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Changed concat syntax into concat1, concat2..., made dtest more readable::

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Fm 2016-05-27 09:55:15 +03:00
parent 2f7d3578d2
commit d32bcdfa3d
5 changed files with 140 additions and 94 deletions
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2
dlib/dnn/core.h
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@ -536,7 +536,7 @@ namespace dlib
subnet_wrapper(const subnet_wrapper&) = delete;
subnet_wrapper& operator=(const subnet_wrapper&) = delete;
subnet_wrapper(T& l_) {}
subnet_wrapper(T& /*l_*/) {}
// Nothing here because in this case T is one of the input layer types
// that doesn't have anything in it.
};

44
dlib/dnn/layers.h
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@ -1953,8 +1953,31 @@ namespace dlib
};
template <typename SUBNET, template<typename> class... TAG_TYPES>
using concat = add_layer<concat_<TAG_TYPES...>, SUBNET>;
// concat layer definitions
template <template<typename> class TAG1, typename SUBNET>
using concat1 = add_layer<concat_<TAG1>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
typename SUBNET>
using concat2 = add_layer<concat_<TAG1, TAG2>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
typename SUBNET>
using concat3 = add_layer<concat_<TAG1, TAG2, TAG3>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
template<typename> class TAG4,
typename SUBNET>
using concat4 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
template<typename> class TAG4,
template<typename> class TAG5,
typename SUBNET>
using concat5 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4, TAG5>, SUBNET>;
// inception layer will use tags internally. If user will use tags too,
// some conflicts possible
@ -1969,30 +1992,35 @@ namespace dlib
template <typename SUBNET> using iskip = add_skip_layer< itag0, SUBNET>;
// here are some templates to be used for creating inception layer groups
template <template<typename>class B1,
typename SUBNET>
using inception1 = concat1<itag1, itag1<B1<iskip< itag0<SUBNET>>>>>;
template <template<typename>class B1,
template<typename>class B2,
typename SUBNET>
using inception2 = concat<itag1<B1<iskip< itag2<B2< itag0<SUBNET>>>>>>, itag1, itag2>;
using inception2 = concat2<itag1, itag2, itag1<B1<iskip< itag2<B2< itag0<SUBNET>>>>>>>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
typename SUBNET>
using inception3 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3< itag0<SUBNET>>>>>>>>>, itag1, itag2, itag3>;
using inception3 = concat3<itag1, itag2, itag3, itag1<B1<iskip< itag2<B2<iskip< itag3<B3< itag0<SUBNET>>>>>>>>>>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
typename SUBNET>
using inception4 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4< itag0<SUBNET>>>>>>>>>>>>,
itag1, itag2, itag3, itag4>;
using inception4 = concat4<itag1, itag2, itag3, itag4,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4< itag0<SUBNET>>>>>>>>>>>>
>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
template<typename>class B5,
typename SUBNET>
using inception5 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>,
itag1, itag2, itag3, itag4, itag5>;
using inception5 = concat5<itag1, itag2, itag3, itag4, itag5,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>
>;
// ----------------------------------------------------------------------------------------
}

44
dlib/dnn/layers_abstract.h
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@ -1693,8 +1693,31 @@ namespace dlib
};
template <typename SUBNET, template<typename> class... TAG_TYPES>
using concat = add_layer<concat_<TAG_TYPES...>, SUBNET>;
// concat layer definitions
template <template<typename> class TAG1, typename SUBNET>
using concat1 = add_layer<concat_<TAG1>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
typename SUBNET>
using concat2 = add_layer<concat_<TAG1, TAG2>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
typename SUBNET>
using concat3 = add_layer<concat_<TAG1, TAG2, TAG3>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
template<typename> class TAG4,
typename SUBNET>
using concat4 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4>, SUBNET>;
template <template<typename> class TAG1,
template<typename> class TAG2,
template<typename> class TAG3,
template<typename> class TAG4,
template<typename> class TAG5,
typename SUBNET>
using concat5 = add_layer<concat_<TAG1, TAG2, TAG3, TAG4, TAG5>, SUBNET>;
// inception layer will use tags internally. If user will use tags too,
// some conflicts possible
@ -1709,30 +1732,35 @@ namespace dlib
template <typename SUBNET> using iskip = add_skip_layer< itag0, SUBNET>;
// here are some templates to be used for creating inception layer groups
template <template<typename>class B1,
typename SUBNET>
using inception1 = concat1<itag1, itag1<B1<iskip< itag0<SUBNET>>>>>;
template <template<typename>class B1,
template<typename>class B2,
typename SUBNET>
using inception2 = concat<itag1<B1<iskip< itag2<B2< itag0<SUBNET>>>>>>, itag1, itag2>;
using inception2 = concat2<itag1, itag2, itag1<B1<iskip< itag2<B2< itag0<SUBNET>>>>>>>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
typename SUBNET>
using inception3 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3< itag0<SUBNET>>>>>>>>>, itag1, itag2, itag3>;
using inception3 = concat3<itag1, itag2, itag3, itag1<B1<iskip< itag2<B2<iskip< itag3<B3< itag0<SUBNET>>>>>>>>>>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
typename SUBNET>
using inception4 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4< itag0<SUBNET>>>>>>>>>>>>,
itag1, itag2, itag3, itag4>;
using inception4 = concat4<itag1, itag2, itag3, itag4,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4< itag0<SUBNET>>>>>>>>>>>>
>;
template <template<typename>class B1,
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
template<typename>class B5,
typename SUBNET>
using inception5 = concat<itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>,
itag1, itag2, itag3, itag4, itag5>;
using inception5 = concat5<itag1, itag2, itag3, itag4, itag5,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>
>;
// ----------------------------------------------------------------------------------------

143
dlib/test/dnn.cpp
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@ -11,83 +11,11 @@
#include "tester.h"
namespace dlib{
template <typename SUBNET> using concat_block1 = con<5,1,1,1,1,SUBNET>;
template <typename SUBNET> using concat_block2 = con<8,3,3,1,1,SUBNET>;
template <typename SUBNET> using concat_block3 = max_pool<3,3,1,1,SUBNET>;
template <typename SUBNET> using concat_incept = inception3<concat_block1,concat_block2,concat_block3,SUBNET>;
// this class is a friend of add_layer and can access private members
class dnn_tester{
public:
// tester function is a member to have access to a private x_grad member of add_layer
static void test_concat()
{
using namespace test;
using namespace std;
using namespace dlib::tt;
print_spinner();
using net_type = concat_incept<input<matrix<float>>>;
resizable_tensor data(10, 1, 111, 222);
data = matrix_cast<float>(gaussian_randm(data.num_samples(), data.k() * data.nr() * data.nc(), 1));
net_type net;
auto& out = net.forward(data);
auto& b1o = layer<itag1>(net).get_output();
auto& b2o = layer<itag2>(net).get_output();
auto& b3o = layer<itag3>(net).get_output();
resizable_tensor dest(10, 14, 111, 222);
copy_tensor(dest, 0, b1o, 0, b1o.k());
copy_tensor(dest, b1o.k(), b2o, 0, b2o.k());
copy_tensor(dest, b1o.k() + b2o.k(), b3o, 0, b3o.k());
DLIB_TEST(dest.size() == out.size());
int error = memcmp(dest.host(), out.host(), dest.size());
DLIB_TEST(error == 0);
resizable_tensor gr(10, 14, 111, 222);
gr = matrix_cast<float>(gaussian_randm(gr.num_samples(), gr.k() * gr.nr() * gr.nc(), 1));
memcpy(net.get_gradient_input(), gr);
net.back_propagate_error(data);
auto& b1g = layer<itag1>(net).subnet().x_grad;
auto& b2g = layer<itag2>(net).subnet().x_grad;
auto& b3g = layer<itag3>(net).subnet().x_grad;
resizable_tensor g1(10, 5, 111, 222);
resizable_tensor g2(10, 8, 111, 222);
resizable_tensor g3(10, 1, 111, 222);
copy_tensor(g1, 0, gr, 0, g1.k());
copy_tensor(g2, 0, gr, g1.k(), g2.k());
copy_tensor(g3, 0, gr, g1.k() + g2.k(), g3.k());
DLIB_TEST(g1.size() == b1g.size());
error = memcmp(g1.host(), b1g.host(), b1g.size());
DLIB_TEST(error == 0);
DLIB_TEST(g2.size() == b2g.size());
error = memcmp(g2.host(), b2g.host(), b2g.size());
DLIB_TEST(error == 0);
DLIB_TEST(g3.size() == b3g.size());
error = memcmp(g3.host(), b3g.host(), b3g.size());
DLIB_TEST(error == 0);
}
};
}
namespace
namespace dlib
{
using namespace test;
using namespace dlib;
using namespace std;
using namespace test;
logger dlog("test.dnn");
@ -1258,7 +1186,7 @@ namespace
r*stride_y+y_offset,
window_width,
window_height)));
float err = abs(image_plane(A,s,k)(r,c) - expected);
float err = std::abs(image_plane(A,s,k)(r,c) - expected);
DLIB_TEST_MSG(err < 1e-5, err << " " << expected << " " << image_plane(A,s,k)(r,c));
}
}
@ -1594,6 +1522,8 @@ namespace
"Runs tests on the deep neural network tools.")
{}
void test_concat();
void perform_test (
)
{
@ -1646,10 +1576,71 @@ namespace
test_layers();
test_visit_funcions();
test_copy_tensor_cpu();
dlib::dnn_tester::test_concat();
test_concat();
}
} a;
template <typename SUBNET> using concat_block1 = con<5,1,1,1,1,SUBNET>;
template <typename SUBNET> using concat_block2 = con<8,3,3,1,1,SUBNET>;
template <typename SUBNET> using concat_block3 = max_pool<3,3,1,1,SUBNET>;
template <typename SUBNET> using concat_incept = inception3<concat_block1,concat_block2,concat_block3,SUBNET>;
void dnn_tester::test_concat()
{
using namespace dlib::tt;
print_spinner();
using net_type = concat_incept<input<matrix<float>>>;
resizable_tensor data(10, 1, 111, 222);
data = matrix_cast<float>(gaussian_randm(data.num_samples(), data.k() * data.nr() * data.nc(), 1));
net_type net;
auto& out = net.forward(data);
auto& b1o = layer<itag1>(net).get_output();
auto& b2o = layer<itag2>(net).get_output();
auto& b3o = layer<itag3>(net).get_output();
resizable_tensor dest(10, 14, 111, 222);
copy_tensor(dest, 0, b1o, 0, b1o.k());
copy_tensor(dest, b1o.k(), b2o, 0, b2o.k());
copy_tensor(dest, b1o.k() + b2o.k(), b3o, 0, b3o.k());
DLIB_TEST(dest.size() == out.size());
int error = memcmp(dest.host(), out.host(), dest.size());
DLIB_TEST(error == 0);
resizable_tensor gr(10, 14, 111, 222);
gr = matrix_cast<float>(gaussian_randm(gr.num_samples(), gr.k() * gr.nr() * gr.nc(), 1));
memcpy(net.get_gradient_input(), gr);
net.back_propagate_error(data);
auto& b1g = layer<itag1>(net).subnet().x_grad;
auto& b2g = layer<itag2>(net).subnet().x_grad;
auto& b3g = layer<itag3>(net).subnet().x_grad;
resizable_tensor g1(10, 5, 111, 222);
resizable_tensor g2(10, 8, 111, 222);
resizable_tensor g3(10, 1, 111, 222);
copy_tensor(g1, 0, gr, 0, g1.k());
copy_tensor(g2, 0, gr, g1.k(), g2.k());
copy_tensor(g3, 0, gr, g1.k() + g2.k(), g3.k());
DLIB_TEST(g1.size() == b1g.size());
error = memcmp(g1.host(), b1g.host(), b1g.size());
DLIB_TEST(error == 0);
DLIB_TEST(g2.size() == b2g.size());
error = memcmp(g2.host(), b2g.host(), b2g.size());
DLIB_TEST(error == 0);
DLIB_TEST(g3.size() == b3g.size());
error = memcmp(g3.host(), b3g.host(), b3g.size());
DLIB_TEST(error == 0);
}
}

1
examples/dnn_inception_ex.cpp
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@ -11,7 +11,6 @@
For further reading refer http://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf
*/
#include <dlib/dnn.h>
#include <iostream>
#include <dlib/data_io.h>