diff options
Diffstat (limited to 'ml/dlib/examples/dnn_mmod_ex.cpp')
| -rw-r--r-- | ml/dlib/examples/dnn_mmod_ex.cpp | 230 |
1 files changed, 230 insertions, 0 deletions
diff --git a/ml/dlib/examples/dnn_mmod_ex.cpp b/ml/dlib/examples/dnn_mmod_ex.cpp new file mode 100644 index 000000000..9565d514c --- /dev/null +++ b/ml/dlib/examples/dnn_mmod_ex.cpp @@ -0,0 +1,230 @@ +// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt +/* + This example shows how to train a CNN based object detector using dlib's + loss_mmod loss layer. This loss layer implements the Max-Margin Object + Detection loss as described in the paper: + Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046). + This is the same loss used by the popular SVM+HOG object detector in dlib + (see fhog_object_detector_ex.cpp) except here we replace the HOG features + with a CNN and train the entire detector end-to-end. This allows us to make + much more powerful detectors. + + It would be a good idea to become familiar with dlib's DNN tooling before + reading this example. So you should read dnn_introduction_ex.cpp and + dnn_introduction2_ex.cpp before reading this example program. + + Just like in the fhog_object_detector_ex.cpp example, we are going to train + a simple face detector based on the very small training dataset in the + examples/faces folder. As we will see, even with this small dataset the + MMOD method is able to make a working face detector. However, for real + applications you should train with more data for an even better result. +*/ + + +#include <iostream> +#include <dlib/dnn.h> +#include <dlib/data_io.h> +#include <dlib/gui_widgets.h> + +using namespace std; +using namespace dlib; + +// The first thing we do is define our CNN. The CNN is going to be evaluated +// convolutionally over an entire image pyramid. Think of it like a normal +// sliding window classifier. This means you need to define a CNN that can look +// at some part of an image and decide if it is an object of interest. In this +// example I've defined a CNN with a receptive field of a little over 50x50 +// pixels. This is reasonable for face detection since you can clearly tell if +// a 50x50 image contains a face. Other applications may benefit from CNNs with +// different architectures. +// +// In this example our CNN begins with 3 downsampling layers. These layers will +// reduce the size of the image by 8x and output a feature map with +// 32 dimensions. Then we will pass that through 4 more convolutional layers to +// get the final output of the network. The last layer has only 1 channel and +// the values in that last channel are large when the network thinks it has +// found an object at a particular location. + + +// Let's begin the network definition by creating some network blocks. + +// A 5x5 conv layer that does 2x downsampling +template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>; +// A 3x3 conv layer that doesn't do any downsampling +template <long num_filters, typename SUBNET> using con3 = con<num_filters,3,3,1,1,SUBNET>; + +// Now we can define the 8x downsampling block in terms of conv5d blocks. We +// also use relu and batch normalization in the standard way. +template <typename SUBNET> using downsampler = relu<bn_con<con5d<32, relu<bn_con<con5d<32, relu<bn_con<con5d<32,SUBNET>>>>>>>>>; + +// The rest of the network will be 3x3 conv layers with batch normalization and +// relu. So we define the 3x3 block we will use here. +template <typename SUBNET> using rcon3 = relu<bn_con<con3<32,SUBNET>>>; + +// Finally, we define the entire network. The special input_rgb_image_pyramid +// layer causes the network to operate over a spatial pyramid, making the detector +// scale invariant. +using net_type = loss_mmod<con<1,6,6,1,1,rcon3<rcon3<rcon3<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>; + +// ---------------------------------------------------------------------------------------- + +int main(int argc, char** argv) try +{ + // In this example we are going to train a face detector based on the + // small faces dataset in the examples/faces directory. So the first + // thing we do is load that dataset. This means you need to supply the + // path to this faces folder as a command line argument so we will know + // where it is. + if (argc != 2) + { + cout << "Give the path to the examples/faces directory as the argument to this" << endl; + cout << "program. For example, if you are in the examples folder then execute " << endl; + cout << "this program by running: " << endl; + cout << " ./dnn_mmod_ex faces" << endl; + cout << endl; + return 0; + } + const std::string faces_directory = argv[1]; + // The faces directory contains a training dataset and a separate + // testing dataset. The training data consists of 4 images, each + // annotated with rectangles that bound each human face. The idea is + // to use this training data to learn to identify human faces in new + // images. + // + // Once you have trained an object detector it is always important to + // test it on data it wasn't trained on. Therefore, we will also load + // a separate testing set of 5 images. Once we have a face detector + // created from the training data we will see how well it works by + // running it on the testing images. + // + // So here we create the variables that will hold our dataset. + // images_train will hold the 4 training images and face_boxes_train + // holds the locations of the faces in the training images. So for + // example, the image images_train[0] has the faces given by the + // rectangles in face_boxes_train[0]. + std::vector<matrix<rgb_pixel>> images_train, images_test; + std::vector<std::vector<mmod_rect>> face_boxes_train, face_boxes_test; + + // Now we load the data. These XML files list the images in each dataset + // and also contain the positions of the face boxes. Obviously you can use + // any kind of input format you like so long as you store the data into + // images_train and face_boxes_train. But for convenience dlib comes with + // tools for creating and loading XML image datasets. Here you see how to + // load the data. To create the XML files you can use the imglab tool which + // can be found in the tools/imglab folder. It is a simple graphical tool + // for labeling objects in images with boxes. To see how to use it read the + // tools/imglab/README.txt file. + load_image_dataset(images_train, face_boxes_train, faces_directory+"/training.xml"); + load_image_dataset(images_test, face_boxes_test, faces_directory+"/testing.xml"); + + + cout << "num training images: " << images_train.size() << endl; + cout << "num testing images: " << images_test.size() << endl; + + + // The MMOD algorithm has some options you can set to control its behavior. However, + // you can also call the constructor with your training annotations and a "target + // object size" and it will automatically configure itself in a reasonable way for your + // problem. Here we are saying that faces are still recognizably faces when they are + // 40x40 pixels in size. You should generally pick the smallest size where this is + // true. Based on this information the mmod_options constructor will automatically + // pick a good sliding window width and height. It will also automatically set the + // non-max-suppression parameters to something reasonable. For further details see the + // mmod_options documentation. + mmod_options options(face_boxes_train, 40,40); + // The detector will automatically decide to use multiple sliding windows if needed. + // For the face data, only one is needed however. + cout << "num detector windows: "<< options.detector_windows.size() << endl; + for (auto& w : options.detector_windows) + cout << "detector window width by height: " << w.width << " x " << w.height << endl; + cout << "overlap NMS IOU thresh: " << options.overlaps_nms.get_iou_thresh() << endl; + cout << "overlap NMS percent covered thresh: " << options.overlaps_nms.get_percent_covered_thresh() << endl; + + // Now we are ready to create our network and trainer. + net_type net(options); + // The MMOD loss requires that the number of filters in the final network layer equal + // options.detector_windows.size(). So we set that here as well. + net.subnet().layer_details().set_num_filters(options.detector_windows.size()); + dnn_trainer<net_type> trainer(net); + trainer.set_learning_rate(0.1); + trainer.be_verbose(); + trainer.set_synchronization_file("mmod_sync", std::chrono::minutes(5)); + trainer.set_iterations_without_progress_threshold(300); + + + // Now let's train the network. We are going to use mini-batches of 150 + // images. The images are random crops from our training set (see + // random_cropper_ex.cpp for a discussion of the random_cropper). + std::vector<matrix<rgb_pixel>> mini_batch_samples; + std::vector<std::vector<mmod_rect>> mini_batch_labels; + random_cropper cropper; + cropper.set_chip_dims(200, 200); + // Usually you want to give the cropper whatever min sizes you passed to the + // mmod_options constructor, which is what we do here. + cropper.set_min_object_size(40,40); + dlib::rand rnd; + // Run the trainer until the learning rate gets small. This will probably take several + // hours. + while(trainer.get_learning_rate() >= 1e-4) + { + cropper(150, images_train, face_boxes_train, mini_batch_samples, mini_batch_labels); + // We can also randomly jitter the colors and that often helps a detector + // generalize better to new images. + for (auto&& img : mini_batch_samples) + disturb_colors(img, rnd); + + trainer.train_one_step(mini_batch_samples, mini_batch_labels); + } + // wait for training threads to stop + trainer.get_net(); + cout << "done training" << endl; + + // Save the network to disk + net.clean(); + serialize("mmod_network.dat") << net; + + + // Now that we have a face detector we can test it. The first statement tests it + // on the training data. It will print the precision, recall, and then average precision. + // This statement should indicate that the network works perfectly on the + // training data. + cout << "training results: " << test_object_detection_function(net, images_train, face_boxes_train) << endl; + // However, to get an idea if it really worked without overfitting we need to run + // it on images it wasn't trained on. The next line does this. Happily, + // this statement indicates that the detector finds most of the faces in the + // testing data. + cout << "testing results: " << test_object_detection_function(net, images_test, face_boxes_test) << endl; + + + // If you are running many experiments, it's also useful to log the settings used + // during the training experiment. This statement will print the settings we used to + // the screen. + cout << trainer << cropper << endl; + + // Now lets run the detector on the testing images and look at the outputs. + image_window win; + for (auto&& img : images_test) + { + pyramid_up(img); + auto dets = net(img); + win.clear_overlay(); + win.set_image(img); + for (auto&& d : dets) + win.add_overlay(d); + cin.get(); + } + return 0; + + // Now that you finished this example, you should read dnn_mmod_train_find_cars_ex.cpp, + // which is a more advanced example. It discusses many issues surrounding properly + // setting the MMOD parameters and creating a good training dataset. + +} +catch(std::exception& e) +{ + cout << e.what() << endl; +} + + + + |