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Commit ecb7095e authored by OranjeeGeneral's avatar OranjeeGeneral
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refactored interface to reduce complexity so conv and convt layers forward… 

refactored interface to reduce complexity so conv and convt layers forward passes have to call setup explicit now and there is only one ()-operator
parent 3a91295e
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Showing with 24 additions and 137 deletions
dlib/dnn/cpu_dlib.cpp
View file @ ecb7095e
......@@ -1739,65 +1739,32 @@ namespace dlib
}
}
void tensor_conv::forward_without_setup(
resizable_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());
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);
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);
output.set_sample(n, mat(filters)*trans(temp));
}
}
void tensor_conv::operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters,
int stride_y,
int stride_x,
int padding_y,
int padding_x
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(stride_y > 0 && stride_x > 0);
DLIB_CASSERT(0 <= padding_y && padding_y < filters.nr());
DLIB_CASSERT(0 <= padding_x && padding_x < filters.nc());
DLIB_CASSERT(filters.nr() <= data.nr() + 2*padding_y,
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*padding_x,
DLIB_CASSERT(filters.nc() <= data.nc() + 2*last_padding_x,
"Filter windows must be small enough to fit into the padded image.");
output.set_size(data.num_samples(),
filters.num_samples(),
1+(data.nr()+2*padding_y-filters.nr())/stride_y,
1+(data.nc()+2*padding_x-filters.nc())/stride_x);
1+(data.nr()+2*last_padding_y-filters.nr())/last_stride_y,
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(), stride_y, stride_x, padding_y, padding_x);
img2col(temp, data, n, filters.nr(), filters.nc(), last_stride_y, last_stride_x, last_padding_y, last_padding_x);
output.set_sample(n, mat(filters)*trans(temp));
}
last_stride_y = stride_y;
last_stride_x = stride_x;
last_padding_y = padding_y;
last_padding_x = padding_x;
}
// ------------------------------------------------------------------------------------
......
dlib/dnn/cpu_dlib.h
View file @ ecb7095e
......@@ -378,27 +378,19 @@ namespace dlib
)
{
(void)data; /* silence compiler */
(void)filters; /* silence compiler */
DLIB_CASSERT(stride_y > 0 && stride_x > 0);
DLIB_CASSERT(0 <= padding_y && padding_y < filters.nr());
DLIB_CASSERT(0 <= padding_x && padding_x < filters.nc());
last_stride_y = stride_y;
last_stride_x = stride_x;
last_padding_y = padding_y;
last_padding_x = padding_x;
}
void forward_without_setup(
resizable_tensor& output,
const tensor& data,
const tensor& filters
);
void operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters,
int stride_y,
int stride_x,
int padding_y,
int padding_x
const tensor& filters
);
void get_gradient_for_data (
......
dlib/dnn/cudnn_dlibapi.cpp
View file @ ecb7095e
......@@ -950,63 +950,10 @@ namespace dlib
clear();
}
void tensor_conv::forward_without_setup(
resizable_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(filters.nc() <= data.nc() + 2*padding_x,
"Filter windows must be small enough to fit into the padded image."
<< "\n\t filters.nc(): " << filters.nc()
<< "\n\t data.nc(): " << data.nc()
<< "\n\t padding_x: " << padding_x
);
DLIB_CASSERT(filters.nr() <= data.nr() + 2*padding_y,
"Filter windows must be small enough to fit into the padded image."
<< "\n\t filters.nr(): " << filters.nr()
<< "\n\t data.nr(): " << data.nr()
<< "\n\t padding_y: " << padding_y
);
output.set_size(out_num_samples, out_k, out_nr, out_nc);
DLIB_ASSERT(output.num_samples() == data.num_samples(),out_num_samples << " " << data.num_samples());
DLIB_ASSERT(output.k() == filters.num_samples());
DLIB_ASSERT(output.nr() == 1+(data.nr()+2*padding_y-filters.nr())/stride_y);
DLIB_ASSERT(output.nc() == 1+(data.nc()+2*padding_x-filters.nc())/stride_x);
const float alpha = 1;
const float beta = 0;
CHECK_CUDNN(cudnnConvolutionForward(
context(),
&alpha,
descriptor(data),
data.device(),
(const cudnnFilterDescriptor_t)filter_handle,
filters.device(),
(const cudnnConvolutionDescriptor_t)conv_handle,
(cudnnConvolutionFwdAlgo_t)forward_algo,
forward_workspace,
forward_workspace_size_in_bytes,
&beta,
descriptor(output),
output.device()));
}
void tensor_conv::operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters,
int stride_y,
int stride_x,
int padding_y,
int padding_x
const tensor& filters
)
{
DLIB_CASSERT(is_same_object(output,data) == false);
......@@ -1027,8 +974,6 @@ namespace dlib
);
setup(data,filters,stride_y,stride_x,padding_y,padding_x);
output.set_size(out_num_samples, out_k, out_nr, out_nc);
DLIB_ASSERT(output.num_samples() == data.num_samples(),out_num_samples << " " << data.num_samples());
......
dlib/dnn/cudnn_dlibapi.h
View file @ ecb7095e
......@@ -205,11 +205,7 @@ namespace dlib
void operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters,
int stride_y,
int stride_x,
int padding_y,
int padding_x
const tensor& filters
);
/*!
requires
......@@ -279,11 +275,6 @@ namespace dlib
int padding_x
);
void forward_without_setup(
resizable_tensor& output,
const tensor& data,
const tensor& filters);
private:
/*!
......
dlib/dnn/layers.h
View file @ ecb7095e
......@@ -147,14 +147,16 @@ namespace dlib
template <typename SUBNET>
void forward(const SUBNET& sub, resizable_tensor& output)
{
conv(output,
sub.get_output(),
conv.setup(sub.get_output(),
filters(params,0),
_stride_y,
_stride_x,
padding_y_,
padding_x_
);
padding_x_);
conv(output,
sub.get_output(),
filters(params,0));
tt::add(1,output,1,biases(params,filters.size()));
}
......
dlib/dnn/tensor_tools.h
View file @ ecb7095e
......@@ -879,12 +879,8 @@ namespace dlib { namespace tt
void operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters,
int stride_y,
int stride_x,
int padding_y,
int padding_x
) { impl(output,data,filters,stride_y,stride_x,padding_y,padding_x); }
const tensor& filters
) { impl(output,data,filters); }
/*!
requires
- stride_y > 0
......@@ -947,14 +943,6 @@ namespace dlib { namespace tt
this gradient to filters_gradient.
!*/
void operator() (
resizable_tensor& output,
const tensor& data,
const tensor& filters
)
{
impl.forward_without_setup(output,data,filters);
}
void setup(
const tensor& data,
......
dlib/test/dnn.cpp
View file @ ecb7095e
......@@ -805,8 +805,10 @@ namespace
padding_y = (filters.nr()-data.nr()+1)/2;
if (!(filters.nc() <= data.nc() + 2*padding_x))
padding_x = (filters.nc()-data.nc()+1)/2;
conv1(output1, data, filters, stride_y,stride_x, padding_y, padding_x);
conv2(output2, data, filters, stride_y,stride_x, padding_y, padding_x);
conv1.setup(data,filters,stride_y,stride_x,padding_y,padding_x);
conv1(output1, data, filters);
conv2.setup(data,filters,stride_y,stride_x,padding_y,padding_x);
conv2(output2, data, filters);
dlog << LINFO << "forward error: "<< max(abs(mat(output1)-mat(output2)));
DLIB_TEST_MSG(max(abs(mat(output1)-mat(output2))) < 1e-3, max(abs(mat(output1)-mat(output2)))
<<"\n\t padding_y: "<< padding_y
......
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