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Commit 771ca2e0 authored by Davis King's avatar Davis King
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clarified spec

parent 53e9c158
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dlib/dnn/layers.h
View file @ 771ca2e0
......@@ -1920,7 +1920,7 @@ namespace dlib
template <typename SUBNET>
void backward(const tensor& gradient_input, SUBNET& sub, tensor&)
{
// Gradient is splitted into parts for each tag layer
// Gradient is split into parts for each tag layer
impl::concat_helper_impl<TAG_TYPES...>::split(gradient_input, sub, 0);
}
......@@ -1987,9 +1987,8 @@ namespace dlib
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
// to exclude them, here are new tags specially for inceptions
// inception layer will use tags internally. If user will use tags too, some conflicts
// possible to exclude them, here are new tags specially for inceptions
template <typename SUBNET> using itag0 = add_tag_layer< 1000 + 0, SUBNET>;
template <typename SUBNET> using itag1 = add_tag_layer< 1000 + 1, SUBNET>;
template <typename SUBNET> using itag2 = add_tag_layer< 1000 + 2, SUBNET>;
......@@ -2027,8 +2026,7 @@ namespace dlib
template<typename>class B5,
typename SUBNET>
using inception5 = concat5<itag1, itag2, itag3, itag4, itag5,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>
>;
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>>;
// ----------------------------------------------------------------------------------------
}
......
dlib/dnn/layers_abstract.h
View file @ 771ca2e0
......@@ -1662,23 +1662,17 @@ namespace dlib
/*!
WHAT THIS OBJECT REPRESENTS
This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface
defined above. This layer simply concatenates the output of requiered layers
In particular, it copies each layer's output from TAG_TYPES into the corresponding
place of the result tensor, those producing combined output
The output of each tag layer is stored in a separate part of final output.
FORWARD:
for each (tag in TAG_TYPES)
outout[i, k + tag.k(), r, c] = layer<tag>(subnet).get_output[i, k, r, c]
BACKWARD:
for each (tag in TAG_TYPES)
layer<tag>(subnet).get_gradient_input[i, k, r, c] = input[i, k + tag.k(), r, c]
This layer can be only used with tags inside.
Each tagged layer should have identical num_samples, R and C size
The output will have K = sum(k) of tags, and the, and the output's num_samples,
R and C will be the same as tagged layers
defined above. This layer simply concatenates the output of tagged layers.
Importantly, each input layer must have the same dimensions (i.e.
num_samples, nr, and nc) except for the k channel, which may vary. This is
because the concatenation happens along the k dimension. That is, the
output of this network is a tensor, OUT, that is the concatenation of the
tensors:
for each (tag in TAG_TYPES)
layer<tag>(subnet).get_output()
Therefore, out.num_samples(), out.nr(), and out.nc() match the dimensions
of the input tensors while OUT.k() is the sum of the input layer's k()
dimensions.
!*/
public:
......@@ -1696,32 +1690,40 @@ namespace dlib
// 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>
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>
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>
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>
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
// to exclude them, here are new tags specially for inceptions
// ----------------------------------------------------------------------------------------
// Now define inception layer tag types. These layer aliases allow creating
// the networks described in the paper:
// Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of
// the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
// Note that we use tag ID numbers >= 1000 to avoid conflict with user's tag layers.
template <typename SUBNET> using itag0 = add_tag_layer< 1000 + 0, SUBNET>;
template <typename SUBNET> using itag1 = add_tag_layer< 1000 + 1, SUBNET>;
template <typename SUBNET> using itag2 = add_tag_layer< 1000 + 2, SUBNET>;
......@@ -1732,35 +1734,35 @@ 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>
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>
template<typename>class B2,
typename SUBNET>
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>
template<typename>class B2,
template<typename>class B3,
typename SUBNET>
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>
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
typename SUBNET>
using inception4 = concat4<itag1, itag2, itag3, itag4,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4< itag0<SUBNET>>>>>>>>>>>>
>;
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>
template<typename>class B2,
template<typename>class B3,
template<typename>class B4,
template<typename>class B5,
typename SUBNET>
using inception5 = concat5<itag1, itag2, itag3, itag4, itag5,
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>
>;
itag1<B1<iskip< itag2<B2<iskip< itag3<B3<iskip< itag4<B4<iskip< itag5<B5< itag0<SUBNET>>>>>>>>>>>>>>>>;
// ----------------------------------------------------------------------------------------
......
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