n-files

nn_ds_inmem4_tmp.py

@@ -42,6 +42,7 @@ SHUFFLE_EPOCH =    True
 NET_ARCH =           3 # overwrite with argv?
 #DEBUG_PACK_TILES = True
 SUFFIX=str(NET_ARCH)+ (["R","A"][ABSOLUTE_DISPARITY])
+MAX_TRAIN_FILES_TFR = 1
 #http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
 class bcolors:
     HEADER = '\033[95m'
@@ -126,6 +127,8 @@ if os.path.isdir(train_filenameTFR):
 else:
     train_filesTFR = [train_filenameTFR]
 
+print("Train tfrecords: "+str(train_filesTFR))
+     
 # tfrecords' paths for testing    
 try:
     test_filenameTFR =  sys.argv[2]
@@ -139,12 +142,29 @@ if os.path.isdir(test_filenameTFR):
 else:
     test_filesTFR = [test_filenameTFR]
 
+print("Test tfrecords: "+str(test_filesTFR))
+    
+# Now we are left with 2 lists - train and test list
+n_allowed_train_filesTFR = min(MAX_TRAIN_FILES_TFR,len(train_filesTFR))
+
 
 import tensorflow as tf
 import tensorflow.contrib.slim as slim
 
 print_time("Importing training data... ", end="")
-corr2d_train, target_disparity_train, gt_ds_train = readTFRewcordsEpoch(train_filenameTFR)
+
+corr2d_trains = [None]*n_allowed_train_filesTFR
+target_disparity_trains = [None]*n_allowed_train_filesTFR
+gt_ds_trains = [None]*n_allowed_train_filesTFR
+
+# Load maximum files from the list
+for i in range(n_allowed_train_filesTFR):
+    corr2d_trains[i], target_disparity_trains[i], gt_ds_trains[i] = readTFRewcordsEpoch(train_filesTFR[i])
+
+corr2d_train = corr2d_trains[0] 
+target_disparity_train = target_disparity_trains[0]
+gt_ds_train = gt_ds_trains[0]
+    
 print_time("  Done")
 
 corr2d_train_placeholder =           tf.placeholder(corr2d_train.dtype,           (None,324)) # corr2d_train.shape)
@@ -390,14 +410,19 @@ shutil.rmtree(TEST_PATH, ignore_errors=True)
 # threading
 from threading import Thread
 
+thr_result = []
+
 def read_new_tfrecord_file(filename,result):
+    global thr_result
     a,b,c = readTFRewcordsEpoch(filename)
-    result = [a,b,c]
+    #result = [a,b,c]
+    result.append(a)
+    result.append(b)
+    result.append(c)
     print("Loaded new tfrecord file: "+str(filename))
 
-tfrecord_filename = train_filenameTFR
-
-tfrecord_file_counter = 0 
+train_record_index_counter = 0 
+train_file_index = 0
 
 with tf.Session()  as sess:
     
@@ -418,26 +443,44 @@ with tf.Session()  as sess:
     
     for epoch in range(EPOCHS_TO_RUN):
         
-        if epoch%30==0:
+        train_file_index = epoch%n_allowed_train_filesTFR
         
-            print_time("Time to begin loading a new tfrecord file")
+        if epoch%10==0:
             
+            # if there are more files than python3 memory allows 
+            if (n_allowed_train_filesTFR<len(train_filesTFR)):
+                # circular loading?
+                tmp_train_index = (n_allowed_train_filesTFR+train_record_index_counter)%len(train_filesTFR)
                 # wait for old thread 
                 if epoch!=0:
+                    if thr.is_alive(): 
+                        print_time("Waiting until tfrecord gets loaded")
                     thr.join()
+                    # do replacement
+                    ## remove the first 
+                    corr2d_trains.pop(0)
+                    target_disparity_trains.pop(0)
+                    gt_ds_trains.pop(0)
+                    ## append
+                    corr2d_trains.append(thr_result[0])
+                    target_disparity_trains.append(thr_result[1])
+                    gt_ds_trains.append(thr_result[2])
             
+                print_time("Time to begin loading a new tfrecord file")
                 # new thread  
                 thr_result = []              
-            thr = Thread(target=read_new_tfrecord_file, args=(tfrecord_filename,thr_result))
-            
+                thr = Thread(target=read_new_tfrecord_file, args=(train_filesTFR[tmp_train_index],thr_result))
                 # start
                 thr.start()
 
+                train_record_index_counter += 1
+        
 #       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})
+
+        sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[train_file_index],
+                                                        target_disparity_train_placeholder: target_disparity_trains[train_file_index],
+                                                        gt_ds_train_placeholder: gt_ds_trains[train_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(