COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/tests/validation/NEON/BatchNormalizationLayer.cpp b/tests/validation/NEON/BatchNormalizationLayer.cpp
new file mode 100644
index 0000000..7656b2f
--- /dev/null
+++ b/tests/validation/NEON/BatchNormalizationLayer.cpp
@@ -0,0 +1,195 @@
+/*
+ * 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/Helper.h"
+#include "NEON/NEAccessor.h"
+#include "TypePrinter.h"
+#include "dataset/BatchNormalizationLayerDataset.h"
+#include "tests/validation/Helpers.h"
+#include "validation/Datasets.h"
+#include "validation/Reference.h"
+#include "validation/Validation.h"
+
+#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"
+
+#include <random>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+using namespace arm_compute::test::neon;
+using namespace arm_compute::test::validation;
+
+namespace
+{
+const float tolerance_f = 1e-05; /**< Tolerance value for comparing reference's output against floating point implementation's output */
+const float tolerance_q = 3; /**< Tolerance value for comparing reference's output against quantized implementation's output */
+
+/** Compute Neon batch normalization function.
+ *
+ * @param[in] shape Shape of the input and output tensors.
+ * @param[in] dt Data type of input and output tensors.
+ * @param[in] norm_info Normalization Layer information.
+ *
+ * @return Computed output tensor.
+ */
+Tensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0)
+{
+ // Create tensors
+ Tensor src = create_tensor(shape0, dt, 1, fixed_point_position);
+ Tensor dst = create_tensor(shape0, dt, 1, fixed_point_position);
+ Tensor mean = create_tensor(shape1, dt, 1, fixed_point_position);
+ Tensor var = create_tensor(shape1, dt, 1, fixed_point_position);
+ Tensor beta = create_tensor(shape1, dt, 1, fixed_point_position);
+ Tensor gamma = create_tensor(shape1, dt, 1, fixed_point_position);
+
+ // Create and configure function
+ NEBatchNormalizationLayer norm;
+ norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon);
+
+ // Allocate tensors
+ src.allocator()->allocate();
+ dst.allocator()->allocate();
+ mean.allocator()->allocate();
+ var.allocator()->allocate();
+ beta.allocator()->allocate();
+ gamma.allocator()->allocate();
+
+ BOOST_TEST(!src.info()->is_resizable());
+ BOOST_TEST(!dst.info()->is_resizable());
+ BOOST_TEST(!mean.info()->is_resizable());
+ BOOST_TEST(!var.info()->is_resizable());
+ BOOST_TEST(!beta.info()->is_resizable());
+ BOOST_TEST(!gamma.info()->is_resizable());
+
+ // Fill tensors
+ if(dt == DataType::F32)
+ {
+ float min_bound = 0.f;
+ float max_bound = 0.f;
+ std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<float>();
+ std::uniform_real_distribution<> distribution(min_bound, max_bound);
+ std::uniform_real_distribution<> distribution_var(0, max_bound);
+ library->fill(NEAccessor(src), distribution, 0);
+ library->fill(NEAccessor(mean), distribution, 1);
+ library->fill(NEAccessor(var), distribution_var, 0);
+ library->fill(NEAccessor(beta), distribution, 3);
+ library->fill(NEAccessor(gamma), distribution, 4);
+ }
+ else
+ {
+ int min_bound = 0;
+ int max_bound = 0;
+ std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
+ std::uniform_int_distribution<> distribution(min_bound, max_bound);
+ std::uniform_int_distribution<> distribution_var(0, max_bound);
+ library->fill(NEAccessor(src), distribution, 0);
+ library->fill(NEAccessor(mean), distribution, 1);
+ library->fill(NEAccessor(var), distribution_var, 0);
+ library->fill(NEAccessor(beta), distribution, 3);
+ library->fill(NEAccessor(gamma), distribution, 4);
+ }
+
+ // Compute function
+ norm.run();
+
+ return dst;
+}
+} // namespace
+
+#ifndef DOXYGEN_SKIP_THIS
+BOOST_AUTO_TEST_SUITE(NEON)
+BOOST_AUTO_TEST_SUITE(BatchNormalizationLayer)
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly"))
+BOOST_DATA_TEST_CASE(Configuration, RandomBatchNormalizationLayerDataset() * (boost::unit_test::data::make(DataType::F32) + boost::unit_test::data::make(DataType::QS8)), obj, dt)
+{
+ // Set fixed point position data type allowed
+ int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;
+
+ // Create tensors
+ Tensor src = create_tensor(obj.shape0, dt, 1, fixed_point_position);
+ Tensor dst = create_tensor(obj.shape0, dt, 1, fixed_point_position);
+ Tensor mean = create_tensor(obj.shape1, dt, 1, fixed_point_position);
+ Tensor var = create_tensor(obj.shape1, dt, 1, fixed_point_position);
+ Tensor beta = create_tensor(obj.shape1, dt, 1, fixed_point_position);
+ Tensor gamma = create_tensor(obj.shape1, dt, 1, fixed_point_position);
+
+ BOOST_TEST(src.info()->is_resizable());
+ BOOST_TEST(dst.info()->is_resizable());
+ BOOST_TEST(mean.info()->is_resizable());
+ BOOST_TEST(var.info()->is_resizable());
+ BOOST_TEST(beta.info()->is_resizable());
+ BOOST_TEST(gamma.info()->is_resizable());
+
+ // Create and configure function
+ NEBatchNormalizationLayer norm;
+ norm.configure(&src, &dst, &mean, &var, &beta, &gamma, obj.epsilon);
+
+ // Validate valid region
+ const ValidRegion valid_region = shape_to_valid_region(obj.shape0);
+ const ValidRegion valid_region_vec = shape_to_valid_region(obj.shape1);
+ validate(src.info()->valid_region(), valid_region);
+ validate(dst.info()->valid_region(), valid_region);
+ validate(mean.info()->valid_region(), valid_region_vec);
+ validate(var.info()->valid_region(), valid_region_vec);
+ validate(beta.info()->valid_region(), valid_region_vec);
+ validate(gamma.info()->valid_region(), valid_region_vec);
+}
+
+BOOST_AUTO_TEST_SUITE(Float)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(Random,
+ RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F32),
+ obj, dt)
+{
+ // Compute function
+ Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);
+
+ // Compute reference
+ RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon);
+
+ // Validate output
+ validate(NEAccessor(dst), ref_dst, tolerance_f, 0);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE(Quantized)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(Random,
+ RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 6),
+ obj, dt, fixed_point_position)
+{
+ // Compute function
+ Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);
+
+ // Compute reference
+ RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position);
+
+ // Validate output
+ validate(NEAccessor(dst), ref_dst, tolerance_q, 0);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
+#endif