/* * Copyright (c) 2019-2020 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 "FuseBatchNormalization.h" #include "tests/validation/Helpers.h" namespace arm_compute { namespace test { namespace validation { namespace reference { template void fuse_batch_normalization_dwc_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon) { const auto *w_data = w.data(); const auto *b_data = b.data(); auto *w_fused_data = w_fused.data(); auto *b_fused_data = b_fused.data(); const unsigned int width = w.shape()[0]; const unsigned int height = w.shape()[1]; const unsigned int dim2 = w.shape()[2]; #if defined(_OPENMP) #pragma omp parallel for #endif /* _OPENMP */ for(unsigned int b = 0; b < dim2; ++b) { const auto mean_val = mean.data()[b]; const auto var_val = var.data()[b]; const auto beta_val = beta.data()[b]; const auto gamma_val = gamma.data()[b]; for(unsigned int i = 0; i < width * height; ++i) { unsigned int index = i + b * width * height; w_fused_data[index] = (gamma_val * (w_data[index])) / sqrt(var_val + epsilon); } b_fused_data[b] = (b_data[b] - mean_val) / sqrt(var_val + epsilon) * gamma_val + beta_val; } } template void fuse_batch_normalization_conv_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon) { const auto *w_data = w.data(); const auto *b_data = b.data(); auto *w_fused_data = w_fused.data(); auto *b_fused_data = b_fused.data(); const unsigned int width = w.shape()[0]; const unsigned int height = w.shape()[1]; const unsigned int dim2 = w.shape()[2]; const unsigned int dim3 = w.shape()[3]; for(unsigned int b = 0; b < dim3; ++b) { const auto mean_val = mean.data()[b]; const auto var_val = var.data()[b]; const auto beta_val = beta.data()[b]; const auto gamma_val = gamma.data()[b]; for(unsigned int i = 0; i < width * height * dim2; ++i) { unsigned int index = i + b * width * height * dim2; w_fused_data[index] = (gamma_val * (w_data[index])) / sqrt(var_val + epsilon); } b_fused_data[b] = (b_data[b] - mean_val) / sqrt(var_val + epsilon) * gamma_val + beta_val; } } template void fuse_batch_normalization_dwc_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon); template void fuse_batch_normalization_dwc_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon); template void fuse_batch_normalization_conv_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon); template void fuse_batch_normalization_conv_layer(const SimpleTensor &w, const SimpleTensor &mean, const SimpleTensor &var, SimpleTensor &w_fused, SimpleTensor &b_fused, const SimpleTensor &b, const SimpleTensor &beta, const SimpleTensor &gamma, float epsilon); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute