// Copyright (C) 2015 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_DNn_LOSS_ABSTRACT_H_ #ifdef DLIB_DNn_LOSS_ABSTRACT_H_ #include "core_abstract.h" #include "../image_processing/full_object_detection_abstract.h" namespace dlib{// ---------------------------------------------------------------------------------------- classEXAMPLE_LOSS_LAYER_{/*! WHAT THIS OBJECT REPRESENTS A loss layer is the final layer in a deep neural network. It computes the task loss. That is, it computes a number that tells us how well the network is performing on some task, such as predicting a binary label. You can use one of the loss layers that comes with dlib (defined below). But importantly, you are able to define your own loss layers to suit your needs. You do this by creating a class that defines an interface matching the one described by this EXAMPLE_LOSS_LAYER_ class. Note that there is no dlib::EXAMPLE_LOSS_LAYER_ type. It is shown here purely to document the interface that a loss layer must implement. A loss layer can optionally provide a to_label() method that converts the output of a network into a user defined type. If to_label() is not provided then the operator() methods of add_loss_layer will not be available, but otherwise everything will function as normal. Finally, note that there are two broad flavors of loss layer, supervised and unsupervised. The EXAMPLE_LOSS_LAYER_ as shown here is a supervised layer. To make an unsupervised loss you simply leave out the training_label_type typedef and the truth iterator argument to compute_loss_value_and_gradient(). !*/ public: // In most cases training_label_type and output_label_type will be the same type. typedef whatever_type_you_use_for_training_labels training_label_type; typedef whatever_type_you_use_for_outout_labels output_label_type;EXAMPLE_LOSS_LAYER_( ); /*! ensures - EXAMPLE_LOSS_LAYER_ objects are default constructable. !*/EXAMPLE_LOSS_LAYER_( const EXAMPLE_LOSS_LAYER_& item ); /*! ensures - EXAMPLE_LOSS_LAYER_ objects are copy constructable. !*/ // Implementing to_label() is optional. template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! requires - SUBNET implements the SUBNET interface defined at the top of layers_abstract.h. - input_tensor was given as input to the network sub and the outputs are now visible in layer<i>(sub).get_output(), for all valid i. - input_tensor.num_samples() > 0 - input_tensor.num_samples()%sub.sample_expansion_factor() == 0. - iter == an iterator pointing to the beginning of a range of input_tensor.num_samples()/sub.sample_expansion_factor() elements. Moreover, they must be output_label_type elements. ensures - Converts the output of the provided network to output_label_type objects and stores the results into the range indicated by iter. In particular, for all valid i, it will be the case that: *(iter+i/sub.sample_expansion_factor()) is populated based on the output of sub and corresponds to the ith sample in input_tensor. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! requires - SUBNET implements the SUBNET interface defined at the top of layers_abstract.h. - input_tensor was given as input to the network sub and the outputs are now visible in layer<i>(sub).get_output(), for all valid i. - input_tensor.num_samples() > 0 - input_tensor.num_samples()%sub.sample_expansion_factor() == 0. - for all valid i: - layer<i>(sub).get_gradient_input() has the same dimensions as layer<i>(sub).get_output(). - layer<i>(sub).get_gradient_input() contains all zeros (i.e. initially, all input gradients are 0). - truth == an iterator pointing to the beginning of a range of input_tensor.num_samples()/sub.sample_expansion_factor() elements. Moreover, they must be training_label_type elements. - for all valid i: - *(truth+i/sub.sample_expansion_factor()) is the label of the ith sample in input_tensor. ensures - This function computes a loss function that describes how well the output of sub matches the expected labels given by truth. Let's write the loss function as L(input_tensor, truth, sub). - Then compute_loss_value_and_gradient() computes the gradient of L() with respect to the outputs in sub. Specifically, compute_loss_value_and_gradient() assigns the gradients into sub by performing the following tensor assignments, for all valid i: - layer<i>(sub).get_gradient_input() = the gradient of L(input_tensor,truth,sub) with respect to layer<i>(sub).get_output(). Note that, since get_gradient_input() is zero initialized, you don't have to write gradient information to layers that have a zero loss gradient. - returns L(input_tensor,truth,sub) !*/}; std::ostream&operator<<(std::ostream& out, const EXAMPLE_LOSS_LAYER_& item); /*! print a string describing this layer. !*/voidto_xml(const EXAMPLE_LOSS_LAYER_& item, std::ostream& out); /*! This function is optional, but required if you want to print your networks with net_to_xml(). Therefore, to_xml() prints a layer as XML. !*/voidserialize(const EXAMPLE_LOSS_LAYER_& item, std::ostream& out);voiddeserialize(EXAMPLE_LOSS_LAYER_& item, std::istream& in); /*! provides serialization support !*/ // For each loss layer you define, always define an add_loss_layer template so that // layers can be easily composed. Moreover, the convention is that the layer class // ends with an _ while the add_loss_layer template has the same name but without the // trailing _. template <typename SUBNET> using EXAMPLE_LOSS_LAYER = add_loss_layer<EXAMPLE_LOSS_LAYER_, SUBNET>; // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- classloss_binary_hinge_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the hinge loss, which is appropriate for binary classification problems. Therefore, the possible labels when using this loss are +1 and -1. Moreover, it will cause the network to produce outputs > 0 when predicting a member of the +1 class and values < 0 otherwise. !*/ public: typedeffloattraining_label_type; typedeffloatoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the raw score for each classified object. If the score is > 0 then the classifier is predicting the +1 class, otherwise it is predicting the -1 class. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all values pointed to by truth are +1 or -1. !*/}; template <typename SUBNET> using loss_binary_hinge = add_loss_layer<loss_binary_hinge_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_binary_log_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the log loss, which is appropriate for binary classification problems. Therefore, there are two possible classes of labels: positive (> 0) and negative (< 0) when using this loss. The absolute value of the label represents its weight. Putting a larger weight on a sample increases the importance of getting its prediction correct during training. A good rule of thumb is to use weights with absolute value 1 unless you have a very unbalanced training dataset, in that case, give larger weight to the class with less training examples. This loss will cause the network to produce outputs > 0 when predicting a member of the positive class and values < 0 otherwise. To be more specific, this object contains a sigmoid layer followed by a cross-entropy layer. !*/ public: typedeffloattraining_label_type; typedeffloatoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the raw score for each classified object. If the score is > 0 then the classifier is predicting the +1 class, otherwise it is predicting the -1 class. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all values pointed to by truth are non-zero. Nominally they should be +1 or -1, each indicating the desired class label. !*/}; template <typename SUBNET> using loss_binary_log = add_loss_layer<loss_binary_log_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_multiclass_log_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the multiclass logistic regression loss (e.g. negative log-likelihood loss), which is appropriate for multiclass classification problems. This means that the possible labels when using this loss are integers >= 0. Moreover, if after training you were to replace the loss layer of the network with a softmax layer, the network outputs would give the probabilities of each class assignment. That is, if you have K classes then the network should output tensors with the tensor::k()'th dimension equal to K. Applying softmax to these K values gives the probabilities of each class. The index into that K dimensional vector with the highest probability is the predicted class label. !*/ public: typedefunsignedlongtraining_label_type; typedefunsignedlongoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted class for each classified object. The number of possible output classes is sub.get_output().k(). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all values pointed to by truth are < sub.get_output().k() !*/}; template <typename SUBNET> using loss_multiclass_log = add_loss_layer<loss_multiclass_log_, SUBNET>; // ---------------------------------------------------------------------------------------- template <typename label_type> structweighted_label{/*! WHAT THIS OBJECT REPRESENTS This object represents the truth label of a single sample, together with an associated weight (the higher the weight, the more emphasis the corresponding sample is given during the training). For technical reasons, it is defined in misc.h This object is used in the following loss layers: - loss_multiclass_log_weighted_ with unsigned long as label_type - loss_multiclass_log_per_pixel_weighted_ with uint16_t as label_type, since, in semantic segmentation, 65536 classes ought to be enough for anybody. !*/weighted_label(){}weighted_label(label_type label,floatweight = 1.f) : label(label), weight(weight){}// The ground truth label label_type label{}; // The weight of the corresponding samplefloatweight = 1.f;}; // ---------------------------------------------------------------------------------------- classloss_multiclass_log_weighted_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the multiclass logistic regression loss (e.g. negative log-likelihood loss), which is appropriate for multiclass classification problems. It is basically just like the loss_multiclass_log except that it lets you define per-sample weights, which might be useful e.g. if you want to emphasize rare classes while training. If the classification problem is difficult, a flat weight structure may lead the network to always predict the most common label, in particular if the degree of imbalance is high. To emphasize a certain class or classes, simply increase the weights of the corresponding samples, relative to the weights of other pixels. Note that if you set all the weights equals to 1, then you get loss_multiclass_log_ as a special case. !*/ public: typedef dlib::weighted_label<unsignedlong> weighted_label; typedef weighted_label training_label_type; typedefunsignedlongoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted class for each classified object. The number of possible output classes is sub.get_output().k(). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all values pointed to by truth are < sub.get_output().k() !*/}; template <typename SUBNET> using loss_multiclass_log_weighted = add_loss_layer<loss_multiclass_log_weighted_, SUBNET>;// ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- classloss_multimulticlass_log_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements a collection of multiclass classifiers. An example will make its use clear. So suppose, for example, that you want to make something that takes a picture of a vehicle and answers the following questions: - What type of vehicle is it? A sedan or a truck? - What color is it? red, green, blue, gray, or black? You need two separate multi-class classifiers to do this. One to decide the type of vehicle, and another to decide the color. The loss_multimulticlass_log_ allows you to pack these two classifiers into one neural network. This means that when you use the network to process an image it will output 2 labels for each image, the type label and the color label. To create a loss_multimulticlass_log_ for the above case you would construct it as follows: std::map<std::string,std::vector<std::string>> labels; labels["type"] = {"sedan", "truck"}; labels["color"] = {"red", "green", "blue", "gray", "black"}; loss_multimulticlass_log_ myloss(labels); Then you could use myloss with a network object and train it to do this task. More generally, you can use any number of classifiers and labels when using this object. Finally, each of the classifiers uses a standard multi-class logistic regression loss. !*/ public:loss_multimulticlass_log_( ); /*! ensures - #number_of_labels() == 0 - #get_labels().size() == 0 !*/loss_multimulticlass_log_( const std::map<std::string,std::vector<std::string>>& labels ); /*! requires - Each vector in labels must contain at least 2 strings. I.e. each classifier must have at least two possible labels. ensures - #number_of_labels() == the total number of strings in all the std::vectors in labels. - #number_of_classifiers() == labels.size() - #get_labels() == labels !*/unsignedlongnumber_of_labels( ) const; /*! ensures - returns the total number of labels known to this loss. This is the count of all the labels in each classifier. !*/unsignedlongnumber_of_classifiers( ) const; /*! ensures - returns the number of classifiers defined by this loss. !*/ std::map<std::string,std::vector<std::string>>get_labels( ) const; /*! ensures - returns the names of the classifiers and labels used by this loss. In particular, if the returned object is L then: - L[CLASS] == the set of labels used by the classifier CLASS. - L.size() == number_of_classifiers() - The count of strings in the vectors in L == number_of_labels() !*/ classclassifier_output{/*! WHAT THIS OBJECT REPRESENTS This object stores the predictions from one of the classifiers in loss_multimulticlass_log_. It allows you to find out the most likely string label predicted by that classifier, as well as get the class conditional probability of any of the classes in the classifier. !*/ public:classifier_output( ); /*! ensures - #num_classes() == 0 !*/size_tnum_classes( ) const; /*! ensures - returns the number of possible classes output by this classifier. !*/doubleprobability_of_class(size_ti ) const; /*! requires - i < num_classes() ensures - returns the probability that the true class has a label of label(i). - The sum of probability_of_class(j) for j in the range [0, num_classes()) is always 1. !*/ const std::string&label(size_ti ) const; /*! requires - i < num_classes() ensures - returns the string label for the ith class. !*/operatorstd::string( ) const; /*! requires - num_classes() != 0 ensures - returns the string label for the most probable class. !*/ friend std::ostream&operator<< (std::ostream& out, const classifier_output& item); /*! requires - num_classes() != 0 ensures - prints the most probable class label to out. !*/}; // Both training_label_type and output_label_type should always have sizes equal to // number_of_classifiers(). That is, the std::map should have an entry for every // classifier known to this loss. typedef std::map<std::string,std::string> training_label_type; typedef std::map<std::string,classifier_output> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - number_of_labels() != 0 - sub.get_output().k() == number_of_labels() - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - number_of_labels() != 0 - sub.get_output().k() == number_of_labels() It should be noted that the last layer in your network should usually be an fc layer. If so, you can satisfy this requirement of k() being number_of_labels() by calling set_num_outputs() prior to training your network like so: your_network.subnet().layer_details().set_num_outputs(your_network.loss_details().number_of_labels()); - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - All the std::maps pointed to by truth contain entries for all the classifiers known to this loss. That is, it must be valid to call truth[i][classifier] for any of the classifiers known to this loss. To say this another way, all the training samples must contain labels for each of the classifiers defined by this loss. To really belabor this, this also means that truth[i].size() == get_labels().size() and that both truth[i] and get_labels() have the same set of key strings. It also means that the value strings in truth[i] must be strings known to the loss, i.e. they are valid labels according to get_labels(). !*/}; template <typename SUBNET> using loss_multimulticlass_log = add_loss_layer<loss_multimulticlass_log_, SUBNET>; // Allow comparison between classifier_outputs and std::string to check if the // predicted class is a particular string. inlinebooloperator== (const std::string& lhs, const loss_multimulticlass_log_::classifier_output& rhs){return lhs == static_cast<const std::string&>(rhs);}inlinebooloperator== (const loss_multimulticlass_log_::classifier_output& lhs, const std::string& rhs){return rhs == static_cast<const std::string&>(lhs);}// ---------------------------------------------------------------------------------------- classloss_multibinary_log_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements a collection of binary classifiers using the log loss, which is appropriate for binary classification problems where each sample can belong to zero or more categories. Therefore, there are two possible classes of labels: positive (> 0) and negative (< 0) when using this loss. The absolute value of the label represents its weight. Putting a larger weight on a sample increases its importance of getting its prediction correct during training. A good rule of thumb is to use weights with absolute value 1 unless you have a very unbalanced training dataset, in that case, give larger weight to the class with less training examples. This loss will cause the network to produce outputs > 0 when predicting a member of the positive classes and values < 0 otherwise. To be more specific, this object contains a sigmoid layer followed by a cross-entropy layer. Additionaly, this layer also contains a focusing parameter gamma, which acts as a modulating factor to the cross-entropy layer by reducing the relative loss for well-classified examples, and focusing on the difficult ones. This gamma parameter makes this layer behave like the Focal loss, presented in the paper: Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár (https://arxiv.org/abs/1708.02002) An example will make its use clear. So suppose, for example, that you want to make a classifier for cats and dogs, but what happens if they both appear in one image? Or none of them? This layer allows you to handle those use cases by using the following labels: - std::vector<float> dog_label = {1.f, -1.f}; - std::vector<float> cat_label = {-1.f , 1.f}; - std::vector<float> both_label = {1.f, 1.f}; - std::vector<float> none_label = {-1.f, -1.f}; !*/ public: typedef std::vector<float> training_label_type; typedef std::vector<float> output_label_type;loss_multibinary_log_( ); /*! ensures - #get_gamma() == 0 !*/loss_multibinary_log_(doublegamma); /*! requires - gamma >= 0 ensures - #get_gamma() == gamma !*/doubleget_gamma() const; /*! ensures - returns the gamma value used by the loss function. !*/ template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output labels are the raw scores for each classified object. If a score is > 0 then the classifier is predicting the +1 class for that category, otherwise it is predicting the -1 class. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - truth points to training_label_type elements, each of size sub.get_output.k(). The elements of each truth training_label_type instance are nominally +1 or -1, each representing a binary class label. !*/}; template <typename SUBNET> using loss_multibinary_log = add_loss_layer<loss_multibinary_log_, SUBNET>; // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- enum classuse_image_pyramid: uint8_t{no, yes}; structmmod_options{/*! WHAT THIS OBJECT REPRESENTS This object contains all the parameters that control the behavior of loss_mmod_. !*/ public: structdetector_window_details{detector_window_details() = default;detector_window_details(unsignedlongw,unsignedlongh) : width(w), height(h){}detector_window_details(unsignedlongw,unsignedlongh, const std::string& l) : width(w), height(h), label(l){}unsignedlongwidth = 0;unsignedlongheight = 0; std::string label; friend inlinevoidserialize(const detector_window_details& item, std::ostream& out); friend inlinevoiddeserialize(detector_window_details& item, std::istream& in);};mmod_options() = default; // This kind of object detector is a sliding window detector. The detector_windows // field determines how many sliding windows we will use and what the shape of each // window is. It also determines the output label applied to each detection // identified by each window. Since you will usually use the MMOD loss with an // image pyramid, the detector sizes also determine the size of the smallest object // you can detect. std::vector<detector_window_details> detector_windows; // These parameters control how we penalize different kinds of mistakes. See // Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046) // for further details.doubleloss_per_false_alarm = 1;doubleloss_per_missed_target = 1; // A detection must have an intersection-over-union value greater than this for us // to consider it a match against a ground truth box.doubletruth_match_iou_threshold = 0.5; // When doing non-max suppression, we use overlaps_nms to decide if a box overlaps // an already output detection and should therefore be thrown out. test_box_overlap overlaps_nms =test_box_overlap(0.4); // Any mmod_rect in the training data that has its ignore field set to true defines // an "ignore zone" in an image. Any detection from that area is totally ignored // by the optimizer. Therefore, this overlaps_ignore field defines how we decide // if a box falls into an ignore zone. You use these ignore zones if there are // objects in your dataset that you are unsure if you want to detect or otherwise // don't care if the detector gets them or not. test_box_overlap overlaps_ignore; // Usually the detector would be scale-invariant, and used with an image pyramid. // However, sometimes scale-invariance may not be desired. use_image_pyramid assume_image_pyramid = use_image_pyramid::yes; // By default, the mmod loss doesn't train any bounding box regression model. But // if you set use_bounding_box_regression == true then it expects the network to // output a tensor with detector_windows.size()*5 channels rather than just // detector_windows.size() channels. The 4 extra channels per window are trained // to give a bounding box regression output that improves the positioning of the // output detection box.booluse_bounding_box_regression = false; // When using bounding box regression, bbr_lambda determines how much you care // about getting the bounding box shape correct vs just getting the detector to // find objects. That is, the objective function being optimized is // basic_mmod_loss + bbr_lambda*bounding_box_regression_loss. So setting // bbr_lambda to a larger value will cause the overall loss to care more about // getting the bounding box shape correct.doublebbr_lambda = 100; // Tell the loss not to print warnings about impossible labels. You should think very hard // before turning this off as it's very often telling you something is really wrong with // your training data.boolbe_quiet = false;mmod_options( const std::vector<std::vector<mmod_rect>>& boxes, constunsignedlongtarget_size, constunsignedlongmin_target_size, constdoublemin_detector_window_overlap_iou = 0.75 ); /*! requires - 0 < min_target_size <= target_size - 0.5 < min_detector_window_overlap_iou < 1 ensures - use_image_pyramid_ == use_image_pyramid::yes - This function should be used when scale-invariance is desired, and input_rgb_image_pyramid is therefore used as the input layer. - This function tries to automatically set the MMOD options to reasonable values, assuming you have a training dataset of boxes.size() images, where the ith image contains objects boxes[i] you want to detect. - The most important thing this function does is decide what detector windows should be used. This is done by finding a set of detector windows that are sized such that: - When slid over an image pyramid, each box in boxes will have an intersection-over-union with one of the detector windows of at least min_detector_window_overlap_iou. That is, we will make sure that each box in boxes could potentially be detected by one of the detector windows. This essentially comes down to picking detector windows with aspect ratios similar to the aspect ratios in boxes. Note that we also make sure that each box can be detected by a window with the same label. For example, if all the boxes had the same aspect ratio but there were 4 different labels used in boxes then there would be 4 resulting detector windows, one for each label. - The longest edge of each detector window is target_size pixels in length, unless the window's shortest side would be less than min_target_size pixels in length. In this case the shortest side will be set to min_target_size length, and the other side sized to preserve the aspect ratio of the window. This means that target_size and min_target_size control the size of the detector windows, while the aspect ratios of the detector windows are automatically determined by the contents of boxes. It should also be emphasized that the detector isn't going to be able to detect objects smaller than any of the detector windows. So consider that when setting these sizes. - This function will also set the overlaps_nms tester to the most restrictive tester that doesn't reject anything in boxes. !*/mmod_options( use_image_pyramid use_image_pyramid, const std::vector<std::vector<mmod_rect>>& boxes, constdoublemin_detector_window_overlap_iou = 0.75 ); /*! requires - use_image_pyramid == use_image_pyramid::no - 0.5 < min_detector_window_overlap_iou < 1 ensures - This function should be used when scale-invariance is not desired, and there is no intention to apply an image pyramid. - This function tries to automatically set the MMOD options to reasonable values, assuming you have a training dataset of boxes.size() images, where the ith image contains objects boxes[i] you want to detect. - The most important thing this function does is decide what detector windows should be used. This is done by finding a set of detector windows that are sized such that: - When slid over an image, each box in boxes will have an intersection-over-union with one of the detector windows of at least min_detector_window_overlap_iou. That is, we will make sure that each box in boxes could potentially be detected by one of the detector windows. - This function will also set the overlaps_nms tester to the most restrictive tester that doesn't reject anything in boxes. !*/};voidserialize(const mmod_options& item, std::ostream& out);voiddeserialize(mmod_options& item, std::istream& in); // ---------------------------------------------------------------------------------------- classloss_mmod_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the Max Margin Object Detection loss defined in the paper: Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046). This means you use this loss if you want to detect the locations of objects in images. It should also be noted that this loss layer requires an input layer that defines the following functions: - image_contained_point() - tensor_space_to_image_space() - image_space_to_tensor_space() A reference implementation of them and their definitions can be found in the input_rgb_image_pyramid object, which is the recommended input layer to be used with loss_mmod_. !*/ public: typedef std::vector<mmod_rect> training_label_type; typedef std::vector<mmod_rect> output_label_type;loss_mmod_( ); /*! ensures - #get_options() == mmod_options() !*/loss_mmod_( mmod_options options_ ); /*! ensures - #get_options() == options_ !*/ const mmod_options&get_options( ) const; /*! ensures - returns the options object that defines the general behavior of this loss layer. !*/ template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter,doubleadjust_threshold = 0 ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 Also, the output labels are std::vectors of mmod_rects where, for each mmod_rect R, we have the following interpretations: - R.rect == the location of an object in the image. - R.detection_confidence the score for the object, the bigger the score the more confident the detector is that an object is really there. Only objects with a detection_confidence > adjust_threshold are output. So if you want to output more objects (that are also of less confidence) you can call to_label() with a smaller value of adjust_threshold. - R.ignore == false (this value is unused by to_label()). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 Also, the loss value returned is roughly equal to the average number of mistakes made per image. This is the sum of false alarms and missed detections, weighted by the loss weights for these types of mistakes specified in the mmod_options. !*/}; template <typename SUBNET> using loss_mmod = add_loss_layer<loss_mmod_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_metric_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it allows you to learn to map objects into a vector space where objects sharing the same class label are close to each other, while objects with different labels are far apart. To be specific, it optimizes the following loss function which considers all pairs of objects in a mini-batch and computes a different loss depending on their respective class labels. So if objects A1 and A2 in a mini-batch share the same class label then their contribution to the loss is: max(0, length(A1-A2)-get_distance_threshold() + get_margin()) While if A1 and B1 have different class labels then their contribution to the loss function is: max(0, get_distance_threshold()-length(A1-B1) + get_margin()) Therefore, this loss layer optimizes a version of the hinge loss. Moreover, the loss is trying to make sure that all objects with the same label are within get_distance_threshold() distance of each other. Conversely, if two objects have different labels then they should be more than get_distance_threshold() distance from each other in the learned embedding. So this loss function gives you a natural decision boundary for deciding if two objects are from the same class. Finally, the loss balances the number of negative pairs relative to the number of positive pairs. Therefore, if there are N pairs that share the same identity in a mini-batch then the algorithm will only include the N worst non-matching pairs in the loss. That is, the algorithm performs hard negative mining on the non-matching pairs. This is important since there are in general way more non-matching pairs than matching pairs. So to avoid imbalance in the loss this kind of hard negative mining is useful. !*/ public: typedefunsignedlongtraining_label_type; typedef matrix<float,0,1> output_label_type;loss_metric_( ); /*! ensures - #get_margin() == 0.04 - #get_distance_threshold() == 0.6 !*/loss_metric_(floatmargin,floatdist_thresh ); /*! requires - margin > 0 - dist_thresh > 0 ensures - #get_margin() == margin - #get_distance_threshold() == dist_thresh !*/ template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 This loss expects the network to produce a single vector (per sample) as output. This vector is the learned embedding. Therefore, to_label() just copies these output vectors from the network into the output label_iterators given to this function, one for each sample in the input_tensor. !*/floatget_margin() const; /*! ensures - returns the margin value used by the loss function. See the discussion in WHAT THIS OBJECT REPRESENTS for details. !*/floatget_distance_threshold() const; /*! ensures - returns the distance threshold value used by the loss function. See the discussion in WHAT THIS OBJECT REPRESENTS for details. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 !*/}; template <typename SUBNET> using loss_metric = add_loss_layer<loss_metric_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_ranking_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the pairwise ranking loss described in the paper: Optimizing Search Engines using Clickthrough Data by Thorsten Joachims This is the same loss function used by the dlib::svm_rank_trainer object. Therefore, it is generally appropriate when you have a two class problem and you want to learn a function that ranks one class before the other. So for example, suppose you have two classes of data. Objects of type A and objects of type B. Moreover, suppose that you want to sort the objects so that A objects always come before B objects. This loss will help you learn a function that assigns a real number to each object such that A objects get a larger number assigned to them than B objects. This lets you then sort the objects according to the output of the neural network and obtain the desired result of having A objects come before B objects. The training labels should be positive values for objects you want to get high scores and negative for objects that should get small scores. So relative to our A/B example, you would give A objects labels of +1 and B objects labels of -1. This should cause the learned network to give A objects large positive values and B objects negative values. Finally, the specific loss function is: For all pairs of positive vs negative training examples A_i and B_j respectively: sum_ij: max(0, B_i - A_j + margin_ij) where margin_ij = the label for A_j minus the label for B_i. If you always use +1 and -1 labels then the margin is always 2. However, this formulation allows you to give certain training samples different weight by adjusting the training labels appropriately. !*/ public: typedeffloattraining_label_type; typedeffloatoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted ranking score. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 !*/}; template <typename SUBNET> using loss_ranking = add_loss_layer<loss_ranking_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_epsilon_insensitive_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the epsilon insensitive loss, which is appropriate for regression problems. In particular, this loss function is; loss(y1,y2) = abs(y1-y2)<epsilon ? 0 : abs(y1-y2)-epsilon Therefore, the loss is basically just the abs() loss except there is a dead zone around zero, causing the loss to not care about mistakes of magnitude smaller than epsilon. !*/ public: typedeffloattraining_label_type; typedeffloatoutput_label_type;loss_epsilon_insensitive_( ) = default; /*! ensures - #get_epsilon() == 1 !*/loss_epsilon_insensitive_(doubleeps ); /*! requires - eps >= 0 ensures - #get_epsilon() == eps !*/doubleget_epsilon( ) const; /*! ensures - returns the epsilon value used in the loss function. Mistakes in the regressor smaller than get_epsilon() are ignored by the loss function. !*/voidset_epsilon(doubleeps ); /*! requires - eps >= 0 ensures - #get_epsilon() == eps !*/ template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted continuous variable. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 !*/}; template <typename SUBNET> using loss_epsilon_insensitive = add_loss_layer<loss_epsilon_insensitive_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_mean_squared_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the mean squared loss, which is appropriate for regression problems. !*/ public: typedeffloattraining_label_type; typedeffloatoutput_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted continuous variable. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 !*/}; template <typename SUBNET> using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_mean_squared_multioutput_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the mean squared loss, which is appropriate for regression problems. It is basically just like loss_mean_squared_ except that it lets you define multiple outputs instead of just 1. !*/ public: typedef matrix<float> training_label_type; typedef matrix<float> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted continuous variable. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - (*(truth + idx)).nc() == 1 for all idx such that 0 <= idx < sub.get_output().num_samples() - (*(truth + idx)).nr() == sub.get_output().k() for all idx such that 0 <= idx < sub.get_output().num_samples() !*/}; template <typename SUBNET> using loss_mean_squared_multioutput = add_loss_layer<loss_mean_squared_multioutput_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_binary_log_per_pixel_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the log loss, which is appropriate for binary classification problems. It is basically just like loss_binary_log_ except that it lets you define matrix outputs instead of scalar outputs. It should be useful, for example, in segmentation where we want to classify each pixel of an image, and also get at least some sort of confidence estimate for each pixel. !*/ public: typedef matrix<float> training_label_type; typedef matrix<float> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the raw score for each classified object. If the score is > 0 then the classifier is predicting the +1 class, otherwise it is predicting the -1 class. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all pixel values pointed to by truth correspond to the desired target values. Nominally they should be +1 or -1, each indicating the desired class label, or 0 to indicate that the corresponding pixel is to be ignored. !*/}; template <typename SUBNET> using loss_binary_log_per_pixel = add_loss_layer<loss_binary_log_per_pixel_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_multiclass_log_per_pixel_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the multiclass logistic regression loss (e.g. negative log-likelihood loss), which is appropriate for multiclass classification problems. It is basically just like loss_multiclass_log_ except that it lets you define matrix outputs instead of scalar outputs. It should be useful, for example, in semantic segmentation where we want to classify each pixel of an image. !*/ public: // In semantic segmentation, if you don't know the ground-truth of some pixel, // set the label of that pixel to this value. When you do so, the pixel will be // ignored when computing gradients. static const uint16_t label_to_ignore = std::numeric_limits<uint16_t>::max(); // In semantic segmentation, 65535 classes ought to be enough for anybody. typedef matrix<uint16_t> training_label_type; typedef matrix<uint16_t> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted class for each classified element. The number of possible output classes is sub.get_output().k(). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all values pointed to by truth are < sub.get_output().k() or are equal to label_to_ignore. !*/}; template <typename SUBNET> using loss_multiclass_log_per_pixel = add_loss_layer<loss_multiclass_log_per_pixel_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_multiclass_log_per_pixel_weighted_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the multiclass logistic regression loss (e.g. negative log-likelihood loss), which is appropriate for multiclass classification problems. It is basically just like loss_multiclass_log_per_pixel_ except that it lets you define per-pixel weights, which may be useful e.g. if you want to emphasize rare classes while training. (If the classification problem is difficult, a flat weight structure may lead the network to always predict the most common label, in particular if the degree of imbalance is high. To emphasize a certain class or classes, simply increase the weights of the corresponding pixels, relative to the weights of the other pixels.) Note that if you set the weight to 0 whenever a pixel's label is equal to loss_multiclass_log_per_pixel_::label_to_ignore, and to 1 otherwise, then you essentially get loss_multiclass_log_per_pixel_ as a special case. !*/ public: typedef dlib::weighted_label<uint16_t> weighted_label; typedef matrix<weighted_label> training_label_type; typedef matrix<uint16_t> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output label is the predicted class for each classified element. The number of possible output classes is sub.get_output().k(). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - all labels pointed to by truth are < sub.get_output().k(), or the corresponding weight is zero. !*/}; template <typename SUBNET> using loss_multiclass_log_per_pixel_weighted = add_loss_layer<loss_multiclass_log_per_pixel_weighted_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_mean_squared_per_pixel_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the mean squared loss, which is appropriate for regression problems. It is basically just like loss_mean_squared_multioutput_ except that it lets you define matrix or image outputs, instead of vector. !*/ public: typedef matrix<float> training_label_type; typedef matrix<float> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output labels are the predicted continuous variables. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().k() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - for all idx such that 0 <= idx < sub.get_output().num_samples(): - sub.get_output().nr() == (*(truth + idx)).nr() - sub.get_output().nc() == (*(truth + idx)).nc() !*/}; template <typename SUBNET> using loss_mean_squared_per_pixel = add_loss_layer<loss_mean_squared_per_pixel_, SUBNET>; // ---------------------------------------------------------------------------------------- template<long_num_channels> classloss_mean_squared_per_channel_and_pixel_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the mean squared loss, which is appropriate for regression problems. It is basically just like loss_mean_squared_per_pixel_ except that it computes the loss over all channels, not just the first one. !*/ public: typedef std::array<matrix<float>, _num_channels> training_label_type; typedef std::array<matrix<float>, _num_channels> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.get_output().k() == _num_channels - sub.sample_expansion_factor() == 1 and the output labels are the predicted continuous variables. !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().k() == _num_channels - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - for all idx such that 0 <= idx < sub.get_output().num_samples(): - sub.get_output().nr() == (*(truth + idx)).nr() - sub.get_output().nc() == (*(truth + idx)).nc() !*/}; template <longnum_channels, typename SUBNET> using loss_mean_squared_per_channel_and_pixel = add_loss_layer<loss_mean_squared_per_channel_and_pixel_<num_channels>, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_dot_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, selecting this loss means you want maximize the dot product between the output of a network and a set of training vectors. The loss is therefore the negative dot product. To be very specific, if X is the output vector of a network and Y is a training label (also a vector), then the loss for this training sample is: -dot(X,Y) !*/ public: typedef matrix<float,0,1> training_label_type; typedef matrix<float,0,1> output_label_type; template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 and the output labels are simply the final network outputs stuffed into a vector. To be very specific, the output is the following for all valid i: *(iter+i) == trans(rowm(mat(sub.get_output()),i)) !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 - Let NETWORK_OUTPUT_DIMS == sub.get_output().size()/sub.get_output().num_samples() - for all idx such that 0 <= idx < sub.get_output().num_samples(): - NETWORK_OUTPUT_DIMS == (*(truth + idx)).size() !*/}; template <typename SUBNET> using loss_dot = add_loss_layer<loss_dot_, SUBNET>; // ---------------------------------------------------------------------------------------- structyolo_options{/*! WHAT THIS OBJECT REPRESENTS This object contains all the parameters that control the behavior of loss_yolo_. !*/ public: structanchor_box_details{anchor_box_details() = default;anchor_box_details(unsignedlongw,unsignedlongh) : width(w), height(h){}unsignedlongwidth = 0;unsignedlongheight = 0; friend inlinevoidserialize(const anchor_box_details& item, std::ostream& out); friend inlinevoiddeserialize(anchor_box_details& item, std::istream& in);};yolo_options() = default; // This kind of object detector is a multi-scale object detector with bounding box // regression for anchor boxes. The anchors field determines which anchors will be // used at the output pointed by the tag layer whose id is the key of the map. std::unordered_map<int, std::vector<anchor_box_details>> anchors; template <template <typename> classTAG_TYPE>voidadd_anchors( const std::vector<anchor_box_details>& boxes ); /*! ensures - anchors.at(tag_id<TAG_TYPE>::id) == boxes !*/ // This field contains the labels of all the possible objects this detector can find. std::vector<std::string> labels; // When computing the objectness loss, any detection that has an IoU above // iou_ignore_threshold with a ground truth box will not incur any loss.doubleiou_ignore_threshold = 0.7; // When computing the YOLO loss (objectness + bounding box regression + classification), // the best match between a truth and an anchor is always used, regardless of the IoU. // However, if other anchors have an IoU with a truth box above iou_anchor_threshold, they // will also experience loss against that truth box as well. Setting iou_anchor_threshold to 1 will // make the model use only the best anchor for each ground truth, so other anchors can be // used for other ground truth boxes in the same cell (useful for detecting objects in crowds). // This setting is meant to be used with "high capacity" models, not small ones.doubleiou_anchor_threshold = 1.0; // When doing non-max suppression, we use overlaps_nms to decide if a box overlaps // an already output detection and should therefore be thrown out. test_box_overlap overlaps_nms =test_box_overlap(0.45, 1.0); // When set to true, NMS will only be applied between objects with the same class label.boolclasswise_nms = true; // These parameters control how we penalize different kinds of mistakes: notably the objectness loss, // the box (bounding box regression) loss, and the classification loss.doublelambda_obj = 1.0;doublelambda_box = 1.0;doublelambda_cls = 1.0;};voidserialize(const yolo_options& item, std::ostream& out)voiddeserialize(yolo_options& item, std::istream& in) // ---------------------------------------------------------------------------------------- template <template <typename> class...TAG_TYPES> classloss_yolo_{/*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the YOLO detection loss defined in the paper: YOLOv3: An Incremental Improvement by Joseph Redmon and Ali Farhadi. This means you use this loss if you want to detect the locations of objects in images. It should also be noted that this loss layer requires tag layers as template parameters, which in turn require a subnetwork to be of type: layer<TAG_TYPE>(net).subnet(): sig<con<(num_classes + 5) * num_anchors), SUBNET>> Where num_classes is the number of categories that the detector is trained on, and num_anchors is the number of priors or anchor boxes at the output pointed by the tag layer. The number 5 corresponds to the objectness plus the 4 coordinates for performing bounding box regression. !*/ public: typedef std::vector<yolo_rect> training_label_type; typedef std::vector<yolo_rect> output_label_type;loss_yolo_( ); /*! ensures - #get_options() == yolo_options() !*/loss_yolo_( yolo_options options_ ); /*! ensures - #get_options() == options_ !*/ const yolo_options&get_options( ) const; /*! ensures - returns the options object that defines the general behavior of this loss layer. !*/ template < typename SUB_TYPE, typename label_iterator >voidto_label( const tensor& input_tensor, const SUB_TYPE& sub, label_iterator iter,doubleadjust_threshold = 0.25 ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::to_label() except it has the additional calling requirements that: - layer<TAG_TYPE>(sub).get_output().k() == options.anchors.at(tag_id<TAG_TYPE>::id).size() * (5 + options.labels.size()); - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 Also, the output labels are std::vectors of yolo_rects where, for each yolo_rect R, we have the following interpretations: - R.rect == the location of an object in the image. - R.detection_confidence == the score for the object, between 0 and 1. Only objects with a detection_confidence > adjust_threshold are output. So if you want to output more objects (that are also of less confidence) you can call to_label() with a smaller value of adjust_threshold. - R.label == the label of the detected object. - R.labels == a std::vector<std::pair<double, std::string>> containing all the confidence values and labels that have a detection score > adjust_threshold, since this loss allows for multi-label outputs. Note that the following is true: - R.labels[0].first == R.detection_confidence - R.labels[0].second == R.label - R.ignore == false (this value is unused by to_label()). !*/ template < typename const_label_iterator, typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, const_label_iterator truth, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - layer<TAG_TYPE>(sub).get_output().k() == options.anchors.at(tag_id<TAG_TYPE>::id).size() * (5 + options.labels.size()); - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 1 Also, the loss value returned corresponds to the squared norm of the error gradient. !*/voidadjust_nms(doubleiou_thresh,doublepercent_covered_thresh = 1,boolclasswise = true ); /*! ensures - #get_options().overlaps_nms == test_box_overlap(iou_thresh, percent_covered_thresh) - #get_options().classwise_nms == classwise !*/}; template <typename SUBNET> using loss_yolo = add_loss_layer<loss_yolo_, SUBNET>; // ---------------------------------------------------------------------------------------- classloss_barlow_twins_{public: /*! WHAT THIS OBJECT REPRESENTS This object implements the loss layer interface defined above by EXAMPLE_LOSS_LAYER_. In particular, it implements the Barlow Twins loss layer presented in the paper: Barlow Twins: Self-Supervised Learning via Redundancy Reduction by Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny (https://arxiv.org/abs/2103.03230) This means you use this loss to learn useful representations from data that has no label information. Useful representations mean that can be used to train another downstream task, such as classification. In particular, this loss function applies the redundancy reduction principle to the representations learned by the network it sits on top of. To be specific, this layer requires the sample_expansion_factor to be 2, and in each batch, the second half contains distorted versions of the first half. Let Z_A and Z_B be the first and second half of the batch that goes into this loss layer, respectively. Z_A and Z_B have dimensions N rows and D columns, where N is half the batch size and D is the dimensionality of the output tensor. Each row in Z_B should contain a distorted version of the corresponding row in Z_A. Then, this loss computes the empirical cross-correlation matrix between the batch-normalized versions of Z_A and Z_B: C = trans(bn(Z_A)) * bn(Z_B) It then applies the redundancy reduction principle by trying to make C as close to the identity matrix as possible: L = squared(diag(C) - 1) + lambda * squared(off_diag(C)) where off_diag grabs all the elements that are not on the diagonal of C and lambda provides a trade-off between both terms in the loss function. The C matrix has dimensions D x D: there are only D diagonal terms, but D * (D - 1) off-diagonal elements. A reasonable value for lambda is 1 / D. !*/loss_barlow_twins_( ); /*! ensures - #get_lambda() == 0.0051 !*/loss_barlow_twins_(floatlambda); /*! ensures - #get_lambda() == lambda !*/floatget_lambda() const; /*! ensures - returns the lambda value used by the loss function. See the discussion in WHAT THIS OBJECT REPRESENTS for details. !*/ template < typename SUBNET >doublecompute_loss_value_and_gradient( const tensor& input_tensor, SUBNET& sub ) const; /*! This function has the same interface as EXAMPLE_LOSS_LAYER_::compute_loss_value_and_gradient() except it has the additional calling requirements that: - sub.get_output().nr() == 1 - sub.get_output().nc() == 1 - sub.get_output().num_samples() == input_tensor.num_samples() - sub.sample_expansion_factor() == 2 !*/}; template <typename SUBNET> using loss_barlow_twins = add_loss_layer<loss_barlow_twins_, SUBNET>;}#endif // DLIB_DNn_LOSS_ABSTRACT_H_