fixed backward pass in cont layer to accumulate gradients this will pass the...

fixed backward pass in cont layer to accumulate gradients this will pass the layer test now also removed compile warnings and changed some comments

dlib/dnn/cpu_dlib.h

@@ -355,14 +355,16 @@ namespace dlib
             ) {}
 
             void setup(
-                const tensor& data,
-                const tensor& filters,
+                const tensor& data,    /* not used but required for interface */
+                const tensor& filters, /* not used but required for interface */
                 int stride_y,
                 int stride_x,
                 int padding_y,
                 int padding_x
             ) 
             {
+                (void)data;    /* silence compiler */
+                (void)filters; /* silence compiler */
                 last_stride_y = stride_y;
                 last_stride_x = stride_x;
                 last_padding_y = padding_y;

dlib/dnn/layers.h

@@ -454,9 +454,9 @@ namespace dlib
             temp.copy_size(sub.get_gradient_input());
             auto filt = filters(params,0);           
             conv(temp,gradient_input, filt);
-            tt::copy_tensor(sub.get_gradient_input(),0,temp,0,sub.get_gradient_input().k());
+            // need to add the new gradients on top of the previous ones
+            tt::add(1,sub.get_gradient_input(),1,temp);
             // no point computing the parameter gradients if they won't be used.
-            
             if (learning_rate_multiplier != 0)
             {
                 auto filt = filters(params_grad,0);                
@@ -582,8 +582,6 @@ namespace dlib
         double bias_learning_rate_multiplier;
         double bias_weight_decay_multiplier;
 
-        // These are here only because older versions of con (which you might encounter
-        // serialized to disk) used different padding settings.
         int padding_y_;
         int padding_x_;
 

dlib/dnn/layers_abstract.h

@@ -850,16 +850,17 @@ namespace dlib
 
             WHAT THIS OBJECT REPRESENTS
                 This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface
-                defined above.  In particular, it defines a convolution layer that takes an
-                input tensor (nominally representing an image) and convolves it with a set
-                of filters and then outputs the results. 
+                defined above.  In particular, it defines a transposed convolution layer 
+                that takes an input tensor (nominally representing an image) and 
+                transpose convolves (deconvolves) it with a set of filters and then outputs the results. 
+                This is basically a convolutional layer with reversed forward/backward passes
                 
                 The dimensions of the tensors output by this layer are as follows (letting
                 IN be the input tensor and OUT the output tensor):
                     - OUT.num_samples() == IN.num_samples()
                     - OUT.k()  == num_filters()
-                    - OUT.nr() == 1+(IN.nr() + 2*padding_y() - nr())/stride_y()
-                    - OUT.nc() == 1+(IN.nc() + 2*padding_x() - nc())/stride_x()
+                    - OUT.nr() == stride_y * (IN.nr() -1) + nr) - 2*padding_y
+                    - OUT.nc() == stride_x * (IN.nc() -1) + nc) - 2*padding_x
         !*/
 
     public:

dlib/test/dnn.cpp

@@ -1410,6 +1410,18 @@ namespace
             auto res = test_layer(l);
             DLIB_TEST_MSG(res, res);
         }
+        {
+            print_spinner();
+            cont_<3,3,3,1,1> l;
+            auto res = test_layer(l);
+            DLIB_TEST_MSG(res, res);
+        }
+        {
+            print_spinner();
+            cont_<3,2,2,2,2> l;
+            auto res = test_layer(l);
+            DLIB_TEST_MSG(res, res);
+        }
         {
             print_spinner();
             con_<3,2,2,2,2> l;