next change

explore_data.py

@@ -395,9 +395,10 @@ class ExploreData:
             lst = []
             for i in range (self.hist_to_batch.max()+1):
                 lst.append([])
-#            bb1d = bb[findx].reshape(self.num_tiles)    
-            disp_var_tiles =   disp_var[findx].reshape(self.num_tiles)
-            disp_neibs_tiles = disp_neibs[findx].reshape(self.num_tiles)
+#            bb1d = bb[findx].reshape(self.num_tiles)
+            if use_neibs:    
+                disp_var_tiles =   disp_var[findx].reshape(self.num_tiles)
+                disp_neibs_tiles = disp_neibs[findx].reshape(self.num_tiles)
             for n, indx in enumerate(bb[findx].reshape(self.num_tiles)):
                 if indx >= 0:
                     if use_neibs:
@@ -718,10 +719,10 @@ if __name__ == "__main__":
       ml_subdir =   "ml"
 
   #Parameters to generate neighbors data. Set radius to 0 to generate single-tile     
-  RADIUS = 1
+  RADIUS = 0
   MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9
   VARIANCE_THRESHOLD = 1.5
-  NUM_TRAIN_SETS = 2
+  NUM_TRAIN_SETS = 6
  
   if RADIUS == 0:
     BATCH_DISP_BINS = 20

nn_ds_inmem4.py

@@ -36,6 +36,7 @@ ABSOLUTE_DISPARITY = False # True # False
 DEBUG_PLT_LOSS =     True
 FEATURES_PER_TILE =  324
 EPOCHS_TO_RUN =     10000 #0
+EPOCHS_SAME_FILE =   20
 RUN_TOT_AVG =       100 # last batches to average. Epoch is 307 training  batches  
 BATCH_SIZE =       1000 # Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
 SHUFFLE_EPOCH =    True
@@ -115,12 +116,13 @@ def read_and_decode(filename_queue):
 try:
     train_filenameTFR =  sys.argv[1]
 except IndexError:
-    train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train.tfrecords"
+    train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_00.tfrecords"
 try:
     test_filenameTFR =  sys.argv[2]
 except IndexError:
     test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
 #FILES_PER_SCENE
+train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"
 
 import tensorflow as tf
 import tensorflow.contrib.slim as slim
@@ -128,6 +130,13 @@ import tensorflow.contrib.slim as slim
 print_time("Importing training data... ", end="")
 corr2d_train, target_disparity_train, gt_ds_train = readTFRewcordsEpoch(train_filenameTFR)
 print_time("  Done")
+print_time("Importing second training data... ", end="")
+corr2d_train1, target_disparity_train1, gt_ds_train1 = readTFRewcordsEpoch(train_filenameTFR1)
+print_time("  Done")
+
+corr2d_trains =           [corr2d_train,            corr2d_train1] 
+target_disparity_trains = [target_disparity_train,  target_disparity_train1] 
+gt_ds_trains =            [gt_ds_train,             gt_ds_train1] 
 
 corr2d_train_placeholder =           tf.placeholder(corr2d_train.dtype,           (None,324)) # corr2d_train.shape)
 target_disparity_train_placeholder = tf.placeholder(target_disparity_train.dtype, (None,1))  #target_disparity_train.shape)
@@ -382,14 +391,14 @@ with tf.Session()  as sess:
     train2_avg = 0.0
     test_avg = 0.0     
     test2_avg = 0.0
-    
-    for epoch in range(EPOCHS_TO_RUN):
-        
-#       if SHUFFLE_EPOCH:
-#        dataset_train = dataset_train.shuffle(buffer_size=10000)
-        sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_train,
-                                                        target_disparity_train_placeholder: target_disparity_train,
-                                                        gt_ds_train_placeholder: gt_ds_train})
+    for epoch in range (EPOCHS_TO_RUN):
+#        file_index = (epoch // 20) % 2 
+        file_index = (epoch // 1) % 2 
+    #       if SHUFFLE_EPOCH:
+    #        dataset_train = dataset_train.shuffle(buffer_size=10000)
+        sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[file_index],
+                                                        target_disparity_train_placeholder: target_disparity_trains[file_index],
+                                                        gt_ds_train_placeholder: gt_ds_trains[file_index]})
         for i in range(dataset_train_size):
             try:
                 train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out  = sess.run(