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Diffstat (limited to 'ml/dlib/examples/dnn_imagenet_ex.cpp')
| -rw-r--r-- | ml/dlib/examples/dnn_imagenet_ex.cpp | 171 |
1 files changed, 0 insertions, 171 deletions
diff --git a/ml/dlib/examples/dnn_imagenet_ex.cpp b/ml/dlib/examples/dnn_imagenet_ex.cpp deleted file mode 100644 index d1fa82823..000000000 --- a/ml/dlib/examples/dnn_imagenet_ex.cpp +++ /dev/null @@ -1,171 +0,0 @@ -// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt -/* - This example shows how to classify an image into one of the 1000 imagenet - categories using the deep learning tools from the dlib C++ Library. We will - use the pretrained ResNet34 model available on the dlib website. - - The ResNet34 architecture is from the paper Deep Residual Learning for Image - Recognition by He, Zhang, Ren, and Sun. The model file that comes with dlib - was trained using the dnn_imagenet_train_ex.cpp program on a Titan X for - about 2 weeks. This pretrained model has a top5 error of 7.572% on the 2012 - imagenet validation dataset. - - For an introduction to dlib's DNN module read the dnn_introduction_ex.cpp and - dnn_introduction2_ex.cpp example programs. - - - Finally, these tools will use CUDA and cuDNN to drastically accelerate - network training and testing. CMake should automatically find them if they - are installed and configure things appropriately. If not, the program will - still run but will be much slower to execute. -*/ - - - -#include <dlib/dnn.h> -#include <iostream> -#include <dlib/data_io.h> -#include <dlib/gui_widgets.h> -#include <dlib/image_transforms.h> - -using namespace std; -using namespace dlib; - -// ---------------------------------------------------------------------------------------- - -// This block of statements defines the resnet-34 network - -template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET> -using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>; - -template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET> -using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>; - -template <int N, template <typename> class BN, int stride, typename SUBNET> -using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>; - -template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>; -template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>; - -template <typename SUBNET> using level1 = ares<512,ares<512,ares_down<512,SUBNET>>>; -template <typename SUBNET> using level2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>; -template <typename SUBNET> using level3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>; -template <typename SUBNET> using level4 = ares<64,ares<64,ares<64,SUBNET>>>; - -using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything< - level1< - level2< - level3< - level4< - max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2, - input_rgb_image_sized<227> - >>>>>>>>>>>; - -// ---------------------------------------------------------------------------------------- - -rectangle make_random_cropping_rect_resnet( - const matrix<rgb_pixel>& img, - dlib::rand& rnd -) -{ - // figure out what rectangle we want to crop from the image - double mins = 0.466666666, maxs = 0.875; - auto scale = mins + rnd.get_random_double()*(maxs-mins); - auto size = scale*std::min(img.nr(), img.nc()); - rectangle rect(size, size); - // randomly shift the box around - point offset(rnd.get_random_32bit_number()%(img.nc()-rect.width()), - rnd.get_random_32bit_number()%(img.nr()-rect.height())); - return move_rect(rect, offset); -} - -// ---------------------------------------------------------------------------------------- - -void randomly_crop_images ( - const matrix<rgb_pixel>& img, - dlib::array<matrix<rgb_pixel>>& crops, - dlib::rand& rnd, - long num_crops -) -{ - std::vector<chip_details> dets; - for (long i = 0; i < num_crops; ++i) - { - auto rect = make_random_cropping_rect_resnet(img, rnd); - dets.push_back(chip_details(rect, chip_dims(227,227))); - } - - extract_image_chips(img, dets, crops); - - for (auto&& img : crops) - { - // Also randomly flip the image - if (rnd.get_random_double() > 0.5) - img = fliplr(img); - - // And then randomly adjust the colors. - apply_random_color_offset(img, rnd); - } -} - -// ---------------------------------------------------------------------------------------- - -int main(int argc, char** argv) try -{ - if (argc == 1) - { - cout << "Give this program image files as command line arguments.\n" << endl; - cout << "You will also need a copy of the file resnet34_1000_imagenet_classifier.dnn " << endl; - cout << "available at http://dlib.net/files/resnet34_1000_imagenet_classifier.dnn.bz2" << endl; - cout << endl; - return 1; - } - - std::vector<string> labels; - anet_type net; - deserialize("resnet34_1000_imagenet_classifier.dnn") >> net >> labels; - - // Make a network with softmax as the final layer. We don't have to do this - // if we just want to output the single best prediction, since the anet_type - // already does this. But if we instead want to get the probability of each - // class as output we need to replace the last layer of the network with a - // softmax layer, which we do as follows: - softmax<anet_type::subnet_type> snet; - snet.subnet() = net.subnet(); - - dlib::array<matrix<rgb_pixel>> images; - matrix<rgb_pixel> img, crop; - - dlib::rand rnd; - image_window win; - - // Read images from the command prompt and print the top 5 best labels for each. - for (int i = 1; i < argc; ++i) - { - load_image(img, argv[i]); - const int num_crops = 16; - // Grab 16 random crops from the image. We will run all of them through the - // network and average the results. - randomly_crop_images(img, images, rnd, num_crops); - // p(i) == the probability the image contains object of class i. - matrix<float,1,1000> p = sum_rows(mat(snet(images.begin(), images.end())))/num_crops; - - win.set_image(img); - // Print the 5 most probable labels - for (int k = 0; k < 5; ++k) - { - unsigned long predicted_label = index_of_max(p); - cout << p(predicted_label) << ": " << labels[predicted_label] << endl; - p(predicted_label) = 0; - } - - cout << "Hit enter to process the next image"; - cin.get(); - } - -} -catch(std::exception& e) -{ - cout << e.what() << endl; -} - |