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batchnorm_forward_training.cc
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/*
* Copyright Codeplay Software Ltd.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use these files except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// DO NOT MODIFY BY HAND
// This file was automatically generated by generate_batchnorm_tests.py.
// Results calculated using Tensorflow v2.8.0.
#include <gtest/gtest.h>
#include "sycldnn/data_format.h"
#include "sycldnn/batchnorm/direction.h"
#include "sycldnn/batchnorm/params.h"
#include "test/batchnorm/batchnorm_fixture.h"
#include "test/types/cartesian_product.h"
#include "test/types/data_format_types.h"
#include "test/types/kernel_data_types.h"
#include "test/types/nested_pairs_to_triple.h"
#include "test/types/test_backend_types.h"
#include "test/types/to_gtest_types.h"
#include <vector>
using DataTypeList = sycldnn::types::KernelDataTypes;
using Backends = sycldnn::types::AllBackendTypes;
using DataFormats = sycldnn::types::DataFormatTypes;
using TypeBackendPairs =
sycldnn::types::CartesianProduct<DataTypeList, Backends>::type;
using TypeBackendFormatTriple =
sycldnn::types::CartesianProduct<TypeBackendPairs, DataFormats>::type;
using TestTriples =
sycldnn::types::NestedPairsToTriple<TypeBackendFormatTriple>::type;
using GTestTypeTriples = sycldnn::types::ToGTestTypes<TestTriples>::type;
using namespace sycldnn; // NOLINT(google-build-using-namespace)
template <typename Triple>
using BatchnormForwardTraining = BatchNormFixture<Triple, batchnorm::Forward>;
TYPED_TEST_CASE(BatchnormForwardTraining, GTestTypeTriples);
TYPED_TEST(BatchnormForwardTraining, 1x1x1x1) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.};
const std::vector<DataType> exp_running_var = {0.99};
const std::vector<DataType> exp_out = {1.};
const std::array<int, 4> in_shape = {{1, 1, 1, 1}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x1x5) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1., 2., 3., 4., 5.};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95};
const std::vector<DataType> exp_out = {1., 2., 3., 4., 1.};
const std::array<int, 4> in_shape = {{1, 1, 1, 5}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x1x8) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {
1., 2., 3., 4., 5., 5.949999999999999, 1.01, 2.01};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95, 5.9399999999999995,
6.93, 0.99};
const std::vector<DataType> exp_out = {1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319};
const std::array<int, 4> in_shape = {{1, 1, 1, 8}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x8x1) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.01625};
const std::vector<DataType> exp_running_var = {1.00734375};
const std::vector<DataType> exp_out = {1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466};
const std::array<int, 4> in_shape = {{1, 1, 8, 1}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x8x5) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1., 2., 3., 4., 5.};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95};
const std::vector<DataType> exp_out = {
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4.,
1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.};
const std::array<int, 4> in_shape = {{1, 1, 8, 5}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x8x8) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {
1.0175, 2.01125, 2.99875, 3.9925,
4.98, 5.967499999999999, 1.02125, 2.00875};
const std::vector<DataType> exp_running_var = {
1.009375, 1.99859375, 2.9885937499999997, 3.979375,
4.9725, 5.959375, 6.94859375, 1.00859375};
const std::vector<DataType> exp_out = {1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664};
const std::array<int, 4> in_shape = {{1, 1, 8, 8}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x9x1) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.017777777777778};
const std::vector<DataType> exp_running_var = {1.007283950617284};
const std::vector<DataType> exp_out = {1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197};
const std::array<int, 4> in_shape = {{1, 1, 9, 1}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x9x5) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1., 2., 3., 4., 5.};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95};
const std::vector<DataType> exp_out = {
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.,
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.,
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.};
const std::array<int, 4> in_shape = {{1, 1, 9, 5}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x1x9x8) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.02,
2.008888888888889,
2.9977777777777774,
3.9922222222222223,
4.981111111111112,
5.97,
1.018888888888889,
2.0077777777777777};
const std::vector<DataType> exp_running_var = {
1.0122222222222221, 2.0009876543209875, 2.9872839506172837,
3.977283950617284, 4.970987654320988, 5.962222222222222,
6.950987654320987, 1.007283950617284};
const std::vector<DataType> exp_out = {1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.};
const std::array<int, 4> in_shape = {{1, 1, 9, 8}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x1x1) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.01625};
const std::vector<DataType> exp_running_var = {1.00734375};
const std::vector<DataType> exp_out = {1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466};
const std::array<int, 4> in_shape = {{1, 8, 1, 1}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x1x5) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1., 2., 3., 4., 5.};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95};
const std::vector<DataType> exp_out = {
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4.,
1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.};
const std::array<int, 4> in_shape = {{1, 8, 1, 5}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x1x8) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {
1.0175, 2.01125, 2.99875, 3.9925,
4.98, 5.967499999999999, 1.02125, 2.00875};
const std::vector<DataType> exp_running_var = {
1.009375, 1.99859375, 2.9885937499999997, 3.979375,
4.9725, 5.959375, 6.94859375, 1.00859375};
const std::vector<DataType> exp_out = {1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664};
const std::array<int, 4> in_shape = {{1, 8, 1, 8}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x8x1) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1.0196875};
const std::vector<DataType> exp_running_var = {1.009677734375};
const std::vector<DataType> exp_out = {1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197,
4.998001498751092,
1.,
1.999500374687773,
2.9990007493755466,
3.9985011240633197};
const std::array<int, 4> in_shape = {{1, 8, 8, 1}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x8x5) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {1., 2., 3., 4., 5.};
const std::vector<DataType> exp_running_var = {0.99, 1.98, 2.9699999999999998,
3.96, 4.95};
const std::vector<DataType> exp_out = {
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1.,
2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4.,
1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2.,
3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.,
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1.,
2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4.,
1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2.,
3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.,
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1.,
2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4.,
1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2.,
3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.,
1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3.,
4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1., 1.,
2., 3., 4., 1., 1., 2., 3., 4., 1., 1., 2., 3., 4., 1.};
const std::array<int, 4> in_shape = {{1, 8, 8, 5}};
const bool is_training = true;
const float momentum = 0.99;
const float epsilon = 0.001;
const auto params =
getBatchNormParams(in_shape, is_training, momentum, epsilon);
const DataType max_input_val = 5.0;
const DataType max_beta_val = 4.0;
const DataType max_gamma_val = 5.0;
const DataType max_input_mean_val = 6.0;
const DataType max_input_var_val = 7.0;
this->test_batchnorm(exp_running_mean, exp_running_var, exp_out, params,
max_input_val, max_beta_val, max_gamma_val,
max_input_mean_val, max_input_var_val);
}
TYPED_TEST(BatchnormForwardTraining, 1x8x8x8) {
using DataType = typename TestFixture::DataType;
const std::vector<DataType> exp_running_mean = {
1.02, 2.00984375, 2.9996875, 3.9903125,
4.98015625, 5.97, 1.01984375, 2.0096875};
const std::vector<DataType> exp_running_var = {
1.0103125, 2.00015380859375, 2.989677734375, 3.979677734375,
4.97015380859375, 5.9603125, 6.9501538085937495, 1.009677734375};
const std::vector<DataType> exp_out = {1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
6.463524409166052,
-1.9992501405957088,
-5.70753321271241,
0.7753571779250394,
5.267624870634663,
12.99550337218996,
1.,
2.,
3.,
4.,
1.,
-0.04107137012493428,
3.7558749568782206,
6.998501124063319,
3.9985011240633197,
6.241580424529413,
-0.4635244091660504,
0.0004999062695274503,
-3.4716888084749407,
1.1835714519500262,
6.023499827512882,
1.0014988759366803,
1.999500374687773,
3.413860141509805,
4.731762204583026,
5.999750046865236,
-7.9433776169498795,
0.36714290390005255,
4.511749913756441,
9.99700224812664,
4.998001498751092,
0.5861398584901953,
1.268237795416975,
2.000249953134764,
-1.2358444042374703,
1.5917857259750132,
3.,
4.,
2.9990007493755466,
4.827720283019609,
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