coco
2274 lines
81 KiB
C++
2274 lines
81 KiB
C++
// Copyright (C) 2015 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_DNN_CPU_cPP_
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#define DLIB_DNN_CPU_cPP_
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// This file contains CPU implementations of the GPU based functions in cuda_dlib.h
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#include "cpu_dlib.h"
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#include "tensor_tools.h"
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#include "../image_transforms/interpolation.h"
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#include "../threads.h"
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namespace dlib
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{
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namespace cpu
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{
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// -----------------------------------------------------------------------------------
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void multiply (
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bool add_to,
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tensor& dest,
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const tensor& src1,
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const tensor& src2
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)
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{
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DLIB_CASSERT(dest.k() == src1.k() && src1.k() == src2.k() &&
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dest.nr() == src1.nr() && src1.nr() == src2.nr() &&
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dest.nc() == src1.nc() && src1.nc() == src2.nc() );
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const long MD = std::max(std::max(dest.num_samples(),src1.num_samples()),src2.num_samples());
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DLIB_CASSERT((dest.num_samples()==1 || dest.num_samples()==MD) &&
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(src1.num_samples()==1 || src1.num_samples()==MD) &&
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(src2.num_samples()==1 || src2.num_samples()==MD) );
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if (dest.size() == 0)
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return;
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const size_t max_size = std::max(std::max(dest.size(),src1.size()),src2.size());
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const auto d = dest.host();
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const auto s1 = src1.host();
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const auto s2 = src2.host();
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if (dest.size() == src1.size() && src1.size() == src2.size())
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{
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if (add_to)
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{
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for (size_t i = 0; i < src1.size(); ++i)
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d[i] += s1[i]*s2[i];
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}
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else
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{
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for (size_t i = 0; i < src1.size(); ++i)
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d[i] = s1[i]*s2[i];
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}
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}
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else if (dest.num_samples() == 1)
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{
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if (!add_to)
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{
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for (size_t i = 0; i < dest.size(); ++i)
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d[i] = 0;
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}
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for (size_t i = 0; i < max_size; ++i)
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d[i%dest.size()] += s1[i%src1.size()]*s2[i%src2.size()];
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}
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else
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{
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if (add_to)
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{
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for (size_t i = 0; i < max_size; ++i)
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d[i] += s1[i%src1.size()]*s2[i%src2.size()];
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}
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else
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{
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for (size_t i = 0; i < max_size; ++i)
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d[i] = s1[i%src1.size()]*s2[i%src2.size()];
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}
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}
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}
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// ------------------------------------------------------------------------------------
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void multiply_conv (
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bool add_to,
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tensor& dest,
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const tensor& src1,
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const tensor& src2
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)
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{
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auto d = dest.host();
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auto s1 = src1.host();
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auto s2 = src2.host();
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if (have_same_dimensions(dest,src1))
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{
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DLIB_CASSERT(src2.num_samples() == 1 && src2.nr() == 1 && src2.nc() == 1 && src2.k() == src1.k());
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if (add_to)
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{
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for (long n = 0; n < dest.num_samples(); ++n)
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{
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for (long k = 0; k < dest.k(); ++k)
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{
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for (long r = 0; r < dest.nr(); ++r)
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{
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for (long c = 0; c < dest.nc(); ++c)
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{
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*d++ += (*s1++)*s2[k];
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}
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}
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}
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}
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}
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else
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{
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for (long n = 0; n < dest.num_samples(); ++n)
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{
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for (long k = 0; k < dest.k(); ++k)
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{
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for (long r = 0; r < dest.nr(); ++r)
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{
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for (long c = 0; c < dest.nc(); ++c)
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{
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*d++ = (*s1++)*s2[k];
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}
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}
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}
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}
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}
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}
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else
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{
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DLIB_CASSERT(have_same_dimensions(src1,src2));
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DLIB_CASSERT(dest.num_samples() == 1 && dest.nr() == 1 && dest.nc() == 1 && dest.k() == src1.k());
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if (!add_to)
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{
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for (long k = 0; k < src1.k(); ++k)
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d[k] = 0;
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}
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for (long n = 0; n < src1.num_samples(); ++n)
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{
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for (long k = 0; k < src1.k(); ++k)
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{
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for (long r = 0; r < src1.nr(); ++r)
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{
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for (long c = 0; c < src1.nc(); ++c)
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{
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d[k] += (*s1++)*(*s2++);
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}
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}
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}
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}
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}
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}
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// ------------------------------------------------------------------------------------
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void scale_channels (
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bool add_to,
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tensor& dest,
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const tensor& src,
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const tensor& scales
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)
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{
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DLIB_CASSERT(have_same_dimensions(dest,src) &&
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scales.num_samples() == src.num_samples() &&
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scales.k() == src.k() &&
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scales.nr() == 1 &&
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scales.nc() == 1 );
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if (dest.size() == 0)
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return;
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if (add_to)
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{
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auto d = dest.host();
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auto s = src.host();
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auto scal = scales.host();
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for (long n = 0; n < src.num_samples(); ++n)
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{
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for (long k = 0; k < src.k(); ++k)
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{
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const auto scale = scal[n*scales.k() + k];
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for (long r = 0; r < src.nr(); ++r)
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{
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for (long c = 0; c < src.nc(); ++c)
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{
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*d++ += (*s++) * scale;
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}
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}
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}
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}
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}
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else
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{
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auto d = dest.host_write_only();
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auto s = src.host();
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auto scal = scales.host();
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for (long n = 0; n < src.num_samples(); ++n)
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{
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for (long k = 0; k < src.k(); ++k)
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{
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const auto scale = scal[n*scales.k() + k];
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for (long r = 0; r < src.nr(); ++r)
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{
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for (long c = 0; c < src.nc(); ++c)
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{
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*d++ = (*s++) * scale;
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}
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}
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}
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}
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}
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}
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// ------------------------------------------------------------------------------------
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void add(
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float beta,
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tensor& dest,
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float alpha,
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const tensor& src
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)
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{
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DLIB_CASSERT(
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(have_same_dimensions(src, dest) ||
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(src.num_samples()==1 && src.k()==dest.k() && src.nr()==1 && src.nc()==1) ||
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(src.num_samples()==1 && src.k()==dest.k() && src.nr()==dest.nr() && src.nc()==dest.nc()) ||
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(src.num_samples()==1 && src.k()==1 && src.nr()==dest.nr() && src.nc()==dest.nc()) ||
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(src.num_samples()==dest.num_samples() && src.k()==1 && src.nr()==1 && src.nc()==1)) &&
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is_same_object(src,dest) == false ,
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"\n\t dest.num_samples(): " << dest.num_samples()
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<<"\n\t dest.k(): " << dest.k()
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<<"\n\t dest.nr(): " << dest.nr()
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<<"\n\t dest.nc(): " << dest.nc()
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<<"\n\t src.num_samples(): " << src.num_samples()
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<<"\n\t src.k(): " << src.k()
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<<"\n\t src.nr(): " << src.nr()
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<<"\n\t src.nc(): " << src.nc()
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);
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if (beta == 0 && alpha == 0)
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{
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dest = 0;
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return;
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}
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auto d = dest.host();
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auto s = src.host();
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for (long n = 0; n < dest.num_samples(); ++n)
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{
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const auto sn = src.num_samples()==1 ? 0:n;
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for (long k = 0; k < dest.k(); ++k)
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{
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const auto sk = src.k()==1 ? 0:k;
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for (long r = 0; r < dest.nr(); ++r)
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{
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const auto sr = src.nr()==1 ? 0:r;
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for (long c = 0; c < dest.nc(); ++c)
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{
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const auto sc = src.nc()==1 ? 0:c;
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const auto s_idx = ((sn*src.k() + sk)*src.nr() + sr)*src.nc() + sc;
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*d = beta*(*d) + alpha*s[s_idx];
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++d;
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}
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}
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}
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}
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}
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// ----------------------------------------------------------------------------------------
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void add (
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tensor& dest,
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const tensor& src1,
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const tensor& src2
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)
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{
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auto d = dest.host();
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auto s1 = src1.host();
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auto s2 = src2.host();
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// Do the simple and fast version if everything has the same dimensions
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if (have_same_dimensions(dest, src1) &&
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have_same_dimensions(dest, src2))
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{
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for (size_t i = 0; i < dest.size(); ++i)
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d[i] = s1[i] + s2[i];
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return;
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}
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// Otherwise, do the more complex version with bounds checking.
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for (long n = 0; n < dest.num_samples(); ++n)
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{
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for (long k = 0; k < dest.k(); ++k)
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{
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for (long r = 0; r < dest.nr(); ++r)
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{
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for (long c = 0; c < dest.nc(); ++c)
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{
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float v1 = 0;
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float v2 = 0;
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// if this index is inside src1
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if (n < src1.num_samples() &&
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k < src1.k() &&
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r < src1.nr() &&
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c < src1.nc() )
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{
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const auto s_idx = ((n*src1.k() + k)*src1.nr() + r)*src1.nc() + c;
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v1 = s1[s_idx];
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}
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// if this index is inside src2
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if (n < src2.num_samples() &&
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k < src2.k() &&
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r < src2.nr() &&
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c < src2.nc() )
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{
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const auto s_idx = ((n*src2.k() + k)*src2.nr() + r)*src2.nc() + c;
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v2 = s2[s_idx];
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}
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*d = v1 + v2;
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++d;
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}
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}
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}
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}
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}
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// ----------------------------------------------------------------------------------------
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void multiply_zero_padded (
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bool add_to,
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tensor& dest,
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const tensor& src1,
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const tensor& src2
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)
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{
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auto d = dest.host();
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auto s1 = src1.host();
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auto s2 = src2.host();
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// Do the simple and fast version if everything has the same dimensions
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if (have_same_dimensions(dest, src1) &&
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have_same_dimensions(dest, src2))
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{
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if (add_to)
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{
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for (size_t i = 0; i < dest.size(); ++i)
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d[i] += s1[i] * s2[i];
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}
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else
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{
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for (size_t i = 0; i < dest.size(); ++i)
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d[i] = s1[i] * s2[i];
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}
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return;
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}
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// Otherwise, do the more complex version with bounds checking.
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for (long n = 0; n < dest.num_samples(); ++n)
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{
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for (long k = 0; k < dest.k(); ++k)
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{
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for (long r = 0; r < dest.nr(); ++r)
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{
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for (long c = 0; c < dest.nc(); ++c)
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{
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float v1 = 0;
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float v2 = 0;
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// if this index is inside src1
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if (n < src1.num_samples() &&
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k < src1.k() &&
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r < src1.nr() &&
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c < src1.nc() )
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{
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const auto s_idx = ((n*src1.k() + k)*src1.nr() + r)*src1.nc() + c;
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v1 = s1[s_idx];
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}
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// if this index is inside src2
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if (n < src2.num_samples() &&
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k < src2.k() &&
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r < src2.nr() &&
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c < src2.nc() )
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{
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const auto s_idx = ((n*src2.k() + k)*src2.nr() + r)*src2.nc() + c;
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v2 = s2[s_idx];
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}
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if (add_to)
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*d += v1 * v2;
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else
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*d = v1 * v2;
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++d;
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}
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}
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}
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}
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}
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// ----------------------------------------------------------------------------------------
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void assign_bias_gradient (
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tensor& grad,
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const tensor& gradient_input
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)
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{
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DLIB_CASSERT(
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grad.num_samples() == 1 &&
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gradient_input.k() == grad.k() &&
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gradient_input.nr() == grad.nr() &&
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gradient_input.nc() == grad.nc() &&
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gradient_input.size() > 0);
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auto out = grad.host();
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auto in = gradient_input.host();
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for (size_t i = 0; i < grad.size(); ++i)
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out[i] = *in++;
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for (long j = 1; j < gradient_input.num_samples(); ++j)
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{
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for (size_t i = 0; i < grad.size(); ++i)
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out[i] += *in++;
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}
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}
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// ------------------------------------------------------------------------------------
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void assign_conv_bias_gradient (
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tensor& grad,
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const tensor& gradient_input
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)
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{
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DLIB_CASSERT(
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grad.num_samples() == 1 &&
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grad.k() >= 1 &&
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grad.nr() == 1 &&
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grad.nc() == 1 &&
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gradient_input.k() == grad.k() &&
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gradient_input.size() > 0 &&
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is_same_object(grad,gradient_input) == false
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);
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auto g = grad.host();
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auto gi = gradient_input.host();
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for (long k = 0; k < gradient_input.k(); ++k)
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g[k] = 0;
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for (long n = 0; n < gradient_input.num_samples(); ++n)
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{
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for (long k = 0; k < gradient_input.k(); ++k)
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{
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for (long r = 0; r < gradient_input.nr(); ++r)
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{
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for (long c = 0; c < gradient_input.nc(); ++c)
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{
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g[k] += (*gi++);
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}
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}
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}
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}
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}
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// -----------------------------------------------------------------------------------
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void affine_transform(
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tensor& dest,
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const tensor& src,
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const float A,
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const float B
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)
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{
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DLIB_CASSERT(dest.size()==src.size());
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const auto d = dest.host();
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const auto s = src.host();
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for (size_t i = 0; i < src.size(); ++i)
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d[i] = A*s[i] + B;
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}
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void affine_transform(
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tensor& dest,
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const tensor& src1,
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const tensor& src2,
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const float A,
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const float B,
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const float C
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)
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{
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DLIB_CASSERT(dest.size()==src1.size());
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DLIB_CASSERT(dest.size()==src2.size());
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const auto d = dest.host();
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const auto s1 = src1.host();
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const auto s2 = src2.host();
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for (size_t i = 0; i < src1.size(); ++i)
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d[i] = A*s1[i] + B*s2[i] + C;
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}
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void affine_transform(
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tensor& dest,
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const tensor& src1,
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const tensor& src2,
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const tensor& src3,
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const float A,
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const float B,
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const float C,
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const float D
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)
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{
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DLIB_CASSERT(dest.size()==src1.size());
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DLIB_CASSERT(dest.size()==src2.size());
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DLIB_CASSERT(dest.size()==src3.size());
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const auto d = dest.host();
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const auto s1 = src1.host();
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const auto s2 = src2.host();
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const auto s3 = src3.host();
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for (size_t i = 0; i < src1.size(); ++i)
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d[i] = A*s1[i] + B*s2[i] + C*s3[i] + D;
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}
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void affine_transform_range(
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size_t begin,
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size_t end,
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tensor& dest,
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const tensor& src1,
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const tensor& src2,
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const tensor& src3,
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const float A,
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const float B,
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const float C
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)
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{
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DLIB_CASSERT(dest.size()==src1.size());
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DLIB_CASSERT(dest.size()==src2.size());
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|
DLIB_CASSERT(dest.size()==src3.size());
|
|
DLIB_CASSERT(begin <= end && end <= dest.size());
|
|
const auto d = dest.host();
|
|
const auto s1 = src1.host();
|
|
const auto s2 = src2.host();
|
|
const auto s3 = src3.host();
|
|
for (size_t i = begin; i < end; ++i)
|
|
d[i] = A*s1[i] + B*s2[i] + C*s3[i];
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
void affine_transform(
|
|
tensor& dest,
|
|
const tensor& src,
|
|
const tensor& A,
|
|
const tensor& B
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(dest,src));
|
|
DLIB_CASSERT(
|
|
((A.num_samples()==1 && B.num_samples()==1) ||
|
|
(A.num_samples()==src.num_samples() && B.num_samples()==src.num_samples())) &&
|
|
A.nr()==B.nr() && B.nr()==src.nr() &&
|
|
A.nc()==B.nc() && B.nc()==src.nc() &&
|
|
A.k() ==B.k() && B.k()==src.k());
|
|
|
|
auto d = dest.host();
|
|
auto s = src.host();
|
|
const auto a = A.host();
|
|
const auto b = B.host();
|
|
if (A.num_samples() == 1)
|
|
{
|
|
const long num = src.size()/src.num_samples();
|
|
for (long i = 0; i < src.num_samples(); ++i)
|
|
{
|
|
for (long j = 0; j < num; ++j)
|
|
{
|
|
*d = a[j]*(*s) + b[j];
|
|
d++;
|
|
s++;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
d[i] = a[i]*s[i] + b[i];
|
|
}
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
void affine_transform_conv(
|
|
tensor& dest,
|
|
const tensor& src,
|
|
const tensor& A,
|
|
const tensor& B
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(dest,src));
|
|
DLIB_CASSERT(have_same_dimensions(A,B));
|
|
DLIB_CASSERT(A.num_samples() == 1 &&
|
|
A.nr() == 1 &&
|
|
A.nc() == 1 &&
|
|
A.k() == src.k());
|
|
|
|
auto d = dest.host();
|
|
auto s = src.host();
|
|
const auto a = A.host();
|
|
const auto b = B.host();
|
|
for (long n = 0; n < dest.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < dest.k(); ++k)
|
|
{
|
|
for (long r = 0; r < dest.nr(); ++r)
|
|
{
|
|
for (long c = 0; c < dest.nc(); ++c)
|
|
{
|
|
*d++ = a[k]*(*s++) + b[k];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void affine_transform(
|
|
const rectangle& rect,
|
|
tensor& dest,
|
|
const tensor& src1,
|
|
const tensor& src2,
|
|
const tensor& src3,
|
|
float A,
|
|
float B,
|
|
float C
|
|
)
|
|
{
|
|
DLIB_CASSERT(dest.size() == src1.size());
|
|
DLIB_CASSERT(dest.size() == src2.size());
|
|
DLIB_CASSERT(dest.size() == src3.size());
|
|
DLIB_CASSERT(dest.num_samples() == src1.num_samples());
|
|
DLIB_CASSERT(dest.num_samples() == src2.num_samples());
|
|
DLIB_CASSERT(dest.num_samples() == src3.num_samples());
|
|
DLIB_CASSERT(rectangle(0,0, dest.size()/dest.num_samples()-1, dest.num_samples()-1).contains(rect));
|
|
|
|
|
|
auto d = dest.host();
|
|
auto s1 = src1.host();
|
|
auto s2 = src2.host();
|
|
auto s3 = src3.host();
|
|
|
|
const auto nc = dest.size()/dest.num_samples();
|
|
|
|
for (long r = rect.top(); r <= rect.bottom(); ++r)
|
|
{
|
|
for (long c = rect.left(); c <= rect.right(); ++c)
|
|
{
|
|
auto idx = r*nc + c;
|
|
d[idx] = s1[idx]*A + s2[idx]*B + s3[idx]*C;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
void compute_adam_update (
|
|
size_t begin,
|
|
size_t end,
|
|
tensor& s,
|
|
tensor& m,
|
|
tensor& v,
|
|
const float t,
|
|
const float learning_rate,
|
|
const float weight_decay,
|
|
const float momentum1,
|
|
const float momentum2,
|
|
const tensor& params,
|
|
const tensor& params_grad
|
|
)
|
|
{
|
|
DLIB_CASSERT(s.size() == m.size() &&
|
|
s.size() == v.size() &&
|
|
s.size() == params.size() &&
|
|
s.size() == params_grad.size());
|
|
DLIB_CASSERT(begin <= end && end <= params.size());
|
|
const float eps = 1e-8;
|
|
const float alpha = learning_rate*std::sqrt(1-std::pow(momentum2,t))/(1-std::pow(momentum1, t));
|
|
|
|
// The loop is equivalent to doing this:
|
|
// m = momentum1*m + (1-momentum1) * (weight_decay*params + params_grad);
|
|
// v = momentum2*v + (1-momentum2)*squared(weight_decay*params + params_grad);
|
|
// s = -alpha*m/(sqrt(v) + eps);
|
|
auto pm = m.host();
|
|
auto pv = v.host();
|
|
auto ps = s.host_write_only();
|
|
auto pparams = params.host();
|
|
auto ppgrad = params_grad.host();
|
|
for (size_t i = begin; i < end; ++i)
|
|
{
|
|
float g = weight_decay*pparams[i] + ppgrad[i];
|
|
pm[i] = momentum1*pm[i] + (1-momentum1)*g;
|
|
pv[i] = momentum2*pv[i] + (1-momentum2)*g*g;
|
|
ps[i] = -alpha*pm[i]/(std::sqrt(pv[i]) + eps);
|
|
}
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
void batch_normalize_inference (
|
|
const double eps,
|
|
resizable_tensor& dest,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
const tensor& beta,
|
|
const tensor& running_means,
|
|
const tensor& running_variances
|
|
)
|
|
{
|
|
DLIB_CASSERT(
|
|
gamma.num_samples() == 1 &&
|
|
gamma.nr() == src.nr() &&
|
|
gamma.nc() == src.nc() &&
|
|
gamma.k() == src.k() &&
|
|
have_same_dimensions(gamma, beta) &&
|
|
have_same_dimensions(gamma, running_means) &&
|
|
have_same_dimensions(gamma, running_variances) &&
|
|
eps > 0,
|
|
"\ngamma.num_samples(): " << gamma.num_samples() <<
|
|
"\ngamma.k(): " << gamma.k() <<
|
|
"\ngamma.nr(): " << gamma.nr() <<
|
|
"\ngamma.nc(): " << gamma.nc() <<
|
|
"\nbeta.num_samples(): " << beta.num_samples() <<
|
|
"\nbeta.k(): " << beta.k() <<
|
|
"\nbeta.nr(): " << beta.nr() <<
|
|
"\nbeta.nc(): " << beta.nc() <<
|
|
"\nrunning_means.num_samples(): " << running_means.num_samples() <<
|
|
"\nrunning_means.k(): " << running_means.k() <<
|
|
"\nrunning_means.nr(): " << running_means.nr() <<
|
|
"\nrunning_means.nc(): " << running_means.nc() <<
|
|
"\nrunning_variances.num_samples(): " << running_variances.num_samples() <<
|
|
"\nrunning_variances.k(): " << running_variances.k() <<
|
|
"\nrunning_variances.nr(): " << running_variances.nr() <<
|
|
"\nrunning_variances.nc(): " << running_variances.nc() <<
|
|
"\nsrc.k(): " << src.k() <<
|
|
"\nsrc.nr(): " << src.nr() <<
|
|
"\nsrc.nc(): " << src.nc() <<
|
|
"\neps: " << eps
|
|
);
|
|
dest.copy_size(src);
|
|
|
|
auto d = dest.host();
|
|
auto s = src.host();
|
|
auto g = gamma.host();
|
|
auto b = beta.host();
|
|
auto m = running_means.host();
|
|
auto v = running_variances.host();
|
|
|
|
const long num = src.k()*src.nr()*src.nc();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < num; ++k)
|
|
{
|
|
*d = g[k]*(*s - m[k])/std::sqrt(v[k]+eps) + b[k];
|
|
++d;
|
|
++s;
|
|
}
|
|
}
|
|
}
|
|
|
|
void batch_normalize (
|
|
const double eps,
|
|
resizable_tensor& dest,
|
|
resizable_tensor& means,
|
|
resizable_tensor& invstds,
|
|
const double averaging_factor,
|
|
resizable_tensor& running_means,
|
|
resizable_tensor& running_variances,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
const tensor& beta
|
|
)
|
|
{
|
|
DLIB_CASSERT(0 <= averaging_factor && averaging_factor <= 1, "averaging_factor: " << averaging_factor);
|
|
DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means));
|
|
DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds));
|
|
DLIB_CASSERT(
|
|
src.num_samples() > 1 &&
|
|
gamma.num_samples() == 1 &&
|
|
beta.num_samples() == 1 &&
|
|
gamma.nr() == beta.nr() && beta.nr() == src.nr() &&
|
|
gamma.nc() == beta.nc() && beta.nc() == src.nc() &&
|
|
gamma.k() == beta.k() && beta.k() == src.k() &&
|
|
eps > 0,
|
|
"\ngamma.num_samples(): " << gamma.num_samples() <<
|
|
"\ngamma.k(): " << gamma.k() <<
|
|
"\ngamma.nr(): " << gamma.nr() <<
|
|
"\ngamma.nc(): " << gamma.nc() <<
|
|
"\nbeta.num_samples(): " << beta.num_samples() <<
|
|
"\nbeta.k(): " << beta.k() <<
|
|
"\nbeta.nr(): " << beta.nr() <<
|
|
"\nbeta.nc(): " << beta.nc() <<
|
|
"\nsrc.k(): " << src.k() <<
|
|
"\nsrc.nr(): " << src.nr() <<
|
|
"\nsrc.nc(): " << src.nc() <<
|
|
"\neps: " << eps
|
|
);
|
|
|
|
dest.copy_size(src);
|
|
means.set_size(1, src.k(), src.nr(), src.nc());
|
|
invstds.set_size(1, src.k(), src.nr(), src.nc());
|
|
|
|
// first compute means and invstds
|
|
means = 0;
|
|
invstds = 0;
|
|
const auto p_invstds = invstds.host();
|
|
const auto p_means = means.host();
|
|
auto p_src = src.host();
|
|
const long num = src.k()*src.nr()*src.nc();
|
|
// compute means, and sum of squares
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
float val = p_src[n*num+i];
|
|
p_means[i] += val;
|
|
p_invstds[i] += val*val;
|
|
}
|
|
}
|
|
means /= src.num_samples();
|
|
invstds /= src.num_samples();
|
|
// copy data back to host
|
|
invstds.host(); means.host();
|
|
|
|
// compute variances
|
|
running_variances.copy_size(invstds);
|
|
auto rvar = running_variances.host();
|
|
// This scale makes the running variances unbiased.
|
|
const double scale = (src.num_samples())/(src.num_samples()-1.0);
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
auto actual_var = p_invstds[i] - p_means[i]*p_means[i];
|
|
if (averaging_factor == 1)
|
|
rvar[i] = scale*actual_var;
|
|
else
|
|
rvar[i] = (1-averaging_factor)*rvar[i] + scale*averaging_factor*actual_var;
|
|
|
|
p_invstds[i] = 1.0f/std::sqrt(actual_var + eps);
|
|
}
|
|
|
|
p_src = src.host();
|
|
auto p_dest = dest.host();
|
|
const auto p_gamma = gamma.host();
|
|
const auto p_beta = beta.host();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
*p_dest = (*p_src - p_means[i])*p_invstds[i];
|
|
*p_dest = (*p_dest)*p_gamma[i] + p_beta[i];
|
|
++p_src;
|
|
++p_dest;
|
|
}
|
|
}
|
|
|
|
// now keep track of the running means
|
|
running_means.copy_size(means);
|
|
if (averaging_factor != 1)
|
|
running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means);
|
|
else
|
|
running_means = means;
|
|
}
|
|
|
|
void batch_normalize_gradient (
|
|
const double eps,
|
|
const tensor& gradient_input,
|
|
const tensor& means,
|
|
const tensor& invstds,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
tensor& src_grad,
|
|
tensor& gamma_grad,
|
|
tensor& beta_grad
|
|
)
|
|
{
|
|
|
|
const long num = src.k()*src.nr()*src.nc();
|
|
DLIB_CASSERT(src.num_samples() > 1);
|
|
DLIB_CASSERT(num == (long)means.size());
|
|
DLIB_CASSERT(num == (long)invstds.size());
|
|
DLIB_CASSERT(num == (long)gamma.size());
|
|
DLIB_CASSERT(num == (long)gamma_grad.size());
|
|
DLIB_CASSERT(num == (long)beta_grad.size());
|
|
DLIB_CASSERT(have_same_dimensions(gradient_input, src));
|
|
DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad));
|
|
DLIB_CASSERT(eps > 0);
|
|
|
|
beta_grad = 0;
|
|
gamma_grad = 0;
|
|
auto p_grad = gradient_input.host();
|
|
auto p_src = src.host();
|
|
const auto p_gamma = gamma.host();
|
|
const auto p_gamma_grad = gamma_grad.host();
|
|
const auto p_beta_grad = beta_grad.host();
|
|
const auto p_invstds = invstds.host();
|
|
const auto p_means = means.host();
|
|
|
|
resizable_tensor dvars, dmeans;
|
|
dvars.copy_size(invstds);
|
|
dmeans.copy_size(means);
|
|
dvars = 0;
|
|
dmeans = 0;
|
|
const auto p_dvars = dvars.host();
|
|
const auto p_dmeans = dmeans.host();
|
|
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float x_hat = (*p_src - p_means[i])*p_invstds[i];
|
|
p_beta_grad[i] += *p_grad;
|
|
p_gamma_grad[i] += (*p_grad)*x_hat;
|
|
|
|
const float dx = *p_grad * p_gamma[i];
|
|
|
|
p_dvars[i] += dx*(*p_src - p_means[i])*-0.5*std::pow(p_invstds[i], 3.0f);
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
}
|
|
}
|
|
|
|
const float invnum = 1.0f/src.num_samples();
|
|
p_grad = gradient_input.host();
|
|
p_src = src.host();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float dx = *p_grad * p_gamma[i];
|
|
|
|
p_dmeans[i] += dx*-p_invstds[i] + p_dvars[i] * -2*(*p_src - p_means[i])*invnum;
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
}
|
|
}
|
|
p_grad = gradient_input.host();
|
|
p_src = src.host();
|
|
auto p_src_grad = src_grad.host();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float dx = *p_grad * p_gamma[i];
|
|
|
|
*p_src_grad += dx*p_invstds[i] +
|
|
p_dvars[i] *2*(*p_src - p_means[i])*invnum +
|
|
p_dmeans[i]*invnum;
|
|
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
++p_src_grad;
|
|
}
|
|
}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void batch_normalize_conv_inference (
|
|
const double eps,
|
|
resizable_tensor& dest,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
const tensor& beta,
|
|
const tensor& running_means,
|
|
const tensor& running_variances
|
|
)
|
|
{
|
|
DLIB_CASSERT(
|
|
gamma.num_samples() == 1 &&
|
|
gamma.nr() == 1 &&
|
|
gamma.nc() == 1 &&
|
|
gamma.k() == src.k() &&
|
|
have_same_dimensions(gamma, beta) &&
|
|
have_same_dimensions(gamma, running_means) &&
|
|
have_same_dimensions(gamma, running_variances) &&
|
|
eps > 0,
|
|
"\ngamma.num_samples(): " << gamma.num_samples() <<
|
|
"\ngamma.k(): " << gamma.k() <<
|
|
"\ngamma.nr(): " << gamma.nr() <<
|
|
"\ngamma.nc(): " << gamma.nc() <<
|
|
"\nbeta.num_samples(): " << beta.num_samples() <<
|
|
"\nbeta.k(): " << beta.k() <<
|
|
"\nbeta.nr(): " << beta.nr() <<
|
|
"\nbeta.nc(): " << beta.nc() <<
|
|
"\nrunning_means.num_samples(): " << running_means.num_samples() <<
|
|
"\nrunning_means.k(): " << running_means.k() <<
|
|
"\nrunning_means.nr(): " << running_means.nr() <<
|
|
"\nrunning_means.nc(): " << running_means.nc() <<
|
|
"\nrunning_variances.num_samples(): " << running_variances.num_samples() <<
|
|
"\nrunning_variances.k(): " << running_variances.k() <<
|
|
"\nrunning_variances.nr(): " << running_variances.nr() <<
|
|
"\nrunning_variances.nc(): " << running_variances.nc() <<
|
|
"\nsrc.k(): " << src.k() <<
|
|
"\nsrc.nr(): " << src.nr() <<
|
|
"\nsrc.nc(): " << src.nc() <<
|
|
"\neps: " << eps
|
|
);
|
|
dest.copy_size(src);
|
|
|
|
auto d = dest.host();
|
|
auto s = src.host();
|
|
auto g = gamma.host();
|
|
auto b = beta.host();
|
|
auto m = running_means.host();
|
|
auto v = running_variances.host();
|
|
|
|
const long num = src.nr()*src.nc();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
const float invstd = 1.0f/std::sqrt(v[k] + eps);
|
|
for (long j = 0; j < num; ++j)
|
|
{
|
|
*d = g[k]*(*s - m[k])*invstd + b[k];
|
|
++d;
|
|
++s;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void batch_normalize_conv (
|
|
const double eps,
|
|
resizable_tensor& dest,
|
|
resizable_tensor& means,
|
|
resizable_tensor& invstds,
|
|
const double averaging_factor,
|
|
resizable_tensor& running_means,
|
|
resizable_tensor& running_variances,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
const tensor& beta
|
|
)
|
|
{
|
|
DLIB_CASSERT(0 <= averaging_factor && averaging_factor <= 1, "averaging_factor: " << averaging_factor);
|
|
DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means));
|
|
DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds));
|
|
DLIB_CASSERT(
|
|
src.num_samples() > 1 &&
|
|
gamma.num_samples() == 1 &&
|
|
beta.num_samples() == 1 &&
|
|
gamma.nr() == 1 &&
|
|
beta.nr() == 1 &&
|
|
gamma.nc() == 1 &&
|
|
beta.nc() == 1 &&
|
|
gamma.k() == beta.k() && beta.k() == src.k() &&
|
|
eps > 0,
|
|
"\ngamma.num_samples(): " << gamma.num_samples() <<
|
|
"\ngamma.k(): " << gamma.k() <<
|
|
"\ngamma.nr(): " << gamma.nr() <<
|
|
"\ngamma.nc(): " << gamma.nc() <<
|
|
"\nbeta.num_samples(): " << beta.num_samples() <<
|
|
"\nbeta.k(): " << beta.k() <<
|
|
"\nbeta.nr(): " << beta.nr() <<
|
|
"\nbeta.nc(): " << beta.nc() <<
|
|
"\nsrc.k(): " << src.k() <<
|
|
"\nsrc.nr(): " << src.nr() <<
|
|
"\nsrc.nc(): " << src.nc() <<
|
|
"\neps: " << eps
|
|
);
|
|
|
|
dest.copy_size(src);
|
|
means.set_size(1, src.k());
|
|
invstds.set_size(1, src.k());
|
|
|
|
// first compute means and invstds
|
|
means = 0;
|
|
invstds = 0;
|
|
const auto p_invstds = invstds.host();
|
|
const auto p_means = means.host();
|
|
const auto p_gamma = gamma.host();
|
|
const auto p_beta = beta.host();
|
|
auto p_src = src.host();
|
|
const long num = src.nr()*src.nc();
|
|
// compute means, and sum of squares
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
p_means[k] += *p_src;
|
|
p_invstds[k] += (*p_src)*(*p_src);
|
|
++p_src;
|
|
}
|
|
}
|
|
}
|
|
means /= src.num_samples()*num;
|
|
invstds /= src.num_samples()*num;
|
|
// copy data back to host
|
|
invstds.host(); means.host();
|
|
|
|
p_src = src.host();
|
|
// compute variances
|
|
running_variances.copy_size(invstds);
|
|
auto rvar = running_variances.host();
|
|
// This scale makes the running variances unbiased.
|
|
const double scale = (src.num_samples()*num)/(src.num_samples()*num-1.0);
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
float actual_var = p_invstds[k] - p_means[k]*p_means[k];
|
|
if (averaging_factor == 1)
|
|
rvar[k] = scale*actual_var;
|
|
else
|
|
rvar[k] = (1-averaging_factor)*rvar[k] + scale*averaging_factor*actual_var;
|
|
|
|
p_invstds[k] = 1.0f/std::sqrt(actual_var + eps);
|
|
}
|
|
|
|
p_src = src.host();
|
|
auto p_dest = dest.host();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
*p_dest = (*p_src - p_means[k])*p_invstds[k];
|
|
*p_dest = (*p_dest)*p_gamma[k] + p_beta[k];
|
|
++p_src;
|
|
++p_dest;
|
|
}
|
|
}
|
|
}
|
|
|
|
// now keep track of the running means
|
|
running_means.copy_size(means);
|
|
if (averaging_factor != 1)
|
|
running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means);
|
|
else
|
|
running_means = means;
|
|
}
|
|
|
|
void batch_normalize_conv_gradient(
|
|
const double eps,
|
|
const tensor& gradient_input,
|
|
const tensor& means,
|
|
const tensor& invstds,
|
|
const tensor& src,
|
|
const tensor& gamma,
|
|
tensor& src_grad,
|
|
tensor& gamma_grad,
|
|
tensor& beta_grad
|
|
)
|
|
{
|
|
|
|
const long num = src.nr()*src.nc();
|
|
DLIB_CASSERT(src.num_samples() > 1);
|
|
DLIB_CASSERT(src.k() == (long)means.size());
|
|
DLIB_CASSERT(src.k() == (long)invstds.size());
|
|
DLIB_CASSERT(src.k() == (long)gamma.size());
|
|
DLIB_CASSERT(src.k() == (long)gamma_grad.size());
|
|
DLIB_CASSERT(src.k() == (long)beta_grad.size());
|
|
DLIB_CASSERT(have_same_dimensions(gradient_input, src));
|
|
DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad));
|
|
DLIB_CASSERT(eps > 0);
|
|
|
|
beta_grad = 0;
|
|
gamma_grad = 0;
|
|
|
|
auto p_grad = gradient_input.host();
|
|
auto p_src = src.host();
|
|
const auto p_gamma = gamma.host();
|
|
const auto p_gamma_grad = gamma_grad.host();
|
|
const auto p_beta_grad = beta_grad.host();
|
|
const auto p_invstds = invstds.host();
|
|
const auto p_means = means.host();
|
|
|
|
resizable_tensor dvars, dmeans;
|
|
dvars.copy_size(invstds);
|
|
dmeans.copy_size(means);
|
|
dvars = 0;
|
|
dmeans = 0;
|
|
const auto p_dvars = dvars.host();
|
|
const auto p_dmeans = dmeans.host();
|
|
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
const float invstd_pow = -0.5*std::pow(p_invstds[k], 3.0f);
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float x_hat = (*p_src - p_means[k])*p_invstds[k];
|
|
p_beta_grad[k] += *p_grad;
|
|
p_gamma_grad[k] += (*p_grad)*x_hat;
|
|
|
|
const float dx = *p_grad * p_gamma[k];
|
|
|
|
p_dvars[k] += dx*(*p_src - p_means[k])*invstd_pow;
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
}
|
|
}
|
|
}
|
|
|
|
p_grad = gradient_input.host();
|
|
p_src = src.host();
|
|
const float invnum = 1.0f/(src.num_samples()*num);
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float dx = *p_grad * p_gamma[k];
|
|
|
|
p_dmeans[k] += -dx*p_invstds[k] + p_dvars[k] * -2*(*p_src - p_means[k])*invnum;
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
}
|
|
}
|
|
}
|
|
p_grad = gradient_input.host();
|
|
p_src = src.host();
|
|
auto p_src_grad = src_grad.host();
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < src.k(); ++k)
|
|
{
|
|
for (long i = 0; i < num; ++i)
|
|
{
|
|
const float dx = *p_grad * p_gamma[k];
|
|
|
|
*p_src_grad += dx*p_invstds[k] +
|
|
p_dvars[k]*2*(*p_src - p_means[k])*invnum +
|
|
p_dmeans[k]*invnum;
|
|
|
|
|
|
++p_grad;
|
|
++p_src;
|
|
++p_src_grad;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
void threshold (
|
|
tensor& data,
|
|
float thresh
|
|
)
|
|
{
|
|
const auto d = data.host();
|
|
for (size_t i = 0; i < data.size(); ++i)
|
|
d[i] = d[i]>thresh ? 1:0;
|
|
}
|
|
|
|
void dot (
|
|
const tensor& a,
|
|
const tensor& b,
|
|
tensor& result,
|
|
size_t idx
|
|
)
|
|
{
|
|
DLIB_CASSERT(a.size() == b.size());
|
|
DLIB_CASSERT(idx < result.size());
|
|
|
|
const auto aa = a.host();
|
|
const auto bb = b.host();
|
|
auto r = result.host();
|
|
for (size_t i = 0; i < a.size(); ++i)
|
|
r[idx] += aa[i]*bb[i];
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------------
|
|
// -----------------------------------------------------------------------------------
|
|
// -----------------------------------------------------------------------------------
|
|
|
|
namespace ttimpl
|
|
{
|
|
void softmax (
|
|
const long num_locations,
|
|
const long num_channels,
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
DLIB_ASSERT(num_channels*num_locations == src.nr()*src.nc()*src.k());
|
|
DLIB_CASSERT(have_same_dimensions(dest,src));
|
|
const auto d = dest.host();
|
|
const auto s = src.host();
|
|
|
|
// Note that we subtract out the max values in each channel before applying
|
|
// exp() to avoid numeric overflow in the subsequent computations. Doing this
|
|
// doesn't change the resulting output, it just makes it more numerically
|
|
// stable.
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
auto ss = s + num_locations*num_channels*n;
|
|
auto dd = d + num_locations*num_channels*n;
|
|
for (long i = 0; i < num_locations; ++i)
|
|
{
|
|
float max_val = -std::numeric_limits<float>::infinity();
|
|
for (long k = 0; k < num_channels; ++k)
|
|
max_val = std::max(max_val, ss[k*num_locations]);
|
|
|
|
for (long k = 0; k < num_channels; ++k)
|
|
dd[k*num_locations] = std::exp(ss[k*num_locations]-max_val);
|
|
|
|
++ss;
|
|
++dd;
|
|
}
|
|
}
|
|
|
|
// Now normalize each channel so they sum to 1.
|
|
for (long n = 0; n < src.num_samples(); ++n)
|
|
{
|
|
const auto dd = d + num_locations*num_channels*n;
|
|
for (long i = 0; i < num_locations; ++i)
|
|
{
|
|
const auto ddd = dd+i;
|
|
|
|
float temp = 0;
|
|
for (long k = 0; k < num_channels; ++k)
|
|
temp += ddd[k*num_locations];
|
|
for (long k = 0; k < num_channels; ++k)
|
|
ddd[k*num_locations] /= temp;
|
|
}
|
|
}
|
|
}
|
|
|
|
void softmax_gradient (
|
|
const long num_locations,
|
|
const long num_channels,
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
DLIB_ASSERT(num_channels*num_locations == grad.nr()*grad.nc()*grad.k());
|
|
DLIB_CASSERT(have_same_dimensions(grad,dest));
|
|
DLIB_CASSERT(have_same_dimensions(grad,gradient_input));
|
|
const auto d = dest.host();
|
|
const auto g = grad.host();
|
|
const auto in = gradient_input.host();
|
|
|
|
|
|
for (long n = 0; n < grad.num_samples(); ++n)
|
|
{
|
|
const auto d2 = d + num_locations*num_channels*n;
|
|
const auto g2 = g + num_locations*num_channels*n;
|
|
const auto in2 = in + num_locations*num_channels*n;
|
|
for (long i = 0; i < num_locations; ++i)
|
|
{
|
|
const auto d3 = d2+i;
|
|
const auto g3 = g2+i;
|
|
const auto in3 = in2+i;
|
|
|
|
float temp = 0;
|
|
for (long k = 0; k < num_channels; ++k)
|
|
temp += -d3[k*num_locations]*in3[k*num_locations];
|
|
if (is_same_object(gradient_input, grad))
|
|
{
|
|
for (long k = 0; k < num_channels; ++k)
|
|
g3[k*num_locations] = d3[k*num_locations]*(temp+in3[k*num_locations]);
|
|
}
|
|
else
|
|
{
|
|
for (long k = 0; k < num_channels; ++k)
|
|
g3[k*num_locations] += d3[k*num_locations]*(temp+in3[k*num_locations]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void softmax (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(dest,src));
|
|
ttimpl::softmax(src.nr()*src.nc(), src.k(), dest, src);
|
|
}
|
|
|
|
void softmax_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(grad,dest));
|
|
DLIB_CASSERT(have_same_dimensions(grad,gradient_input));
|
|
ttimpl::softmax_gradient(grad.nr()*grad.nc(), grad.k(), grad, dest, gradient_input);
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void softmax_all (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(dest,src));
|
|
ttimpl::softmax(1, src.nr()*src.nc()*src.k(), dest, src);
|
|
}
|
|
|
|
void softmax_all_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(grad,dest));
|
|
DLIB_CASSERT(have_same_dimensions(grad,gradient_input));
|
|
ttimpl::softmax_gradient(1, grad.nr()*grad.nc()*grad.k(), grad, dest, gradient_input);
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void sigmoid (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
const auto d = dest.host();
|
|
const auto s = src.host();
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
d[i] = 1/(1+std::exp(-s[i]));
|
|
}
|
|
|
|
void sigmoid_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
const auto g = grad.host();
|
|
const auto d = dest.host();
|
|
const auto in = gradient_input.host();
|
|
if (is_same_object(gradient_input, grad))
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
g[i] = in[i]*d[i]*(1-d[i]);
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
g[i] += in[i]*d[i]*(1-d[i]);
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void mish (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
const auto d = dest.host_write_only();
|
|
const auto s = src.host();
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
{
|
|
const auto e = std::exp(s[i]);
|
|
const auto delta = 2*e + e*e + 2;
|
|
d[i] = s[i] - 2*s[i]/delta;
|
|
}
|
|
}
|
|
|
|
void mish_gradient(
|
|
tensor& grad,
|
|
const tensor& src,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
const auto g = grad.host();
|
|
const auto s = src.host();
|
|
const auto in = gradient_input.host();
|
|
|
|
const auto calculate_gradient = [](float x)
|
|
{
|
|
if (x >= 8)
|
|
return 1.f;
|
|
if (x <= -8)
|
|
return 0.f;
|
|
|
|
const auto e = std::exp(x);
|
|
const auto delta = 2*e + e*e + 2;
|
|
const auto omega = 4*(x + 1) + 4*e*e + e*e*e + e*(4*x + 6);
|
|
return e*omega/(delta*delta);
|
|
};
|
|
|
|
if (is_same_object(gradient_input, grad))
|
|
{
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
g[i] = in[i]*calculate_gradient(s[i]);
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
g[i] += in[i]*calculate_gradient(s[i]);
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void relu (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
dest = lowerbound(mat(src), 0);
|
|
}
|
|
|
|
void relu_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
const float* gi = gradient_input.host();
|
|
const float* in = dest.host();
|
|
float* out = grad.host();
|
|
if (is_same_object(grad, gradient_input))
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (in[i] > 0)
|
|
out[i] = gi[i];
|
|
else
|
|
out[i] = 0;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (in[i] > 0)
|
|
out[i] += gi[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void prelu (
|
|
tensor& dest,
|
|
const tensor& src,
|
|
const tensor& param
|
|
)
|
|
{
|
|
const float p = param.host()[0];
|
|
const float* s = src.host();
|
|
float* d = dest.host();
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (s[i] > 0)
|
|
d[i] = s[i];
|
|
else
|
|
d[i] = p*s[i];
|
|
}
|
|
}
|
|
|
|
void prelu_gradient (
|
|
tensor& grad,
|
|
const tensor& src,
|
|
const tensor& gradient_input,
|
|
const tensor& param,
|
|
tensor& params_grad
|
|
)
|
|
{
|
|
DLIB_CASSERT(is_same_object(grad, gradient_input) == false);
|
|
const float p = param.host()[0];
|
|
const float* gi = gradient_input.host();
|
|
const float* s = src.host();
|
|
float* out = grad.host();
|
|
float pgrad = 0;
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
{
|
|
if (s[i] > 0)
|
|
{
|
|
out[i] += gi[i];
|
|
}
|
|
else
|
|
{
|
|
out[i] += p*gi[i];
|
|
pgrad += gi[i]*s[i];
|
|
}
|
|
}
|
|
params_grad.host()[0] = pgrad;
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void leaky_relu (
|
|
tensor& dest,
|
|
const tensor& src,
|
|
const float alpha
|
|
)
|
|
{
|
|
const float* s = src.host();
|
|
float* d = dest.host();
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (s[i] > 0)
|
|
d[i] = s[i];
|
|
else
|
|
d[i] = alpha * s[i];
|
|
}
|
|
}
|
|
|
|
void leaky_relu_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input,
|
|
const float alpha
|
|
)
|
|
{
|
|
const float* gi = gradient_input.host();
|
|
const float* in = dest.host();
|
|
float* out = grad.host();
|
|
if (is_same_object(grad, gradient_input))
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (in[i] > 0)
|
|
out[i] = gi[i];
|
|
else
|
|
out[i] = alpha * gi[i];
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
{
|
|
if (in[i] > 0)
|
|
out[i] += gi[i];
|
|
else
|
|
out[i] += alpha * gi[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void tanh (
|
|
tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
const auto d = dest.host();
|
|
const auto s = src.host();
|
|
for (size_t i = 0; i < src.size(); ++i)
|
|
d[i] = std::tanh(s[i]);
|
|
}
|
|
|
|
void tanh_gradient (
|
|
tensor& grad,
|
|
const tensor& dest,
|
|
const tensor& gradient_input
|
|
)
|
|
{
|
|
const auto g = grad.host();
|
|
const auto d = dest.host();
|
|
const auto in = gradient_input.host();
|
|
if (is_same_object(grad, gradient_input))
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
g[i] = in[i]*(1-d[i]*d[i]);
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < dest.size(); ++i)
|
|
g[i] += in[i]*(1-d[i]*d[i]);
|
|
}
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------------------
|
|
|
|
void resize_bilinear (
|
|
tensor& dest,
|
|
long dest_row_stride,
|
|
long dest_channel_stride,
|
|
const tensor& src,
|
|
long src_row_stride,
|
|
long src_channel_stride
|
|
)
|
|
{
|
|
DLIB_CASSERT(is_same_object(dest, src)==false);
|
|
DLIB_CASSERT(dest.num_samples() == src.num_samples());
|
|
DLIB_CASSERT(dest.k() == src.k());
|
|
|
|
if (dest.size() == 0 || src.size() == 0)
|
|
return;
|
|
|
|
const float* s = src.host();
|
|
float* d = dest.host();
|
|
|
|
parallel_for(0, dest.k()*dest.num_samples(), [&](long i)
|
|
{
|
|
auto simg = sub_image(s+i*src_channel_stride, src.nr(), src.nc(), src_row_stride);
|
|
auto dimg = sub_image(d+i*dest_channel_stride, dest.nr(), dest.nc(), dest_row_stride);
|
|
|
|
resize_image(simg, dimg);
|
|
});
|
|
}
|
|
|
|
void resize_bilinear_gradient (
|
|
tensor& grad,
|
|
long grad_row_stride,
|
|
long grad_channel_stride,
|
|
const tensor& gradient_input,
|
|
long gradient_input_row_stride,
|
|
long gradient_input_channel_stride
|
|
)
|
|
{
|
|
DLIB_CASSERT(is_same_object(grad, gradient_input)==false);
|
|
DLIB_CASSERT(gradient_input.num_samples() == grad.num_samples());
|
|
DLIB_CASSERT(gradient_input.k() == grad.k());
|
|
|
|
if (gradient_input.size() == 0 || grad.size() == 0)
|
|
return;
|
|
|
|
const float* gi = gradient_input.host();
|
|
float* g = grad.host();
|
|
const float x_scale = (grad.nc()-1)/(float)std::max<long>((gradient_input.nc()-1),1);
|
|
const float y_scale = (grad.nr()-1)/(float)std::max<long>((gradient_input.nr()-1),1);
|
|
for (long long samp = 0; samp < gradient_input.num_samples(); ++samp)
|
|
{
|
|
for (long long k = 0; k < gradient_input.k(); ++k)
|
|
{
|
|
for (long long r = 0; r < gradient_input.nr(); ++r)
|
|
{
|
|
const float y = r*y_scale;
|
|
const long long top = static_cast<long long>(std::floor(y));
|
|
const long long bottom = std::min(top+1, grad.nr()-1);
|
|
const float tb_frac = y - top;
|
|
for (long long c = 0; c < gradient_input.nc(); ++c)
|
|
{
|
|
const float x = c*x_scale;
|
|
const long long left = static_cast<long long>(std::floor(x));
|
|
const long long right = std::min(left+1, grad.nc()-1);
|
|
const float lr_frac = x - left;
|
|
|
|
const float tmp = gi[r*gradient_input_row_stride+c];
|
|
|
|
g[top*grad_row_stride+left] += tmp*(1-tb_frac)*(1-lr_frac);
|
|
g[top*grad_row_stride+right] += tmp*(1-tb_frac)*(lr_frac);
|
|
g[bottom*grad_row_stride+left] += tmp*(tb_frac)*(1-lr_frac);
|
|
g[bottom*grad_row_stride+right] += tmp*(tb_frac)*(lr_frac);
|
|
}
|
|
}
|
|
|
|
g += grad_channel_stride;
|
|
gi += gradient_input_channel_stride;
|
|
}
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
pooling::pooling (
|
|
) : window_height(0),window_width(0),stride_y(0),stride_x(0),padding_y(0),padding_x(0),do_max_pooling(true)
|
|
{
|
|
}
|
|
|
|
void pooling::
|
|
clear(
|
|
)
|
|
{
|
|
window_height = 0;
|
|
window_width = 0;
|
|
stride_y = 0;
|
|
stride_x = 0;
|
|
padding_y = 0;
|
|
padding_x = 0;
|
|
}
|
|
|
|
void pooling::
|
|
setup_max_pooling(
|
|
int window_height_,
|
|
int window_width_,
|
|
int stride_y_,
|
|
int stride_x_,
|
|
int padding_y_,
|
|
int padding_x_
|
|
)
|
|
{
|
|
DLIB_CASSERT(window_width_ > 0);
|
|
DLIB_CASSERT(window_height_ > 0);
|
|
DLIB_CASSERT(stride_y_ > 0);
|
|
DLIB_CASSERT(stride_x_ > 0);
|
|
DLIB_CASSERT(0 <= padding_y_ && padding_y_ < window_height_);
|
|
DLIB_CASSERT(0 <= padding_x_ && padding_x_ < window_width_);
|
|
|
|
window_height = window_height_;
|
|
window_width = window_width_;
|
|
stride_y = stride_y_;
|
|
stride_x = stride_x_;
|
|
padding_y = padding_y_;
|
|
padding_x = padding_x_;
|
|
do_max_pooling = true;
|
|
}
|
|
|
|
void pooling::
|
|
setup_avg_pooling(
|
|
int window_height_,
|
|
int window_width_,
|
|
int stride_y_,
|
|
int stride_x_,
|
|
int padding_y_,
|
|
int padding_x_
|
|
)
|
|
{
|
|
DLIB_CASSERT(window_width_ > 0);
|
|
DLIB_CASSERT(window_height_ > 0);
|
|
DLIB_CASSERT(stride_y_ > 0);
|
|
DLIB_CASSERT(stride_x_ > 0);
|
|
DLIB_CASSERT(0 <= padding_y_ && padding_y_ < window_height_);
|
|
DLIB_CASSERT(0 <= padding_x_ && padding_x_ < window_width_);
|
|
|
|
window_height = window_height_;
|
|
window_width = window_width_;
|
|
stride_y = stride_y_;
|
|
stride_x = stride_x_;
|
|
padding_y = padding_y_;
|
|
padding_x = padding_x_;
|
|
do_max_pooling = false;
|
|
}
|
|
|
|
void pooling::
|
|
operator() (
|
|
resizable_tensor& dest,
|
|
const tensor& src
|
|
)
|
|
{
|
|
DLIB_CASSERT(window_width > 0);
|
|
DLIB_CASSERT(window_height > 0);
|
|
DLIB_CASSERT(stride_y > 0);
|
|
DLIB_CASSERT(stride_x > 0);
|
|
DLIB_CASSERT(0 <= padding_y && padding_y < window_height);
|
|
DLIB_CASSERT(0 <= padding_x && padding_x < window_width);
|
|
DLIB_CASSERT(window_width <= src.nc() + 2*padding_x,
|
|
"Pooling windows must be small enough to fit into the padded image.");
|
|
DLIB_CASSERT(window_height <= src.nr() + 2*padding_y,
|
|
"Pooling windows must be small enough to fit into the padded image.");
|
|
|
|
dest.set_size(
|
|
src.num_samples(),
|
|
src.k(),
|
|
1+(src.nr()+2*padding_y-window_height)/stride_y,
|
|
1+(src.nc()+2*padding_x-window_width)/stride_x
|
|
);
|
|
|
|
if (src.size() == 0)
|
|
{
|
|
dest = 0;
|
|
return;
|
|
}
|
|
|
|
|
|
auto d = dest.host();
|
|
const long x_offset = window_width/2 - padding_x;
|
|
const long y_offset = window_height/2 - padding_y;
|
|
if (does_max_pooling())
|
|
{
|
|
for (long n = 0; n < dest.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < dest.k(); ++k)
|
|
{
|
|
auto simg = image_plane(src,n,k);
|
|
auto dimg = d + (n*dest.k() + k)*dest.nr()*dest.nc();
|
|
|
|
for (long r = 0; r < dest.nr(); ++r)
|
|
{
|
|
for (long c = 0; c < dest.nc(); ++c)
|
|
{
|
|
auto win = centered_rect(c*stride_x+x_offset,
|
|
r*stride_y+y_offset,
|
|
window_width,
|
|
window_height);
|
|
dimg[r*dest.nc() + c] = max(subm_clipped(simg,win));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (long n = 0; n < dest.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < dest.k(); ++k)
|
|
{
|
|
auto simg = image_plane(src,n,k);
|
|
auto dimg = d + (n*dest.k() + k)*dest.nr()*dest.nc();
|
|
|
|
for (long r = 0; r < dest.nr(); ++r)
|
|
{
|
|
for (long c = 0; c < dest.nc(); ++c)
|
|
{
|
|
auto win = centered_rect(c*stride_x+x_offset,
|
|
r*stride_y+y_offset,
|
|
window_width,
|
|
window_height);
|
|
dimg[r*dest.nc() + c] = mean(subm_clipped(simg,win));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
void pooling::get_gradient(
|
|
const tensor& gradient_input,
|
|
const tensor& dest,
|
|
const tensor& src,
|
|
tensor& grad
|
|
)
|
|
{
|
|
DLIB_CASSERT(have_same_dimensions(gradient_input,dest));
|
|
DLIB_CASSERT(have_same_dimensions(src,grad));
|
|
|
|
|
|
if (src.size() == 0)
|
|
{
|
|
return;
|
|
}
|
|
|
|
|
|
auto gi = gradient_input.host();
|
|
auto g = grad.host();
|
|
const long x_offset = window_width/2 - padding_x;
|
|
const long y_offset = window_height/2 - padding_y;
|
|
if (does_max_pooling())
|
|
{
|
|
for (long n = 0; n < dest.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < dest.k(); ++k)
|
|
{
|
|
auto simg = image_plane(src,n,k);
|
|
auto gimg = g + (n*grad.k() + k)*grad.nr()*grad.nc();
|
|
auto giimg = gi + (n*dest.k() + k)*dest.nr()*dest.nc();
|
|
auto imgbox = get_rect(simg);
|
|
|
|
for (long r = 0; r < dest.nr(); ++r)
|
|
{
|
|
for (long c = 0; c < dest.nc(); ++c)
|
|
{
|
|
auto win = centered_rect(c*stride_x+x_offset,
|
|
r*stride_y+y_offset,
|
|
window_width,
|
|
window_height).intersect(imgbox);
|
|
auto p = max_point(subm(simg,win))+win.tl_corner();
|
|
gimg[p.y()*grad.nc()+p.x()] += giimg[r*dest.nc()+c];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (long n = 0; n < dest.num_samples(); ++n)
|
|
{
|
|
for (long k = 0; k < dest.k(); ++k)
|
|
{
|
|
auto simg = image_plane(src,n,k);
|
|
auto gimg = g + (n*grad.k() + k)*grad.nr()*grad.nc();
|
|
auto giimg = gi + (n*dest.k() + k)*dest.nr()*dest.nc();
|
|
auto imgbox = get_rect(simg);
|
|
|
|
for (long r = 0; r < dest.nr(); ++r)
|
|
{
|
|
for (long c = 0; c < dest.nc(); ++c)
|
|
{
|
|
auto win = centered_rect(c*stride_x+x_offset,
|
|
r*stride_y+y_offset,
|
|
window_width,
|
|
window_height).intersect(imgbox);
|
|
const float delta = giimg[r*dest.nc()+c]/win.area();
|
|
for (long y = win.top(); y <= win.bottom(); ++y)
|
|
{
|
|
for (long x = win.left(); x <= win.right(); ++x)
|
|
{
|
|
gimg[y*grad.nc()+x] += delta;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void img2col(
|
|
matrix<float>& output,
|
|
const tensor& data,
|
|
long n,
|
|
long filter_nr,
|
|
long filter_nc,
|
|
long stride_y,
|
|
long stride_x,
|
|
long padding_y,
|
|
long padding_x
|
|
)
|
|
{
|
|
const auto d = data.host() + data.k()*data.nr()*data.nc()*n;
|
|
const rectangle boundary = get_rect(data);
|
|
|
|
const long out_nr = 1+(data.nr()+2*padding_y-filter_nr)/stride_y;
|
|
const long out_nc = 1+(data.nc()+2*padding_x-filter_nc)/stride_x;
|
|
|
|
output.set_size(out_nr*out_nc,
|
|
data.k()*filter_nr*filter_nc);
|
|
DLIB_CASSERT(output.size() != 0);
|
|
float* t = &output(0,0);
|
|
|
|
// now fill in the Toeplitz output matrix for the n-th sample in data.
|
|
size_t cnt = 0;
|
|
const long max_r = data.nr() + padding_y-(filter_nr-1);
|
|
const long max_c = data.nc() + padding_x-(filter_nc-1);
|
|
for (long r = -padding_y; r < max_r; r+=stride_y)
|
|
{
|
|
for (long c = -padding_x; c < max_c; c+=stride_x)
|
|
{
|
|
for (long k = 0; k < data.k(); ++k)
|
|
{
|
|
for (long y = 0; y < filter_nr; ++y)
|
|
{
|
|
for (long x = 0; x < filter_nc; ++x)
|
|
{
|
|
DLIB_ASSERT(cnt < output.size());
|
|
long xx = c+x;
|
|
long yy = r+y;
|
|
if (boundary.contains(xx,yy))
|
|
*t = d[(k*data.nr() + yy)*data.nc() + xx];
|
|
else
|
|
*t = 0;
|
|
++t;
|
|
++cnt;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void col2img(
|
|
const matrix<float>& output,
|
|
tensor& data,
|
|
long n,
|
|
long filter_nr,
|
|
long filter_nc,
|
|
long stride_y,
|
|
long stride_x,
|
|
long padding_y,
|
|
long padding_x
|
|
)
|
|
{
|
|
const auto d = data.host() + data.k()*data.nr()*data.nc()*n;
|
|
const rectangle boundary = get_rect(data);
|
|
|
|
DLIB_CASSERT(output.size() != 0);
|
|
const float* t = &output(0,0);
|
|
|
|
// now fill in the Toeplitz output matrix for the n-th sample in data.
|
|
const long max_r = data.nr() + padding_y-(filter_nr-1);
|
|
const long max_c = data.nc() + padding_x-(filter_nc-1);
|
|
for (long r = -padding_y; r < max_r; r+=stride_y)
|
|
{
|
|
for (long c = -padding_x; c < max_c; c+=stride_x)
|
|
{
|
|
for (long k = 0; k < data.k(); ++k)
|
|
{
|
|
for (long y = 0; y < filter_nr; ++y)
|
|
{
|
|
for (long x = 0; x < filter_nc; ++x)
|
|
{
|
|
long xx = c+x;
|
|
long yy = r+y;
|
|
if (boundary.contains(xx,yy))
|
|
d[(k*data.nr() + yy)*data.nc() + xx] += *t;
|
|
++t;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void tensor_conv::operator() (
|
|
const bool add_to_output,
|
|
resizable_tensor& output,
|
|
const tensor& data,
|
|
const tensor& filters
|
|
)
|
|
{
|
|
DLIB_CASSERT(last_stride_y > 0 && last_stride_x > 0, "You must call setup() before calling this function.");
|
|
output.set_size(data.num_samples(),
|
|
filters.num_samples(),
|
|
1+(data.nr()+2*last_padding_y-filters.nr())/last_stride_y,
|
|
1+(data.nc()+2*last_padding_x-filters.nc())/last_stride_x);
|
|
(*this)(add_to_output, static_cast<tensor&>(output),data,filters);
|
|
}
|
|
|
|
void tensor_conv::operator() (
|
|
const bool add_to_output,
|
|
tensor& output,
|
|
const tensor& data,
|
|
const tensor& filters
|
|
)
|
|
{
|
|
DLIB_CASSERT(is_same_object(output,data) == false);
|
|
DLIB_CASSERT(is_same_object(output,filters) == false);
|
|
DLIB_CASSERT(filters.k() == data.k());
|
|
DLIB_CASSERT(last_stride_y > 0 && last_stride_x > 0, "You must call setup() before calling this function.");
|
|
DLIB_CASSERT(filters.nr() <= data.nr() + 2*last_padding_y,
|
|
"Filter windows must be small enough to fit into the padded image.");
|
|
DLIB_CASSERT(filters.nc() <= data.nc() + 2*last_padding_x,
|
|
"Filter windows must be small enough to fit into the padded image.");
|
|
|
|
DLIB_CASSERT(output.num_samples() == data.num_samples());
|
|
DLIB_CASSERT(output.k() == filters.num_samples());
|
|
DLIB_CASSERT(output.nr() == 1+(data.nr()+2*last_padding_y-filters.nr())/last_stride_y);
|
|
DLIB_CASSERT(output.nc() == 1+(data.nc()+2*last_padding_x-filters.nc())/last_stride_x);
|
|
|
|
|
|
matrix<float> temp;
|
|
for (long n = 0; n < data.num_samples(); ++n)
|
|
{
|
|
img2col(temp, data, n, filters.nr(), filters.nc(), last_stride_y, last_stride_x, last_padding_y, last_padding_x);
|
|
|
|
if (add_to_output)
|
|
output.add_to_sample(n, mat(filters)*trans(temp));
|
|
else
|
|
output.set_sample(n, mat(filters)*trans(temp));
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void tensor_conv::
|
|
get_gradient_for_data (
|
|
const bool add_to_output,
|
|
const tensor& gradient_input,
|
|
const tensor& filters,
|
|
tensor& data_gradient
|
|
)
|
|
{
|
|
matrix<float> temp;
|
|
if (!add_to_output)
|
|
data_gradient = 0;
|
|
for (long n = 0; n < gradient_input.num_samples(); ++n)
|
|
{
|
|
auto gi = mat(gradient_input.host()+gradient_input.k()*gradient_input.nr()*gradient_input.nc()*n,
|
|
gradient_input.k(),
|
|
gradient_input.nr()*gradient_input.nc());
|
|
|
|
|
|
temp = trans(gi)*mat(filters);
|
|
col2img(temp, data_gradient, n, filters.nr(), filters.nc(), last_stride_y, last_stride_x, last_padding_y, last_padding_x);
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void tensor_conv::
|
|
get_gradient_for_filters (
|
|
const bool add_to_output,
|
|
const tensor& gradient_input,
|
|
const tensor& data,
|
|
tensor& filters_gradient
|
|
)
|
|
{
|
|
matrix<float> temp;
|
|
for (long n = 0; n < gradient_input.num_samples(); ++n)
|
|
{
|
|
auto gi = mat(gradient_input.host()+gradient_input.k()*gradient_input.nr()*gradient_input.nc()*n,
|
|
gradient_input.k(),
|
|
gradient_input.nr()*gradient_input.nc());
|
|
|
|
|
|
img2col(temp, data, n, filters_gradient.nr(), filters_gradient.nc(), last_stride_y, last_stride_x, last_padding_y, last_padding_x);
|
|
if (n == 0)
|
|
{
|
|
if (add_to_output)
|
|
filters_gradient += gi*temp;
|
|
else
|
|
filters_gradient = gi*temp;
|
|
}
|
|
else
|
|
{
|
|
filters_gradient += gi*temp;
|
|
}
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
void copy_tensor(
|
|
bool add_to,
|
|
tensor& dest,
|
|
size_t dest_k_offset,
|
|
const tensor& src,
|
|
size_t src_k_offset,
|
|
size_t count_k
|
|
)
|
|
{
|
|
const size_t dest_sample_size = static_cast<size_t>(dest.nc() * dest.nr() * dest.k());
|
|
const size_t src_sample_size = static_cast<size_t>(src.nc() * src.nr() * src.k());
|
|
|
|
const size_t block_size = count_k * dest.nc() * dest.nr();
|
|
|
|
DLIB_CASSERT(dest.num_samples() == src.num_samples() &&
|
|
dest.nc() == src.nc() && dest.nr() == src.nr(), "All sources should fit into dest tensor size");
|
|
DLIB_CASSERT(dest.k() - dest_k_offset >= count_k, "Not enough space in dest tensor");
|
|
DLIB_CASSERT(src.k() - src_k_offset >= count_k, "Not enough space in src tensor");
|
|
|
|
float* dest_p = dest.host() + dest_k_offset * dest.nc() * dest.nr();
|
|
const float* src_p = src.host() + src_k_offset * src.nc() * src.nr();
|
|
|
|
for (long i = 0; i < src.num_samples(); ++i)
|
|
{
|
|
if (add_to)
|
|
{
|
|
for (size_t j = 0; j < block_size; ++j)
|
|
dest_p[j] += src_p[j];
|
|
}
|
|
else
|
|
{
|
|
::memcpy(dest_p, src_p, block_size * sizeof(float));
|
|
}
|
|
|
|
dest_p += dest_sample_size;
|
|
src_p += src_sample_size;
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
// ------------------------------------------------------------------------------------
|
|
|
|
}
|
|
}
|
|
|
|
|
|
#endif // DLIB_DNN_CPU_cPP_
|
|
|
|
|