_x = reshaped_weights_shape.x();
+ reshaped_weights_shape.set(0, reshaped_weights_shape.y());
+ reshaped_weights_shape.set(1, shape_x);
+
+ // Weights have to be passed reshaped
+ // Transpose 1xW for batched version
+ if(!reshape_weights && output_shape.y() > 1)
+ {
+ const int transpose_width = 16 / data_size_from_type(data_type);
+ const float shape_x = reshaped_weights_shape.x();
+ reshaped_weights_shape.set(0, reshaped_weights_shape.y() * transpose_width);
+ reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
+ }
+ }
+
+ // Create tensors
+ TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
+ TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
+ TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
+ TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
+
+ // Create and configure function.
+ FunctionType fc;
+ fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights);
+
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ src.allocator()->allocate();
+ weights.allocator()->allocate();
+ bias.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(src), 0);
+ fill(AccessorType(bias), 2);
+
+ if(!reshape_weights || !transpose_weights)
+ {
+ TensorShape tmp_shape(weights_shape);
+ RawTensor tmp(tmp_shape, data_type, 1, fixed_point_position);
+
+ // Fill with original shape
+ fill(tmp, 1);
+
+ // Transpose elementwise
+ tmp = transpose(tmp);
+
+ // Reshape weights for batched runs
+ if(!reshape_weights && output_shape.y() > 1)
+ {
+ // Transpose with interleave
+ const int interleave_size = 16 / tmp.element_size();
+ tmp = transpose(tmp, interleave_size);
+ }
+
+ AccessorType weights_accessor(weights);
+
+ for(int i = 0; i < tmp.num_elements(); ++i)
+ {
+ Coordinates coord = index2coord(tmp.shape(), i);
+ std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
+ tmp.element_size(),
+ static_cast<RawTensor::value_type *>(weights_accessor(coord)));
+ }
+ }
+ else
+ {
+ fill(AccessorType(weights), 1);
+ }
+
+ // Compute NEFullyConnectedLayer function
+ fc.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
+ bool reshape_weights, DataType data_type, int fixed_point_position = 0)
+ {
+ // Create reference
+ SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
+ SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
+ SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
+
+ // Fill reference
+ fill(src, 0);
+ fill(weights, 1);
+ fill(bias, 2);
+
+ return reference::fully_connected_layer<T>(src, weights, bias, output_shape);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ int _fractional_bits{};
+ DataType _data_type{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
+ {
+ FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type,
+ 0);
+ }
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */