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* Add instance segmentation example - first version of training code * Add MMOD options; get rid of the cache approach, and instead load all MMOD rects upfront * Improve console output * Set filter count * Minor tweaking * Inference - first version, at least compiles! * Ignore overlapped boxes * Ignore even small instances * Set overlaps_ignore * Add TODO remarks * Revert "Set overlaps_ignore" This reverts commit65adeff1f8. * Set result size * Set label image size * Take ignore-color into account * Fix the cropping rect's aspect ratio; also slightly expand the rect * Draw the largest findings last * Improve masking of the current instance * Add some perturbation to the inputs * Simplify ground-truth reading; fix random cropping * Read even class labels * Tweak default minibatch size * Learn only one class * Really train only instances of the selected class * Remove outdated TODO remark * Automatically skip images with no detections * Print to console what was found * Fix class index problem * Fix indentation * Allow to choose multiple classes * Draw rect in the color of the corresponding class * Write detector window classes to ostream; also group detection windows by class (when ostreaming) * Train a separate instance segmentation network for each classlabel * Use separate synchronization file for each seg net of each class * Allow more overlap * Fix sorting criterion * Fix interpolating the predicted mask * Improve bilinear interpolation: if output type is an integer, round instead of truncating * Add helpful comments * Ignore large aspect ratios; refactor the code; tweak some network parameters * Simplify the segmentation network structure; make the object detection network more complex in turn * Problem: CUDA errors not reported properly to console Solution: stop and join data loader threads even in case of exceptions * Minor parameters tweaking * Loss may have increased, even if prob_loss_increasing_thresh > prob_loss_increasing_thresh_max_value * Add previous_loss_values_dump_amount to previous_loss_values.size() when deciding if loss has been increasing * Improve behaviour when loss actually increased after disk sync * Revert some of the earlier change * Disregard dumped loss values only when deciding if learning rate should be shrunk, but *not* when deciding if loss has been going up since last disk sync * Revert "Revert some of the earlier change" This reverts commit6c852124ef. * Keep enough previous loss values, until the disk sync * Fix maintaining the dumped (now "effectively disregarded") loss values count * Detect cats instead of aeroplanes * Add helpful logging * Clarify the intention and the code * Review fixes * Add operator== for the other pixel types as well; remove the inline * If available, use constexpr if * Revert "If available, use constexpr if" This reverts commit503d4dd335. * Simplify code as per review comments * Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh * Clarify console output * Revert "Keep estimating steps_without_progress, even if steps_since_last_learning_rate_shrink < iter_without_progress_thresh" This reverts commit9191ebc776. * To keep the changes to a bare minimum, revert the steps_since_last_learning_rate_shrink change after all (at least for now) * Even empty out some of the previous test loss values * Minor review fixes * Can't use C++14 features here * Do not use the struct name as a variable name
168 lines
9.3 KiB
C++
168 lines
9.3 KiB
C++
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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Semantic segmentation using the PASCAL VOC2012 dataset.
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In segmentation, the task is to assign each pixel of an input image
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a label - for example, 'dog'. Then, the idea is that neighboring
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pixels having the same label can be connected together to form a
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larger region, representing a complete (or partially occluded) dog.
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So technically, segmentation can be viewed as classification of
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individual pixels (using the relevant context in the input images),
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however the goal usually is to identify meaningful regions that
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represent complete entities of interest (such as dogs).
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Instructions how to run the example:
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1. Download the PASCAL VOC2012 data, and untar it somewhere.
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http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
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2. Build the dnn_semantic_segmentation_train_ex example program.
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3. Run:
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./dnn_semantic_segmentation_train_ex /path/to/VOC2012
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4. Wait while the network is being trained.
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5. Build the dnn_semantic_segmentation_ex example program.
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6. Run:
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./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
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An alternative to steps 2-4 above is to download a pre-trained network
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from here: http://dlib.net/files/semantic_segmentation_voc2012net_v2.dnn
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It would be a good idea to become familiar with dlib's DNN tooling before reading this
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example. So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
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before reading this example program.
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*/
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#ifndef DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
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#define DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
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#include <dlib/dnn.h>
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#include "pascal_voc_2012.h"
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// ----------------------------------------------------------------------------------------
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// Introduce the building blocks used to define the segmentation network.
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// The network first does residual downsampling (similar to the dnn_imagenet_(train_)ex
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// example program), and then residual upsampling. In addition, U-Net style skip
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// connections are used, so that not every simple detail needs to reprented on the low
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// levels. (See Ronneberger et al. (2015), U-Net: Convolutional Networks for Biomedical
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// Image Segmentation, https://arxiv.org/pdf/1505.04597.pdf)
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template <int N, template <typename> class BN, int stride, typename SUBNET>
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using block = BN<dlib::con<N,3,3,1,1,dlib::relu<BN<dlib::con<N,3,3,stride,stride,SUBNET>>>>>;
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template <int N, template <typename> class BN, int stride, typename SUBNET>
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using blockt = BN<dlib::cont<N,3,3,1,1,dlib::relu<BN<dlib::cont<N,3,3,stride,stride,SUBNET>>>>>;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual = dlib::add_prev1<block<N,BN,1,dlib::tag1<SUBNET>>>;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual_down = dlib::add_prev2<dlib::avg_pool<2,2,2,2,dlib::skip1<dlib::tag2<block<N,BN,2,dlib::tag1<SUBNET>>>>>>;
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template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
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using residual_up = dlib::add_prev2<dlib::cont<N,2,2,2,2,dlib::skip1<dlib::tag2<blockt<N,BN,2,dlib::tag1<SUBNET>>>>>>;
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template <int N, typename SUBNET> using res = dlib::relu<residual<block,N,dlib::bn_con,SUBNET>>;
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template <int N, typename SUBNET> using ares = dlib::relu<residual<block,N,dlib::affine,SUBNET>>;
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template <int N, typename SUBNET> using res_down = dlib::relu<residual_down<block,N,dlib::bn_con,SUBNET>>;
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template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block,N,dlib::affine,SUBNET>>;
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template <int N, typename SUBNET> using res_up = dlib::relu<residual_up<block,N,dlib::bn_con,SUBNET>>;
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template <int N, typename SUBNET> using ares_up = dlib::relu<residual_up<block,N,dlib::affine,SUBNET>>;
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// ----------------------------------------------------------------------------------------
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template <typename SUBNET> using res64 = res<64,SUBNET>;
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template <typename SUBNET> using res128 = res<128,SUBNET>;
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template <typename SUBNET> using res256 = res<256,SUBNET>;
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template <typename SUBNET> using res512 = res<512,SUBNET>;
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template <typename SUBNET> using ares64 = ares<64,SUBNET>;
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template <typename SUBNET> using ares128 = ares<128,SUBNET>;
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template <typename SUBNET> using ares256 = ares<256,SUBNET>;
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template <typename SUBNET> using ares512 = ares<512,SUBNET>;
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template <typename SUBNET> using level1 = dlib::repeat<2,res64,res<64,SUBNET>>;
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template <typename SUBNET> using level2 = dlib::repeat<2,res128,res_down<128,SUBNET>>;
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template <typename SUBNET> using level3 = dlib::repeat<2,res256,res_down<256,SUBNET>>;
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template <typename SUBNET> using level4 = dlib::repeat<2,res512,res_down<512,SUBNET>>;
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template <typename SUBNET> using alevel1 = dlib::repeat<2,ares64,ares<64,SUBNET>>;
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template <typename SUBNET> using alevel2 = dlib::repeat<2,ares128,ares_down<128,SUBNET>>;
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template <typename SUBNET> using alevel3 = dlib::repeat<2,ares256,ares_down<256,SUBNET>>;
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template <typename SUBNET> using alevel4 = dlib::repeat<2,ares512,ares_down<512,SUBNET>>;
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template <typename SUBNET> using level1t = dlib::repeat<2,res64,res_up<64,SUBNET>>;
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template <typename SUBNET> using level2t = dlib::repeat<2,res128,res_up<128,SUBNET>>;
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template <typename SUBNET> using level3t = dlib::repeat<2,res256,res_up<256,SUBNET>>;
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template <typename SUBNET> using level4t = dlib::repeat<2,res512,res_up<512,SUBNET>>;
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template <typename SUBNET> using alevel1t = dlib::repeat<2,ares64,ares_up<64,SUBNET>>;
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template <typename SUBNET> using alevel2t = dlib::repeat<2,ares128,ares_up<128,SUBNET>>;
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template <typename SUBNET> using alevel3t = dlib::repeat<2,ares256,ares_up<256,SUBNET>>;
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template <typename SUBNET> using alevel4t = dlib::repeat<2,ares512,ares_up<512,SUBNET>>;
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// ----------------------------------------------------------------------------------------
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template <
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template<typename> class TAGGED,
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template<typename> class PREV_RESIZED,
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typename SUBNET
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>
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using resize_and_concat = dlib::add_layer<
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dlib::concat_<TAGGED,PREV_RESIZED>,
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PREV_RESIZED<dlib::resize_prev_to_tagged<TAGGED,SUBNET>>>;
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template <typename SUBNET> using utag1 = dlib::add_tag_layer<2100+1,SUBNET>;
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template <typename SUBNET> using utag2 = dlib::add_tag_layer<2100+2,SUBNET>;
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template <typename SUBNET> using utag3 = dlib::add_tag_layer<2100+3,SUBNET>;
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template <typename SUBNET> using utag4 = dlib::add_tag_layer<2100+4,SUBNET>;
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template <typename SUBNET> using utag1_ = dlib::add_tag_layer<2110+1,SUBNET>;
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template <typename SUBNET> using utag2_ = dlib::add_tag_layer<2110+2,SUBNET>;
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template <typename SUBNET> using utag3_ = dlib::add_tag_layer<2110+3,SUBNET>;
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template <typename SUBNET> using utag4_ = dlib::add_tag_layer<2110+4,SUBNET>;
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template <typename SUBNET> using concat_utag1 = resize_and_concat<utag1,utag1_,SUBNET>;
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template <typename SUBNET> using concat_utag2 = resize_and_concat<utag2,utag2_,SUBNET>;
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template <typename SUBNET> using concat_utag3 = resize_and_concat<utag3,utag3_,SUBNET>;
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template <typename SUBNET> using concat_utag4 = resize_and_concat<utag4,utag4_,SUBNET>;
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// ----------------------------------------------------------------------------------------
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static const char* semantic_segmentation_net_filename = "semantic_segmentation_voc2012net_v2.dnn";
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// ----------------------------------------------------------------------------------------
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// training network type
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using bnet_type = dlib::loss_multiclass_log_per_pixel<
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dlib::cont<class_count,1,1,1,1,
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dlib::relu<dlib::bn_con<dlib::cont<64,7,7,2,2,
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concat_utag1<level1t<
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concat_utag2<level2t<
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concat_utag3<level3t<
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concat_utag4<level4t<
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level4<utag4<
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level3<utag3<
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level2<utag2<
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level1<dlib::max_pool<3,3,2,2,utag1<
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dlib::relu<dlib::bn_con<dlib::con<64,7,7,2,2,
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dlib::input<dlib::matrix<dlib::rgb_pixel>>
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>>>>>>>>>>>>>>>>>>>>>>>>>;
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// testing network type (replaced batch normalization with fixed affine transforms)
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using anet_type = dlib::loss_multiclass_log_per_pixel<
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dlib::cont<class_count,1,1,1,1,
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dlib::relu<dlib::affine<dlib::cont<64,7,7,2,2,
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concat_utag1<alevel1t<
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concat_utag2<alevel2t<
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concat_utag3<alevel3t<
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concat_utag4<alevel4t<
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alevel4<utag4<
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alevel3<utag3<
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alevel2<utag2<
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alevel1<dlib::max_pool<3,3,2,2,utag1<
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dlib::relu<dlib::affine<dlib::con<64,7,7,2,2,
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dlib::input<dlib::matrix<dlib::rgb_pixel>>
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>>>>>>>>>>>>>>>>>>>>>>>>>;
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// ----------------------------------------------------------------------------------------
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#endif // DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
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