COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 187 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
new file mode 100644
index 0000000000..9a216aecde
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+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
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+/*
+ * 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 "arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/NEON/NEFixedPoint.h"
+#include "arm_compute/core/NEON/NEMath.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"
+
+using namespace arm_compute;
+
+NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
+    : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon()
+{
+}
+
+void batch_normalization_q8(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+{
+    Iterator input(in, window);
+    Iterator output(out, window);
+
+    // Hold information about the current feature map we are iterating.
+    // Only compute denominator and NEON vectors once per feature map.
+    int slice = -1;
+
+    int        fixed_point_position = in->info()->fixed_point_position();
+    const auto input_mean           = reinterpret_cast<const qint8_t *>(mean->ptr_to_element(Coordinates(0, 0)));
+    const auto input_var            = reinterpret_cast<const qint8_t *>(var->ptr_to_element(Coordinates(0, 0)));
+    const auto input_gamma          = reinterpret_cast<const qint8_t *>(gamma->ptr_to_element(Coordinates(0, 0)));
+    const auto input_beta           = reinterpret_cast<const qint8_t *>(beta->ptr_to_element(Coordinates(0, 0)));
+
+    qint8x16_t       mean_vec    = vdupq_n_qs8(0);
+    qint8x16_t       var_vec     = vdupq_n_qs8(0);
+    qint8x16_t       gamma_vec   = vdupq_n_qs8(0);
+    qint8x16_t       beta_vec    = vdupq_n_qs8(0);
+    qint8x16_t       denominator = vdupq_n_qs8(0);
+    const qint8x16_t epsilon_vec = vdupq_n_qs8(scvt_qs8_f32(epsilon, fixed_point_position));
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        if(slice != id.z())
+        {
+            // Conctruct vectors
+            mean_vec  = vdupq_n_qs8(*(input_mean + id.z()));
+            var_vec   = vdupq_n_qs8(*(input_var + id.z()));
+            gamma_vec = vdupq_n_qs8(*(input_gamma + id.z()));
+            beta_vec  = vdupq_n_qs8(*(input_beta + id.z()));
+
+            // Calculate denominator
+            denominator = vqinvsqrtq_qs8(vqaddq_qs8(var_vec, epsilon_vec), fixed_point_position);
+            slice       = id.z();
+        }
+
+        // Calculate x bar and store results
+        const qint8x16_t numerator = vqsubq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), mean_vec);
+        const qint8x16_t x_bar     = vqmulq_qs8(numerator, denominator, fixed_point_position);
+        vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqmlaq_qs8(beta_vec, x_bar, gamma_vec, fixed_point_position));
+    },
+    input, output);
+}
+
+void batch_normalization_fp32(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+{
+    Iterator input(in, window);
+    Iterator output(out, window);
+
+    // Hold information about the current feature map we are iterating.
+    // Only compute denominator and NEON vectors once per feature map.
+    int slice = -1;
+
+    const auto input_mean  = reinterpret_cast<const float *>(mean->ptr_to_element(Coordinates(0, 0)));
+    const auto input_var   = reinterpret_cast<const float *>(var->ptr_to_element(Coordinates(0, 0)));
+    const auto input_gamma = reinterpret_cast<const float *>(gamma->ptr_to_element(Coordinates(0, 0)));
+    const auto input_beta  = reinterpret_cast<const float *>(beta->ptr_to_element(Coordinates(0, 0)));
+
+    float32x4_t       mean_vec    = vdupq_n_f32(0.0);
+    float32x4_t       var_vec     = vdupq_n_f32(0.0);
+    float32x4_t       gamma_vec   = vdupq_n_f32(0.0);
+    float32x4_t       beta_vec    = vdupq_n_f32(0.0);
+    float32x4_t       denominator = vdupq_n_f32(0.0);
+    const float32x4_t epsilon_vec = vdupq_n_f32(epsilon);
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        if(slice != id.z())
+        {
+            // Conctruct vectors
+            mean_vec  = vdupq_n_f32(*(input_mean + id.z()));
+            var_vec   = vdupq_n_f32(*(input_var + id.z()));
+            gamma_vec = vdupq_n_f32(*(input_gamma + id.z()));
+            beta_vec  = vdupq_n_f32(*(input_beta + id.z()));
+
+            // Calculate denominator
+            denominator = vinvsqrtq_f32(vaddq_f32(var_vec, epsilon_vec));
+            slice       = id.z();
+        }
+
+        // Calculate x bar and store results
+        const float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec);
+        const float32x4_t x_bar     = vmulq_f32(numerator, denominator);
+        vst1q_f32(reinterpret_cast<float *>(output.ptr()), vmlaq_f32(beta_vec, x_bar, gamma_vec));
+    },
+    input, output);
+}
+
+void NEBatchNormalizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mean, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(var, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(gamma, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(beta, 1, DataType::QS8, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
+
+    _input   = input;
+    _output  = output;
+    _mean    = mean;
+    _var     = var;
+    _gamma   = gamma;
+    _beta    = beta;
+    _epsilon = epsilon;
+
+    unsigned int num_elems_processed_per_iteration = 0;
+
+    switch(input->info()->data_type())
+    {
+        case DataType::QS8:
+            _func                             = &batch_normalization_q8;
+            num_elems_processed_per_iteration = 16;
+            break;
+        case DataType::F32:
+            _func                             = &batch_normalization_fp32;
+            num_elems_processed_per_iteration = 4;
+            break;
+        default:
+            ARM_COMPUTE_ERROR("Element size not supported");
+            break;
+    }
+
+    Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+    update_window_and_padding(win, input_access, output_access);
+
+    output_access.set_valid_region(win, input->info()->valid_region());
+
+    INEKernel::configure(win);
+}
+
+void NEBatchNormalizationLayerKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _output, _mean, _var, _beta, _gamma, _epsilon, window);
+}