// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_DNN_CPU_cPP_
#define DLIB_DNN_CPU_cPP_
// This file contains CPU implementations of the GPU based functions in cuda_dlib.h
#include "cpu_dlib.h"
#include "tensor_tools.h"
#include "../image_transforms/interpolation.h"
#include "../threads.h"
namespace dlib
{
namespace cpu
{
// -----------------------------------------------------------------------------------
void multiply (
bool add_to,
tensor& dest,
const tensor& src1,
const tensor& src2
)
{
DLIB_CASSERT(dest.k() == src1.k() && src1.k() == src2.k() &&
dest.nr() == src1.nr() && src1.nr() == src2.nr() &&
dest.nc() == src1.nc() && src1.nc() == src2.nc() );
const long MD = std::max(std::max(dest.num_samples(),src1.num_samples()),src2.num_samples());
DLIB_CASSERT((dest.num_samples()==1 || dest.num_samples()==MD) &&
(src1.num_samples()==1 || src1.num_samples()==MD) &&
(src2.num_samples()==1 || src2.num_samples()==MD) );
if (dest.size() == 0)
return;
const size_t max_size = std::max(std::max(dest.size(),src1.size()),src2.size());
const auto d = dest.host();
const auto s1 = src1.host();
const auto s2 = src2.host();
if (dest.size() == src1.size() && src1.size() == src2.size())
{
if (add_to)
{
for (size_t i = 0; i < src1.size(); ++i)
d[i] += s1[i]*s2[i];
}
else
{
for (size_t i = 0; i < src1.size(); ++i)
d[i] = s1[i]*s2[i];
}
}
else if (dest.num_samples() == 1)
{
if (!add_to)
{
for (size_t i = 0; i < dest.size(); ++i)
d[i] = 0;
}
for (size_t i = 0; i < max_size; ++i)
d[i%dest.size()] += s1[i%src1.size()]*s2[i%src2.size()];
}
else
{
if (add_to)
{
for (size_t i = 0; i < max_size; ++i)
d[i] += s1[i%src1.size()]*s2[i%src2.size()];
}
else
{
for (size_t i = 0; i < max_size; ++i)
d[i] = s1[i%src1.size()]*s2[i%src2.size()];
}
}
}
// ------------------------------------------------------------------------------------
void multiply_conv (
bool add_to,
tensor& dest,
const tensor& src1,
const tensor& src2
)
{
auto d = dest.host();
auto s1 = src1.host();
auto s2 = src2.host();
if (have_same_dimensions(dest,src1))
{
DLIB_CASSERT(src2.num_samples() == 1 && src2.nr() == 1 && src2.nc() == 1 && src2.k() == src1.k());
if (add_to)
{
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++ += (*s1++)*s2[k];
}
}
}
}
}
else
{
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++ = (*s1++)*s2[k];
}
}
}
}
}
}
else
{
DLIB_CASSERT(have_same_dimensions(src1,src2));
DLIB_CASSERT(dest.num_samples() == 1 && dest.nr() == 1 && dest.nc() == 1 && dest.k() == src1.k());
if (!add_to)
{
for (long k = 0; k < src1.k(); ++k)
d[k] = 0;
}
for (long n = 0; n < src1.num_samples(); ++n)
{
for (long k = 0; k < src1.k(); ++k)
{
for (long r = 0; r < src1.nr(); ++r)
{
for (long c = 0; c < src1.nc(); ++c)
{
d[k] += (*s1++)*(*s2++);
}
}
}
}
}
}
// ------------------------------------------------------------------------------------
void scale_channels (
bool add_to,
tensor& dest,
const tensor& src,
const tensor& scales
)
{
DLIB_CASSERT(have_same_dimensions(dest,src) &&
scales.num_samples() == src.num_samples() &&
scales.k() == src.k() &&
scales.nr() == 1 &&
scales.nc() == 1 );
if (dest.size() == 0)
return;
if (add_to)
{
auto d = dest.host();
auto s = src.host();
auto scal = scales.host();
for (long n = 0; n < src.num_samples(); ++n)
{
for (long k = 0; k < src.k(); ++k)
{
const auto scale = scal[n*scales.k() + k];
for (long r = 0; r < src.nr(); ++r)
{
for (long c = 0; c < src.nc(); ++c)
{
*d++ += (*s++) * scale;
}
}
}
}
}
else
{
auto d = dest.host_write_only();
auto s = src.host();
auto scal = scales.host();
for (long n = 0; n < src.num_samples(); ++n)
{
for (long k = 0; k < src.k(); ++k)
{
const auto scale = scal[n*scales.k() + k];
for (long r = 0; r < src.nr(); ++r)
{
for (long c = 0; c < src.nc(); ++c)
{
*d++ = (*s++) * scale;
}
}
}
}
}
}
// ------------------------------------------------------------------------------------
void add(
float beta,
tensor& dest,
float alpha,
const tensor& src
)
{
DLIB_CASSERT(
(have_same_dimensions(src, dest) ||
(src.num_samples()==1 && src.k()==dest.k() && src.nr()==1 && src.nc()==1) ||
(src.num_samples()==1 && src.k()==dest.k() && src.nr()==dest.nr() && src.nc()==dest.nc()) ||
(src.num_samples()==1 && src.k()==1 && src.nr()==dest.nr() && src.nc()==dest.nc()) ||
(src.num_samples()==dest.num_samples() && src.k()==1 && src.nr()==1 && src.nc()==1)) &&
is_same_object(src,dest) == false ,
"\n\t dest.num_samples(): " << dest.num_samples()
<<"\n\t dest.k(): " << dest.k()
<<"\n\t dest.nr(): " << dest.nr()
<<"\n\t dest.nc(): " << dest.nc()
<<"\n\t src.num_samples(): " << src.num_samples()
<<"\n\t src.k(): " << src.k()
<<"\n\t src.nr(): " << src.nr()
<<"\n\t src.nc(): " << src.nc()
);
if (beta == 0 && alpha == 0)
{
dest = 0;
return;
}
auto d = dest.host();
auto s = src.host();
for (long n = 0; n < dest.num_samples(); ++n)
{
const auto sn = src.num_samples()==1 ? 0:n;
for (long k = 0; k < dest.k(); ++k)
{
const auto sk = src.k()==1 ? 0:k;
for (long r = 0; r < dest.nr(); ++r)
{
const auto sr = src.nr()==1 ? 0:r;
for (long c = 0; c < dest.nc(); ++c)
{
const auto sc = src.nc()==1 ? 0:c;
const auto s_idx = ((sn*src.k() + sk)*src.nr() + sr)*src.nc() + sc;
*d = beta*(*d) + alpha*s[s_idx];
++d;
}
}
}
}
}
// ----------------------------------------------------------------------------------------
void add (
tensor& dest,
const tensor& src1,
const tensor& src2
)
{
auto d = dest.host();
auto s1 = src1.host();
auto s2 = src2.host();
// Do the simple and fast version if everything has the same dimensions
if (have_same_dimensions(dest, src1) &&
have_same_dimensions(dest, src2))
{
for (size_t i = 0; i < dest.size(); ++i)
d[i] = s1[i] + s2[i];
return;
}
// Otherwise, do the more complex version with bounds checking.
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)
{
float v1 = 0;
float v2 = 0;
// if this index is inside src1
if (n < src1.num_samples() &&
k < src1.k() &&
r < src1.nr() &&
c < src1.nc() )
{
const auto s_idx = ((n*src1.k() + k)*src1.nr() + r)*src1.nc() + c;
v1 = s1[s_idx];
}
// if this index is inside src2
if (n < src2.num_samples() &&
k < src2.k() &&
r < src2.nr() &&
c < src2.nc() )
{
const auto s_idx = ((n*src2.k() + k)*src2.nr() + r)*src2.nc() + c;
v2 = s2[s_idx];
}
*d = v1 + v2;
++d;
}
}
}
}
}
// ----------------------------------------------------------------------------------------
void multiply_zero_padded (
bool add_to,
tensor& dest,
const tensor& src1,
const tensor& src2
)
{
auto d = dest.host();
auto s1 = src1.host();
auto s2 = src2.host();
// Do the simple and fast version if everything has the same dimensions
if (have_same_dimensions(dest, src1) &&
have_same_dimensions(dest, src2))
{
if (add_to)
{
for (size_t i = 0; i < dest.size(); ++i)
d[i] += s1[i] * s2[i];
}
else
{
for (size_t i = 0; i < dest.size(); ++i)
d[i] = s1[i] * s2[i];
}
return;
}
// Otherwise, do the more complex version with bounds checking.
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)
{
float v1 = 0;
float v2 = 0;
// if this index is inside src1
if (n < src1.num_samples() &&
k < src1.k() &&
r < src1.nr() &&
c < src1.nc() )
{
const auto s_idx = ((n*src1.k() + k)*src1.nr() + r)*src1.nc() + c;
v1 = s1[s_idx];
}
// if this index is inside src2
if (n < src2.num_samples() &&
k < src2.k() &&
r < src2.nr() &&
c < src2.nc() )
{
const auto s_idx = ((n*src2.k() + k)*src2.nr() + r)*src2.nc() + c;
v2 = s2[s_idx];
}
if (add_to)
*d += v1 * v2;
else
*d = v1 * v2;
++d;
}
}
}
}
}
// ----------------------------------------------------------------------------------------
void assign_bias_gradient (
tensor& grad,
const tensor& gradient_input
)
{
DLIB_CASSERT(
grad.num_samples() == 1 &&
gradient_input.k() == grad.k() &&
gradient_input.nr() == grad.nr() &&
gradient_input.nc() == grad.nc() &&
gradient_input.size() > 0);
auto out = grad.host();
auto in = gradient_input.host();
for (size_t i = 0; i < grad.size(); ++i)
out[i] = *in++;
for (long j = 1; j < gradient_input.num_samples(); ++j)
{
for (size_t i = 0; i < grad.size(); ++i)
out[i] += *in++;
}
}
// ------------------------------------------------------------------------------------
void assign_conv_bias_gradient (
tensor& grad,
const tensor& gradient_input
)
{
DLIB_CASSERT(
grad.num_samples() == 1 &&
grad.k() >= 1 &&
grad.nr() == 1 &&
grad.nc() == 1 &&
gradient_input.k() == grad.k() &&
gradient_input.size() > 0 &&
is_same_object(grad,gradient_input) == false
);
auto g = grad.host();
auto gi = gradient_input.host();
for (long k = 0; k < gradient_input.k(); ++k)
g[k] = 0;
for (long n = 0; n < gradient_input.num_samples(); ++n)
{
for (long k = 0; k < gradient_input.k(); ++k)
{
for (long r = 0; r < gradient_input.nr(); ++r)
{
for (long c = 0; c < gradient_input.nc(); ++c)
{
g[k] += (*gi++);
}
}
}
}
}
// -----------------------------------------------------------------------------------
void affine_transform(
tensor& dest,
const tensor& src,
const float A,
const float B
)
{
DLIB_CASSERT(dest.size()==src.size());
const auto d = dest.host();
const auto s = src.host();
for (size_t i = 0; i < src.size(); ++i)
d[i] = A*s[i] + B;
}
void affine_transform(
tensor& dest,
const tensor& src1,
const tensor& src2,
const float A,
const float B,
const float C
)
{
DLIB_CASSERT(dest.size()==src1.size());
DLIB_CASSERT(dest.size()==src2.size());
const auto d = dest.host();
const auto s1 = src1.host();
const auto s2 = src2.host();
for (size_t i = 0; i < src1.size(); ++i)
d[i] = A*s1[i] + B*s2[i] + C;
}
void affine_transform(
tensor& dest,
const tensor& src1,
const tensor& src2,
const tensor& src3,
const float A,
const float B,
const float C,
const float D
)
{
DLIB_CASSERT(dest.size()==src1.size());
DLIB_CASSERT(dest.size()==src2.size());
DLIB_CASSERT(dest.size()==src3.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 = 0; i < src1.size(); ++i)
d[i] = A*s1[i] + B*s2[i] + C*s3[i] + D;
}
void affine_transform_range(
size_t begin,
size_t end,
tensor& dest,
const tensor& src1,
const tensor& src2,
const tensor& src3,
const float A,
const float B,
const float C
)
{
DLIB_CASSERT(dest.size()==src1.size());
DLIB_CASSERT(dest.size()==src2.size());
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 layer_normalize (
const double eps,
resizable_tensor& dest,
resizable_tensor& means,
resizable_tensor& invstds,
const tensor& src,
const tensor& gamma,
const tensor& beta
)
{
DLIB_CASSERT(
have_same_dimensions(gamma, beta) &&
gamma.k() == src.k() &&
gamma.nr() == 1 &&
gamma.nc() == 1 &&
eps > 0,
"\nsrc.k(): " << src.k() <<
"\ngamma.k(): " << gamma.k() <<
"\ngamma.nr(): " << gamma.nr() <<
"\ngamma.nc(): " << gamma.nc() <<
"\nbeta.k(): " << beta.k() <<
"\nbeta.nr(): " << beta.nr() <<
"\nbeta.nc(): " << beta.nc() <<
"\neps: " << eps
);
dest.copy_size(src);
means.set_size(src.num_samples());
invstds.set_size(src.num_samples());
// first compute means and invstds
means = 0;
invstds = 0;
const float* p_src = src.host();
float* p_invstds = invstds.host();
float* p_means = means.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[n] += *p_src;
p_invstds[n] += (*p_src) * (*p_src);
++p_src;
}
}
}
means /= src.k() * num;
invstds /= src.k () * num;
// copy data back to host
invstds.host();
means.host();
// compute variances
for (long n = 0; n < src.num_samples(); ++n)
{
p_invstds[n] = 1.0f / std::sqrt(p_invstds[n] - p_means[n] * p_means[n] + eps);
}
p_src = src.host();
float* p_dest = dest.host();
const float* p_gamma = gamma.host();
const float* p_beta = beta.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[n]) * p_invstds[n];
*p_dest = (*p_dest) * p_gamma[k] + p_beta[k];
++p_src;
++p_dest;
}
}
}
}
void layer_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,
resizable_tensor& dmeans,
resizable_tensor& dvars
)
{
const long num = src.nr() * src.nc();
DLIB_CASSERT(src.num_samples() == means.size());
DLIB_CASSERT(src.num_samples() == invstds.size());
DLIB_CASSERT(have_same_dimensions(gamma, gamma_grad));
DLIB_CASSERT(have_same_dimensions(gamma_grad, beta_grad));
DLIB_CASSERT(gamma.k() == src.k());
DLIB_CASSERT(gamma.nr() == 1);
DLIB_CASSERT(gamma.nc() == 1);
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();
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)
{
const float invstd_pow = -0.5 * std::pow(p_invstds[n], 3.0f);
for (long k = 0; k < src.k(); ++k)
{
for (long i = 0; i < num; ++i)
{
const float x_hat = (*p_src - p_means[n]) * p_invstds[n];
p_beta_grad[k] += *p_grad;
p_gamma_grad[k] += (*p_grad) * x_hat;
const float dx = *p_grad * p_gamma[k];
p_dvars[n] += dx * (*p_src - p_means[n]) * invstd_pow;
++p_grad;
++p_src;
}
}
}
p_grad = gradient_input.host();
p_src = src.host();
const float invnum = 1.0f / (src.k() * 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[n] += -dx * p_invstds[n] + p_dvars[n] * -2 * (*p_src - p_means[n]) * 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[n] +
p_dvars[n] * 2 * (*p_src - p_means[n]) * invnum +
p_dmeans[n] * invnum;
++p_grad;
++p_src;
++p_src_grad;
}
}
}
}
// -----------------------------------------------------------------------------------
void rms_normalize(
const double eps,
resizable_tensor& dest,
resizable_tensor& scale,
const tensor& src,
const tensor& gamma
)
{
DLIB_CASSERT(
gamma.k() == src.k() &&
gamma.nr() == 1 &&
gamma.nc() == 1 &&
eps > 0,
"\nsrc.k(): " << src.k() <<
"\ngamma.k(): " << gamma.k() <<
"\ngamma.nr(): " << gamma.nr() <<
"\ngamma.nc(): " << gamma.nc() <<
"\neps: " << eps
);
const long ns = src.num_samples();
const long ks = src.k();
const long num = src.nr() * src.nc();
dest.copy_size(src);
scale.set_size(ns);
// Compute RMS values
scale = 0;
const float* p_src = src.host();
float* p_scale = scale.host();
for (long n = 0; n < ns; ++n)
{
for (long k = 0; k < ks; ++k)
{
for (long i = 0; i < num; ++i)
{
p_scale[n] += (*p_src) * (*p_src);
++p_src;
}
}
p_scale[n] = 1.0f / std::sqrt(p_scale[n] / (ks * num) + static_cast<float>(eps));
}
scale.host();
// Apply RMS normalization
p_src = src.host();
float* p_dest = dest.host();
const float* p_gamma = gamma.host();
for (long n = 0; n < ns; ++n)
{
for (long k = 0; k < ks; ++k)
{
for (long i = 0; i < num; ++i)
{
*p_dest = (*p_src) * p_scale[n] * p_gamma[k];
++p_src;
++p_dest;
}
}
}
}
void rms_normalize_gradient(
const tensor& gradient_input,
const tensor& scale,
const tensor& src,
const tensor& gamma,
tensor& src_grad,
tensor& gamma_grad,
resizable_tensor& dscale
)
{
DLIB_CASSERT(src.num_samples() == scale.size());
DLIB_CASSERT(have_same_dimensions(gamma, gamma_grad));
DLIB_CASSERT(gamma.k() == src.k());
DLIB_CASSERT(gamma.nr() == 1);
DLIB_CASSERT(gamma.nc() == 1);
DLIB_CASSERT(have_same_dimensions(gradient_input, src));
DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad));
const long ns = src.num_samples();
const long ks = src.k();
const long num = src.nr() * src.nc();
gamma_grad = 0;
dscale.copy_size(scale);
dscale = 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_scale = scale.host();
auto p_dscale = dscale.host();
for (long n = 0; n < ns; ++n)
{
const float scale_pow = -0.5f * std::pow(p_scale[n], 3.0f);
for (long k = 0; k < ks; ++k)
{
for (long i = 0; i < num; ++i)
{
const float x_hat = *p_src * p_scale[n];
p_gamma_grad[k] += (*p_grad) * x_hat;
const float dx = *p_grad * p_gamma[k];
p_dscale[n] += dx * *p_src * scale_pow;
++p_grad;
++p_src;
}
}
}
p_grad = gradient_input.host();
p_src = src.host();
auto p_src_grad = src_grad.host();
const float invnum = 1.0f / (ks * num);
for (long n = 0; n < ns; ++n)
{
for (long k = 0; k < ks; ++k)
{
for (long i = 0; i < num; ++i)
{
const float dx = *p_grad * p_gamma[k];
*p_src_grad += dx * p_scale[n] + p_dscale[n] * 2 * *p_src * 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,
operation_mode mode = operation_mode::CHANNEL_WISE
)
{
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();
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;
if (mode == operation_mode::CHANNEL_WISE)
{
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]);
float sum = 0.0f;
for (long k = 0; k < num_channels; ++k)
{
dd[k * num_locations] = std::exp(ss[k * num_locations] - max_val);
sum += dd[k * num_locations];
}
for (long k = 0; k < num_channels; ++k)
dd[k * num_locations] /= sum;
++ss;
++dd;
}
}
else if (mode == operation_mode::PLANE_WISE)
{
for (long k = 0; k < num_channels; ++k)
{
auto s_channel = ss + k * num_locations;
auto d_channel = dd + k * num_locations;
for (long r = 0; r < src.nr(); ++r)
{
float max_val = -std::numeric_limits<float>::infinity();
for (long c = 0, idx = r * src.nc(); c < src.nc(); ++c, ++idx)
max_val = std::max(max_val, s_channel[idx]);
if (max_val == -std::numeric_limits<float>::infinity())
{
for (long c = 0, idx = r * src.nc(); c < src.nc(); ++c, ++idx)
d_channel[idx] = 0.0f;
}
else
{
float sum = 0.0f;
for (long c = 0, idx = r * src.nc(); c < src.nc(); ++c, ++idx)
{
d_channel[idx] = std::exp(s_channel[idx] - max_val);
sum += d_channel[idx];
}
for (long c = 0, idx = r * src.nc(); c < src.nc(); ++c, ++idx)
d_channel[idx] /= sum;
}
}
}
}
}
}
void softmax_gradient(
const long num_locations,
const long num_channels,
tensor& grad,
const tensor& dest,
const tensor& gradient_input,
operation_mode mode = operation_mode::CHANNEL_WISE
)
{
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;
if (mode == operation_mode::CHANNEL_WISE)
{
for (long i = 0; i < num_locations; ++i)
{
const auto d3 = d2 + i;
const auto g3 = g2 + i;
const auto in3 = in2 + i;
float sum = 0.0f;
for (long k = 0; k < num_channels; ++k)
sum += -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] * (sum + in3[k * num_locations]);
}
else
{
for (long k = 0; k < num_channels; ++k)
g3[k * num_locations] += d3[k * num_locations] * (sum + in3[k * num_locations]);
}
}
}
else if (mode == operation_mode::PLANE_WISE)
{
for (long k = 0; k < num_channels; ++k)
{
const auto d_channel = d2 + k * num_locations;
const auto g_channel = g2 + k * num_locations;
const auto in_channel = in2 + k * num_locations;
for (long r = 0; r < grad.nr(); ++r)
{
float sum = 0.0f;
for (long c = 0, idx = r * grad.nc(); c < grad.nc(); ++c, ++idx)
sum += -d_channel[idx] * in_channel[idx];
if (is_same_object(gradient_input, grad))
{
for (long c = 0, idx = r * grad.nc(); c < grad.nc(); ++c, ++idx)
g_channel[idx] = d_channel[idx] * (sum + in_channel[idx]);
}
else
{
for (long c = 0, idx = r * grad.nc(); c < grad.nc(); ++c, ++idx)
g_channel[idx] += d_channel[idx] * (sum + in_channel[idx]);
}
}
}
}
}
}
}
// ----------------------------------------------------------------------------------------
void softmax(
tensor& dest,
const tensor& src,
operation_mode mode
)
{
DLIB_CASSERT(have_same_dimensions(dest, src));
DLIB_CASSERT(mode == operation_mode::CHANNEL_WISE || mode == operation_mode::PLANE_WISE, "Invalid softmax mode");
ttimpl::softmax(src.nr() * src.nc(), src.k(), dest, src, mode);
}
void softmax_gradient(
tensor& grad,
const tensor& dest,
const tensor& gradient_input,
operation_mode mode
)
{
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, mode);
}
// ------------------------------------------------------------------------------------
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 clipped_relu (
tensor& dest,
const tensor& src,
const float ceiling
)
{
dest = upperbound(lowerbound(mat(src), 0), ceiling);
}
void clipped_relu_gradient (
tensor& grad,
const tensor& dest,
const tensor& gradient_input,
const float ceiling
)
{
const auto out = grad.host();
const auto in = dest.host();
const auto gi = gradient_input.host();
if (is_same_object(grad, gradient_input))
{
for (size_t i = 0; i < dest.size(); ++i)
{
if (in[i] > 0 && in[i] < ceiling)
out[i] = gi[i];
else
out[i] = 0;
}
}
else
{
for (size_t i = 0; i < dest.size(); ++i)
{
if (in[i] > 0 && in[i] < ceiling)
out[i] += gi[i];
}
}
}
// ----------------------------------------------------------------------------------------
void elu (
tensor& dest,
const tensor& src,
const float alpha
)
{
const auto d = dest.host();
const auto s = src.host();
for (size_t i = 0; i < src.size(); ++i)
{
if (s[i] > 0)
d[i] = s[i];
else
d[i] = alpha * (std::exp(s[i]) - 1.0f);
}
}
void elu_gradient (
tensor& grad,
const tensor& dest,
const tensor& gradient_input,
const float alpha
)
{
const auto out = grad.host();
const auto in = dest.host();
const auto gi = gradient_input.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 + in[i]) * gi[i];
}
}
else
{
for (size_t i = 0; i < dest.size(); ++i)
{
if (in[i] > 0)
out[i] += gi[i];
else
out[i] += (alpha + in[i]) * gi[i];
}
}
}
// ----------------------------------------------------------------------------------------
void gelu (
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] = 0.5f*s[i]*(1.0f + std::erf(s[i]/sqrt_2));
}
void gelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input
)
{
const float beta = 1.0f / std::sqrt(2.0f * pi);
const auto compute_gradient = [beta](float x)
{
const float cdf = 0.5f*(1.0f + std::erf(x/sqrt_2));
const float pdf = beta*std::exp(-0.5f*x*x);
return cdf + x * pdf;
};
const auto g = grad.host();
const auto s = src.host();
const auto in = gradient_input.host();
if (is_same_object(grad, gradient_input))
{
for (size_t i = 0; i < src.size(); ++i)
g[i] = in[i]*compute_gradient(s[i]);
}
else
{
for (size_t i = 0; i < src.size(); ++i)
g[i] += in[i]*compute_gradient(s[i]);
}
}
// ----------------------------------------------------------------------------------------
void smelu (
tensor& dest,
const tensor& src,
const float beta
)
{
const float* s = src.host();
float* d = dest.host();
for (size_t i = 0; i < dest.size(); ++i)
{
if (s[i] >= beta)
d[i] = s[i];
else if (s[i] <= -beta)
d[i] = 0;
else
d[i] = (s[i] + beta) * (s[i] + beta) / (4 * beta);
}
}
void smelu_gradient (
tensor& grad,
const tensor& dest,
const tensor& gradient_input,
const float beta
)
{
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] >= beta)
out[i] = gi[i];
else if (in[i] == 0)
out[i] = 0;
else
out[i] = std::sqrt(beta * in[i]) / beta * gi[i];
}
}
else
{
for (size_t i = 0; i < dest.size(); ++i)
{
if (in[i] >= beta)
out[i] += gi[i];
else if (in[i] == 0)
continue;
else
out[i] += std::sqrt(beta * in[i]) / beta * gi[i];
}
}
}
// ----------------------------------------------------------------------------------------
void silu (
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] = s[i] * impl::sigmoid(s[i]);
}
void silu_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();
if (is_same_object(grad, gradient_input))
{
for (size_t i = 0; i < src.size(); ++i)
{
const auto sig_s = impl::sigmoid(s[i]);
g[i] = in[i] * (sig_s * (1.0f + s[i] * (1.0f - sig_s)));
}
}
else
{
for (size_t i = 0; i < src.size(); ++i)
{
const auto sig_s = impl::sigmoid(s[i]);
g[i] += in[i] * (sig_s * (1.0f + s[i] * (1.0f - sig_s)));
}
}
}
// ----------------------------------------------------------------------------------------
void resize_bilinear (
tensor& dest,
long long dest_row_stride,
long long dest_channel_stride,
const tensor& src,
long long src_row_stride,
long 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 long grad_row_stride,
long long grad_channel_stride,
const tensor& gradient_input,
long long gradient_input_row_stride,
long 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;
}
}
}
// ----------------------------------------------------------------------------------------
void reorg(
bool add_to,
tensor& dest,
const int row_stride,
const int col_stride,
const tensor& src
)
{
DLIB_CASSERT(!is_same_object(dest, src), "Destination and source must be distinct objects.");
DLIB_CASSERT(src.nr() % row_stride == 0, "The number of rows in src must be divisible by row_stride.");
DLIB_CASSERT(src.nc() % col_stride == 0, "The number of columns in src must be divisible by col_stride.");
DLIB_CASSERT(dest.num_samples() == src.num_samples(), "The number of samples must match.");
DLIB_CASSERT(dest.k() == src.k() * row_stride * col_stride, "The number of channels must match.");
DLIB_CASSERT(dest.nr() == src.nr() / row_stride, "The number of rows must match.");
DLIB_CASSERT(dest.nc() == src.nc() / col_stride, "The number of columns must match.");
const float* s = src.host();
float* d = dest.host();
const size_t sk = src.k(), snr = src.nr(), snc = src.nc();
const size_t dk = dest.k(), dnr = dest.nr(), dnc = dest.nc(), dsize = dest.size();
dlib::parallel_for(0, dsize, [&](long i)
{
const size_t out_plane_size = dnr * dnc;
const size_t out_sample_size = dk * out_plane_size;
const size_t n = i / out_sample_size;
const size_t out_idx = i % out_sample_size;
const size_t out_k = out_idx / out_plane_size;
const size_t out_rc = out_idx % out_plane_size;
const size_t out_r = out_rc / dnc;
const size_t out_c = out_rc % dnc;
const size_t in_k = out_k % sk;
const size_t in_r = out_r * row_stride + (out_k / sk) / col_stride;
const size_t in_c = out_c * col_stride + (out_k / sk) % col_stride;
const size_t in_idx = ((n * sk + in_k) * snr + in_r) * snc + in_c;
if (add_to) d[i] += s[in_idx];
else d[i] = s[in_idx];
});
}
void reorg_gradient(
bool add_to,
tensor& grad,
const int row_stride,
const int col_stride,
const tensor& gradient_input
)
{
DLIB_CASSERT(!is_same_object(grad, gradient_input), "Grad and gradient_input must be distinct objects.");
DLIB_CASSERT(grad.nr() % row_stride == 0, "The number of rows in grad must be divisible by row_stride.");
DLIB_CASSERT(grad.nc() % col_stride == 0, "The number of columns in grad must be divisible by col_stride.");
DLIB_CASSERT(grad.num_samples() == gradient_input.num_samples(), "The number of samples in grad and gradient_input must match.");
DLIB_CASSERT(grad.k() == gradient_input.k() / row_stride / col_stride, "The number of channels in grad must be gradient_input.k() divided by row_stride and col_stride.");
DLIB_CASSERT(grad.nr() == gradient_input.nr() * row_stride, "The number of rows in grad must be gradient_input.nr() multiplied by row_stride.");
DLIB_CASSERT(grad.nc() == gradient_input.nc() * col_stride, "The number of columns in grad must be gradient_input.nc() multiplied by col_stride.");
const float* gi = gradient_input.host();
float* g = grad.host();
parallel_for(0, gradient_input.num_samples(), [&](long n)
{
for (long k = 0; k < gradient_input.k(); ++k)
{
for (long r = 0; r < gradient_input.nr(); ++r)
{
for (long c = 0; c < gradient_input.nc(); ++c)
{
const auto in_idx = tensor_index(gradient_input, n, k, r, c);
const auto out_idx = tensor_index(grad,
n,
k % grad.k(),
r * row_stride + (k / grad.k()) / col_stride,
c * col_stride + (k / grad.k()) % col_stride);
if (add_to) g[out_idx] += gi[in_idx];
else g[out_idx] = gi[in_idx];
}
}
}
});
}
// ------------------------------------------------------------------------------------
void embeddings(
resizable_tensor& dest,
const tensor& src,
const tensor& embs
)
{
DLIB_CASSERT(
src.nr() > 0 &&
embs.num_samples() > 0 &&
embs.k() > 0 &&
embs.nr() == 1 &&
embs.nc() == 1,
"\nsrc.num_samples(): " << src.num_samples() <<
"\nsrc.k(): " << src.k() <<
"\nsrc.nr(): " << src.nr() <<
"\nsrc.nc(): " << src.nc() <<
"\nembs.num_samples(): " << embs.num_samples() <<
"\nembs.k(): " << embs.k() <<
"\nembs.nr(): " << embs.nr() <<
"\nembs.nc(): " << embs.nc()
);
long ns = dest.num_samples(), nk = dest.k(), nr = dest.nr(), nc = dest.nc();
const float* src_data = src.host();
float* dest_data = dest.host();
const float* embs_data = embs.host();
for (long s = 0; s < ns; ++s)
{
for (long k = 0; k < nk; ++k)
{
for (long r = 0; r < nr; ++r)
{
const unsigned long token_idx = static_cast<unsigned long>(src_data[tensor_index(src, s, k, r, 0)]);
if (token_idx < embs.num_samples())
{
for (long c = 0; c < nc; ++c)
dest_data[tensor_index(dest, s, k, r, c)] = embs_data[tensor_index(embs, token_idx, c, 0, 0)];
}
else
{
for (long c = 0; c < nc; ++c)
dest_data[tensor_index(dest, s, k, r, c)] = 0;
}
}
}
}
}
void embeddings_gradient(
const tensor& prev,
const tensor& gradient_input,
tensor& grads,
const tensor& freqs,
float learning_rate,
bool scale
)
{
DLIB_CASSERT(
prev.nr() > 0 &&
gradient_input.num_samples() == prev.num_samples() &&
gradient_input.k() == prev.k() &&
gradient_input.nr() == prev.nr() &&
gradient_input.nc() == grads.k() &&
grads.num_samples() > 0 &&
grads.k() > 0 &&
grads.nr() == 1 &&
grads.nc() == 1,
"\ngradient_input.num_samples(): " << gradient_input.num_samples() <<
"\ngradient_input.k(): " << gradient_input.k() <<
"\ngradient_input.nr(): " << gradient_input.nr() <<
"\ngradient_input.nc(): " << gradient_input.nc() <<
"\nprev.num_samples(): " << prev.num_samples() <<
"\nprev.k(): " << prev.k() <<
"\nprev.nr(): " << prev.nr() <<
"\nprev.nc(): " << prev.nc() <<
"\ngrads.num_samples(): " << grads.num_samples() <<
"\ngrads.k(): " << grads.k() <<
"\ngrads.nr(): " << grads.nr() <<
"\ngrads.nc(): " << grads.nc()
);
const float* prev_data = prev.host();
const float* gradient_input_data = gradient_input.host();
const float* freqs_data = freqs.host();
float* grads_data = grads.host();
long ns = gradient_input.num_samples(), nk = gradient_input.k();
long nr = gradient_input.nr(), nc = gradient_input.nc();
std::vector<dlib::mutex> embedding_mutexes(grads.num_samples());
parallel_for(0, ns * nk, [&](long i)
{
long s = i / nk;
long k = i % nk;
for (long r = 0; r < nr; ++r)
{
const unsigned long token_idx = static_cast<unsigned long>(prev_data[tensor_index(prev, s, k, r, 0)]);
if (token_idx < grads.num_samples())
{
const float freg_token = freqs_data[token_idx];
float freq_scale = 1.0f;
if (scale && freg_token != 0.0f) freq_scale = std::min(0.15f, std::max(1.0f / freg_token, 1.0f));
auto_mutex locker(embedding_mutexes[token_idx]);
for (long c = 0; c < nc; ++c)
{
const float gradient = gradient_input_data[tensor_index(gradient_input, s, k, r, c)];
grads_data[tensor_index(grads, token_idx, c, 0, 0)] -= (gradient * learning_rate * freq_scale);
}
}
}
});
}
// ------------------------------------------------------------------------------------
// ------------------------------------------------------------------------------------
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.
long 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::operator() (
const bool add_to_output,
resizable_tensor& output,
const tensor& data,
const tensor& filters,
const tensor& biases,
bool use_relu
)
{
DLIB_CASSERT(filters.num_samples() == biases.k());
(*this)(add_to_output, output,data,filters);
tt::add(1, output, 1, biases);
if (use_relu) tt::relu(output, output);
}
void tensor_conv::operator() (
const bool add_to_output,
tensor& output,
const tensor& data,
const tensor& filters,
const tensor& biases,
bool use_relu
)
{
DLIB_CASSERT(filters.num_samples() == biases.k());
(*this)(add_to_output, output, data, filters);
tt::add(1, output, 1, biases);
if (use_relu) tt::relu(output, output);
}
// ------------------------------------------------------------------------------------
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;
}
}
// ------------------------------------------------------------------------------------
void copy_tensor(
bool add_to,
tensor& dest,
size_t dk, size_t dnr, size_t dnc,
const tensor& src,
size_t sk, size_t snr, size_t snc,
size_t k, size_t nr, size_t nc
)
{
size_t dest_stride_sample = static_cast<size_t>(dest.nc() * dest.nr() * dest.k());
size_t dest_stride_k = static_cast<size_t>(dest.nc() * dest.nr());
size_t dest_stride_nr = static_cast<size_t>(dest.nc());
size_t src_stride_sample = static_cast<size_t>(src.nc() * src.nr() * src.k());
size_t src_stride_k = static_cast<size_t>(src.nc() * src.nr());
size_t src_stride_nr = static_cast<size_t>(src.nc());
DLIB_CASSERT(dest.num_samples() == src.num_samples(), "All sources should fit into dest tensor size");
DLIB_CASSERT(dest.k() - dk >= k &&
dest.nr() - dnr >= nr &&
dest.nc() - dnc >= nc, "Not enough space in dest tensor");
DLIB_CASSERT(src.k() - sk >= k &&
src.nr() - snr >= nr &&
src.nc() - snc >= nc, "Not enough space in src tensor");
float* dest_p = dest.host() + dk * dest_stride_k \
+ dnr * dest_stride_nr \
+ dnc;
const float* src_p = src.host() + sk * src_stride_k \
+ snr * src_stride_nr \
+ snc;
for (long i = 0; i < src.num_samples(); ++i)
{
float* dest_channel_p = dest_p;
const float* src_channel_p = src_p;
for (long j = 0; j < k; ++j)
{
float* dest_row_p = dest_channel_p;
const float* src_row_p = src_channel_p;
for (long r = 0; r < nr; ++r)
{
if (add_to)
{
for (size_t c = 0; c < nc; ++c)
dest_row_p[c] += src_row_p[c];
}
else
{
::memcpy(dest_row_p, src_row_p, nc * sizeof(float));
}
dest_row_p += dest_stride_nr;
src_row_p += src_stride_nr;
}
dest_channel_p += dest_stride_k;
src_channel_p += src_stride_k;
}
dest_p += dest_stride_sample;
src_p += src_stride_sample;
}
}
// ------------------------------------------------------------------------------------
void transpose(
bool add,
tensor& dest,
const tensor& src
)
{
DLIB_CASSERT(dest.num_samples() == src.num_samples() &&
dest.k() == src.k() &&
dest.nr() == src.nc() &&
dest.nc() == src.nr(),
"Incompatible tensor dimensions.");
const float* src_data = src.host();
float* dest_data = dest.host();
const long num_samples = src.num_samples();
const long k_dim = src.k();
const long src_nr = src.nr();
const long src_nc = src.nc();
const long dest_nr = dest.nr();
const long dest_nc = dest.nc();
parallel_for(0, num_samples * k_dim, [&](long i) {
const long n = i / k_dim;
const long k = i % k_dim;
const long src_nk_offset = (n * src.k() + k) * src_nr;
const long dest_nk_offset = (n * dest.k() + k) * dest_nr;
for (long r = 0; r < src_nr; ++r) {
for (long c = 0; c < src_nc; ++c) {
const long src_idx = (src_nk_offset + r) * src_nc + c;
const long dest_idx = (dest_nk_offset + c) * dest_nc + r;
if (add) dest_data[dest_idx] += src_data[src_idx];
else dest_data[dest_idx] = src_data[src_idx];
}
}
});
}
// ------------------------------------------------------------------------------------
}
}
#endif // DLIB_DNN_CPU_cPP_