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Diffstat (limited to 'compute/cker/include/cker/operation/DepthwiseConv.h')
-rw-r--r-- | compute/cker/include/cker/operation/DepthwiseConv.h | 217 |
1 files changed, 217 insertions, 0 deletions
diff --git a/compute/cker/include/cker/operation/DepthwiseConv.h b/compute/cker/include/cker/operation/DepthwiseConv.h new file mode 100644 index 000000000..7d022477d --- /dev/null +++ b/compute/cker/include/cker/operation/DepthwiseConv.h @@ -0,0 +1,217 @@ +/* + * 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 ¶ms, 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 ¶ms, 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__ |