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Commit 5039f0ba authored by Davis King's avatar Davis King
Browse files

Changed the API for functions that can operate in-place to a more appropriate 

form.
parent c1433b3d
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Showing with 90 additions and 121 deletions
dlib/dnn/cudnn_dlibapi.cpp
View file @ 5039f0ba
......@@ -639,24 +639,24 @@ namespace dlib
void softmax_gradient (
tensor& grad,
const tensor& softmaxed_data,
const tensor& dest,
const tensor& gradient_input
)
{
DLIB_CASSERT(
have_same_dimensions(softmaxed_data,gradient_input) == true &&
have_same_dimensions(softmaxed_data,grad) == true , "");
if (softmaxed_data.size() == 0)
have_same_dimensions(dest,gradient_input) == true &&
have_same_dimensions(dest,grad) == true , "");
if (dest.size() == 0)
return;
const float alpha = 1;
const float beta = 1;
const float beta = 0;
check(cudnnSoftmaxBackward(context(),
CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_CHANNEL,
&alpha,
descriptor(softmaxed_data),
softmaxed_data.device(),
descriptor(dest),
dest.device(),
descriptor(gradient_input),
gradient_input.device(),
&beta,
......@@ -691,19 +691,17 @@ namespace dlib
void sigmoid_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
DLIB_CASSERT(
have_same_dimensions(src,gradient_input) == true &&
have_same_dimensions(src,grad) == true &&
have_same_dimensions(src,dest) == true , "");
if (src.size() == 0)
have_same_dimensions(dest,gradient_input) == true &&
have_same_dimensions(dest,grad) == true , "");
if (dest.size() == 0)
return;
const float alpha = 1;
const float beta = 1;
const float beta = 0;
check(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_SIGMOID,
&alpha,
......@@ -711,8 +709,8 @@ namespace dlib
dest.device(),
descriptor(gradient_input),
gradient_input.device(),
descriptor(src),
src.device(),
descriptor(dest),
dest.device(),
&beta,
descriptor(grad),
grad.device()));
......@@ -744,19 +742,17 @@ namespace dlib
void relu_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
DLIB_CASSERT(
have_same_dimensions(src,gradient_input) == true &&
have_same_dimensions(src,grad) == true &&
have_same_dimensions(src,dest) == true , "");
if (src.size() == 0)
have_same_dimensions(dest,gradient_input) == true &&
have_same_dimensions(dest,grad) == true , "");
if (dest.size() == 0)
return;
const float alpha = 1;
const float beta = 1;
const float beta = 0;
check(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_RELU,
&alpha,
......@@ -764,8 +760,8 @@ namespace dlib
dest.device(),
descriptor(gradient_input),
gradient_input.device(),
descriptor(src),
src.device(),
descriptor(dest),
dest.device(),
&beta,
descriptor(grad),
grad.device()));
......@@ -797,19 +793,17 @@ namespace dlib
void tanh_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
DLIB_CASSERT(
have_same_dimensions(src,gradient_input) == true &&
have_same_dimensions(src,grad) == true &&
have_same_dimensions(src,dest) == true , "");
if (src.size() == 0)
have_same_dimensions(dest,gradient_input) == true &&
have_same_dimensions(dest,grad) == true, "");
if (dest.size() == 0)
return;
const float alpha = 1;
const float beta = 1;
const float beta = 0;
check(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_TANH,
&alpha,
......@@ -817,8 +811,8 @@ namespace dlib
dest.device(),
descriptor(gradient_input),
gradient_input.device(),
descriptor(src),
src.device(),
descriptor(dest),
dest.device(),
&beta,
descriptor(grad),
grad.device()));
......
dlib/dnn/cudnn_dlibapi.h
View file @ 5039f0ba
......@@ -346,19 +346,18 @@ namespace dlib
void softmax_gradient (
tensor& grad,
const tensor& softmaxed_data,
const tensor& dest,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(softmaxed_data,gradient_input) == true
- have_same_dimensions(softmaxed_data,grad) == true
- is_same_object(grad, softmaxed_data)==false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad, dest)==false
ensures
- We interpret softmaxed_data as the output of softmax(softmaxed_data,SRC)
for some SRC tensor. Then let f(SRC) == dot(gradient_input,softmaxed_data)
Then this function computes the gradient of f() with respect to SRC and
adds it to grad.
- We interpret dest as the output of softmax(dest,SRC) for some SRC tensor.
Then let f(SRC) == dot(gradient_input,dest) Then this function computes
the gradient of f() with respect to SRC and assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -381,22 +380,18 @@ namespace dlib
void sigmoid_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling sigmoid(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of sigmoid(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
adds it to grad.
- Recalling that dest is the output of sigmoid(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -419,22 +414,18 @@ namespace dlib
void relu_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling relu(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of relu(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
adds it to grad.
- Recalling that dest is the output of relu(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -457,22 +448,18 @@ namespace dlib
void tanh_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling tanh(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of tanh(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
adds it to grad.
- Recalling that dest is the output of tanh(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......
dlib/dnn/tensor_tools.cpp
View file @ 5039f0ba
......@@ -86,7 +86,7 @@ namespace dlib { namespace tt
{
DLIB_CASSERT(have_same_dimensions(dest,src) == true,"");
#ifdef DLIB_USE_CUDA
cuda::multiply(dest, src);
//cuda::multiply(dest, src);
#else
cpu::multiply(dest, src);
#endif
......@@ -103,7 +103,7 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
cuda::affine_transform(dest,src,A,B);
//cuda::affine_transform(dest,src,A,B);
#else
cpu::affine_transform(dest,src,A,B);
#endif
......@@ -119,7 +119,7 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
cuda::affine_transform(dest,src,A,B);
//cuda::affine_transform(dest,src,A,B);
#else
cpu::affine_transform(dest,src,A,B);
#endif
......@@ -137,7 +137,7 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
cuda::batch_normalize(dest,means,vars,src,gamma,beta);
//cuda::batch_normalize(dest,means,vars,src,gamma,beta);
#else
cpu::batch_normalize(dest,means,vars,src,gamma,beta);
#endif
......@@ -157,8 +157,10 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
/*
cuda::batch_normalize_gradient(gradient_input,means,vars,src,gamma,
src_grad,gamma_grad,beta_grad);
*/
#else
cpu::batch_normalize_gradient(gradient_input,means,vars,src,gamma,
src_grad,gamma_grad,beta_grad);
......@@ -177,7 +179,7 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
cuda::batch_normalize_conv(dest,means,vars,src,gamma,beta);
//cuda::batch_normalize_conv(dest,means,vars,src,gamma,beta);
#else
cpu::batch_normalize_conv(dest,means,vars,src,gamma,beta);
#endif
......@@ -197,8 +199,10 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
/*
cuda::batch_normalize_conv_gradient(gradient_input,means,vars,src,gamma,
src_grad,gamma_grad,beta_grad);
*/
#else
cpu::batch_normalize_conv_gradient(gradient_input,means,vars,src,gamma,
src_grad,gamma_grad,beta_grad);
......@@ -213,7 +217,7 @@ namespace dlib { namespace tt
)
{
#ifdef DLIB_USE_CUDA
cuda::threshold(data,thresh);
//cuda::threshold(data,thresh);
#else
cpu::threshold(data,thresh);
#endif
......@@ -417,12 +421,12 @@ namespace dlib { namespace tt
void softmax_gradient (
tensor& grad,
const tensor& softmaxed_data,
const tensor& dest,
const tensor& gradient_input
)
{
#ifdef DLIB_USE_CUDA
cuda::softmax_gradient(grad, softmaxed_data, gradient_input);
cuda::softmax_gradient(grad, dest, gradient_input);
#else
// TODO
DLIB_CASSERT(false,"");
......@@ -447,12 +451,11 @@ namespace dlib { namespace tt
void sigmoid_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
#ifdef DLIB_USE_CUDA
cuda::sigmoid_gradient(grad, dest, src, gradient_input);
cuda::sigmoid_gradient(grad, dest, gradient_input);
#else
// TODO
DLIB_CASSERT(false,"");
......@@ -477,12 +480,11 @@ namespace dlib { namespace tt
void relu_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
#ifdef DLIB_USE_CUDA
cuda::relu_gradient(grad, dest, src, gradient_input);
cuda::relu_gradient(grad, dest, gradient_input);
#else
// TODO
DLIB_CASSERT(false,"");
......@@ -507,12 +509,11 @@ namespace dlib { namespace tt
void tanh_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
)
{
#ifdef DLIB_USE_CUDA
cuda::tanh_gradient(grad, dest, src, gradient_input);
cuda::tanh_gradient(grad, dest, gradient_input);
#else
// TODO
DLIB_CASSERT(false,"");
......
dlib/dnn/tensor_tools.h
View file @ 5039f0ba
......@@ -519,19 +519,18 @@ namespace dlib { namespace tt
void softmax_gradient (
tensor& grad,
const tensor& softmaxed_data,
const tensor& dest,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(softmaxed_data,gradient_input) == true
- have_same_dimensions(softmaxed_data,grad) == true
- is_same_object(grad, softmaxed_data)==false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad, dest)==false
ensures
- We interpret softmaxed_data as the output of softmax(softmaxed_data,SRC) for
some SRC tensor. Then let f(SRC) == dot(gradient_input,softmaxed_data) Then
this function computes the gradient of f() with respect to SRC and adds it to
grad.
- We interpret dest as the output of softmax(dest,SRC) for some SRC tensor.
Then let f(SRC) == dot(gradient_input,dest) Then this function computes the
gradient of f() with respect to SRC and adds it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -554,22 +553,18 @@ namespace dlib { namespace tt
void sigmoid_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling sigmoid(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of sigmoid(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
adds it to grad.
- Recalling that dest is the output of sigmoid(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -592,22 +587,18 @@ namespace dlib { namespace tt
void relu_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling relu(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of relu(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and adds
it to grad.
- Recalling that dest is the output of relu(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......@@ -630,22 +621,18 @@ namespace dlib { namespace tt
void tanh_gradient (
tensor& grad,
const tensor& dest,
const tensor& src,
const tensor& gradient_input
);
/*!
requires
- have_same_dimensions(src,gradient_input) == true
- have_same_dimensions(src,grad) == true
- have_same_dimensions(src,dest) == true
- dest contains the result of calling tanh(dest,src)
- is_same_object(grad,src) == false
- have_same_dimensions(dest,gradient_input) == true
- have_same_dimensions(dest,grad) == true
- is_same_object(grad,dest) == false
ensures
- Recalling that dest is the output of tanh(dest,src),
let f(src) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and adds
it to grad.
- Recalling that dest is the output of tanh(dest,SRC) for some SRC tensor,
let f(SRC) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to SRC and
assigns it to grad.
- This function supports in-place operation, i.e. having
is_same_object(grad, gradient_input)==true
!*/
......
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