101 lines
2.6 KiB
C++
101 lines
2.6 KiB
C++
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// Copyright 2016, Tobias Hermann.
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// https://github.com/Dobiasd/frugally-deep
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// Distributed under the MIT License.
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// (See accompanying LICENSE file or at
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// https://opensource.org/licenses/MIT)
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#pragma once
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#include "fdeep/common.hpp"
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#include "fdeep/tensor5.hpp"
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#include "fdeep/shape5.hpp"
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#include <cassert>
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#include <cstddef>
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#include <vector>
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namespace fdeep { namespace internal
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{
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class filter
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{
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public:
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filter(const tensor5& m, float_type bias) : m_(m), bias_(bias)
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{
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}
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const shape5& shape() const
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{
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return m_.shape();
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}
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std::size_t volume() const
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{
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return m_.shape().volume();
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}
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const tensor5& get_tensor5() const
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{
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return m_;
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}
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float_type get(std::size_t y, size_t x, std::size_t z) const
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{
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return m_.get(0, 0, y, x, z);
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}
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float_type get_bias() const
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{
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return bias_;
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}
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void set_params(const float_vec& weights, float_type bias)
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{
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assertion(weights.size() == m_.shape().volume(),
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"invalid parameter count");
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m_ = tensor5(m_.shape(), float_vec(weights));
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bias_ = bias;
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}
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private:
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tensor5 m_;
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float_type bias_;
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};
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typedef std::vector<filter> filter_vec;
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inline filter dilate_filter(const shape2& dilation_rate, const filter& undilated)
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{
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return filter(dilate_tensor5(dilation_rate, undilated.get_tensor5()),
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undilated.get_bias());
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}
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inline filter_vec generate_filters(
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const shape2& dilation_rate,
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const shape5& filter_shape, std::size_t k,
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const float_vec& weights, const float_vec& bias)
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{
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filter_vec filters(k, filter(tensor5(filter_shape, 0), 0));
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assertion(!filters.empty(), "at least one filter needed");
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const std::size_t param_count = fplus::sum(fplus::transform(
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fplus_c_mem_fn_t(filter, volume, std::size_t), filters));
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assertion(static_cast<std::size_t>(weights.size()) == param_count,
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"invalid weight size");
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const auto filter_param_cnt = filters.front().shape().volume();
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auto filter_weights =
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fplus::split_every(filter_param_cnt, weights);
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assertion(filter_weights.size() == filters.size(),
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"invalid size of filter weights");
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assertion(bias.size() == filters.size(), "invalid bias size");
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auto it_filter_val = std::begin(filter_weights);
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auto it_filter_bias = std::begin(bias);
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for (auto& filt : filters)
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{
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filt.set_params(*it_filter_val, *it_filter_bias);
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filt = dilate_filter(dilation_rate, filt);
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++it_filter_val;
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++it_filter_bias;
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}
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return filters;
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}
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} } // namespace fdeep, namespace internal
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