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diff --git a/ml/dlib/examples/dnn_semantic_segmentation_ex.h b/ml/dlib/examples/dnn_semantic_segmentation_ex.h new file mode 100644 index 00000000..47fc102c --- /dev/null +++ b/ml/dlib/examples/dnn_semantic_segmentation_ex.h @@ -0,0 +1,200 @@ +// 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.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. The network could be improved e.g. +// by introducing skip connections from the input image, and/or the first layers, to the +// last layer(s). (See Long et al., Fully Convolutional Networks for Semantic Segmentation, +// https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.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 res512 = res<512, SUBNET>; +template <typename SUBNET> using res256 = res<256, SUBNET>; +template <typename SUBNET> using res128 = res<128, SUBNET>; +template <typename SUBNET> using res64 = res<64, SUBNET>; +template <typename SUBNET> using ares512 = ares<512, SUBNET>; +template <typename SUBNET> using ares256 = ares<256, SUBNET>; +template <typename SUBNET> using ares128 = ares<128, SUBNET>; +template <typename SUBNET> using ares64 = ares<64, SUBNET>; + + +template <typename SUBNET> using level1 = dlib::repeat<2,res512,res_down<512,SUBNET>>; +template <typename SUBNET> using level2 = dlib::repeat<2,res256,res_down<256,SUBNET>>; +template <typename SUBNET> using level3 = dlib::repeat<2,res128,res_down<128,SUBNET>>; +template <typename SUBNET> using level4 = dlib::repeat<2,res64,res<64,SUBNET>>; + +template <typename SUBNET> using alevel1 = dlib::repeat<2,ares512,ares_down<512,SUBNET>>; +template <typename SUBNET> using alevel2 = dlib::repeat<2,ares256,ares_down<256,SUBNET>>; +template <typename SUBNET> using alevel3 = dlib::repeat<2,ares128,ares_down<128,SUBNET>>; +template <typename SUBNET> using alevel4 = dlib::repeat<2,ares64,ares<64,SUBNET>>; + +template <typename SUBNET> using level1t = dlib::repeat<2,res512,res_up<512,SUBNET>>; +template <typename SUBNET> using level2t = dlib::repeat<2,res256,res_up<256,SUBNET>>; +template <typename SUBNET> using level3t = dlib::repeat<2,res128,res_up<128,SUBNET>>; +template <typename SUBNET> using level4t = dlib::repeat<2,res64,res_up<64,SUBNET>>; + +template <typename SUBNET> using alevel1t = dlib::repeat<2,ares512,ares_up<512,SUBNET>>; +template <typename SUBNET> using alevel2t = dlib::repeat<2,ares256,ares_up<256,SUBNET>>; +template <typename SUBNET> using alevel3t = dlib::repeat<2,ares128,ares_up<128,SUBNET>>; +template <typename SUBNET> using alevel4t = dlib::repeat<2,ares64,ares_up<64,SUBNET>>; + +// ---------------------------------------------------------------------------------------- + +// training network type +using net_type = dlib::loss_multiclass_log_per_pixel< + dlib::cont<class_count,7,7,2,2, + level4t<level3t<level2t<level1t< + level1<level2<level3<level4< + dlib::max_pool<3,3,2,2,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,7,7,2,2, + alevel4t<alevel3t<alevel2t<alevel1t< + alevel1<alevel2<alevel3<alevel4< + dlib::max_pool<3,3,2,2,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_ |