Add new loss layer for binary loss per pixel (#1976)

* Add new loss layer for binary loss per pixel

dlib/cuda/cuda_dlib.cu

@@ -1681,6 +1681,48 @@ namespace dlib
             }
         }
 
+    // ----------------------------------------------------------------------------------------
+
+        __device__ float cuda_log1pexp(float x)
+        {
+            if (x <= -18)
+                return std::exp(x);
+            else if (-18 < x && x <= 9)
+                return std::log1p(std::exp(x));
+            else if (9 < x && x <= 16)
+                return x + std::exp(-x);
+            else
+                return x;
+        }
+
+        __global__ void _cuda_compute_loss_binary_log_per_pixel(float* loss_out, float* g, const float* truth, const float* out_data, size_t n, const float scale)
+        {
+            float loss = 0;
+            for(auto i : grid_stride_range(0, n))
+            {
+                const float y = truth[i];
+
+                if (y > 0.f)
+                {
+                    const float temp = cuda_log1pexp(-out_data[i]);
+                    loss += y*temp;
+                    g[i] = y*scale*(g[i]-1);
+                }
+                else if (y < 0.f)
+                {
+                    const float temp = -(-out_data[i]-cuda_log1pexp(-out_data[i]));
+                    loss += -y*temp;
+                    g[i] = -y*scale*g[i];
+                }
+                else
+                {
+                    g[i] = 0.f;
+                }
+            }
+
+            warp_reduce_atomic_add(*loss_out, loss);
+        }
+
     // ----------------------------------------------------------------------------------------
 
         __device__ float cuda_safe_log(float x, float epsilon = 1e-10)
@@ -1720,29 +1762,52 @@ namespace dlib
             warp_reduce_atomic_add(*loss_out, loss);
         }
 
+    // ----------------------------------------------------------------------------------------
+
+        void compute_loss_binary_log_per_pixel::
+        do_work(
+            cuda_data_ptr<float> loss_work_buffer,
+            cuda_data_ptr<const float> truth_buffer,
+            const tensor& subnetwork_output,
+            tensor& gradient,
+            double& loss
+        )
+        {
+            CHECK_CUDA(cudaMemset(loss_work_buffer, 0, sizeof(float)));
+            sigmoid(gradient, subnetwork_output);
+
+            // The loss we output is the average loss over the mini-batch, and also over each element of the matrix output.
+            const double scale = 1.0 / (subnetwork_output.num_samples() * subnetwork_output.nr() * subnetwork_output.nc());
+
+            launch_kernel(_cuda_compute_loss_binary_log_per_pixel, max_jobs(gradient.size()),
+                loss_work_buffer.data(), gradient.device(), truth_buffer.data(), subnetwork_output.device(), gradient.size(), scale);
+
+            float floss;
+            dlib::cuda::memcpy(&floss, loss_work_buffer);
+            loss = scale*floss;
+        }
 
         void compute_loss_multiclass_log_per_pixel::
         do_work(
-            float* loss_cuda_work_buffer,
-            const uint16_t* truth_buffer,
+            cuda_data_ptr<float> loss_work_buffer,
+            cuda_data_ptr<const uint16_t> truth_buffer,
             const tensor& subnetwork_output,
             tensor& gradient,
             double& loss
         )
         {
-            CHECK_CUDA(cudaMemset(loss_cuda_work_buffer, 0, sizeof(float)));
+            CHECK_CUDA(cudaMemset(loss_work_buffer, 0, sizeof(float)));
             softmax(gradient, subnetwork_output);
             static const uint16_t label_to_ignore = std::numeric_limits<uint16_t>::max();
 
             // The loss we output is the average loss over the mini-batch, and also over each element of the matrix output.
             const double scale = 1.0 / (subnetwork_output.num_samples() * subnetwork_output.nr() * subnetwork_output.nc());
 
-
             launch_kernel(_cuda_compute_loss_multiclass_log_per_pixel, max_jobs(gradient.size()),
-                loss_cuda_work_buffer, gradient.device(), truth_buffer, gradient.size(), gradient.nr()*gradient.nc(), gradient.nr()*gradient.nc()*gradient.k(), gradient.k(), label_to_ignore, scale);
+                loss_work_buffer.data(), gradient.device(), truth_buffer.data(), gradient.size(), gradient.nr()*gradient.nc(), gradient.nr()*gradient.nc()*gradient.k(), gradient.k(), label_to_ignore, scale);
 
             float floss;
-            CHECK_CUDA(cudaMemcpy(&floss, loss_cuda_work_buffer,  sizeof(float), cudaMemcpyDefault));
+            dlib::cuda::memcpy(&floss, loss_work_buffer);
             loss = scale*floss;
         }
 

dlib/cuda/cuda_dlib.h

@@ -424,11 +424,70 @@ namespace dlib
 
     // ----------------------------------------------------------------------------------------
 
+        class compute_loss_binary_log_per_pixel
+        {
+            /*!
+                The point of this class is to compute the loss computed by
+                loss_binary_log_per_pixel_, but to do so with CUDA.
+            !*/
+        public:
+
+            compute_loss_binary_log_per_pixel(
+            )
+            {
+            }
+
+            template <
+                typename const_label_iterator
+                >
+            void operator() (
+                const_label_iterator truth,
+                const tensor& subnetwork_output,
+                tensor& gradient,
+                double& loss
+            ) const
+            {
+                const auto image_size = subnetwork_output.nr()*subnetwork_output.nc();
+                const size_t bytes_per_plane = image_size*sizeof(float);
+                // Allocate a cuda buffer to store all the truth images and also one float
+                // for the scalar loss output.
+                buf = device_global_buffer(subnetwork_output.num_samples()*bytes_per_plane + sizeof(float));
+
+                cuda_data_ptr<float> loss_buf = static_pointer_cast<float>(buf, 1);
+                buf = buf+sizeof(float);
+
+                // copy the truth data into a cuda buffer.
+                for (long i = 0; i < subnetwork_output.num_samples(); ++i, ++truth)
+                {
+                    const matrix<float>& t = *truth;
+                    DLIB_ASSERT(t.nr() == subnetwork_output.nr());
+                    DLIB_ASSERT(t.nc() == subnetwork_output.nc());
+                    memcpy(buf + i*bytes_per_plane, &t(0,0), bytes_per_plane);
+                }
+
+                auto truth_buf = static_pointer_cast<const float>(buf, subnetwork_output.num_samples()*image_size);
+
+                do_work(loss_buf, truth_buf, subnetwork_output, gradient, loss);
+            }
+
+        private:
+
+            static void do_work(
+                cuda_data_ptr<float> loss_work_buffer,
+                cuda_data_ptr<const float> truth_buffer,
+                const tensor& subnetwork_output,
+                tensor& gradient,
+                double& loss
+            );
+            
+            mutable cuda_data_void_ptr buf;
+        };
+
         class compute_loss_multiclass_log_per_pixel
         {
             /*!
                 The point of this class is to compute the loss computed by
-                loss_multiclass_log_per_pixel, but to do so with CUDA.
+                loss_multiclass_log_per_pixel_, but to do so with CUDA.
             !*/
         public:
 
@@ -447,12 +506,13 @@ namespace dlib
                 double& loss
             ) const
             {
-                const size_t bytes_per_plane = subnetwork_output.nr()*subnetwork_output.nc()*sizeof(uint16_t);
+                const auto image_size = subnetwork_output.nr()*subnetwork_output.nc();
+                const size_t bytes_per_plane = image_size*sizeof(uint16_t);
                 // Allocate a cuda buffer to store all the truth images and also one float
                 // for the scalar loss output.
                 buf = device_global_buffer(subnetwork_output.num_samples()*bytes_per_plane + sizeof(float));
 
-                cuda_data_void_ptr loss_buf = buf;
+                cuda_data_ptr<float> loss_buf = static_pointer_cast<float>(buf, 1);
                 buf = buf+sizeof(float);
 
                 // copy the truth data into a cuda buffer.
@@ -464,14 +524,16 @@ namespace dlib
                     memcpy(buf + i*bytes_per_plane, &t(0,0), bytes_per_plane);
                 }
 
-                do_work(static_cast<float*>(loss_buf.data()), static_cast<uint16_t*>(buf.data()), subnetwork_output, gradient, loss);
+                auto truth_buf = static_pointer_cast<const uint16_t>(buf, subnetwork_output.num_samples()*image_size);
+
+                do_work(loss_buf, truth_buf, subnetwork_output, gradient, loss);
             }
 
         private:
 
             static void do_work(
-                float* loss_cuda_work_buffer,
-                const uint16_t* truth_buffer,
+                cuda_data_ptr<float> loss_work_buffer,
+                cuda_data_ptr<const uint16_t> truth_buffer,
                 const tensor& subnetwork_output,
                 tensor& gradient,
                 double& loss

dlib/dnn/loss.h

@@ -2481,6 +2481,164 @@ namespace dlib
     template <typename SUBNET>
     using loss_mean_squared_multioutput = add_loss_layer<loss_mean_squared_multioutput_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+
+    class loss_binary_log_per_pixel_
+    {
+    public:
+
+        typedef matrix<float> training_label_type;
+        typedef matrix<float> output_label_type;
+
+        template <
+            typename SUB_TYPE,
+            typename label_iterator
+            >
+        static void to_label (
+            const tensor& input_tensor,
+            const SUB_TYPE& sub,
+            label_iterator iter
+        )
+        {
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+
+            const tensor& output_tensor = sub.get_output();
+
+            DLIB_CASSERT(output_tensor.k() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
+
+            const float* const out_data = output_tensor.host();
+
+            for (long i = 0; i < output_tensor.num_samples(); ++i, ++iter) 
+            {
+                iter->set_size(output_tensor.nr(), output_tensor.nc());
+                for (long r = 0; r < output_tensor.nr(); ++r) 
+                {
+                    for (long c = 0; c < output_tensor.nc(); ++c) 
+                    {
+                        iter->operator()(r, c) = out_data[tensor_index(output_tensor, i, r, c)];
+                    }
+                }
+            }
+        }
+
+        template <
+            typename const_label_iterator,
+            typename SUBNET
+            >
+        double compute_loss_value_and_gradient (
+            const tensor& input_tensor,
+            const_label_iterator truth,
+            SUBNET& sub
+        ) const
+        {
+            const tensor& output_tensor = sub.get_output();
+            tensor& grad = sub.get_gradient_input();
+
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() != 0);
+            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
+            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
+            DLIB_CASSERT(output_tensor.k() == 1);
+            DLIB_CASSERT(output_tensor.nr() == grad.nr() &&
+                         output_tensor.nc() == grad.nc() &&
+                         output_tensor.k() == grad.k());
+            for (long idx = 0; idx < output_tensor.num_samples(); ++idx)
+            {
+                const_label_iterator truth_matrix_ptr = (truth + idx);
+                DLIB_CASSERT(truth_matrix_ptr->nr() == output_tensor.nr() &&
+                             truth_matrix_ptr->nc() == output_tensor.nc(),
+                             "truth size = " << truth_matrix_ptr->nr() << " x " << truth_matrix_ptr->nc() << ", "
+                             "output size = " << output_tensor.nr() << " x " << output_tensor.nc());
+            }
+
+#ifdef DLIB_USE_CUDA
+            double loss;
+            cuda_compute(truth, output_tensor, grad, loss);
+            return loss;
+#else
+
+            tt::sigmoid(grad, output_tensor);
+
+            // The loss we output is the average loss over the mini-batch, and also over each element of the matrix output.
+            const double scale = 1.0/(output_tensor.num_samples()*output_tensor.nr()*output_tensor.nc());
+            double loss = 0;
+            float* const g = grad.host();
+            const float* const out_data = output_tensor.host();
+            for (long i = 0; i < output_tensor.num_samples(); ++i, ++truth)
+            {
+                for (long r = 0; r < output_tensor.nr(); ++r)
+                {
+                    for (long c = 0; c < output_tensor.nc(); ++c)
+                    {
+                        const float y = truth->operator()(r, c);
+                        const size_t idx = tensor_index(output_tensor, i, r, c);
+
+                        if (y > 0.f)
+                        {
+                            const float temp = log1pexp(-out_data[idx]);
+                            loss += y*scale*temp;
+                            g[idx] = y*scale*(g[idx]-1);
+                        }
+                        else if (y < 0.f)
+                        {
+                            const float temp = -(-out_data[idx]-log1pexp(-out_data[idx]));
+                            loss += -y*scale*temp;
+                            g[idx] = -y*scale*g[idx];
+                        }
+                        else
+                        {
+                            g[idx] = 0.f;
+                        }
+                    }
+                }
+            }
+            return loss;
+#endif
+        }
+
+        friend void serialize(const loss_binary_log_per_pixel_& , std::ostream& out)
+        {
+            serialize("loss_binary_log_per_pixel_", out);
+        }
+
+        friend void deserialize(loss_binary_log_per_pixel_& , std::istream& in)
+        {
+            std::string version;
+            deserialize(version, in);
+            if (version != "loss_binary_log_per_pixel_")
+                throw serialization_error("Unexpected version found while deserializing dlib::loss_binary_log_per_pixel_.");
+        }
+
+        friend std::ostream& operator<<(std::ostream& out, const loss_binary_log_per_pixel_& )
+        {
+            out << "loss_binary_log_per_pixel";
+            return out;
+        }
+
+        friend void to_xml(const loss_binary_log_per_pixel_& /*item*/, std::ostream& out)
+        {
+            out << "<loss_binary_log_per_pixel/>";
+        }
+
+    private:
+        static size_t tensor_index(const tensor& t, long sample, long row, long column)
+        {
+            DLIB_ASSERT(t.k() == 1);
+
+            // See: https://github.com/davisking/dlib/blob/4dfeb7e186dd1bf6ac91273509f687293bd4230a/dlib/dnn/tensor_abstract.h#L38
+            return (sample * t.nr() + row) * t.nc() + column;
+        }
+
+#ifdef DLIB_USE_CUDA
+        cuda::compute_loss_binary_log_per_pixel cuda_compute;
+#endif
+    };
+
+    template <typename SUBNET>
+    using loss_binary_log_per_pixel = add_loss_layer<loss_binary_log_per_pixel_, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     class loss_multiclass_log_per_pixel_

dlib/dnn/loss_abstract.h

@@ -1283,6 +1283,68 @@ namespace dlib
     template <typename SUBNET>
     using loss_mean_squared_multioutput = add_loss_layer<loss_mean_squared_multioutput_, SUBNET>;
 
+// ----------------------------------------------------------------------------------------
+
+    class loss_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
+            >
+        void to_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
+            >
+        double compute_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>;
+
 // ----------------------------------------------------------------------------------------
 
     class loss_multiclass_log_per_pixel_

dlib/test/dnn.cpp

@@ -2587,6 +2587,202 @@ namespace
         DLIB_TEST_MSG(error_after < error_before, "multi channel error increased after training");
     }
 
+// ----------------------------------------------------------------------------------------
+
+    void test_loss_binary_log_per_pixel_learned_params_on_trivial_two_pixel_task()
+    {
+        print_spinner();
+
+        ::std::vector<matrix<float>> x({ matrix<float,2,1>({ -1, 1 }) });
+        ::std::vector<matrix<float>> y({ matrix<float,2,1>({ -1, 1 }) });
+
+        using net_type = loss_binary_log_per_pixel<con<1,1,1,1,1,input<matrix<float>>>>;
+        net_type net;
+
+        dnn_trainer<net_type> trainer(net, sgd(0,0));
+        trainer.set_learning_rate(1e7);
+        trainer.set_max_num_epochs(1);
+        trainer.train(x, y);
+
+        const tensor& learned_params = layer<1>(net).layer_details().get_layer_params();
+        const float* learned_params_data = learned_params.host();
+
+        DLIB_TEST(learned_params_data[0] > 1e5);
+        DLIB_TEST(abs(learned_params_data[1]) < 1);
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    void test_loss_binary_log_per_pixel_outputs_on_trivial_task()
+    {
+        print_spinner();
+
+        constexpr int input_height = 7;
+        constexpr int input_width = 5;
+        constexpr int output_height = input_height;
+        constexpr int output_width = input_width;
+        constexpr int num_samples = 7;
+        constexpr int filter_height = 3;
+        constexpr int filter_width = 3;
+
+        ::std::vector<matrix<double>> x(num_samples);
+        ::std::vector<matrix<float>> y(num_samples);
+
+        matrix<double> xtmp(input_height, input_width);
+        matrix<float> ytmp(output_height, output_width);
+
+        ::std::default_random_engine generator(16);
+        ::std::normal_distribution<double> n01(0);
+
+        const auto z = 0.674490; // This should give us a 50/50 split between the classes
+
+        // Generate training data: random inputs x, and the corresponding target outputs y
+        for (int ii = 0; ii < num_samples; ++ii) {
+            for (int jj = 0; jj < input_height; ++jj) {
+                for (int kk = 0; kk < input_width; ++kk) {
+                    xtmp(jj, kk) = n01(generator);
+                    ytmp(jj, kk) = std::abs(xtmp(jj, kk)) > z ? 1.f : -1.f;
+                }
+            }
+            x[ii] = xtmp;
+            y[ii] = ytmp;
+        }
+
+        using net_type = loss_binary_log_per_pixel<con<1,1,1,1,1,relu<con<10,1,1,1,1,input<matrix<double>>>>>>;
+        net_type net;
+
+        dnn_trainer<net_type> trainer(net, sgd(0, 0.9));
+        trainer.set_learning_rate(1);
+        trainer.set_max_num_epochs(800);
+        trainer.train(x, y);
+
+        // The learning task is easy, so the net should have no problem
+        // getting all the outputs right.
+        const auto response = net(x);
+        for (int ii = 0; ii < num_samples; ++ii)
+            for (int jj = 0; jj < output_height; ++jj)
+                for (int kk = 0; kk < output_width; ++kk)
+                    DLIB_TEST((response[ii](jj,kk) > 0) == (y[ii](jj,kk) > 0));
+    }
+
+// ----------------------------------------------------------------------------------------
+
+    void test_loss_binary_log_per_pixel_with_noise_and_pixels_to_ignore()
+    {
+        // Test learning when some pixels are to be ignored, etc.
+
+        print_spinner();
+
+        constexpr int input_height = 5;
+        constexpr int input_width = 7;
+        constexpr int output_height = input_height;
+        constexpr int output_width = input_width;
+        const int num_samples = 1000;
+        const double ignore_probability = 0.5;
+        const double noise_probability = 0.05;
+
+        ::std::default_random_engine generator(16);
+        ::std::bernoulli_distribution ignore(ignore_probability);
+        ::std::bernoulli_distribution noise_occurrence(noise_probability);
+        ::std::bernoulli_distribution noisy_label(0.5);
+
+        ::std::vector<matrix<double>> x(num_samples);
+        ::std::vector<matrix<float>> y(num_samples);
+
+        ::std::vector<int> truth_histogram(2);
+
+        matrix<double> xtmp(input_height, input_width);
+        matrix<float> ytmp(output_height, output_width);
+
+        // The function to be learned.
+        const auto ground_truth = [](const matrix<double>& x, int row, int column) {
+            double sum = 0.0;
+            const int first_column = std::max(0, column - 1);
+            const int last_column = std::min(static_cast<int>(x.nc() - 1), column + 1);
+            for (int c = first_column; c <= last_column; ++c) {
+                sum += x(row, c);
+            }
+            DLIB_TEST(sum < 2.0 * (last_column - first_column + 1));
+            return sum > (last_column - first_column + 1);
+        };
+
+        for ( int ii = 0; ii < num_samples; ++ii ) {
+            for ( int jj = 0; jj < input_height; ++jj ) {
+                for ( int kk = 0; kk < input_width; ++kk ) {
+                    // Generate numbers between 0 and 2.
+                    double value = static_cast<double>(ii + jj + kk) / 10.0;
+                    value -= (static_cast<int>(value) / 2) * 2;
+                    DLIB_TEST(value >= 0.0 && value < 2.0);
+                    xtmp(jj, kk) = value;
+                }
+            }
+            x[ii] = xtmp;
+
+            for ( int jj = 0; jj < output_height; ++jj ) {
+                for ( int kk = 0; kk < output_width; ++kk ) {
+                    const bool truth = ground_truth(x[ii], jj, kk);
+                    ++truth_histogram[truth];
+                    if (ignore(generator)) {
+                        ytmp(jj, kk) = 0.f;
+                    }
+                    else if (noise_occurrence(generator)) {
+                        ytmp(jj, kk) = noisy_label(generator) ? 1.f : -1.f;
+                    }
+                    else {
+                        ytmp(jj, kk) = truth ? 1.f : -1.f;
+                    }
+                }
+            }
+
+            y[ii] = ytmp;
+        }
+
+        const int num_total_elements = num_samples * output_height * output_width;
+
+        { // Require a reasonably balanced truth histogram in order to make sure that a trivial classifier is not enough
+            const int required_min_histogram_value = static_cast<int>(::std::ceil(num_total_elements / 2.0 * 0.375));
+            for (auto histogram_value : truth_histogram) {
+                DLIB_TEST_MSG(histogram_value >= required_min_histogram_value,
+                              "Histogram value = " << histogram_value << ", required = " << required_min_histogram_value);
+            }
+        }
+
+        using net_type = loss_binary_log_per_pixel<con<1,1,input_width,1,1,input<matrix<double>>>>;
+        net_type net;
+        sgd defsolver(0,0.9);
+        dnn_trainer<net_type> trainer(net, defsolver);
+        trainer.set_learning_rate(0.1);
+        trainer.set_min_learning_rate(0.01);
+        trainer.set_mini_batch_size(50);
+        trainer.set_max_num_epochs(170);
+        trainer.train(x, y);
+
+        const ::std::vector<matrix<float>> predictions = net(x);
+
+        int num_correct = 0;
+
+        for ( int ii = 0; ii < num_samples; ++ii ) {
+            const matrix<float>& prediction = predictions[ii];
+            DLIB_TEST(prediction.nr() == output_height);
+            DLIB_TEST(prediction.nc() == output_width);
+            for ( int jj = 0; jj < output_height; ++jj )
+                for ( int kk = 0; kk < output_width; ++kk )
+                    if ( (prediction(jj, kk) > 0.f) == ground_truth(x[ii], jj, kk) )
+                        ++num_correct;
+        }
+
+        // First some sanity checks.
+        const int num_correct_max = num_total_elements;
+        DLIB_TEST(num_correct_max == ::std::accumulate(truth_histogram.begin(), truth_histogram.end(), 0));
+        DLIB_TEST_MSG(num_correct <= num_correct_max,
+                      "Number of correctly classified elements = " << num_correct << ", max = " << num_correct_max);
+
+        // This is the real test, verifying that we have actually learned something.
+        const int num_correct_required = static_cast<int>(::std::ceil(0.9 * num_correct_max));
+        DLIB_TEST_MSG(num_correct >= num_correct_required,
+                      "Number of correctly classified elements = " << num_correct << ", required = " << num_correct_required);
+    }
+
 // ----------------------------------------------------------------------------------------
 
     void test_loss_multiclass_per_pixel_learned_params_on_trivial_single_pixel_task()
@@ -3429,6 +3625,9 @@ namespace
             test_multioutput_linear_regression();
             test_simple_autoencoder();
             test_loss_mean_squared_per_channel_and_pixel();
+            test_loss_binary_log_per_pixel_learned_params_on_trivial_two_pixel_task();
+            test_loss_binary_log_per_pixel_outputs_on_trivial_task();
+            test_loss_binary_log_per_pixel_with_noise_and_pixels_to_ignore();
             test_loss_multiclass_per_pixel_learned_params_on_trivial_single_pixel_task();
             test_loss_multiclass_per_pixel_activations_on_trivial_single_pixel_task();
             test_loss_multiclass_per_pixel_outputs_on_trivial_task();

examples/dnn_instance_segmentation_ex.cpp

@@ -16,7 +16,7 @@
        ./dnn_instance_segmentation_ex /path/to/VOC2012-or-other-images
 
     An alternative to steps 2-4 above is to download a pre-trained network
-    from here: http://dlib.net/files/instance_segmentation_voc2012net.dnn
+    from here: http://dlib.net/files/instance_segmentation_voc2012net_v2.dnn
 
     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
@@ -71,26 +71,21 @@ int main(int argc, char** argv) try
     {
         // Load the input image.
         load_image(input_image, file.full_name());
-
-        // Draw largest objects last
-        const auto sort_instances = [](const std::vector<mmod_rect>& input) {
-            auto output = input;
-            const auto compare_area = [](const mmod_rect& lhs, const mmod_rect& rhs) {
-                return lhs.rect.area() < rhs.rect.area();
-            };
-            std::sort(output.begin(), output.end(), compare_area);
-            return output;
-        };
-
+        
         // Find instances in the input image
-        const auto instances = sort_instances(det_net(input_image));
+        const auto instances = det_net(input_image);
 
         matrix<rgb_pixel> rgb_label_image;
+        matrix<float> label_image_confidence;
+
         matrix<rgb_pixel> input_chip;
 
         rgb_label_image.set_size(input_image.nr(), input_image.nc());
         rgb_label_image = rgb_pixel(0, 0, 0);
 
+        label_image_confidence.set_size(input_image.nr(), input_image.nc());
+        label_image_confidence = 0.0;
+
         bool found_something = false;
 
         for (const auto& instance : instances)
@@ -131,7 +126,7 @@ int main(int argc, char** argv) try
                 rnd.get_random_8bit_number()
             );
 
-            dlib::matrix<uint16_t> resized_mask(
+            dlib::matrix<float> resized_mask(
                 static_cast<int>(chip_details.rect.height()),
                 static_cast<int>(chip_details.rect.width())
             );
@@ -142,12 +137,29 @@ int main(int argc, char** argv) try
             {
                 for (int c = 0; c < resized_mask.nc(); ++c)
                 {
-                    if (resized_mask(r, c))
+                    const auto new_confidence = resized_mask(r, c);
+                    if (new_confidence > 0)
                     {
                         const auto y = chip_details.rect.top() + r;
                         const auto x = chip_details.rect.left() + c;
                         if (y >= 0 && y < rgb_label_image.nr() && x >= 0 && x < rgb_label_image.nc())
-                            rgb_label_image(y, x) = random_color;
+                        {
+                            auto& current_confidence = label_image_confidence(y, x);
+                            if (new_confidence > current_confidence)
+                            {
+                                auto rgb_label = random_color;
+                                const auto baseline_confidence = 5;
+                                if (new_confidence < baseline_confidence)
+                                {
+                                    // Scale label intensity if confidence isn't high
+                                    rgb_label.red   *= new_confidence / baseline_confidence;
+                                    rgb_label.green *= new_confidence / baseline_confidence;
+                                    rgb_label.blue  *= new_confidence / baseline_confidence;
+                                }
+                                rgb_label_image(y, x) = rgb_label;
+                                current_confidence = new_confidence;
+                            }
+                        }
                     }
                 }
             }

examples/dnn_instance_segmentation_ex.h

@@ -23,7 +23,7 @@
        ./dnn_instance_segmentation_ex /path/to/VOC2012-or-other-images
 
     An alternative to steps 2-4 above is to download a pre-trained network
-    from here: http://dlib.net/files/instance_segmentation_voc2012net.dnn
+    from here: http://dlib.net/files/instance_segmentation_voc2012net_v2.dnn
 
     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
@@ -159,13 +159,13 @@ template <typename SUBNET> using concat_utag4 = resize_and_concat<utag4,utag4_,S
 
 // ----------------------------------------------------------------------------------------
 
-static const char* instance_segmentation_net_filename = "instance_segmentation_voc2012net.dnn";
+static const char* instance_segmentation_net_filename = "instance_segmentation_voc2012net_v2.dnn";
 
 // ----------------------------------------------------------------------------------------
 
 // training network type
-using seg_bnet_type = dlib::loss_multiclass_log_per_pixel<
-                              dlib::cont<2,1,1,1,1,
+using seg_bnet_type = dlib::loss_binary_log_per_pixel<
+                              dlib::cont<1,1,1,1,1,
                               dlib::relu<dlib::bn_con<dlib::cont<16,7,7,2,2,
                               concat_utag1<level1t<
                               concat_utag2<level2t<
@@ -180,8 +180,8 @@ using seg_bnet_type = dlib::loss_multiclass_log_per_pixel<
                               >>>>>>>>>>>>>>>>>>>>>>>>>;
 
 // testing network type (replaced batch normalization with fixed affine transforms)
-using seg_anet_type = dlib::loss_multiclass_log_per_pixel<
-                              dlib::cont<2,1,1,1,1,
+using seg_anet_type = dlib::loss_binary_log_per_pixel<
+                              dlib::cont<1,1,1,1,1,
                               dlib::relu<dlib::affine<dlib::cont<16,7,7,2,2,
                               concat_utag1<alevel1t<
                               concat_utag2<alevel2t<

examples/dnn_instance_segmentation_train_ex.cpp

@@ -49,7 +49,7 @@ struct det_training_sample
 struct seg_training_sample
 {
     matrix<rgb_pixel> input_image;
-    matrix<uint16_t> label_image; // The ground-truth label of each pixel.
+    matrix<float> label_image; // The ground-truth label of each pixel. (+1 or -1)
 };
 
 // ----------------------------------------------------------------------------------------
@@ -321,12 +321,12 @@ det_bnet_type train_detection_network(
 
 // ----------------------------------------------------------------------------------------
 
-matrix<uint16_t> keep_only_current_instance(const matrix<rgb_pixel>& rgb_label_image, const rgb_pixel rgb_label)
+matrix<float> keep_only_current_instance(const matrix<rgb_pixel>& rgb_label_image, const rgb_pixel rgb_label)
 {
     const auto nr = rgb_label_image.nr();
     const auto nc = rgb_label_image.nc();
 
-    matrix<uint16_t> result(nr, nc);
+    matrix<float> result(nr, nc);
 
     for (long r = 0; r < nr; ++r)
     {
@@ -334,11 +334,11 @@ matrix<uint16_t> keep_only_current_instance(const matrix<rgb_pixel>& rgb_label_i
         {
             const auto& index = rgb_label_image(r, c);
             if (index == rgb_label)
-                result(r, c) = 1;
+                result(r, c) = +1;
             else if (index == dlib::rgb_pixel(224, 224, 192))
-                result(r, c) = dlib::loss_multiclass_log_per_pixel_::label_to_ignore;
-            else
                 result(r, c) = 0;
+            else
+                result(r, c) = -1;
         }
     }
 
@@ -373,7 +373,7 @@ seg_bnet_type train_segmentation_network(
     cout << seg_trainer << endl;
 
     std::vector<matrix<rgb_pixel>> samples;
-    std::vector<matrix<uint16_t>> labels;
+    std::vector<matrix<float>> labels;
 
     // Start a bunch of threads that read images from disk and pull out random crops.  It's
     // important to be sure to feed the GPU fast enough to keep it busy.  Using multiple