| /* |
| * Copyright (c) 2018-2019 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/NEON/kernels/NEFuseBatchNormalizationKernel.h" |
| |
| #include "arm_compute/core/CPP/Validate.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include "utils/TypePrinter.h" |
| #include <map> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, |
| const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, |
| const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_weights, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_bias == nullptr && fused_bias == nullptr); |
| ARM_COMPUTE_RETURN_ERROR_ON(bn_mean->num_dimensions() > 1); |
| |
| if(fbn_type == FuseBatchNormalizationType::CONVOLUTION) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(3) != bn_mean->dimension(0)); |
| } |
| else |
| { |
| const size_t channel_idx = get_data_layout_dimension_index(input_weights->data_layout(), DataLayoutDimension::CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(channel_idx) != bn_mean->dimension(0)); |
| } |
| // Validate bias |
| if(input_bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, input_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, input_bias); |
| } |
| // Validate beta |
| if(bn_beta != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_beta); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_beta); |
| } |
| // Validate gamma |
| if(bn_gamma != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_gamma); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_gamma); |
| } |
| |
| // Validate output weights |
| if(fused_weights != nullptr && fused_weights->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_weights, fused_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input_weights, fused_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_weights); |
| } |
| // Validate output bias |
| if(fused_bias != nullptr && fused_bias->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, fused_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_bias); |
| } |
| |
| return Status{}; |
| } |
| |
| template <typename VectorType> |
| void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias, |
| const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window) |
| { |
| using ScalarType = typename VectorType::scalar_type; |
| const int size = 16 / conv_weights->info()->element_size(); |
| using ExactTagType = typename VectorType::tag_type; |
| |
| const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); |
| const bool run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias); |
| |
| // Set build options |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const int window_step_x = size; |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Iterator conv_w_in(conv_weights, win); |
| Iterator conv_w_out(run_in_place_weights ? conv_weights : fused_weights, win); |
| |
| const auto conv_bias_in = (conv_bias != nullptr ? reinterpret_cast<ScalarType *>(conv_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); |
| auto conv_bias_out = (run_in_place_bias ? conv_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0)))); |
| |
| const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{}); |
| auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{}); |
| |
| auto mean = ScalarType(0.0); |
| auto var = ScalarType(0.0); |
| auto gamma = ScalarType(1.0); |
| auto beta = ScalarType(0.0); |
| auto conv_bias_in_scalar = ScalarType(0.0); |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| var = input_var[id[3]]; |
| if(input_gamma != nullptr) |
| { |
| gamma = input_gamma[id[3]]; |
| } |
| |
| if((id[0] == 0) && (id[1] == 0) && (id[2] == 0)) |
| { |
| if(input_beta != nullptr) |
| { |
| beta = input_beta[id[3]]; |
| beta_vec = wrapper::vdup_n(beta, ExactTagType{}); |
| } |
| |
| // Construct vectors |
| mean = input_mean[id[3]]; |
| mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| |
| if(conv_bias_in != nullptr) |
| { |
| conv_bias_in_scalar = conv_bias_in[id[3]]; |
| } |
| auto conv_bias_tmp_scalar = (conv_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); |
| conv_bias_out[id[3]] = (conv_bias_tmp_scalar * gamma) + beta; |
| } |
| |
| int x = window_start_x; |
| auto conv_w_in_ptr = reinterpret_cast<const ScalarType *>(conv_w_in.ptr()); |
| auto conv_w_out_ptr = reinterpret_cast<ScalarType *>(conv_w_out.ptr()); |
| var_vec = wrapper::vdup_n(var, ExactTagType{}); |
| gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); |
| rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); |
| |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| auto wn = wrapper::vloadq(conv_w_in_ptr + x); |
| wn = wrapper::vmul(wn, rvar_vec); |
| wn = wrapper::vmul(wn, gamma_vec); |
| |
| // Store results |
| wrapper::vstore(conv_w_out_ptr + x, wn); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; |
| } |
| }, |
| conv_w_in, conv_w_out); |
| } |
| |
| template <typename VectorType> |
| void fused_batch_normalization_dwc_nhwc(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias, |
| const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window) |
| { |
| using ScalarType = typename VectorType::scalar_type; |
| const int size = 16 / dwc_weights->info()->element_size(); |
| using ExactTagType = typename VectorType::tag_type; |
| |
| const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights); |
| const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias); |
| |
| // Set build options |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const int window_step_x = size; |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Iterator dwc_w_in(dwc_weights, win); |
| Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win); |
| |
| const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); |
| auto dwc_bias_out = (run_in_place_bias ? dwc_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0)))); |
| |
| const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{}); |
| auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto dwc_bias_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{}); |
| |
| auto gamma = ScalarType(1.0); |
| auto beta = ScalarType(0.0); |
| auto dwc_bias_in_scalar = ScalarType(0); |
| |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| var_vec = wrapper::vloadq(input_var + x); |
| if(input_gamma != nullptr) |
| { |
| gamma_vec = wrapper::vloadq(input_gamma + x); |
| } |
| |
| if((id[2] == 0) && (id[1] == 0)) |
| { |
| mean_vec = wrapper::vloadq(input_mean + x); |
| |
| // Construct vectors |
| if(input_beta != nullptr) |
| { |
| beta_vec = wrapper::vloadq(input_beta + x); |
| } |
| |
| if(dwc_bias_in != nullptr) |
| { |
| dwc_bias_vec = wrapper::vloadq(dwc_bias_in + x); |
| } |
| |
| auto dwc_bias_tmp_vec = wrapper::vmul(wrapper::vsub(dwc_bias_vec, mean_vec), wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec))); |
| dwc_bias_tmp_vec = wrapper::vadd(wrapper::vmul(dwc_bias_tmp_vec, gamma_vec), beta_vec); |
| wrapper::vstore(dwc_bias_out + x, dwc_bias_tmp_vec); |
| } |
| |
| auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr()); |
| auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr()); |
| |
| auto wn = wrapper::vloadq(dwc_w_in_ptr + x); |
| rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); |
| wn = wrapper::vmul(wn, rvar_vec); |
| wn = wrapper::vmul(wn, gamma_vec); |
| |
| // Store results |
| wrapper::vstore(dwc_w_out_ptr + x, wn); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| auto var = input_var[x]; |
| if(input_gamma != nullptr) |
| { |
| gamma = input_gamma[x]; |
| } |
| |
| if(id[2] == 0 && id[1] == 0) |
| { |
| auto mean = input_mean[x]; |
| if(input_beta != nullptr) |
| { |
| beta = input_beta[x]; |
| } |
| if(dwc_bias_in != nullptr) |
| { |
| dwc_bias_in_scalar = dwc_bias_in[x]; |
| } |
| |
| auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); |
| dwc_bias_out[x] = (dwc_bias_tmp_scalar * gamma) + beta; |
| } |
| |
| const auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr()); |
| auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr()); |
| |
| *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; |
| } |
| }, |
| dwc_w_in, dwc_w_out); |
| } |
| |
| template <typename VectorType> |
| void fused_batch_normalization_dwc_nchw(const ITensor *dwc_weights, const ITensor *dwc_bias, ITensor *fused_weights, ITensor *fused_bias, |
| const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window) |
| { |
| using ScalarType = typename VectorType::scalar_type; |
| const int size = 16 / dwc_weights->info()->element_size(); |
| using ExactTagType = typename VectorType::tag_type; |
| |
| const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights); |
| const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias); |
| |
| // Set build options |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const int window_step_x = size; |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Iterator dwc_w_in(dwc_weights, win); |
| Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win); |
| |
| const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); |
| auto dwc_bias_out = (run_in_place_bias ? dwc_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0)))); |
| |
| const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0))); |
| const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0))); |
| const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; |
| |
| auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{}); |
| auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{}); |
| const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{}); |
| |
| auto mean = ScalarType(0.0); |
| auto var = ScalarType(0.0); |
| auto gamma = ScalarType(1.0); |
| auto beta = ScalarType(0.0); |
| auto dwc_bias_in_scalar = ScalarType(0.0); |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| var = input_var[id[2]]; |
| if(input_gamma != nullptr) |
| { |
| gamma = input_gamma[id[2]]; |
| } |
| |
| if(id[1] == 0) |
| { |
| mean = input_mean[id[2]]; |
| |
| // Construct vectors |
| mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| if(input_beta != nullptr) |
| { |
| beta = input_beta[id[2]]; |
| beta_vec = wrapper::vdup_n(beta, ExactTagType{}); |
| } |
| |
| if(dwc_bias_in != nullptr) |
| { |
| dwc_bias_in_scalar = dwc_bias_in[id[2]]; |
| } |
| |
| auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); |
| dwc_bias_out[id[2]] = (dwc_bias_tmp_scalar * gamma) + beta; |
| } |
| |
| int x = window_start_x; |
| auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr()); |
| auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr()); |
| var_vec = wrapper::vdup_n(var, ExactTagType{}); |
| gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); |
| rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); |
| |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| auto wn = wrapper::vloadq(dwc_w_in_ptr + x); |
| wn = wrapper::vmul(wn, rvar_vec); |
| wn = wrapper::vmul(wn, gamma_vec); |
| |
| // Store results |
| wrapper::vstore(dwc_w_out_ptr + x, wn); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; |
| } |
| }, |
| dwc_w_in, dwc_w_out); |
| } |
| |
| } // namespace |
| |
| NEFuseBatchNormalizationKernel::NEFuseBatchNormalizationKernel() |
| : _input_weights(nullptr), _input_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(), |
| _run_in_place_weights(false), _run_in_place_bias(false), _func(nullptr) |
| { |
| } |
| |
| void NEFuseBatchNormalizationKernel::configure(const ITensor *input_weights, const ITensor *bn_mean, const ITensor *bn_var, |
| ITensor *fused_weights, ITensor *fused_bias, |
| const ITensor *input_bias, const ITensor *bn_beta, const ITensor *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); |
| |
| _input_weights = input_weights; |
| _input_bias = input_bias; |
| _bn_mean = bn_mean; |
| _bn_var = bn_var; |
| _bn_beta = bn_beta; |
| _bn_gamma = bn_gamma; |
| _fused_weights = fused_weights; |
| _fused_bias = fused_bias; |
| _epsilon = epsilon; |
| |
| _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == input_weights); |
| _run_in_place_bias = (fused_bias == nullptr) || (input_bias != nullptr && fused_bias == input_bias); |
| |
| // Auto initialize outputs |
| if(_fused_weights != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*_fused_weights->info(), *_input_weights->info()->clone()); |
| fused_weights->info()->set_valid_region(input_weights->info()->valid_region()); |
| } |
| if(_fused_bias != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*_fused_bias->info(), *_bn_mean->info()->clone()); |
| _fused_bias->info()->set_valid_region(bn_mean->info()->valid_region()); |
| } |
| |
| // Validate arguments |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_weights->info(), bn_mean->info(), bn_var->info(), |
| (fused_weights != nullptr) ? fused_weights->info() : nullptr, |
| (fused_bias != nullptr) ? fused_bias->info() : nullptr, |
| (input_bias != nullptr) ? input_bias->info() : nullptr, |
| (bn_beta != nullptr) ? bn_beta->info() : nullptr, |
| (bn_gamma != nullptr) ? bn_gamma->info() : nullptr, |
| epsilon, fbn_type)); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input_weights->info()); |
| INEKernel::configure(win); |
| |
| // Configure function |
| static std::map<std::string, FuseBatchNormFunction *> map_function = |
| { |
| { "fused_batch_normalization_conv_NHWC_F32", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float, 4>> }, |
| { "fused_batch_normalization_conv_NCHW_F32", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float, 4>> }, |
| { "fused_batch_normalization_dwc_NHWC_F32", &fused_batch_normalization_dwc_nhwc<wrapper::traits::neon_vector<float, 4>> }, |
| { "fused_batch_normalization_dwc_NCHW_F32", &fused_batch_normalization_dwc_nchw<wrapper::traits::neon_vector<float, 4>> }, |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| { "fused_batch_normalization_conv_NHWC_F16", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float16_t, 8>> }, |
| { "fused_batch_normalization_conv_NCHW_F16", &fused_batch_normalization_conv<wrapper::traits::neon_vector<float16_t, 8>> }, |
| { "fused_batch_normalization_dwc_NHWC_F16", &fused_batch_normalization_dwc_nhwc<wrapper::traits::neon_vector<float16_t, 8>> }, |
| { "fused_batch_normalization_dwc_NCHW_F16", &fused_batch_normalization_dwc_nchw<wrapper::traits::neon_vector<float16_t, 8>> }, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| }; |
| |
| std::string function_to_call("fused_batch_normalization_"); |
| function_to_call += fbn_type == FuseBatchNormalizationType::CONVOLUTION ? "conv_" : "dwc_"; |
| function_to_call += string_from_data_layout(_input_weights->info()->data_layout()); |
| function_to_call += "_"; |
| function_to_call += string_from_data_type(_input_weights->info()->data_type()); |
| |
| auto it = map_function.find(function_to_call); |
| |
| if(it != map_function.end()) |
| { |
| _func = it->second; |
| } |
| } |
| |
| Status NEFuseBatchNormalizationKernel::validate(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, |
| const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, |
| const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_weights, bn_mean, bn_var, fused_weights, fused_bias, input_bias, bn_beta, bn_gamma, epsilon, fbn_type)); |
| return Status{}; |
| } |
| |
| void NEFuseBatchNormalizationKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| (*_func)(_input_weights, _input_bias, _fused_weights, _fused_bias, _bn_mean, _bn_var, _bn_beta, _bn_gamma, _epsilon, window); |
| } |
| } // namespace arm_compute |