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diff --git a/compute/cker/include/cker/operation/DepthwiseConv.h b/compute/cker/include/cker/operation/DepthwiseConv.h
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+/*
+ * 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_DEPTHWISE_CONV_H__
+#define __NNFW_CKER_DEPTHWISE_CONV_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+namespace nnfw
+{
+namespace cker
+{
+
+struct DepthwiseConvParams
+{
+ PaddingType padding_type;
+ PaddingValues padding_values;
+ int16_t stride_width;
+ int16_t stride_height;
+ int16_t dilation_width_factor;
+ int16_t dilation_height_factor;
+ int16_t depth_multiplier;
+ // uint8 inference params.
+ // TODO(b/65838351): Use smaller types if appropriate.
+ int32_t input_offset;
+ int32_t weights_offset;
+ int32_t output_offset;
+ int32_t output_multiplier;
+ int output_shift;
+ // 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 DepthwiseConv(const DepthwiseConvParams &params, const Shape &input_shape,
+ const uint8_t *input_data, const Shape &filter_shape,
+ const uint8_t *filter_data, const Shape &bias_shape,
+ const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data)
+{
+ const int stride_width = params.stride_width;
+ const int stride_height = params.stride_height;
+ const int dilation_width_factor = params.dilation_width_factor;
+ const int dilation_height_factor = params.dilation_height_factor;
+ const int pad_width = params.padding_values.width;
+ const int pad_height = params.padding_values.height;
+ const int depth_multiplier = params.depth_multiplier;
+ const int32_t output_activation_min = params.quantized_activation_min;
+ const int32_t output_activation_max = params.quantized_activation_max;
+ const int32_t input_offset = params.input_offset;
+ const int32_t filter_offset = params.weights_offset;
+ const int32_t output_offset = params.output_offset;
+ const int32_t output_multiplier = params.output_multiplier;
+ const int output_shift = params.output_shift;
+ assert(input_shape.DimensionsCount() == 4);
+ assert(filter_shape.DimensionsCount() == 4);
+ assert(output_shape.DimensionsCount() == 4);
+
+ assert(output_activation_min <= output_activation_max);
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int input_depth = input_shape.Dims(3);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ assert(output_depth == input_depth * depth_multiplier);
+ assert(bias_shape.FlatSize() == output_depth);
+ UNUSED_RELEASE(output_depth);
+ UNUSED_RELEASE(bias_shape);
+
+ for (int b = 0; b < batches; ++b)
+ {
+ for (int out_y = 0; out_y < output_height; ++out_y)
+ {
+ for (int out_x = 0; out_x < output_width; ++out_x)
+ {
+ for (int ic = 0; ic < input_depth; ++ic)
+ {
+ for (int m = 0; m < depth_multiplier; m++)
+ {
+ const int oc = m + ic * depth_multiplier;
+ const int in_x_origin = (out_x * stride_width) - pad_width;
+ const int in_y_origin = (out_y * stride_height) - pad_height;
+ int32_t acc = 0;
+ for (int filter_y = 0; filter_y < filter_height; ++filter_y)
+ {
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x)
+ {
+ const int in_x = in_x_origin + dilation_width_factor * filter_x;
+ const int in_y = in_y_origin + dilation_height_factor * filter_y;
+ // If the location is outside the bounds of the input image,
+ // use zero as a default value.
+ if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height))
+ {
+ int32_t input_val = input_data[Offset(input_shape, b, in_y, in_x, ic)];
+ int32_t filter_val = filter_data[Offset(filter_shape, 0, filter_y, filter_x, oc)];
+ acc += (filter_val + filter_offset) * (input_val + input_offset);
+ }
+ }
+ }
+ if (bias_data)
+ {
+ acc += bias_data[oc];
+ }
+ acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift);
+ acc += output_offset;
+ acc = std::max(acc, output_activation_min);
+ acc = std::min(acc, output_activation_max);
+ output_data[Offset(output_shape, b, out_y, out_x, oc)] = static_cast<uint8_t>(acc);
+ }
+ }
+ }
+ }
+ }
+}
+
+inline void DepthwiseConv(const DepthwiseConvParams &params, const Shape &input_shape,
+ const float *input_data, const Shape &filter_shape,
+ const float *filter_data, const Shape &bias_shape, const float *bias_data,
+ const Shape &output_shape, float *output_data)
+{
+ const int stride_width = params.stride_width;
+ const int stride_height = params.stride_height;
+ const int dilation_width_factor = params.dilation_width_factor;
+ const int dilation_height_factor = params.dilation_height_factor;
+ const int pad_width = params.padding_values.width;
+ const int pad_height = params.padding_values.height;
+ const int depth_multiplier = params.depth_multiplier;
+ const float output_activation_min = params.float_activation_min;
+ const float output_activation_max = params.float_activation_max;
+ assert(input_shape.DimensionsCount() == 4);
+ assert(filter_shape.DimensionsCount() == 4);
+ assert(output_shape.DimensionsCount() == 4);
+
+ const int batches = MatchingDim(input_shape, 0, output_shape, 0);
+ const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
+ const int input_height = input_shape.Dims(1);
+ const int input_width = input_shape.Dims(2);
+ const int input_depth = input_shape.Dims(3);
+ const int filter_height = filter_shape.Dims(1);
+ const int filter_width = filter_shape.Dims(2);
+ const int output_height = output_shape.Dims(1);
+ const int output_width = output_shape.Dims(2);
+ assert(output_depth == input_depth * depth_multiplier);
+ assert(bias_shape.FlatSize() == output_depth);
+ UNUSED_RELEASE(output_depth);
+ UNUSED_RELEASE(bias_shape);
+
+ for (int b = 0; b < batches; ++b)
+ {
+ for (int out_y = 0; out_y < output_height; ++out_y)
+ {
+ for (int out_x = 0; out_x < output_width; ++out_x)
+ {
+ for (int ic = 0; ic < input_depth; ++ic)
+ {
+ for (int m = 0; m < depth_multiplier; m++)
+ {
+ const int oc = m + ic * depth_multiplier;
+ const int in_x_origin = (out_x * stride_width) - pad_width;
+ const int in_y_origin = (out_y * stride_height) - pad_height;
+ float total = 0.f;
+ for (int filter_y = 0; filter_y < filter_height; ++filter_y)
+ {
+ for (int filter_x = 0; filter_x < filter_width; ++filter_x)
+ {
+ const int in_x = in_x_origin + dilation_width_factor * filter_x;
+ const int in_y = in_y_origin + dilation_height_factor * filter_y;
+ // If the location is outside the bounds of the input image,
+ // use zero as a default value.
+ if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && (in_y < input_height))
+ {
+ float input_value = input_data[Offset(input_shape, b, in_y, in_x, ic)];
+ float filter_value = filter_data[Offset(filter_shape, 0, filter_y, filter_x, oc)];
+ total += (input_value * filter_value);
+ }
+ }
+ }
+ float bias_value = 0.0f;
+ if (bias_data)
+ {
+ bias_value = bias_data[oc];
+ }
+ output_data[Offset(output_shape, b, out_y, out_x, oc)] = ActivationFunctionWithMinMax(
+ total + bias_value, output_activation_min, output_activation_max);
+ }
+ }
+ }
+ }
+ }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_DEPTHWISE_CONV_H__