This commit applies optimized cpu kernel for AvgPoolFloat32.
- Introduce introduce optimized cpu kernel of AvgPool2D op
- Apply that kernel for AvgPoolFloat32
Signed-off-by: jiseob.jang <jiseob.jang@samsung.com>
option(BUILD_GTEST "Download and build Google Test" OFF)
option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF)
option(BUILD_TENSORFLOW_LITE "Build TensorFlow Lite from the downloaded source" OFF)
+option(DOWNLOAD_EIGEN "Download Eigen source" OFF)
option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF)
option(DOWNLOAD_NNPACK "Download NNPACK source" OFF)
option(BUILD_GTEST "Download and build Google Test" OFF)
option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF)
option(BUILD_TENSORFLOW_LITE "Build TensorFlow Lite from the downloaded source" OFF)
+option(DOWNLOAD_EIGEN "Download Eigen source" OFF)
option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF)
option(DOWNLOAD_NNPACK "Download NNPACK source" OFF)
add_library(nnfw_lib_cker INTERFACE)
+
+nnfw_find_package(Eigen QUIET)
+if(Eigen_FOUND)
+ target_link_libraries(nnfw_lib_cker INTERFACE eigen)
+ target_compile_definitions(nnfw_lib_cker INTERFACE CKER_OPTIMIZED_EIGEN)
+endif(Eigen_FOUND)
+
target_include_directories(nnfw_lib_cker INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include)
int16_t height;
};
+struct AveragePoolParams
+{
+ FusedActivationFunctionType activation;
+ PaddingType padding_type;
+ PaddingValues padding_values;
+ int stride_height;
+ int stride_width;
+ int filter_height;
+ int filter_width;
+ // uint8, etc, activation params.
+ int32_t quantized_activation_min;
+ int32_t quantized_activation_max;
+ // float activation params.
+ float float_activation_min;
+ float float_activation_max;
+};
+
} // namespace cker
} // namespace nnfw
return gemmlowp::SaturatingRoundingDoublingHighMul(x * (1 << left_shift), quantized_multiplier);
}
+inline int NodeOffset(int b, int h, int w, int height, int width)
+{
+ return (b * height + h) * width + w;
+}
+
inline int CountLeadingZeros(uint32_t integer_input)
{
const uint32_t one_in_leading_positive = 1U << 31;
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file 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.
+ */
+
+#ifndef __NNFW_CKER_EIGEN_UTILS_H__
+#define __NNFW_CKER_EIGEN_UTILS_H__
+
+#if defined(CKER_OPTIMIZED_EIGEN)
+
+#include <Eigen/Core>
+#include <type_traits>
+#include "cker/Shape.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+// Make a local VectorMap typedef allowing to map a float array
+// as a Eigen matrix expression. The same explanation as for VectorMap
+// above also applies here.
+template <typename Scalar>
+using MatrixMap = typename std::conditional<
+ std::is_const<Scalar>::value,
+ Eigen::Map<const Eigen::Matrix<typename std::remove_const<Scalar>::type, Eigen::Dynamic,
+ Eigen::Dynamic>>,
+ Eigen::Map<Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>>>::type;
+
+template <typename Scalar>
+MatrixMap<Scalar> MapAsMatrixWithLastDimAsRows(Scalar *data, const Shape &shape)
+{
+ const int dims_count = shape.DimensionsCount();
+ const int rows = shape.Dims(dims_count - 1);
+ const int cols = FlatSizeSkipDim(shape, dims_count - 1);
+ return MatrixMap<Scalar>(data, rows, cols);
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // defined(CKER_OPTIMIZED_EIGEN)
+
+#endif // __NNFW_CKER_EIGEN_UTILS_H__
#ifndef __NNFW_CKER_AVERAGE_POOL_H__
#define __NNFW_CKER_AVERAGE_POOL_H__
-#include "cker/Shape.h"
-#include "cker/Types.h"
-#include "cker/Utils.h"
+#if defined(CKER_OPTIMIZED_EIGEN)
+#include "cker/operation/optimized/AveragePool.h"
+#endif // defined(CKER_OPTIMIZED_EIGEN)
+
+#include "cker/operation/reference/AveragePool.h"
namespace nnfw
{
namespace cker
{
-struct AveragePoolParams
-{
- FusedActivationFunctionType activation;
- PaddingType padding_type;
- PaddingValues padding_values;
- int stride_height;
- int stride_width;
- int filter_height;
- int filter_width;
- // uint8, etc, activation params.
- int32_t quantized_activation_min;
- int32_t quantized_activation_max;
- // float activation params.
- float float_activation_min;
- float float_activation_max;
-};
-
inline void AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
const float *input_data, const Shape &output_shape, float *output_data)
{
- assert(input_shape.DimensionsCount() == 4);
- assert(output_shape.DimensionsCount() == 4);
- const int batches = MatchingDim(input_shape, 0, output_shape, 0);
- const int depth = MatchingDim(input_shape, 3, output_shape, 3);
- const int input_height = input_shape.Dims(1);
- const int input_width = input_shape.Dims(2);
- const int output_height = output_shape.Dims(1);
- const int output_width = output_shape.Dims(2);
- const int stride_height = params.stride_height;
- const int stride_width = params.stride_width;
- for (int batch = 0; batch < batches; ++batch)
- {
- for (int out_y = 0; out_y < output_height; ++out_y)
- {
- for (int out_x = 0; out_x < output_width; ++out_x)
- {
- const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
- const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
- // Compute the boundaries of the filter region clamped so as to
- // ensure that the filter window fits in the input array.
- const int filter_x_start = std::max(0, -in_x_origin);
- const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
- const int filter_y_start = std::max(0, -in_y_origin);
- const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
- int filter_count = (filter_y_end - filter_y_start) * (filter_x_end - filter_x_start);
- if (filter_count <= 0)
- {
- continue;
- }
- for (int channel = 0; channel < depth; ++channel)
- {
- float total = 0.f;
- for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y)
- {
- for (int filter_x = filter_x_start; filter_x < filter_x_end; ++filter_x)
- {
- const int in_x = in_x_origin + filter_x;
- const int in_y = in_y_origin + filter_y;
- total += input_data[Offset(input_shape, batch, in_y, in_x, channel)];
- }
- }
- const float average = total / (float)filter_count;
- output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
- ActivationFunctionWithMinMax(average, params.float_activation_min,
- params.float_activation_max);
- }
- }
- }
- }
+#if defined(CKER_OPTIMIZED_EIGEN)
+ optimized::AveragePool(params, input_shape, input_data, output_shape, output_data);
+#else // defined(CKER_OPTIMIZED_EIGEN)
+ reference::AveragePool(params, input_shape, input_data, output_shape, output_data);
+#endif // defined(CKER_OPTIMIZED_EIGEN)
}
inline void AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file 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.
+ */
+
+#ifndef __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__
+#define __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__
+
+#if defined(CKER_OPTIMIZED_EIGEN)
+
+#include "cker/eigen/Utils.h"
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+#include <Eigen/Core>
+
+namespace nnfw
+{
+namespace cker
+{
+namespace optimized
+{
+
+// TODO Change to apply neon for this function if it is faster
+inline void AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
+ const float *input_data, const Shape &output_shape, float *output_data)
+{
+ assert(input_shape.DimensionsCount() == 4);
+ assert(output_shape.DimensionsCount() == 4);
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ const int stride_height = params.stride_height;
+ const int stride_width = params.stride_width;
+
+ // TODO(benoitjacob) make this a proper reference impl without Eigen!
+ const auto in_mat = MapAsMatrixWithLastDimAsRows(input_data, input_shape);
+ auto out_mat = MapAsMatrixWithLastDimAsRows(output_data, output_shape);
+ // TODO(benoitjacob) get rid of the dynamic memory allocation here!
+ Eigen::VectorXf out_count(out_mat.cols());
+ out_count.setZero();
+ // Prefill the output to 0.
+ out_mat.setZero();
+ for (int b = 0; b < batches; ++b)
+ {
+ for (int h = 0; h < input_height; ++h)
+ {
+ for (int w = 0; w < input_width; ++w)
+ {
+ // (h_start, h_end) * (w_start, w_end) is the range that the input
+ // vector projects to.
+ int hpad = h + params.padding_values.height;
+ int wpad = w + params.padding_values.width;
+ int h_start =
+ (hpad < params.filter_height) ? 0 : (hpad - params.filter_height) / stride_height + 1;
+ int h_end = std::min(hpad / stride_height + 1, output_height);
+ int w_start =
+ (wpad < params.filter_width) ? 0 : (wpad - params.filter_width) / stride_width + 1;
+ int w_end = std::min(wpad / stride_width + 1, output_width);
+ // compute elementwise sum
+ for (int ph = h_start; ph < h_end; ++ph)
+ {
+ for (int pw = w_start; pw < w_end; ++pw)
+ {
+ int out_offset = NodeOffset(b, ph, pw, output_height, output_width);
+ out_mat.col(out_offset) += in_mat.col(NodeOffset(b, h, w, input_height, input_width));
+ out_count(out_offset)++;
+ }
+ }
+ }
+ }
+ }
+ // Divide the output by the actual number of elements being averaged over
+ assert(out_count.minCoeff() > 0);
+ out_mat.array().rowwise() /= out_count.transpose().array();
+
+ const int flat_size = output_shape.FlatSize();
+ for (int i = 0; i < flat_size; ++i)
+ {
+ output_data[i] = ActivationFunctionWithMinMax(output_data[i], params.float_activation_min,
+ params.float_activation_max);
+ }
+}
+
+} // namespace optimized
+} // namespace cker
+} // namespace nnfw
+
+#endif // defined(CKER_OPTIMIZED_EIGEN)
+
+#endif // __NNFW_CKER_OPTIMIZED_AVERAGE_POOL_H__
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file 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.
+ */
+
+#ifndef __NNFW_CKER_REFERENCE_AVERAGE_POOL_H__
+#define __NNFW_CKER_REFERENCE_AVERAGE_POOL_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+namespace nnfw
+{
+namespace cker
+{
+namespace reference
+{
+
+inline void AveragePool(const AveragePoolParams ¶ms, const Shape &input_shape,
+ const float *input_data, const Shape &output_shape, float *output_data)
+{
+ assert(input_shape.DimensionsCount() == 4);
+ assert(output_shape.DimensionsCount() == 4);
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int depth = MatchingDim(input_shape, 3, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ const int stride_height = params.stride_height;
+ const int stride_width = params.stride_width;
+ for (int batch = 0; batch < batches; ++batch)
+ {
+ for (int out_y = 0; out_y < output_height; ++out_y)
+ {
+ for (int out_x = 0; out_x < output_width; ++out_x)
+ {
+ const int in_x_origin = (out_x * stride_width) - params.padding_values.width;
+ const int in_y_origin = (out_y * stride_height) - params.padding_values.height;
+ // Compute the boundaries of the filter region clamped so as to
+ // ensure that the filter window fits in the input array.
+ const int filter_x_start = std::max(0, -in_x_origin);
+ const int filter_x_end = std::min(params.filter_width, input_width - in_x_origin);
+ const int filter_y_start = std::max(0, -in_y_origin);
+ const int filter_y_end = std::min(params.filter_height, input_height - in_y_origin);
+ int filter_count = (filter_y_end - filter_y_start) * (filter_x_end - filter_x_start);
+ if (filter_count <= 0)
+ {
+ continue;
+ }
+ for (int channel = 0; channel < depth; ++channel)
+ {
+ float total = 0.f;
+ for (int filter_y = filter_y_start; filter_y < filter_y_end; ++filter_y)
+ {
+ for (int filter_x = filter_x_start; filter_x < filter_x_end; ++filter_x)
+ {
+ const int in_x = in_x_origin + filter_x;
+ const int in_y = in_y_origin + filter_y;
+ total += input_data[Offset(input_shape, batch, in_y, in_x, channel)];
+ }
+ }
+ const float average = total / (float)filter_count;
+ output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
+ ActivationFunctionWithMinMax(average, params.float_activation_min,
+ params.float_activation_max);
+ }
+ }
+ }
+ }
+}
+
+} // namespace reference
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_REFERENCE_AVERAGE_POOL_H__
projects / platform / core / ml / nnfw.git / commitdiff