99 lines
4.9 KiB
C++
99 lines
4.9 KiB
C++
/*
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// Copyright (c) 2019 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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*/
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///////////////////////////////////////////////////////////////////////////////////////////////////
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#pragma once
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#include "primitive.hpp"
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#include <vector>
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namespace cldnn {
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/// @addtogroup cpp_api C++ API
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/// @{
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/// @addtogroup cpp_topology Network Topology
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/// @{
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/// @addtogroup cpp_primitives Primitives
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/// @{
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/// @brief Performs forward spatial binary_convolution with weight sharing.
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struct binary_convolution : public primitive_base<binary_convolution> {
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CLDNN_DECLARE_PRIMITIVE(binary_convolution)
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/// @brief Constructs binary_convolution primitive.
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/// @param id This primitive id.
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/// @param input Input primitive id.
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/// @param weights List of primitive ids containing weights data.
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/// @param input_offset Defines a shift, relative to (0,0) position of the input buffer,
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/// where (0,0) point of the binary_convolution window should start calculations.
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/// @param stride Defines shift in input buffer between adjacent calculations of output values.
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/// @param dilation Defines gaps in the input - dilation rate k=1 is normal binary_convolution,
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/// k=2 means skipping one pixel per input, k=4 means skipping 3 pixels.
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/// As an example in one dimension, a filter w of size 3 would compute over input x the following: w[0]*x[0] + w[1]*x[1] + w[2]*x[2] for dilation of 1.
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/// For dilation 2 the filter would instead compute w[0]*x[0] + w[1]*x[2] + w[2]*x[4].
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/// @param output_size User-defined output data size of the primitive (w/o padding).
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/// @param groups Number of feature groups (grouped convolution). If more than 1 then weights/bias count needs to be 1.
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/// @param pad_value Logical value of padding. Can be one of 3 values: 1 - pad bits equal to 1; -1 -> pad bits equal to 0; 0 -> pad is not counted
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/// @param calc_precision Precision of intermediate accumulators
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binary_convolution(const primitive_id& id,
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const primitive_id& input,
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const std::vector<primitive_id>& weights,
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tensor stride = {1, 1, 1, 1},
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tensor input_offset = {0, 0, 0, 0},
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tensor dilation = {1, 1, 1, 1},
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tensor output_size = {0, 0, 0, 0},
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int groups = 1,
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float pad_value = 0.0f,
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data_types calc_precision = data_types::f32,
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const padding& output_padding = padding())
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: primitive_base(id, {input}, output_padding, optional_data_type {calc_precision}),
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input_offset(input_offset),
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stride(stride),
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dilation(dilation),
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output_size(output_size),
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groups(groups),
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pad_value(pad_value),
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weights(weights) {}
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/// @brief Defines a shift, relative to (0,0) position of the input buffer, where (0,0) point of the binary_convolution window should start calculations.
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tensor input_offset;
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/// @brief Defines shift in input buffer between adjacent calculations of output values.
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tensor stride;
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/// @brief Defines gaps in the input - dilation rate k=1 is normal binary_convolution, k=2 means skipping one pixel per input, k=4 means skipping 3 pixels.
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/// As an example in one dimension, a filter w of size 3 would compute over input x the following: w[0]*x[0] + w[1]*x[1] + w[2]*x[2] for dilation of 1.
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/// For dilation 2 the filter would instead compute w[0]*x[0] + w[1]*x[2] + w[2]*x[4].
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tensor dilation;
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/// @brief User-defined output data size of the primitive (w/o padding).
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tensor output_size;
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/// @brief Number of feature groups (grouped convolution). If more than 1 then weights/bias count needs to be 1.
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int groups;
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/// @brief Logical value of padding. Can be one of 3 values: 1 - pad bits equal to 1; -1 -> pad bits equal to 0; 0 -> pad is not counted
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float pad_value;
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/// @brief List of primitive ids containing weights data.
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const primitive_id_arr weights;
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int32_t split() const { return static_cast<int32_t>(weights.size()); }
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std::vector<std::reference_wrapper<const primitive_id>> get_dependencies() const override {
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std::vector<std::reference_wrapper<const primitive_id>> ret;
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ret.reserve(weights.size());
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for (auto& w : weights) ret.push_back(std::ref(w));
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return ret;
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}
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};
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/// @}
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/// @}
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/// @}
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} // namespace cldnn
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