Added scope

nn_ds_inmem.py

@@ -194,6 +194,7 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
               conf_pwr =               2.0,
               gt_conf_offset =         0.08,
               gt_conf_pwr =            1.0):
+    with tf.name_scope("BatchLoss"):
         """
         Here confidence should be after relU. Disparity - may be also if absolute, but no activation if output is residual disparity
         """
@@ -209,11 +210,11 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
         if gt_conf_pwr == 0:
             w = tf.ones((out_batch.shape[0]), dtype=tf.float32,name="w_ones")
         else:
-#        w_slice = tf.slice(gt_ds_batch,[0,1],[-1,1],              name = "w_gt_slice")
+    #        w_slice = tf.slice(gt_ds_batch,[0,1],[-1,1],              name = "w_gt_slice")
             w_slice = tf.reshape(gt_ds_batch[:,1],[-1],                     name = "w_gt_slice")
             
             w_sub =   tf.subtract      (w_slice, tf_gt_conf_offset,         name = "w_sub")
-#        w_clip =  tf.clip_by_value(w_sub, tf_0f,tf_maxw,              name = "w_clip")
+    #        w_clip =  tf.clip_by_value(w_sub, tf_0f,tf_maxw,              name = "w_clip")
             w_clip =  tf.maximum(w_sub, tf_0f,                              name = "w_clip")
             if gt_conf_pwr == 1.0:
                 w = w_clip
@@ -222,7 +223,7 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
     
         if use_confidence:
             tf_num_tilesf =      tf.cast(tf_num_tiles, dtype=tf.float32,     name="tf_num_tilesf")
-#        conf_slice =     tf.slice(out_batch,[0,1],[-1,1],                name = "conf_slice")
+    #        conf_slice =     tf.slice(out_batch,[0,1],[-1,1],                name = "conf_slice")
             conf_slice =     tf.reshape(out_batch[:,1],[-1],                 name = "conf_slice")
             conf_sum =       tf.reduce_sum(conf_slice,                       name = "conf_sum")
             conf_avg =       tf.divide(conf_sum, tf_num_tilesf,              name = "conf_avg")
@@ -239,15 +240,15 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
             w_all =          tf.multiply (w, nconf,                          name = "w_all")
         else:
             w_all = w
-        cost2 = 0.0
-        cost3 = 0.0    
+#            cost2 = 0.0
+#            cost3 = 0.0    
         # normalize weights
         w_sum =              tf.reduce_sum(w_all,                            name = "w_sum")
         iw_sum =             tf.divide(tf_1f, w_sum,                         name = "iw_sum")
         w_norm =             tf.multiply (w_all, iw_sum,                     name = "w_norm")
         
-#    disp_slice =         tf.slice(out_batch,[0,0],[-1,1],                name = "disp_slice")
-#    d_gt_slice =         tf.slice(gt_ds_batch,[0,0],[-1,1],              name = "d_gt_slice")
+    #    disp_slice =         tf.slice(out_batch,[0,0],[-1,1],                name = "disp_slice")
+    #    d_gt_slice =         tf.slice(gt_ds_batch,[0,0],[-1,1],              name = "d_gt_slice")
         disp_slice =         tf.reshape(out_batch[:,0],[-1],                 name = "disp_slice")
         d_gt_slice =         tf.reshape(gt_ds_batch[:,0],[-1],               name = "d_gt_slice")
         if absolute_disparity: