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dlib/examples/dnn_semantic_segmentation_ex.h
Juha Reunanen f685cb4249 Add U-net style skip connections to the semantic-segmentation example (#1600) 
* Add concat_prev layer, and U-net example for semantic segmentation

* Allow to supply mini-batch size as command-line parameter

* Decrease default mini-batch size from 30 to 24

* Resize t1, if needed

* Use DenseNet-style blocks instead of residual learning

* Increase default mini-batch size to 50

* Increase default mini-batch size from 50 to 60

* Resize even during the backward step, if needed

* Use resize_bilinear_gradient for the backward step

* Fix function call ambiguity problem

* Clear destination before adding gradient

* Works OK-ish

* Add more U-tags

* Tweak default mini-batch size

* Define a simpler network when using Microsoft Visual C++ compiler; clean up the DenseNet stuff (leaving it for a later PR)

* Decrease default mini-batch size from 24 to 23

* Define separate dnn filename for MSVC++ and not

* Add documentation for the resize_to_prev layer; move the implementation so that it comes after mult_prev

* Fix previous typo

* Minor formatting changes

* Reverse the ordering of levels

* Increase the learning-rate stopping criterion back to 1e-4 (was 1e-8)

* Use more U-tags even on Windows

* Minor formatting

* Latest MSVC 2017 builds fast, so there's no need to limit the depth any longer

* Tweak default mini-batch size again

* Even though latest MSVC can now build the extra layers, it does not mean we should add them!

* Fix naming
2019-01-06 09:11:39 -05:00

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// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
Semantic segmentation using the PASCAL VOC2012 dataset.
In segmentation, the task is to assign each pixel of an input image
a label - for example, 'dog'. Then, the idea is that neighboring
pixels having the same label can be connected together to form a
larger region, representing a complete (or partially occluded) dog.
So technically, segmentation can be viewed as classification of
individual pixels (using the relevant context in the input images),
however the goal usually is to identify meaningful regions that
represent complete entities of interest (such as dogs).
Instructions how to run the example:
1. Download the PASCAL VOC2012 data, and untar it somewhere.
http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
2. Build the dnn_semantic_segmentation_train_ex example program.
3. Run:
./dnn_semantic_segmentation_train_ex /path/to/VOC2012
4. Wait while the network is being trained.
5. Build the dnn_semantic_segmentation_ex example program.
6. Run:
./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
An alternative to steps 2-4 above is to download a pre-trained network
from here: http://dlib.net/files/semantic_segmentation_voc2012net_v2.dnn
It would be a good idea to become familiar with dlib's DNN tooling before reading this
example. So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
before reading this example program.
*/
#ifndef DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
#define DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
#include <dlib/dnn.h>
// ----------------------------------------------------------------------------------------
inline bool operator == (const dlib::rgb_pixel& a, const dlib::rgb_pixel& b)
{
return a.red == b.red && a.green == b.green && a.blue == b.blue;
}
// ----------------------------------------------------------------------------------------
// The PASCAL VOC2012 dataset contains 20 ground-truth classes + background. Each class
// is represented using an RGB color value. We associate each class also to an index in the
// range [0, 20], used internally by the network.
struct Voc2012class {
Voc2012class(uint16_t index, const dlib::rgb_pixel& rgb_label, const std::string& classlabel)
: index(index), rgb_label(rgb_label), classlabel(classlabel)
{}
// The index of the class. In the PASCAL VOC 2012 dataset, indexes from 0 to 20 are valid.
const uint16_t index = 0;
// The corresponding RGB representation of the class.
const dlib::rgb_pixel rgb_label;
// The label of the class in plain text.
const std::string classlabel;
};
namespace {
constexpr int class_count = 21; // background + 20 classes
const std::vector<Voc2012class> classes = {
Voc2012class(0, dlib::rgb_pixel(0, 0, 0), ""), // background
// The cream-colored `void' label is used in border regions and to mask difficult objects
// (see http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/devkit_doc.html)
Voc2012class(dlib::loss_multiclass_log_per_pixel_::label_to_ignore,
dlib::rgb_pixel(224, 224, 192), "border"),
Voc2012class(1, dlib::rgb_pixel(128, 0, 0), "aeroplane"),
Voc2012class(2, dlib::rgb_pixel( 0, 128, 0), "bicycle"),
Voc2012class(3, dlib::rgb_pixel(128, 128, 0), "bird"),
Voc2012class(4, dlib::rgb_pixel( 0, 0, 128), "boat"),
Voc2012class(5, dlib::rgb_pixel(128, 0, 128), "bottle"),
Voc2012class(6, dlib::rgb_pixel( 0, 128, 128), "bus"),
Voc2012class(7, dlib::rgb_pixel(128, 128, 128), "car"),
Voc2012class(8, dlib::rgb_pixel( 64, 0, 0), "cat"),
Voc2012class(9, dlib::rgb_pixel(192, 0, 0), "chair"),
Voc2012class(10, dlib::rgb_pixel( 64, 128, 0), "cow"),
Voc2012class(11, dlib::rgb_pixel(192, 128, 0), "diningtable"),
Voc2012class(12, dlib::rgb_pixel( 64, 0, 128), "dog"),
Voc2012class(13, dlib::rgb_pixel(192, 0, 128), "horse"),
Voc2012class(14, dlib::rgb_pixel( 64, 128, 128), "motorbike"),
Voc2012class(15, dlib::rgb_pixel(192, 128, 128), "person"),
Voc2012class(16, dlib::rgb_pixel( 0, 64, 0), "pottedplant"),
Voc2012class(17, dlib::rgb_pixel(128, 64, 0), "sheep"),
Voc2012class(18, dlib::rgb_pixel( 0, 192, 0), "sofa"),
Voc2012class(19, dlib::rgb_pixel(128, 192, 0), "train"),
Voc2012class(20, dlib::rgb_pixel( 0, 64, 128), "tvmonitor"),
};
}
template <typename Predicate>
const Voc2012class& find_voc2012_class(Predicate predicate)
{
const auto i = std::find_if(classes.begin(), classes.end(), predicate);
if (i != classes.end())
{
return *i;
}
else
{
throw std::runtime_error("Unable to find a matching VOC2012 class");
}
}
// ----------------------------------------------------------------------------------------
// Introduce the building blocks used to define the segmentation network.
// The network first does residual downsampling (similar to the dnn_imagenet_(train_)ex
// example program), and then residual upsampling. In addition, U-Net style skip
// connections are used, so that not every simple detail needs to reprented on the low
// levels. (See Ronneberger et al. (2015), U-Net: Convolutional Networks for Biomedical
// Image Segmentation, https://arxiv.org/pdf/1505.04597.pdf)
template <int N, template <typename> class BN, int stride, typename SUBNET>
using block = BN<dlib::con<N,3,3,1,1,dlib::relu<BN<dlib::con<N,3,3,stride,stride,SUBNET>>>>>;
template <int N, template <typename> class BN, int stride, typename SUBNET>
using blockt = BN<dlib::cont<N,3,3,1,1,dlib::relu<BN<dlib::cont<N,3,3,stride,stride,SUBNET>>>>>;
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual = dlib::add_prev1<block<N,BN,1,dlib::tag1<SUBNET>>>;
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual_down = dlib::add_prev2<dlib::avg_pool<2,2,2,2,dlib::skip1<dlib::tag2<block<N,BN,2,dlib::tag1<SUBNET>>>>>>;
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual_up = dlib::add_prev2<dlib::cont<N,2,2,2,2,dlib::skip1<dlib::tag2<blockt<N,BN,2,dlib::tag1<SUBNET>>>>>>;
template <int N, typename SUBNET> using res = dlib::relu<residual<block,N,dlib::bn_con,SUBNET>>;
template <int N, typename SUBNET> using ares = dlib::relu<residual<block,N,dlib::affine,SUBNET>>;
template <int N, typename SUBNET> using res_down = dlib::relu<residual_down<block,N,dlib::bn_con,SUBNET>>;
template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block,N,dlib::affine,SUBNET>>;
template <int N, typename SUBNET> using res_up = dlib::relu<residual_up<block,N,dlib::bn_con,SUBNET>>;
template <int N, typename SUBNET> using ares_up = dlib::relu<residual_up<block,N,dlib::affine,SUBNET>>;
// ----------------------------------------------------------------------------------------
template <typename SUBNET> using res64 = res<64,SUBNET>;
template <typename SUBNET> using res128 = res<128,SUBNET>;
template <typename SUBNET> using res256 = res<256,SUBNET>;
template <typename SUBNET> using res512 = res<512,SUBNET>;
template <typename SUBNET> using ares64 = ares<64,SUBNET>;
template <typename SUBNET> using ares128 = ares<128,SUBNET>;
template <typename SUBNET> using ares256 = ares<256,SUBNET>;
template <typename SUBNET> using ares512 = ares<512,SUBNET>;
template <typename SUBNET> using level1 = dlib::repeat<2,res64,res<64,SUBNET>>;
template <typename SUBNET> using level2 = dlib::repeat<2,res128,res_down<128,SUBNET>>;
template <typename SUBNET> using level3 = dlib::repeat<2,res256,res_down<256,SUBNET>>;
template <typename SUBNET> using level4 = dlib::repeat<2,res512,res_down<512,SUBNET>>;
template <typename SUBNET> using alevel1 = dlib::repeat<2,ares64,ares<64,SUBNET>>;
template <typename SUBNET> using alevel2 = dlib::repeat<2,ares128,ares_down<128,SUBNET>>;
template <typename SUBNET> using alevel3 = dlib::repeat<2,ares256,ares_down<256,SUBNET>>;
template <typename SUBNET> using alevel4 = dlib::repeat<2,ares512,ares_down<512,SUBNET>>;
template <typename SUBNET> using level1t = dlib::repeat<2,res64,res_up<64,SUBNET>>;
template <typename SUBNET> using level2t = dlib::repeat<2,res128,res_up<128,SUBNET>>;
template <typename SUBNET> using level3t = dlib::repeat<2,res256,res_up<256,SUBNET>>;
template <typename SUBNET> using level4t = dlib::repeat<2,res512,res_up<512,SUBNET>>;
template <typename SUBNET> using alevel1t = dlib::repeat<2,ares64,ares_up<64,SUBNET>>;
template <typename SUBNET> using alevel2t = dlib::repeat<2,ares128,ares_up<128,SUBNET>>;
template <typename SUBNET> using alevel3t = dlib::repeat<2,ares256,ares_up<256,SUBNET>>;
template <typename SUBNET> using alevel4t = dlib::repeat<2,ares512,ares_up<512,SUBNET>>;
// ----------------------------------------------------------------------------------------
template <
template<typename> class TAGGED,
template<typename> class PREV_RESIZED,
typename SUBNET
>
using resize_and_concat = dlib::add_layer<
dlib::concat_<TAGGED,PREV_RESIZED>,
PREV_RESIZED<dlib::resize_prev_to_tagged<TAGGED,SUBNET>>>;
template <typename SUBNET> using utag1 = dlib::add_tag_layer<2100+1,SUBNET>;
template <typename SUBNET> using utag2 = dlib::add_tag_layer<2100+2,SUBNET>;
template <typename SUBNET> using utag3 = dlib::add_tag_layer<2100+3,SUBNET>;
template <typename SUBNET> using utag4 = dlib::add_tag_layer<2100+4,SUBNET>;
template <typename SUBNET> using utag1_ = dlib::add_tag_layer<2110+1,SUBNET>;
template <typename SUBNET> using utag2_ = dlib::add_tag_layer<2110+2,SUBNET>;
template <typename SUBNET> using utag3_ = dlib::add_tag_layer<2110+3,SUBNET>;
template <typename SUBNET> using utag4_ = dlib::add_tag_layer<2110+4,SUBNET>;
template <typename SUBNET> using concat_utag1 = resize_and_concat<utag1,utag1_,SUBNET>;
template <typename SUBNET> using concat_utag2 = resize_and_concat<utag2,utag2_,SUBNET>;
template <typename SUBNET> using concat_utag3 = resize_and_concat<utag3,utag3_,SUBNET>;
template <typename SUBNET> using concat_utag4 = resize_and_concat<utag4,utag4_,SUBNET>;
// ----------------------------------------------------------------------------------------
static const char* semantic_segmentation_net_filename = "semantic_segmentation_voc2012net_v2.dnn";
// ----------------------------------------------------------------------------------------
// training network type
using bnet_type = dlib::loss_multiclass_log_per_pixel<
dlib::cont<class_count,1,1,1,1,
dlib::relu<dlib::bn_con<dlib::cont<64,7,7,2,2,
concat_utag1<level1t<
concat_utag2<level2t<
concat_utag3<level3t<
concat_utag4<level4t<
level4<utag4<
level3<utag3<
level2<utag2<
level1<dlib::max_pool<3,3,2,2,utag1<
dlib::relu<dlib::bn_con<dlib::con<64,7,7,2,2,
dlib::input<dlib::matrix<dlib::rgb_pixel>>
>>>>>>>>>>>>>>>>>>>>>>>>>;
// testing network type (replaced batch normalization with fixed affine transforms)
using anet_type = dlib::loss_multiclass_log_per_pixel<
dlib::cont<class_count,1,1,1,1,
dlib::relu<dlib::affine<dlib::cont<64,7,7,2,2,
concat_utag1<alevel1t<
concat_utag2<alevel2t<
concat_utag3<alevel3t<
concat_utag4<alevel4t<
alevel4<utag4<
alevel3<utag3<
alevel2<utag2<
alevel1<dlib::max_pool<3,3,2,2,utag1<
dlib::relu<dlib::affine<dlib::con<64,7,7,2,2,
dlib::input<dlib::matrix<dlib::rgb_pixel>>
>>>>>>>>>>>>>>>>>>>>>>>>>;
// ----------------------------------------------------------------------------------------
#endif // DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
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