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-rw-r--r--tests/validation_old/CL/BatchNormalizationLayer.cpp227
-rw-r--r--tests/validation_old/NEON/BatchNormalizationLayer.cpp258
-rw-r--r--tests/validation_old/Reference.cpp62
-rw-r--r--tests/validation_old/Reference.h11
-rw-r--r--tests/validation_old/ReferenceCPP.cpp13
-rw-r--r--tests/validation_old/ReferenceCPP.h14
-rw-r--r--tests/validation_old/TensorOperations.h64
-rw-r--r--tests/validation_old/TensorVisitors.h27
8 files changed, 0 insertions, 676 deletions
diff --git a/tests/validation_old/CL/BatchNormalizationLayer.cpp b/tests/validation_old/CL/BatchNormalizationLayer.cpp
deleted file mode 100644
index 75c9a580ea..0000000000
--- a/tests/validation_old/CL/BatchNormalizationLayer.cpp
+++ /dev/null
@@ -1,227 +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 "Utils.h"
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/validation_old/Datasets.h"
-#include "tests/validation_old/Helpers.h"
-#include "tests/validation_old/Reference.h"
-#include "tests/validation_old/Validation.h"
-#include "tests/validation_old/dataset/BatchNormalizationLayerDataset.h"
-
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h"
-#include "arm_compute/runtime/Tensor.h"
-#include "arm_compute/runtime/TensorAllocator.h"
-
-#include <random>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-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_qs8 = 3; /**< Tolerance value for comparing reference's output against quantized implementation's output */
-const float tolerance_qs16 = 6; /**< 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.
- */
-CLTensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0)
-{
- // Create tensors
- CLTensor src = create_tensor<CLTensor>(shape0, dt, 1, fixed_point_position);
- CLTensor dst = create_tensor<CLTensor>(shape0, dt, 1, fixed_point_position);
- CLTensor mean = create_tensor<CLTensor>(shape1, dt, 1, fixed_point_position);
- CLTensor var = create_tensor<CLTensor>(shape1, dt, 1, fixed_point_position);
- CLTensor beta = create_tensor<CLTensor>(shape1, dt, 1, fixed_point_position);
- CLTensor gamma = create_tensor<CLTensor>(shape1, dt, 1, fixed_point_position);
-
- // Create and configure function
- CLBatchNormalizationLayer 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(CLAccessor(src), distribution, 0);
- library->fill(CLAccessor(mean), distribution, 1);
- library->fill(CLAccessor(var), distribution_var, 0);
- library->fill(CLAccessor(beta), distribution, 3);
- library->fill(CLAccessor(gamma), distribution, 4);
- }
- else
- {
- int min_bound = 0;
- int max_bound = 0;
- if(dt == DataType::QS8)
- {
- std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
- }
- else
- {
- std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int16_t>(fixed_point_position);
- }
- std::uniform_int_distribution<> distribution(min_bound, max_bound);
- std::uniform_int_distribution<> distribution_var(0, max_bound);
- library->fill(CLAccessor(src), distribution, 0);
- library->fill(CLAccessor(mean), distribution, 1);
- library->fill(CLAccessor(var), distribution_var, 0);
- library->fill(CLAccessor(beta), distribution, 3);
- library->fill(CLAccessor(gamma), distribution, 4);
- }
-
- // Compute function
- norm.run();
-
- return dst;
-}
-} // namespace
-
-#ifndef DOXYGEN_SKIP_THIS
-BOOST_AUTO_TEST_SUITE(CL)
-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::QS8, DataType::QS16, DataType::F32 }), 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
- CLTensor src = create_tensor<CLTensor>(obj.shape0, dt, 1, fixed_point_position);
- CLTensor dst = create_tensor<CLTensor>(obj.shape0, dt, 1, fixed_point_position);
- CLTensor mean = create_tensor<CLTensor>(obj.shape1, dt, 1, fixed_point_position);
- CLTensor var = create_tensor<CLTensor>(obj.shape1, dt, 1, fixed_point_position);
- CLTensor beta = create_tensor<CLTensor>(obj.shape1, dt, 1, fixed_point_position);
- CLTensor gamma = create_tensor<CLTensor>(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
- CLBatchNormalizationLayer 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
- CLTensor 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(CLAccessor(dst), ref_dst, tolerance_f, 0);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE(Quantized)
-
-BOOST_AUTO_TEST_SUITE(QS8)
-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
- CLTensor 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(CLAccessor(dst), ref_dst, tolerance_qs8, 0);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE(QS16)
-BOOST_DATA_TEST_CASE(Random,
- RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS16) * boost::unit_test::data::xrange(1, 14),
- obj, dt, fixed_point_position)
-{
- // Compute function
- CLTensor 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(CLAccessor(dst), ref_dst, tolerance_qs16, 0);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* DOXYGEN_SKIP_THIS */
diff --git a/tests/validation_old/NEON/BatchNormalizationLayer.cpp b/tests/validation_old/NEON/BatchNormalizationLayer.cpp
deleted file mode 100644
index d98f99a63c..0000000000
--- a/tests/validation_old/NEON/BatchNormalizationLayer.cpp
+++ /dev/null
@@ -1,258 +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 "tests/Globals.h"
-#include "tests/NEON/Helper.h"
-#include "tests/Utils.h"
-#include "tests/validation_old/Datasets.h"
-#include "tests/validation_old/Helpers.h"
-#include "tests/validation_old/Reference.h"
-#include "tests/validation_old/Validation.h"
-#include "tests/validation_old/dataset/BatchNormalizationLayerDataset.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::validation;
-
-namespace
-{
-const float tolerance_qs8 = 6; /**< Tolerance value for comparing reference's output against quantized implementation's output */
-const float tolerance_qs16 = 6; /**< Tolerance value for comparing reference's output against quantized implementation's output */
-const float tolerance_f32 = 1e-05f; /**< Tolerance value for comparing reference's output against floating point implementation's output */
-#ifdef ARM_COMPUTE_ENABLE_FP16
-const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against half precision floating point implementation's output */
-#endif /* ARM_COMPUTE_ENABLE_FP16 */
-
-/** 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<Tensor>(shape0, dt, 1, fixed_point_position);
- Tensor dst = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
- Tensor mean = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
- Tensor var = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
- Tensor beta = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
- Tensor gamma = create_tensor<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
- switch(dt)
- {
- case DataType::QS8:
- {
- const std::pair<int8_t, int8_t> bounds = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
- std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_int_distribution<> distribution_var(0, bounds.second);
- test::fill_tensors(distribution, { 0, 1, 3, 4 }, &src, &mean, &beta, &gamma);
- test::fill_tensors(distribution_var, { 0 }, &var);
- break;
- }
- case DataType::QS16:
- {
- const std::pair<int16_t, int16_t> bounds = get_batchnormalization_layer_test_bounds<int16_t>(fixed_point_position);
- std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_int_distribution<> distribution_var(0, bounds.second);
- test::fill_tensors(distribution, { 0, 1, 3, 4 }, &src, &mean, &beta, &gamma);
- test::fill_tensors(distribution_var, { 0 }, &var);
- break;
- }
-#ifdef ARM_COMPUTE_ENABLE_FP16
- case DataType::F16:
- {
- const std::pair<half_float::half, half_float::half> bounds = get_batchnormalization_layer_test_bounds<half_float::half>();
- std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_real_distribution<> distribution_var(0, bounds.second);
- test::fill_tensors(distribution, { 0, 1, 3, 4 }, &src, &mean, &beta, &gamma);
- test::fill_tensors(distribution_var, { 0 }, &var);
- break;
- }
-#endif /* ARM_COMPUTE_ENABLE_FP16 */
- case DataType::F32:
- {
- const std::pair<float, float> bounds = get_batchnormalization_layer_test_bounds<float>();
- std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_real_distribution<> distribution_var(0, bounds.second);
- test::fill_tensors(distribution, { 0, 1, 3, 4 }, &src, &mean, &beta, &gamma);
- test::fill_tensors(distribution_var, { 0 }, &var);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Not supported");
- break;
- }
- }
-
- // 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::QS8, DataType::QS16, DataType::F32 }), 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<Tensor>(obj.shape0, dt, 1, fixed_point_position);
- Tensor dst = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position);
- Tensor mean = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
- Tensor var = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
- Tensor beta = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position);
- Tensor gamma = create_tensor<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(Accessor(dst), ref_dst, tolerance_f32, 0);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-#ifdef ARM_COMPUTE_ENABLE_FP16
-BOOST_AUTO_TEST_SUITE(Float16)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(Random,
- RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F16),
- 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(Accessor(dst), ref_dst, tolerance_f16, 0);
-}
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* ARM_COMPUTE_ENABLE_FP16 */
-
-BOOST_AUTO_TEST_SUITE(Quantized)
-BOOST_AUTO_TEST_SUITE(QS8)
-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(Accessor(dst), ref_dst, tolerance_qs8);
-}
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE(QS16)
-BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
-BOOST_DATA_TEST_CASE(Random,
- RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS16) * boost::unit_test::data::xrange(1, 14),
- 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(Accessor(dst), ref_dst, tolerance_qs16);
-}
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-
-BOOST_AUTO_TEST_SUITE_END()
-BOOST_AUTO_TEST_SUITE_END()
-#endif /* DOXYGEN_SKIP_THIS */
diff --git a/tests/validation_old/Reference.cpp b/tests/validation_old/Reference.cpp
index 6a52cd016f..fc5484606e 100644
--- a/tests/validation_old/Reference.cpp
+++ b/tests/validation_old/Reference.cpp
@@ -284,68 +284,6 @@ RawTensor Reference::compute_reference_warp_perspective(const TensorShape &shape
return ref_dst;
}
-RawTensor Reference::compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position)
-{
- // Create reference
- RawTensor ref_src(shape0, dt, 1, fixed_point_position);
- RawTensor ref_dst(shape0, dt, 1, fixed_point_position);
- RawTensor ref_mean(shape1, dt, 1, fixed_point_position);
- RawTensor ref_var(shape1, dt, 1, fixed_point_position);
- RawTensor ref_beta(shape1, dt, 1, fixed_point_position);
- RawTensor ref_gamma(shape1, dt, 1, fixed_point_position);
-
- // Fill tensors
- switch(dt)
- {
- case DataType::QS8:
- {
- const std::pair<int8_t, int8_t> bounds = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position);
- std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_int_distribution<> distribution_var(0, bounds.second);
- fill_tensors(distribution, { 0, 1, 3, 4 }, &ref_src, &ref_mean, &ref_beta, &ref_gamma);
- fill_tensors(distribution_var, { 0 }, &ref_var);
- break;
- }
- case DataType::QS16:
- {
- const std::pair<int16_t, int16_t> bounds = get_batchnormalization_layer_test_bounds<int16_t>(fixed_point_position);
- std::uniform_int_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_int_distribution<> distribution_var(0, bounds.second);
- fill_tensors(distribution, { 0, 1, 3, 4 }, &ref_src, &ref_mean, &ref_beta, &ref_gamma);
- fill_tensors(distribution_var, { 0 }, &ref_var);
- break;
- }
- case DataType::F16:
- {
- const std::pair<half_float::half, half_float::half> bounds = get_batchnormalization_layer_test_bounds<half_float::half>();
- std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_real_distribution<> distribution_var(0, bounds.second);
- fill_tensors(distribution, { 0, 1, 3, 4 }, &ref_src, &ref_mean, &ref_beta, &ref_gamma);
- fill_tensors(distribution_var, { 0 }, &ref_var);
- break;
- }
- case DataType::F32:
- {
- const std::pair<float, float> bounds = get_batchnormalization_layer_test_bounds<float>();
- std::uniform_real_distribution<> distribution(bounds.first, bounds.second);
- std::uniform_real_distribution<> distribution_var(0, bounds.second);
- fill_tensors(distribution, { 0, 1, 3, 4 }, &ref_src, &ref_mean, &ref_beta, &ref_gamma);
- fill_tensors(distribution_var, { 0 }, &ref_var);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Not supported");
- break;
- }
- }
-
- // Compute reference
- ReferenceCPP::batch_normalization_layer(ref_src, ref_dst, ref_mean, ref_var, ref_beta, ref_gamma, epsilon, fixed_point_position);
-
- return ref_dst;
-}
-
RawTensor Reference::compute_reference_roi_pooling_layer(const TensorShape &shape, DataType dt, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
{
TensorShape shape_dst;
diff --git a/tests/validation_old/Reference.h b/tests/validation_old/Reference.h
index 9c7baacbf6..e363bb2ecd 100644
--- a/tests/validation_old/Reference.h
+++ b/tests/validation_old/Reference.h
@@ -204,17 +204,6 @@ public:
static RawTensor compute_reference_warp_perspective(const TensorShape &shape, RawTensor &valid_mask, const float *matrix, InterpolationPolicy policy, BorderMode border_mode,
uint8_t constant_border_value);
- /** Compute reference batch normalization layer.
- *
- * @param[in] shape0 Shape of the input and output tensors.
- * @param[in] shape1 Shape of the vector tensors.
- * @param[in] dt Data type of all input and output tensors.
- * @param[in] epsilon Small value to avoid division with zero.
- * @param[in] fixed_point_position Fixed point position.
- *
- * @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 roi pooling layer.
*
* @param[in] shape Shape of the input tensor.
diff --git a/tests/validation_old/ReferenceCPP.cpp b/tests/validation_old/ReferenceCPP.cpp
index 86dc589bb1..eae892af26 100644
--- a/tests/validation_old/ReferenceCPP.cpp
+++ b/tests/validation_old/ReferenceCPP.cpp
@@ -212,19 +212,6 @@ void ReferenceCPP::warp_perspective(const RawTensor &src, RawTensor &dst, RawTen
tensor_operations::warp_perspective(s, d, vmask, matrix, policy, border_mode, constant_border_value);
}
-// Batch Normalization Layer
-void ReferenceCPP::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)
-{
- const TensorVariant s = TensorFactory::get_tensor(src);
- TensorVariant d = TensorFactory::get_tensor(dst);
- const TensorVariant m = TensorFactory::get_tensor(mean);
- const TensorVariant v = TensorFactory::get_tensor(var);
- const TensorVariant b = TensorFactory::get_tensor(beta);
- const TensorVariant g = TensorFactory::get_tensor(gamma);
- boost::apply_visitor(tensor_visitors::batch_normalization_layer_visitor(s, m, v, b, g, epsilon, fixed_point_position), d);
-}
-
// ROI Pooling Layer
void ReferenceCPP::roi_pooling_layer(const RawTensor &src, RawTensor &dst, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
{
diff --git a/tests/validation_old/ReferenceCPP.h b/tests/validation_old/ReferenceCPP.h
index 5bc10a512f..2f02afc30e 100644
--- a/tests/validation_old/ReferenceCPP.h
+++ b/tests/validation_old/ReferenceCPP.h
@@ -198,20 +198,6 @@ public:
* @param[in] constant_border_value Constant value to use for borders if border_mode is set to CONSTANT.
*/
static void warp_perspective(const RawTensor &src, RawTensor &dst, RawTensor &valid_mask, const float *matrix, InterpolationPolicy policy, BorderMode border_mode, uint8_t constant_border_value);
-
- /** Batch Normalization of @p src based on the information from @p norm_info.
- *
- * @param[in] src Input tensor.
- * @param[out] dst Result tensor.
- * @param[out] mean Mean vector tensor.
- * @param[out] var Var vector tensor.
- * @param[out] beta Beta vector tensor.
- * @param[out] gamma Gamma vector tensor.
- * @param[in] epsilon Small value to avoid division with zero.
- * @param[in] fixed_point_position Fixed point position.
- */
- 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);
/** ROI Pooling layer of @p src based on the information from @p pool_info and @p rois.
*
* @param[in] src Input tensor.
diff --git a/tests/validation_old/TensorOperations.h b/tests/validation_old/TensorOperations.h
index 0c1ab4134e..04a79f0de3 100644
--- a/tests/validation_old/TensorOperations.h
+++ b/tests/validation_old/TensorOperations.h
@@ -861,70 +861,6 @@ void warp_perspective(const Tensor<T> &in, Tensor<T> &out, Tensor<T> &valid_mask
}
}
-// Batch Normalization Layer for fixed point type
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type * = nullptr>
-void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position)
-{
- const int cols = static_cast<int>(in.shape()[0]);
- const int rows = static_cast<int>(in.shape()[1]);
- const int depth = static_cast<int>(in.shape()[2]);
- int upper_dims = in.shape().total_size() / (cols * rows * depth);
-
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int i = 0; i < depth; ++i)
- {
- for(int k = 0; k < rows; ++k)
- {
- for(int l = 0; l < cols; ++l)
- {
- const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
- fixed_point_arithmetic::fixed_point<T> in_qs(in[pos], fixed_point_position, true);
- fixed_point_arithmetic::fixed_point<T> var_qs(var[i], fixed_point_position, true);
- fixed_point_arithmetic::fixed_point<T> mean_qs(mean[i], fixed_point_position, true);
- fixed_point_arithmetic::fixed_point<T> beta_qs(beta[i], fixed_point_position, true);
- fixed_point_arithmetic::fixed_point<T> gamma_qs(gamma[i], fixed_point_position, true);
- fixed_point_arithmetic::fixed_point<T> epsilon_qs(epsilon, fixed_point_position);
-
- auto denominator = fixed_point_arithmetic::inv_sqrt(var_qs + epsilon_qs);
- auto numerator = in_qs - mean_qs;
- auto x_bar = numerator * denominator;
- x_bar = beta_qs + x_bar * gamma_qs;
- out[pos] = x_bar.raw();
- }
- }
- }
- }
-}
-
-// Batch Normalization Layer for floating point type
-template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type * = nullptr>
-void batch_normalization_layer(const Tensor<T> &in, Tensor<T> &out, const Tensor<T> &mean, const Tensor<T> &var, const Tensor<T> &beta, const Tensor<T> &gamma, float epsilon, int fixed_point_position)
-{
- const int cols = static_cast<int>(in.shape()[0]);
- const int rows = static_cast<int>(in.shape()[1]);
- const int depth = static_cast<int>(in.shape()[2]);
- int upper_dims = in.shape().total_size() / (cols * rows * depth);
-
- for(int r = 0; r < upper_dims; ++r)
- {
- for(int i = 0; i < depth; ++i)
- {
- for(int k = 0; k < rows; ++k)
- {
- for(int l = 0; l < cols; ++l)
- {
- const int pos = l + k * cols + i * rows * cols + r * cols * rows * depth;
- const float denominator = sqrt(var[i] + epsilon);
- const float numerator = in[pos] - mean[i];
- const float x_bar = numerator / denominator;
- out[pos] = beta[i] + x_bar * gamma[i];
- }
- }
- }
- }
-}
-
// ROI Pooling layer
template <typename T>
void roi_pooling_layer(const Tensor<T> &in, Tensor<T> &out, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
diff --git a/tests/validation_old/TensorVisitors.h b/tests/validation_old/TensorVisitors.h
index dafbfe0235..8af035be08 100644
--- a/tests/validation_old/TensorVisitors.h
+++ b/tests/validation_old/TensorVisitors.h
@@ -128,33 +128,6 @@ private:
RoundingPolicy _rounding_policy;
};
-// Batch Normalization Layer visitor
-struct batch_normalization_layer_visitor : public boost::static_visitor<>
-{
-public:
- explicit batch_normalization_layer_visitor(const TensorVariant &in, const TensorVariant &mean, const TensorVariant &var, const TensorVariant &beta, const TensorVariant &gamma, float epsilon,
- int fixed_point_position = 0)
- : _in(in), _mean(mean), _var(var), _beta(beta), _gamma(gamma), _epsilon(epsilon), _fixed_point_position(fixed_point_position)
- {
- }
-
- template <typename T>
- void operator()(Tensor<T> &out) const
- {
- const Tensor<T> &in = boost::get<Tensor<T>>(_in);
- const Tensor<T> &mean = boost::get<Tensor<T>>(_mean);
- const Tensor<T> &var = boost::get<Tensor<T>>(_var);
- const Tensor<T> &beta = boost::get<Tensor<T>>(_beta);
- const Tensor<T> &gamma = boost::get<Tensor<T>>(_gamma);
- tensor_operations::batch_normalization_layer(in, out, mean, var, beta, gamma, _epsilon, _fixed_point_position);
- }
-
-private:
- const TensorVariant &_in, &_mean, &_var, &_beta, &_gamma;
- float _epsilon;
- int _fixed_point_position;
-};
-
// ROI Pooling layer
struct roi_pooling_layer_visitor : public boost::static_visitor<>
{
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-03 16:57:41 +0000