--- /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_MAX_POOL_H__
+#define __NNFW_CKER_MAX_POOL_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct MaxPoolParams
+{
+ 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 MaxPool(const MaxPoolParams ¶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)
+ {
+ for (int channel = 0; channel < depth; ++channel)
+ {
+ 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);
+ float max = std::numeric_limits<float>::lowest();
+ 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;
+ max = std::max(max, input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
+ }
+ }
+ output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
+ ActivationFunctionWithMinMax(max, params.float_activation_min,
+ params.float_activation_max);
+ }
+ }
+ }
+ }
+}
+
+inline void MaxPool(const MaxPoolParams ¶ms, const Shape &input_shape,
+ const uint8_t *input_data, const Shape &output_shape, uint8_t *output_data)
+{
+ assert(params.quantized_activation_min <= params.quantized_activation_max);
+ assert(params.quantized_activation_min >= 0);
+ assert(params.quantized_activation_max <= 255);
+ 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)
+ {
+ for (int channel = 0; channel < depth; ++channel)
+ {
+ 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);
+ uint8_t max = 0;
+ 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;
+ max = std::max(max, input_data[Offset(input_shape, batch, in_y, in_x, channel)]);
+ }
+ }
+ max = std::max<uint8_t>(max, params.quantized_activation_min);
+ max = std::min<uint8_t>(max, params.quantized_activation_max);
+ output_data[Offset(output_shape, batch, out_y, out_x, channel)] =
+ static_cast<uint8_t>(max);
+ }
+ }
+ }
+ }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_MAX_POOL_H__
#include "MaxPoolLayer.h"
-#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
+#include <cker/operation/MaxPool.h>
+
#include "OperationUtils.h"
namespace neurun
{
#define MAXPOOLING_PARAMETERS \
- tflite::PoolParams op_params; \
+ nnfw::cker::MaxPoolParams op_params; \
op_params.stride_height = _strideHeight; \
op_params.stride_width = _strideWidth; \
op_params.filter_height = _kernelHeight; \
op_params.float_activation_min = output_activation_min;
op_params.float_activation_max = output_activation_max;
- ::tflite::optimized_ops::MaxPool(op_params, convertShapeToTFLiteShape(_inputShape), _inputData.f,
- convertShapeToTFLiteShape(_outputShape), _outputData.f);
+ nnfw::cker::MaxPool(op_params, convertShapeToCkerShape(_inputShape), _inputData.f,
+ convertShapeToCkerShape(_outputShape), _outputData.f);
return true;
}
bool MaxPoolLayer::maxPoolQuant8()
op_params.quantized_activation_min = output_activation_min;
op_params.quantized_activation_max = output_activation_max;
- ::tflite::optimized_ops::MaxPool(op_params, convertShapeToTFLiteShape(_inputShape), _inputData.u8,
- convertShapeToTFLiteShape(_outputShape), _outputData.u8);
+ nnfw::cker::MaxPool(op_params, convertShapeToCkerShape(_inputShape), _inputData.u8,
+ convertShapeToCkerShape(_outputShape), _outputData.u8);
return true;
}