adapt to dlib indentation style

dlib/dnn/loss.h

@@ -1305,70 +1305,70 @@ namespace dlib
             typename SUB_TYPE,
             typename label_iterator
             >
-            void to_label (
-                const tensor& input_tensor,
-                const SUB_TYPE& sub,
-                label_iterator iter
-                ) const
-            {
-                DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+        void to_label (
+            const tensor& input_tensor,
+            const SUB_TYPE& sub,
+            label_iterator iter
+        ) const
+        {
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
 
-                const tensor& output_tensor = sub.get_output();
+            const tensor& output_tensor = sub.get_output();
 
-                DLIB_CASSERT(output_tensor.nr() == 1 &&
-                             output_tensor.nc() == 1 &&
-                             output_tensor.k() == 1);
-                DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
+            DLIB_CASSERT(output_tensor.nr() == 1 &&
+                         output_tensor.nc() == 1 &&
+                         output_tensor.k() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
 
-                const float* out_data = output_tensor.host();
-                for (long i = 0; i < output_tensor.num_samples(); ++i)
-                {
-                    *iter++ = out_data[i];
-                }
+            const float* out_data = output_tensor.host();
+            for (long i = 0; i < output_tensor.num_samples(); ++i)
+            {
+                *iter++ = out_data[i];
             }
+        }
 
 
         template <
             typename const_label_iterator,
             typename SUBNET
             >
-            double compute_loss_value_and_gradient (
-                const tensor& input_tensor,
-                const_label_iterator truth,
-                SUBNET& sub
-                ) const
+        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.nr() == 1 &&
+                         output_tensor.nc() == 1 &&
+                         output_tensor.k() == 1);
+            DLIB_CASSERT(grad.nr() == 1 &&
+                         grad.nc() == 1 &&
+                         grad.k() == 1);
+
+            // The loss we output is the average loss over the mini-batch.
+            const double scale = 1.0/output_tensor.num_samples();
+            double loss = 0;
+            float* g = grad.host_write_only();
+            const float* out_data = output_tensor.host();
+            for (long i = 0; i < output_tensor.num_samples(); ++i)
             {
-                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.nr() == 1 &&
-                             output_tensor.nc() == 1 &&
-                             output_tensor.k() == 1);
-                DLIB_CASSERT(grad.nr() == 1 &&
-                             grad.nc() == 1 &&
-                             grad.k() == 1);
-
-                // The loss we output is the average loss over the mini-batch.
-                const double scale = 1.0/output_tensor.num_samples();
-                double loss = 0;
-                float* g = grad.host_write_only();
-                const float* out_data = output_tensor.host();
-                for (long i = 0; i < output_tensor.num_samples(); ++i)
-                {
-                    const float y = *truth++;
-                    const float temp1 = y - out_data[i];
-                    const float temp2 = scale*temp1;
-                    loss += 0.5*temp2*temp1;
-                    g[i] = -temp2;
+                const float y = *truth++;
+                const float temp1 = y - out_data[i];
+                const float temp2 = scale*temp1;
+                loss += 0.5*temp2*temp1;
+                g[i] = -temp2;
 
-                }
-                return loss;
             }
+            return loss;
+        }
 
         friend void serialize(const loss_mean_squared_& , std::ostream& out)
         {
@@ -1397,7 +1397,7 @@ namespace dlib
     };
 
     template <typename SUBNET>
-        using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
+    using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
 
 // ----------------------------------------------------------------------------------------
 

dlib/dnn/loss_abstract.h

@@ -584,7 +584,6 @@ namespace dlib
     template <typename SUBNET>
     using loss_mean_squared = add_loss_layer<loss_mean_squared_, SUBNET>;
 
-
 }
 
 #endif // DLIB_DNn_LOSS_ABSTRACT_H_

dlib/test/dnn.cpp

@@ -1758,11 +1758,7 @@ namespace
             y[ii] = (true_intercept + true_slope*static_cast<float>(val) + distribution(generator));
         }
 
-        using net_type = loss_mean_squared<
-            fc<
-                1, input<matrix<double>>
-                >
-            >;
+        using net_type = loss_mean_squared<fc<1, input<matrix<double>>>>;
         net_type net;
         layer<1>(net).layer_details().set_bias_learning_rate_multiplier(300);
         sgd defsolver;