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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* Tweak default minibatch size * Learn only one class * Really train only instances of the selected class * Remove outdated TODO remark * Automatically skip images with no detections * Print to console what was found * Fix class index problem * Fix indentation * Allow to choose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
201 lines
10 KiB
C++
201 lines
10 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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Instance segmentation using the PASCAL VOC2012 dataset.
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Instance segmentation sort-of combines object detection with semantic
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segmentation. While each dog, for example, is detected separately,
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the output is not only a bounding-box but a more accurate, per-pixel
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mask.
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For introductions to object detection and semantic segmentation, you
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can have a look at dnn_mmod_ex.cpp and dnn_semantic_segmentation.h,
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respectively.
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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_instance_segmentation_train_ex example program.
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3. Run:
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./dnn_instance_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_instance_segmentation_ex example program.
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6. Run:
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./dnn_instance_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/instance_segmentation_voc2012net.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_INSTANCE_SEGMENTATION_EX_H_
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#define DLIB_DNn_INSTANCE_SEGMENTATION_EX_H_
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#include <dlib/dnn.h>
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// ----------------------------------------------------------------------------------------
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namespace {
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// Segmentation will be performed using patches having this size.
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constexpr int seg_dim = 227;
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}
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dlib::rectangle get_cropping_rect(const dlib::rectangle& rectangle)
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{
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DLIB_ASSERT(!rectangle.is_empty());
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const auto center_point = dlib::center(rectangle);
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const auto max_dim = std::max(rectangle.width(), rectangle.height());
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const auto d = static_cast<long>(std::round(max_dim / 2.0 * 1.5)); // add +50%
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return dlib::rectangle(
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center_point.x() - d,
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center_point.y() - d,
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center_point.x() + d,
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center_point.y() + d
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);
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}
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// ----------------------------------------------------------------------------------------
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// The object detection network.
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// Adapted from dnn_mmod_train_find_cars_ex.cpp and friends.
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template <long num_filters, typename SUBNET> using con5d = dlib::con<num_filters,5,5,2,2,SUBNET>;
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template <long num_filters, typename SUBNET> using con5 = dlib::con<num_filters,5,5,1,1,SUBNET>;
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template <typename SUBNET> using bdownsampler = dlib::relu<dlib::bn_con<con5d<128,dlib::relu<dlib::bn_con<con5d<128,dlib::relu<dlib::bn_con<con5d<32,SUBNET>>>>>>>>>;
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template <typename SUBNET> using adownsampler = dlib::relu<dlib::affine<con5d<128,dlib::relu<dlib::affine<con5d<128,dlib::relu<dlib::affine<con5d<32,SUBNET>>>>>>>>>;
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template <typename SUBNET> using brcon5 = dlib::relu<dlib::bn_con<con5<256,SUBNET>>>;
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template <typename SUBNET> using arcon5 = dlib::relu<dlib::affine<con5<256,SUBNET>>>;
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using det_bnet_type = dlib::loss_mmod<dlib::con<1,9,9,1,1,brcon5<brcon5<brcon5<bdownsampler<dlib::input_rgb_image_pyramid<dlib::pyramid_down<6>>>>>>>>;
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using det_anet_type = dlib::loss_mmod<dlib::con<1,9,9,1,1,arcon5<arcon5<arcon5<adownsampler<dlib::input_rgb_image_pyramid<dlib::pyramid_down<6>>>>>>>>;
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// The segmentation network.
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// For the time being, this is very much copy-paste from dnn_semantic_segmentation.h, although the network is made narrower (smaller feature maps).
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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 res16 = res<16,SUBNET>;
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template <typename SUBNET> using res24 = res<24,SUBNET>;
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template <typename SUBNET> using res32 = res<32,SUBNET>;
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template <typename SUBNET> using res48 = res<48,SUBNET>;
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template <typename SUBNET> using ares16 = ares<16,SUBNET>;
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template <typename SUBNET> using ares24 = ares<24,SUBNET>;
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template <typename SUBNET> using ares32 = ares<32,SUBNET>;
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template <typename SUBNET> using ares48 = ares<48,SUBNET>;
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template <typename SUBNET> using level1 = dlib::repeat<2,res16,res<16,SUBNET>>;
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template <typename SUBNET> using level2 = dlib::repeat<2,res24,res_down<24,SUBNET>>;
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template <typename SUBNET> using level3 = dlib::repeat<2,res32,res_down<32,SUBNET>>;
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template <typename SUBNET> using level4 = dlib::repeat<2,res48,res_down<48,SUBNET>>;
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template <typename SUBNET> using alevel1 = dlib::repeat<2,ares16,ares<16,SUBNET>>;
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template <typename SUBNET> using alevel2 = dlib::repeat<2,ares24,ares_down<24,SUBNET>>;
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template <typename SUBNET> using alevel3 = dlib::repeat<2,ares32,ares_down<32,SUBNET>>;
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template <typename SUBNET> using alevel4 = dlib::repeat<2,ares48,ares_down<48,SUBNET>>;
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template <typename SUBNET> using level1t = dlib::repeat<2,res16,res_up<16,SUBNET>>;
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template <typename SUBNET> using level2t = dlib::repeat<2,res24,res_up<24,SUBNET>>;
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template <typename SUBNET> using level3t = dlib::repeat<2,res32,res_up<32,SUBNET>>;
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template <typename SUBNET> using level4t = dlib::repeat<2,res48,res_up<48,SUBNET>>;
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template <typename SUBNET> using alevel1t = dlib::repeat<2,ares16,ares_up<16,SUBNET>>;
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template <typename SUBNET> using alevel2t = dlib::repeat<2,ares24,ares_up<24,SUBNET>>;
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template <typename SUBNET> using alevel3t = dlib::repeat<2,ares32,ares_up<32,SUBNET>>;
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template <typename SUBNET> using alevel4t = dlib::repeat<2,ares48,ares_up<48,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* instance_segmentation_net_filename = "instance_segmentation_voc2012net.dnn";
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// ----------------------------------------------------------------------------------------
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// training network type
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using seg_bnet_type = dlib::loss_multiclass_log_per_pixel<
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dlib::cont<2,1,1,1,1,
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dlib::relu<dlib::bn_con<dlib::cont<16,7,7,2,2,
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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<16,7,7,2,2,
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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 seg_anet_type = dlib::loss_multiclass_log_per_pixel<
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dlib::cont<2,1,1,1,1,
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dlib::relu<dlib::affine<dlib::cont<16,7,7,2,2,
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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<16,7,7,2,2,
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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_INSTANCE_SEGMENTATION_EX_H_
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