combining low/high disparity cluster variance into the same batch

nn_ds_neibs.py

@@ -46,8 +46,8 @@ RUN_TOT_AVG =       100 # last batches to average. Epoch is 307 training  batche
 #BATCH_SIZE =       1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
 BATCH_SIZE =       2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
 SHUFFLE_EPOCH =    True
-NET_ARCH1 =          0 # 4 # 3 # overwrite with argv?
-NET_ARCH2 =          0 # 3 # overwrite with argv?
+NET_ARCH1 =          6 #0 # 4 # 3 # overwrite with argv?
+NET_ARCH2 =          6 # 0 # 3 # overwrite with argv?
 ONLY_TILE =          None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
 
 
@@ -60,7 +60,9 @@ NN_LAYOUTS = {0:[0,   0,   0,   32,  20,  16],
               1:[0,   0,   0,  256, 128,  64],
               2:[0, 128,  32,   32,  32,  16],
               3:[0,   0,  40,   32,  20,  16],
-              4:[0,   0,   0,   0,   16,  16]
+              4:[0,   0,   0,   0,   16,  16],
+              5:[0,   0,  64,  32,   32,  16],
+              6:[0,   0,  32,  16,   16,  16],
               }
 NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
 NN_LAYOUT2 = NN_LAYOUTS[NET_ARCH2]
@@ -609,12 +611,14 @@ G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
 saver=tf.train.Saver()
 
 ROOT_PATH  = './attic/nn_ds_neibs_graph'+SUFFIX+"/"
-TRAIN_PATH = ROOT_PATH + 'train'
-TEST_PATH  = ROOT_PATH + 'test'
+TRAIN_PATH =  ROOT_PATH + 'train'
+TEST_PATH  =  ROOT_PATH + 'test'
+TEST_PATH1  = ROOT_PATH + 'test1'
 
 # CLEAN OLD STAFF
 shutil.rmtree(TRAIN_PATH, ignore_errors=True)
 shutil.rmtree(TEST_PATH, ignore_errors=True)
+shutil.rmtree(TEST_PATH1, ignore_errors=True)
 
 with tf.Session()  as sess:
     
@@ -624,6 +628,7 @@ with tf.Session()  as sess:
     merged = tf.summary.merge_all()
     train_writer =    tf.summary.FileWriter(TRAIN_PATH, sess.graph)
     test_writer  =    tf.summary.FileWriter(TEST_PATH, sess.graph)
+    test_writer1  =   tf.summary.FileWriter(TEST_PATH1, sess.graph)
     loss_train_hist=  np.empty(dataset_train_size, dtype=np.float32)
     loss_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
     loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)
@@ -682,14 +687,16 @@ with tf.Session()  as sess:
         train2_avg =      np.average(loss2_train_hist).astype(np.float32)
         gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
         
-        for dataset_test in datasets_test:
+        test_summaries = [0.0]*len(datasets_test)
+        for ntest,dataset_test in enumerate(datasets_test):
             sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:      dataset_test['corr2d'],
                                                     target_disparity_train_placeholder: dataset_test['target_disparity'],
                                                     gt_ds_train_placeholder:            dataset_test['gt_ds']})
-    
+            
             for i in range(dataset_test_size):
                 try:
-                    test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
+#                    test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
+                    test_summaries[ntest], G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
                         [merged,
                          G_loss,
                          out,
@@ -717,13 +724,15 @@ with tf.Session()  as sess:
 #        _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})
         
         train_writer.add_summary(train_summary, epoch)
-        test_writer.add_summary(test_summary, epoch)
+        test_writer.add_summary(test_summaries[0], epoch)
+        test_writer1.add_summary(test_summaries[1], epoch)
         
         print_time("%d:%d -> %f %f (%f %f) dbg:%f %f"%(epoch,i,train_avg, test_avg,train2_avg, test2_avg, gtvar_train_avg, gtvar_test_avg))
      
      # Close writers
     train_writer.close()
     test_writer.close()
+    test_writer1.close()
 #reports error: Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7efc5f720ef0>> if there is no print before exit()
 
 print("All done")