Trying next versions with partial second stage - 5x5+3x3+1x1

explore_data2.py

@@ -1030,7 +1030,7 @@ if __name__ == "__main__":
   
   ml_list_train=ex_data.getMLList(ml_subdir, ex_data.files_train)
   ml_list_test= ex_data.getMLList(ml_subdir, ex_data.files_test)
-  num_test_scenes = [ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN]    
+  num_test_scenes = len([ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN])    
           
 
   if RADIUS == 0 :

nn_ds_neibs10.py

+#!/usr/bin/env python3
+from numpy import float64
+from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
+from debian.deb822 import PdiffIndex
+
+__copyright__ = "Copyright 2018, Elphel, Inc."
+__license__   = "GPL-3.0+"
+__email__     = "andrey@elphel.com"
+
+
+from PIL import Image
+
+import os
+import sys
+import glob
+
+import numpy as np
+import itertools
+
+import time
+
+import matplotlib.pyplot as plt
+
+import shutil
+import sys
+from threading import Thread
+
+TIME_START = time.time()
+TIME_LAST  = TIME_START
+DEBUG_LEVEL= 1
+DISP_BATCH_BINS =   20 # Number of batch disparity bins
+STR_BATCH_BINS =    10 # Number of batch strength bins
+FILES_PER_SCENE =    5 # number of random offset files for the scene to select from (0 - use all available)
+#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from 
+#MAX_BATCH_FILES =   10 #maximal number of files to use in a batch
+#MAX_EPOCH =        500
+LR =               1e-3 # learning rate
+LR100 =            3e-4 #LR    # 1e-4
+LR200 =            1e-4 #LR100 # 3e-5
+LR400 =            3e-5 #LR200 # 1e-5
+LR600 =            1e-5 #LR400 # 3e-6
+USE_CONFIDENCE =     False
+ABSOLUTE_DISPARITY = False # True # False # True # False
+DEBUG_PLT_LOSS =     True
+TILE_LAYERS =        4
+FILE_TILE_SIDE =     9
+TILE_SIDE =          9 # 7
+TILE_SIZE =         TILE_SIDE* TILE_SIDE # == 81
+FEATURES_PER_TILE =  TILE_LAYERS * TILE_SIZE# == 324
+EPOCHS_TO_RUN =     3000#0 #0
+EPOCHS_FULL_TEST =   5 # 10 # 25# repeat full image test after this number of epochs
+#EPOCHS_SAME_FILE =   20
+RUN_TOT_AVG =       100 # last batches to average. Epoch is 307 training  batches  
+#BATCH_SIZE =       2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
+TWO_TRAINS =       True # use 2 train sets         
+BATCH_SIZE =       ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
+SHUFFLE_EPOCH =    True
+NET_ARCH1 =       0 # 4 # #4 # 8 # 4 # 0 #3 # 0 #  0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?
+NET_ARCH2 =       0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 #  0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?
+SYM8_SUB =        False # True #False #  True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
+ONLY_TILE =        None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
+ZIP_LHVAR =        True # combine _lvar and _hvar as odd/even elements 
+#DEBUG_PACK_TILES = True
+# CLUSTER_RADIUS should match input data
+CLUSTER_RADIUS =     2 # 1 # 1 - 3x3, 2 - 5x5 tiles
+SHUFFLE_FILES  =     True
+WLOSS_LAMBDA =       0.5 #3.0 # 1.0 # 0.3 # 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
+WBORDERS_ZERO =      True # Border conditions for first layer weights: False - free, True - tied to 0
+MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8
+FILE_UPDATE_EPOCHS =  2 # update train files each this many epochs. 0 - do not update
+PARTIALS_WEIGHTS = [1.0,1.0,1.0] # weight of full 5x5, center 3x3 and center 1x1. len(PARTIALS_WEIGHTS) == CLUSTER_RADIUS + 1. Set to None
+
+SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY]) +(["NS","S8"][SYM8_SUB])+"LMBD"+str(WLOSS_LAMBDA)
+
+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],
+              5:[0,   0,  64,   32,  32,  16],
+              6:[0,   0,  32,   16,  16,  16],
+              7:[0,   0,  64,   16,  16,  16],
+              8:[0,   0,   0,   64,  20,  16],
+              }
+NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
+NN_LAYOUT2 = NN_LAYOUTS[NET_ARCH2]
+USE_PARTIALS =      not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets
+
+#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[38;5;214m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    BOLDWHITE = '\033[1;37m'
+    UNDERLINE = '\033[4m'
+def print_time(txt="",end="\n"):
+    global TIME_LAST
+    t = time.time()
+    if txt:
+        txt +=" "
+    print(("%s"+bcolors.BOLDWHITE+"at %.4fs (+%.4fs)"+bcolors.ENDC)%(txt,t-TIME_START,t-TIME_LAST), end = end, flush=True)
+    TIME_LAST = t
+#reading to memory (testing)
+train_next = [{'file':0, 'slot':0, 'files':0, 'slots':0},
+              {'file':0, 'slot':0, 'files':0, 'slots':0}]
+
+if TWO_TRAINS:
+    train_next +=  [{'file':0, 'slot':0, 'files':0, 'slots':0},
+                    {'file':0, 'slot':0, 'files':0, 'slots':0}]
+def readTFRewcordsEpoch(train_filename):
+#    filenames = [train_filename]
+#    dataset = tf.data.TFRecorDataset(filenames)
+    if not  '.tfrecords' in train_filename:
+        train_filename += '.tfrecords'
+    npy_dir_name = "npy"
+    dirname = os.path.dirname(train_filename) 
+    npy_dir = os.path.join(dirname, npy_dir_name)
+    filebasename, file_extension = os.path.splitext(train_filename)
+    filebasename = os.path.basename(filebasename)
+    file_corr2d =           os.path.join(npy_dir,filebasename + '_corr2d.npy')
+    file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
+    file_gt_ds =            os.path.join(npy_dir,filebasename + '_gt_ds.npy')
+    if (os.path.exists(file_corr2d) and
+        os.path.exists(file_target_disparity) and
+        os.path.exists(file_gt_ds)):
+        corr2d=            np.load (file_corr2d)
+        target_disparity = np.load(file_target_disparity)
+        gt_ds =            np.load(file_gt_ds)
+        pass
+    else:     
+        record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
+        corr2d_list=[]
+        target_disparity_list=[]
+        gt_ds_list = []
+        for string_record in record_iterator:
+            example = tf.train.Example()
+            example.ParseFromString(string_record)
+            corr2d_list.append           (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32))
+            target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32))
+            gt_ds_list.append            (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32))
+            pass
+        corr2d=            np.array(corr2d_list)
+        target_disparity = np.array(target_disparity_list)
+        gt_ds =            np.array(gt_ds_list)
+        try:
+            os.makedirs(os.path.dirname(file_corr2d))
+        except:
+            pass     
+
+        np.save(file_corr2d,           corr2d)
+        np.save(file_target_disparity, target_disparity)
+        np.save(file_gt_ds,            gt_ds)
+    return corr2d, target_disparity, gt_ds
+
+def getMoreFiles(fpaths,rslt):
+    for fpath in fpaths:
+        corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+        dataset = {"corr2d":           corr2d,
+                     "target_disparity": target_disparity,
+                     "gt_ds":            gt_ds}
+        if FILE_TILE_SIDE > TILE_SIDE:
+            reduce_tile_size([dataset],   TILE_LAYERS, TILE_SIDE)
+        reformat_to_clusters([dataset])
+        rslt.append(dataset)
+
+   
+
+#from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
+def read_and_decode(filename_queue):
+    reader = tf.TFRecordReader()
+    _, serialized_example = reader.read(filename_queue)
+
+    features = tf.parse_single_example(
+      serialized_example,
+      # Defaults are not specified since both keys are required.
+      features={
+        'corr2d':           tf.FixedLenFeature([FEATURES_PER_TILE],tf.float32), #string),
+        'target_disparity': tf.FixedLenFeature([1],   tf.float32), #.string),
+        'gt_ds':            tf.FixedLenFeature([2],  tf.float32)  #.string)
+        })
+    corr2d =           features['corr2d'] # tf.decode_raw(features['corr2d'], tf.float32)
+    target_disparity = features['target_disparity'] # tf.decode_raw(features['target_disparity'], tf.float32)
+    gt_ds =            tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], tf.float32)
+    in_features = tf.concat([corr2d,target_disparity],0)
+    # still some nan-s in correlation data?
+#    in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features)     
+#    corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features_clean, target_disparity, gt_ds],
+    corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, target_disparity, gt_ds],
+                                                 batch_size=1000, # 2,
+                                                 capacity=30,
+                                                 num_threads=2,
+                                                 min_after_dequeue=10)
+    return corr2d_out, target_disparity_out, gt_ds_out
+#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
+def add_margins(npa,radius, val = np.nan):
+    npa_ext = np.empty((npa.shape[0]+2*radius, npa.shape[1]+2*radius, npa.shape[2]), dtype = npa.dtype)
+    npa_ext[radius:radius + npa.shape[0],radius:radius + npa.shape[1]] = npa
+    npa_ext[0:radius,:,:] = val  
+    npa_ext[radius + npa.shape[0]:,:,:] = val  
+    npa_ext[:,0:radius,:] = val  
+    npa_ext[:, radius + npa.shape[1]:,:] = val
+    return npa_ext   
+
+def add_neibs(npa_ext,radius):
+    height = npa_ext.shape[0]-2*radius
+    width =  npa_ext.shape[1]-2*radius
+    side = 2 * radius + 1
+    size = side * side
+    npa_neib = np.empty((height,  width, side, side, npa_ext.shape[2]), dtype = npa_ext.dtype)
+    for dy in range (side):
+        for dx in range (side):
+            npa_neib[:,:,dy, dx,:]= npa_ext[dy:dy+height, dx:dx+width]
+    return npa_neib.reshape(height, width, -1)    
+    
+def extend_img_to_clusters(datasets_img,radius):
+    side = 2 * radius + 1
+    size = side * side
+    width =  324
+    if len(datasets_img) ==0:
+        return
+    num_tiles = datasets_img[0]['corr2d'].shape[0]
+    height = num_tiles // width 
+    for rec in datasets_img:
+        rec['corr2d'] =           add_neibs(add_margins(rec['corr2d'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1)) 
+        rec['target_disparity'] = add_neibs(add_margins(rec['target_disparity'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1)) 
+        rec['gt_ds'] =            add_neibs(add_margins(rec['gt_ds'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
+        pass
+
+
+
+def reformat_to_clusters(datasets_data):
+    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    for rec in datasets_data:
+        rec['corr2d'] =           rec['corr2d'].reshape(          (rec['corr2d'].shape[0]//cluster_size,           rec['corr2d'].shape[1] * cluster_size)) 
+        rec['target_disparity'] = rec['target_disparity'].reshape((rec['target_disparity'].shape[0]//cluster_size, rec['target_disparity'].shape[1] * cluster_size)) 
+        rec['gt_ds'] =            rec['gt_ds'].reshape(           (rec['gt_ds'].shape[0]//cluster_size,            rec['gt_ds'].shape[1] * cluster_size))
+
+def replace_nan(datasets_data):
+    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    for rec in datasets_data:
+        np.nan_to_num(rec['corr2d'],           copy = False) 
+        np.nan_to_num(rec['target_disparity'], copy = False) 
+        np.nan_to_num(rec['gt_ds'],            copy = False)
+
+def permute_to_swaps(perm):
+    pairs = []
+    for i in range(len(perm)):
+        w = np.where(perm == i)[0][0]
+        if w != i:
+            pairs.append([i,w])
+            perm[w] = perm[i]
+            perm[i] = i
+    return pairs        
+        
+def shuffle_in_place(datasets_data, indx, period):
+    swaps = permute_to_swaps(np.random.permutation(len(datasets_data)))
+    num_entries = datasets_data[0]['corr2d'].shape[0] // period
+    for swp in swaps:
+        ds0 = datasets_data[swp[0]]
+        ds1 = datasets_data[swp[1]]
+        tmp = ds0['corr2d'][indx::period].copy() 
+        ds0['corr2d'][indx::period] = ds1['corr2d'][indx::period]
+        ds1['corr2d'][indx::period] = tmp
+
+        tmp = ds0['target_disparity'][indx::period].copy() 
+        ds0['target_disparity'][indx::period] = ds1['target_disparity'][indx::period]
+        ds1['target_disparity'][indx::period] = tmp
+
+        tmp = ds0['gt_ds'][indx::period].copy() 
+        ds0['gt_ds'][indx::period] = ds1['gt_ds'][indx::period]
+        ds1['gt_ds'][indx::period] = tmp
+    
+def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
+    """
+    Improve shuffling by preserving indices inside batches (0 <->0, ... 39 <->39 for 40 tile group batches)
+    """
+    num_files = len(datasets_data)
+    #chunks_per_file = datasets_data[0]['target_disparity']
+    for nf, ds in enumerate(datasets_data):
+        groups_per_file = ds['corr2d'].shape[0]
+        chunks_per_file = groups_per_file//tiles_groups_per_chunk
+        permut = np.random.permutation(chunks_per_file)
+        ds['corr2d'] =           ds['corr2d'].          reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+        ds['target_disparity'] = ds['target_disparity'].reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+        ds['gt_ds'] =            ds['gt_ds'].           reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+
+
+def _setFileSlot(train_next,files):
+    train_next['files'] = files
+    train_next['slots'] = min(train_next['files'], MAX_FILES_PER_GROUP)
+     
+def _nextFileSlot(train_next):
+    train_next['file'] = (train_next['file'] + 1) % train_next['files'] 
+    train_next['slot'] = (train_next['slot'] + 1) % train_next['slots'] 
+
+    
+def replaceNextDataset(datasets_data, new_dataset, train_next, nset,period):
+    replaceDataset(datasets_data, new_dataset, nset, period, findx = train_next['slot'])
+#    _nextFileSlot(train_next[nset])
+
+        
+def replaceDataset(datasets_data, new_dataset, nset, period, findx):
+    """
+    Replace one file in the dataset
+    """
+    datasets_data[findx]['corr2d']          [nset::period] =  new_dataset['corr2d'] 
+    datasets_data[findx]['target_disparity'][nset::period] =  new_dataset['target_disparity'] 
+    datasets_data[findx]['gt_ds']           [nset::period] =  new_dataset['gt_ds'] 
+       
+    
+
+def zip_lvar_hvar(datasets_all_data, del_src = True):
+#    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    num_sets_to_combine = len(datasets_all_data)
+    datasets_data = []
+    if num_sets_to_combine:
+        for nrec in range(len(datasets_all_data[0])):
+            recs = [[] for _ in range(num_sets_to_combine)]
+            for nset, datasets in enumerate(datasets_all_data):
+                recs[nset] = datasets[nrec]
+                
+            rec = {'corr2d':           np.empty((recs[0]['corr2d'].shape[0]*num_sets_to_combine,          recs[0]['corr2d'].shape[1]),dtype=np.float32),
+                   'target_disparity': np.empty((recs[0]['target_disparity'].shape[0]*num_sets_to_combine,recs[0]['target_disparity'].shape[1]),dtype=np.float32),
+                   'gt_ds':            np.empty((recs[0]['gt_ds'].shape[0]*num_sets_to_combine,           recs[0]['gt_ds'].shape[1]),dtype=np.float32)}
+            
+            for nset, reci in enumerate(recs):
+                rec['corr2d']          [nset::num_sets_to_combine] =  recs[nset]['corr2d'] 
+                rec['target_disparity'][nset::num_sets_to_combine] =  recs[nset]['target_disparity'] 
+                rec['gt_ds']           [nset::num_sets_to_combine] =  recs[nset]['gt_ds'] 
+            if del_src:
+                for nset in range(num_sets_to_combine):
+                    datasets_all_data[nset][nrec] = None
+            datasets_data.append(rec)
+    return datasets_data    
+         
+
+# list of dictionaries  
+def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side):
+    if (not datasets_data is None) and (len (datasets_data) > 0): 
+        tsz = (datasets_data[0]['corr2d'].shape[1])// num_tile_layers # 81 # list index out of range
+        tss = int(np.sqrt(tsz)+0.5)
+        offs = (tss - reduced_tile_side) // 2
+        for rec in datasets_data:
+            rec['corr2d'] =  (rec['corr2d'].reshape((-1, num_tile_layers,  tss, tss))
+                                 [..., offs:offs+reduced_tile_side, offs:offs+reduced_tile_side].
+                                 reshape(-1,num_tile_layers*reduced_tile_side*reduced_tile_side))
+
+def eval_results(rslt_path, absolute,
+                 min_disp =       -0.1, #minimal GT disparity
+                 max_disp =       20.0, # maximal GT disparity
+                 max_ofst_target = 1.0,
+                 max_ofst_result = 1.0,
+                 str_pow =         2.0,
+                 radius =          0):
+#    for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in [
+    variants = [[         -0.1,         5.0,              0.5,            0.5,          1.0],           
+                [         -0.1,         5.0,              0.5,            0.5,          2.0],
+                [         -0.1,         5.0,              0.2,            0.2,          1.0],
+                [         -0.1,         5.0,              0.2,            0.2,          2.0],
+                [         -0.1,        20.0,              0.5,            0.5,          1.0],           
+                [         -0.1,        20.0,              0.5,            0.5,          2.0],
+                [         -0.1,        20.0,              0.2,            0.2,          1.0],
+                [         -0.1,        20.0,              0.2,            0.2,          2.0],
+                [         -0.1,        20.0,              1.0,            1.0,          1.0],
+                [min_disp, max_disp, max_ofst_target, max_ofst_result, str_pow]]
+    
+
+    rslt = np.load(result_file)
+    not_nan  =  ~np.isnan(rslt[...,0])
+    not_nan &=  ~np.isnan(rslt[...,1])
+    not_nan &=  ~np.isnan(rslt[...,2])
+    not_nan &=  ~np.isnan(rslt[...,3])
+    not_nan_ext = np.zeros((rslt.shape[0] + 2*radius,rslt.shape[1] + 2 * radius),dtype=np.bool) 
+    not_nan_ext[radius:-radius,radius:-radius] = not_nan
+    for dy in range(2*radius+1):
+        for dx in range(2*radius+1):
+            not_nan_ext[dy:dy+not_nan.shape[0], dx:dx+not_nan.shape[1]] &= not_nan
+    not_nan = not_nan_ext[radius:-radius,radius:-radius]         
+        
+    if  not absolute:
+        rslt[...,0] +=  rslt[...,1]
+    nn_disparity =     np.nan_to_num(rslt[...,0], copy = False)
+    target_disparity = np.nan_to_num(rslt[...,1], copy = False)
+    gt_disparity =     np.nan_to_num(rslt[...,2], copy = False)
+    gt_strength =      np.nan_to_num(rslt[...,3], copy = False)
+    rslt = []
+    for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in variants:
+        good_tiles = not_nan.copy();
+        good_tiles &= (gt_disparity >= min_disparity)
+        good_tiles &= (gt_disparity <= max_disparity)
+        good_tiles &= (target_disparity != gt_disparity)
+        good_tiles &= (np.abs(target_disparity - gt_disparity) <= max_offset_target)
+        good_tiles &= (np.abs(target_disparity - nn_disparity) <= max_offset_result)
+        gt_w =  gt_strength * good_tiles
+        gt_w = np.power(gt_w,strength_pow)
+        sw = gt_w.sum() 
+        diff0 = target_disparity - gt_disparity
+        diff1 = nn_disparity -     gt_disparity
+        diff0_2w = gt_w*diff0*diff0
+        diff1_2w = gt_w*diff1*diff1
+        rms0 = np.sqrt(diff0_2w.sum()/sw) 
+        rms1 = np.sqrt(diff1_2w.sum()/sw)
+        print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
+            min_disparity, max_disparity, max_offset_target,  max_offset_result, strength_pow, rms0, rms1, rms0/rms1, good_tiles.sum() ))
+        rslt.append([rms0,rms1])
+    return rslt 
+
+def concentricSquares(radius):
+    side = 2 * radius + 1
+    return [[((i // side) >= var) and
+             ((i // side) < (side - var)) and
+             ((i % side)  >= var) and
+             ((i % side)  < (side - var))  for i in range (side*side) ] for var in range(radius+1)]    
+                         
+
+"""
+Start of the main code
+"""
+"""
+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_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"
+"""
+data_dir =  "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" 
+data_dir1 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_4" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+ 
+#img_dir =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+img_dir =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+
+dir_train_lvar =  data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_hvar =  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_test_lvar =   data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+#dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_img =         os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
+dir_result =      os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"
+
+files_train_lvar = ["train000_R2_LE_1.5.tfrecords",
+                    "train001_R2_LE_1.5.tfrecords",
+                    "train002_R2_LE_1.5.tfrecords",
+                    "train003_R2_LE_1.5.tfrecords",
+                    "train004_R2_LE_1.5.tfrecords",
+                    "train005_R2_LE_1.5.tfrecords",
+                    "train006_R2_LE_1.5.tfrecords",
+                    "train007_R2_LE_1.5.tfrecords",
+                    "train008_R2_LE_1.5.tfrecords",
+                    "train009_R2_LE_1.5.tfrecords",
+                    "train010_R2_LE_1.5.tfrecords",
+                    "train011_R2_LE_1.5.tfrecords",
+                    "train012_R2_LE_1.5.tfrecords",
+                    "train013_R2_LE_1.5.tfrecords",
+                    "train014_R2_LE_1.5.tfrecords",
+                    "train015_R2_LE_1.5.tfrecords",
+                    "train016_R2_LE_1.5.tfrecords",
+                    "train017_R2_LE_1.5.tfrecords",
+                    "train018_R2_LE_1.5.tfrecords",
+                    "train019_R2_LE_1.5.tfrecords",
+                    "train020_R2_LE_1.5.tfrecords",
+                    "train021_R2_LE_1.5.tfrecords",
+                    "train022_R2_LE_1.5.tfrecords",
+                    "train023_R2_LE_1.5.tfrecords",
+                    "train024_R2_LE_1.5.tfrecords",
+                    "train025_R2_LE_1.5.tfrecords",
+                    "train026_R2_LE_1.5.tfrecords",
+                    "train027_R2_LE_1.5.tfrecords",
+                    "train028_R2_LE_1.5.tfrecords",
+                    "train029_R2_LE_1.5.tfrecords",
+                    "train030_R2_LE_1.5.tfrecords",
+                    "train031_R2_LE_1.5.tfrecords",
+                    ]
+files_train_hvar = ["train000_R2_GT_1.5.tfrecords",
+                    "train001_R2_GT_1.5.tfrecords",
+                    "train002_R2_GT_1.5.tfrecords",
+                    "train003_R2_GT_1.5.tfrecords",
+                    "train004_R2_GT_1.5.tfrecords",
+                    "train005_R2_GT_1.5.tfrecords",
+                    "train006_R2_GT_1.5.tfrecords",
+                    "train007_R2_GT_1.5.tfrecords",
+                    "train008_R2_GT_1.5.tfrecords",
+                    "train009_R2_GT_1.5.tfrecords",
+                    "train010_R2_GT_1.5.tfrecords",
+                    "train011_R2_GT_1.5.tfrecords",
+                    "train012_R2_GT_1.5.tfrecords",
+                    "train013_R2_GT_1.5.tfrecords",
+                    "train014_R2_GT_1.5.tfrecords",
+                    "train015_R2_GT_1.5.tfrecords",
+                    "train016_R2_GT_1.5.tfrecords",
+                    "train017_R2_GT_1.5.tfrecords",
+                    "train018_R2_GT_1.5.tfrecords",
+                    "train019_R2_GT_1.5.tfrecords",
+                    "train020_R2_GT_1.5.tfrecords",
+                    "train021_R2_GT_1.5.tfrecords",
+                    "train022_R2_GT_1.5.tfrecords",
+                    "train023_R2_GT_1.5.tfrecords",
+                    "train024_R2_GT_1.5.tfrecords",
+                    "train025_R2_GT_1.5.tfrecords",
+                    "train026_R2_GT_1.5.tfrecords",
+                    "train027_R2_GT_1.5.tfrecords",
+                    "train028_R2_GT_1.5.tfrecords",
+                    "train029_R2_GT_1.5.tfrecords",
+                    "train030_R2_GT_1.5.tfrecords",
+                    "train031_R2_GT_1.5.tfrecords",
+]
+files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
+                    "train001_R2_LE_1.5.tfrecords",
+                    "train002_R2_LE_1.5.tfrecords",
+                    "train003_R2_LE_1.5.tfrecords",
+                    "train004_R2_LE_1.5.tfrecords",
+                    "train005_R2_LE_1.5.tfrecords",
+                    "train006_R2_LE_1.5.tfrecords",
+                    "train007_R2_LE_1.5.tfrecords",
+                    "train008_R2_LE_1.5.tfrecords",
+                    "train009_R2_LE_1.5.tfrecords",
+                    "train010_R2_LE_1.5.tfrecords",
+                    "train011_R2_LE_1.5.tfrecords",
+                    "train012_R2_LE_1.5.tfrecords",
+                    "train013_R2_LE_1.5.tfrecords",
+                    "train014_R2_LE_1.5.tfrecords",
+                    "train015_R2_LE_1.5.tfrecords",
+                    "train016_R2_LE_1.5.tfrecords",
+                    "train017_R2_LE_1.5.tfrecords",
+                    "train018_R2_LE_1.5.tfrecords",
+                    "train019_R2_LE_1.5.tfrecords",
+                    "train020_R2_LE_1.5.tfrecords",
+                    "train021_R2_LE_1.5.tfrecords",
+                    "train022_R2_LE_1.5.tfrecords",
+                    "train023_R2_LE_1.5.tfrecords",
+                    ]
+files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
+                    "train001_R2_GT_1.5.tfrecords",
+                    "train002_R2_GT_1.5.tfrecords",
+                    "train003_R2_GT_1.5.tfrecords",
+                    "train004_R2_GT_1.5.tfrecords",
+                    "train005_R2_GT_1.5.tfrecords",
+                    "train006_R2_GT_1.5.tfrecords",
+                    "train007_R2_GT_1.5.tfrecords",
+                    "train008_R2_GT_1.5.tfrecords",
+                    "train009_R2_GT_1.5.tfrecords",
+                    "train010_R2_GT_1.5.tfrecords",
+                    "train011_R2_GT_1.5.tfrecords",
+                    "train012_R2_GT_1.5.tfrecords",
+                    "train013_R2_GT_1.5.tfrecords",
+                    "train014_R2_GT_1.5.tfrecords",
+                    "train015_R2_GT_1.5.tfrecords",
+                    "train016_R2_GT_1.5.tfrecords",
+                    "train017_R2_GT_1.5.tfrecords",
+                    "train018_R2_GT_1.5.tfrecords",
+                    "train019_R2_GT_1.5.tfrecords",
+                    "train020_R2_GT_1.5.tfrecords",
+                    "train021_R2_GT_1.5.tfrecords",
+                    "train022_R2_GT_1.5.tfrecords",
+                    "train023_R2_GT_1.5.tfrecords",
+]
+
+"""
+Try again - all hvar training, train than different hvar testing.
+tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-bothtrain-test-hvar" --port=7069
+Seems that even different (but used) hvar perfectly match each other, but training for both lvar and hvar never get
+match for either of hvar/lvar, even those that were used for training
+tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-19-tested_with_same_hvar_lvar_as_trained" --port=7070
+
+try same with higher LR - will they eventually converge?
+
+Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as test)
+"""
+#just testing:
+#files_train_lvar = files_train_hvar
+files_test_lvar =  ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]
+files_test_hvar =  ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
+#files_img =        ['1527257933_150165-v04'] # overlook
+#files_img =        ['1527256858_150165-v01'] # State Street
+#files_img =        ['1527256816_150165-v02'] # State Street
+#files_img =        ['1527182802_096892-v02'] # plane near
+files_img =        ['1527182805_096892-v02'] # plane midrange
+#files_img =        ['1527182810_096892-v02'] # plane far
+#MAX_FILES_PER_GROUP
+for i, path in enumerate(files_train_lvar):
+    files_train_lvar[i]=os.path.join(dir_train_lvar, path)
+    
+for i, path in enumerate(files_train_hvar):
+    files_train_hvar[i]=os.path.join(dir_train_hvar, path)
+
+# Second set of files
+
+for i, path in enumerate(files_train_lvar1):
+    files_train_lvar1[i]=os.path.join(dir_train_lvar1, path)
+    
+for i, path in enumerate(files_train_hvar1):
+    files_train_hvar1[i]=os.path.join(dir_train_hvar1, path)
+    
+for i, path in enumerate(files_test_lvar):
+    files_test_lvar[i]=os.path.join(dir_test_lvar, path)
+    
+for i, path in enumerate(files_test_hvar):
+    files_test_hvar[i]=os.path.join(dir_test_hvar, path)
+
+result_files=[]
+for i, path in enumerate(files_img):
+    files_img[i] =      os.path.join(dir_img,    path+'.tfrecords')
+    result_files.append(os.path.join(dir_result, path+"_"+SUFFIX+'.npy'))
+
+files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
+
+#file_test_hvar=  None
+weight_hvar = 0.26
+weight_lvar = 1.0 - weight_hvar 
+partials = None
+partials = concentricSquares(CLUSTER_RADIUS)
+PARTIALS_WEIGHTS = [1.0*pw/sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS]
+if not USE_PARTIALS:
+    partials = partials[0:1]
+    PARTIALS_WEIGHTS = [1.0]
+
+
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+
+for result_file in result_files:
+    try:
+        eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
+    #exit(0)
+    except:
+        pass
+
+datasets_img = []
+for fpath in files_img:
+    print_time("Importing test image data from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_img.append(       {"corr2d":           corr2d,
+                                "target_disparity": target_disparity,
+                                "gt_ds":            gt_ds})
+    print_time("  Done")
+gtruth =   datasets_img[0]['gt_ds'].copy()
+t_disp =   datasets_img[0]['target_disparity'].reshape([-1,1]).copy()
+extend_img_to_clusters(datasets_img, radius = CLUSTER_RADIUS)
+#reformat_to_clusters(datasets_img) already this format   
+replace_nan(datasets_img)
+pass
+pass
+
+
+
+datasets_train_lvar =  []
+datasets_train_hvar =  []
+datasets_train_lvar1 = []
+datasets_train_hvar1 = []
+
+datasets_train_all = [[],[],[],[]]
+#files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
+
+for n_train, f_train in enumerate(files_train):
+    if len(f_train) and ((n_train<2) or TWO_TRAINS):
+        _setFileSlot(train_next[n_train], len(f_train))
+        for i, fpath in enumerate(f_train):
+            if i >= MAX_FILES_PER_GROUP:
+                break
+            print_time("Importing train data "+(["low variance","high variance", "low variance1","high variance1"][n_train]) +" from "+fpath, end="")
+            corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+            datasets_train_all[n_train].append({"corr2d":corr2d,
+                                        "target_disparity":target_disparity,
+                                        "gt_ds":gt_ds})
+            _nextFileSlot(train_next[n_train])
+            print_time("  Done")
+
+datasets_test_lvar = []
+for fpath in files_test_lvar:
+    print_time("Importing test data (low variance) from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_test_lvar.append({"corr2d":corr2d,
+                                "target_disparity":target_disparity,
+                                "gt_ds":gt_ds})
+    print_time("  Done")
+
+datasets_test_hvar = []
+for fpath in files_test_hvar:
+    print_time("Importing test data (high variance) from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_test_hvar.append({"corr2d":corr2d,
+                                "target_disparity":target_disparity,
+                                "gt_ds":gt_ds})
+    print_time("  Done")
+    
+    
+    
+    
+#CLUSTER_RADIUS
+cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
+# Reformat input data
+if FILE_TILE_SIDE > TILE_SIDE:
+    print_time("Reducing correlation tile size from %d to %d"%(FILE_TILE_SIDE, TILE_SIDE), end="")
+    for d_train in datasets_train_all:
+        reduce_tile_size(d_train,  TILE_LAYERS, TILE_SIDE)
+    reduce_tile_size(datasets_test_lvar,   TILE_LAYERS, TILE_SIDE)
+    reduce_tile_size(datasets_test_hvar,   TILE_LAYERS, TILE_SIDE)
+    print_time("  Done")
+pass
+
+# Reformat to 1/9/25 tile clusters
+for n_train, d_train in enumerate(datasets_train_all):
+    print_time("Reshaping train data ("+(["low variance","high variance", "low variance1","high variance1"][n_train])+") ", end="")
+    reformat_to_clusters(d_train)
+    print_time("  Done")
+
+print_time("Reshaping test data (low variance)", end="")
+reformat_to_clusters(datasets_test_lvar)
+print_time("  Done")
+print_time("Reshaping test data (high variance)", end="")
+reformat_to_clusters(datasets_test_hvar)
+print_time("  Done")
+pass    
+
+"""
+datasets_train_lvar & datasets_train_hvar ( that will increase batch size and placeholders twice
+test has to have even original, batches will not zip - just use two batches for one big one
+"""
+
+if ZIP_LHVAR:
+    print_time("Zipping together datasets datasets_train_lvar and datasets_train_hvar", end="")
+    datasets_train = zip_lvar_hvar(datasets_train_all, del_src = True) # no shuffle, delete src
+    print_time("  Done")
+else:
+    #Alternate lvar/hvar
+    datasets_train = []
+    datasets_weights_train = []
+    for indx in range(max(len(datasets_train_lvar),len(datasets_train_hvar))):
+        if (indx < len(datasets_train_lvar)):
+            datasets_train.append(datasets_train_lvar[indx])
+            datasets_weights_train.append(weight_lvar)
+        if (indx < len(datasets_train_hvar)):
+            datasets_train.append(datasets_train_hvar[indx])
+            datasets_weights_train.append(weight_hvar)
+
+datasets_test = []
+datasets_weights_test = []
+for dataset_test_lvar in datasets_test_lvar:
+    datasets_test.append(dataset_test_lvar)
+    datasets_weights_test.append(weight_lvar)
+for dataset_test_hvar in datasets_test_hvar:
+    datasets_test.append(dataset_test_hvar)
+    datasets_weights_test.append(weight_hvar)
+
+    
+corr2d_train_placeholder =           tf.placeholder(datasets_train[0]['corr2d'].dtype,           (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
+target_disparity_train_placeholder = tf.placeholder(datasets_train[0]['target_disparity'].dtype, (None,1 *   cluster_size))  #target_disparity_train.shape)
+gt_ds_train_placeholder =            tf.placeholder(datasets_train[0]['gt_ds'].dtype,            (None,2 *   cluster_size)) #gt_ds_train.shape)
+#dataset_tt    TensorSliceDataset: <TensorSliceDataset shapes: {gt_ds: (50,), corr2d: (8100,), target_disparity: (25,)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>    
+dataset_tt = tf.data.Dataset.from_tensor_slices({
+    "corr2d":corr2d_train_placeholder,
+    "target_disparity": target_disparity_train_placeholder,
+    "gt_ds": gt_ds_train_placeholder})
+
+
+#dataset_train_size = len(corr2d_train)
+
+#dataset_train_size = len(datasets_train_lvar[0]['corr2d'])
+dataset_train_size = len(datasets_train[0]['corr2d'])
+dataset_train_size //= BATCH_SIZE
+dataset_test_size = len(datasets_test_lvar[0]['corr2d'])
+dataset_test_size //= BATCH_SIZE
+dataset_img_size = len(datasets_img[0]['corr2d'])
+dataset_img_size //= BATCH_SIZE
+
+#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
+dataset_tt = dataset_tt.batch(BATCH_SIZE)
+dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
+iterator_tt = dataset_tt.make_initializable_iterator()
+next_element_tt = iterator_tt.get_next()
+#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
+#BatchDataset: <BatchDataset shapes: {gt_ds: (?, 50), corr2d: (?, 8100), target_disparity: (?, 25)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
+"""
+next_element_tt    dict: {'gt_ds': <tf.Tensor 'IteratorGetNext:1' shape=(?, 50) dtype=float32>, 'corr2d': <tf.Tensor 'IteratorGetNext:0' shape=(?, 8100) dtype=float32>, 'target_disparity': <tf.Tensor 'IteratorGetNext:2' shape=(?, 25) dtype=float32>}    
+    'corr2d' (140405473715624)    Tensor: Tensor("IteratorGetNext:0", shape=(?, 8100), dtype=float32)    
+    'gt_ds' (140405473715680)    Tensor: Tensor("IteratorGetNext:1", shape=(?, 50), dtype=float32)    
+    'target_disparity' (140405501995888)    Tensor: Tensor("IteratorGetNext:2", shape=(?, 25), dtype=float32)    
+"""
+
+#https://www.tensorflow.org/versions/r1.5/programmers_guide/datasets
+result_dir = './attic/result_neibs_'+     SUFFIX+'/'
+checkpoint_dir = './attic/result_neibs_'+ SUFFIX+'/'
+save_freq = 500
+def lrelu(x):
+    return tf.maximum(x*0.2,x)
+#    return tf.nn.relu(x)
+
+def sym_inputs8(inp):
+    """
+    get input vector [?:4*9*9+1] (last being target_disparity) and reorder for horizontal flip,
+    vertical flip and transpose (8 variants, mode + 1 - hor, +2 - vert, +4 - transpose)
+    return same lengh, reordered
+    """
+    with tf.name_scope("sym_inputs8"):
+        td =           inp[:,-1:] # tf.reshape(inp,[-1], name = "td")[-1]
+        inp_corr =     tf.reshape(inp[:,:-1],[-1,4,TILE_SIDE,TILE_SIDE], name = "inp_corr")
+        inp_corr_h =   tf.stack([-inp_corr  [:,0,:,-1::-1], inp_corr  [:,1,:,-1::-1], -inp_corr  [:,3,:,-1::-1], -inp_corr  [:,2,:,-1::-1]], axis=1, name = "inp_corr_h")
+        inp_corr_v =   tf.stack([ inp_corr  [:,0,-1::-1,:],-inp_corr  [:,1,-1::-1,:],  inp_corr  [:,3,-1::-1,:],  inp_corr  [:,2,-1::-1,:]], axis=1, name = "inp_corr_v")
+        inp_corr_hv =  tf.stack([ inp_corr_h[:,0,-1::-1,:],-inp_corr_h[:,1,-1::-1,:],  inp_corr_h[:,3,-1::-1,:],  inp_corr_h[:,2,-1::-1,:]], axis=1, name = "inp_corr_hv")
+        inp_corr_t =   tf.stack([tf.transpose(inp_corr   [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr   [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr   [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr   [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_t")
+        inp_corr_ht =  tf.stack([tf.transpose(inp_corr_h [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_h [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_h [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_h [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_ht")
+        inp_corr_vt =  tf.stack([tf.transpose(inp_corr_v [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_v [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_v [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_v [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_vt")
+        inp_corr_hvt = tf.stack([tf.transpose(inp_corr_hv[:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_hv[:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_hv[:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_hv[:,3], perm=[0,2,1])], axis=1, name = "inp_corr_hvt")
+#        return td, [inp_corr, inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
+        """
+        return [tf.concat([tf.reshape(inp_corr,    [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr"),
+                tf.concat([tf.reshape(inp_corr_h,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_h"),
+                tf.concat([tf.reshape(inp_corr_v,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_v"),
+                tf.concat([tf.reshape(inp_corr_hv, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hv"),
+                tf.concat([tf.reshape(inp_corr_t,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_t"),
+                tf.concat([tf.reshape(inp_corr_ht, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_ht"),
+                tf.concat([tf.reshape(inp_corr_vt, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_vt"),
+                tf.concat([tf.reshape(inp_corr_hvt,[inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hvt")]
+        """
+        cl = 4 * TILE_SIDE * TILE_SIDE
+        return [tf.concat([tf.reshape(inp_corr,    [-1,cl]),td], axis=1,name = "out_corr"),
+                tf.concat([tf.reshape(inp_corr_h,  [-1,cl]),td], axis=1,name = "out_corr_h"),
+                tf.concat([tf.reshape(inp_corr_v,  [-1,cl]),td], axis=1,name = "out_corr_v"),
+                tf.concat([tf.reshape(inp_corr_hv, [-1,cl]),td], axis=1,name = "out_corr_hv"),
+                tf.concat([tf.reshape(inp_corr_t,  [-1,cl]),td], axis=1,name = "out_corr_t"),
+                tf.concat([tf.reshape(inp_corr_ht, [-1,cl]),td], axis=1,name = "out_corr_ht"),
+                tf.concat([tf.reshape(inp_corr_vt, [-1,cl]),td], axis=1,name = "out_corr_vt"),
+                tf.concat([tf.reshape(inp_corr_hvt,[-1,cl]),td], axis=1,name = "out_corr_hvt")]
+#                           inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
+    
+
+def network_sub(input, layout, reuse, sym8 = False):
+    last_indx = None;
+    fc = []
+    inp_weights = []
+    for i, num_outs in enumerate (layout):
+        if num_outs:
+           if fc:
+               inp = fc[-1]
+               fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
+           else:
+               inp = input
+               if sym8:
+                   inp8 = sym_inputs8(inp)
+                   num_non_sum = num_outs %  len(inp8) # if number of first layer outputs is not multiple of 8
+                   num_sym8 =    num_outs // len(inp8) # number of symmetrical groups
+                   fc_sym = []
+                   for j in range (len(inp8)): # ==8
+                       reuse_this = reuse | (j > 0)
+                       scp = 'g_fc_sub'+str(i)
+                       fc_sym.append(slim.fully_connected(inp8[j],    num_sym8, activation_fn=lrelu, scope= scp,     reuse = reuse_this))
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+                   if num_non_sum > 0:
+                       reuse_this = reuse
+                       scp = 'g_fc_sub'+str(i)+"r"
+                       fc_sym.append(slim.fully_connected(inp,     num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))    
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+                   fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
+               else:
+                   scp = 'g_fc_sub'+str(i)
+                   fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
+                   if not reuse:
+                       with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
+                          inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+           
+    return fc[-1], inp_weights
+
+def network_inter(input, layout, reuse=False):
+    last_indx = None;
+    fc = []
+    for i, num_outs in enumerate (layout):
+        if num_outs:
+           if fc:
+               inp = fc[-1]
+           else:
+               inp = input
+           fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_inter'+str(i), reuse = reuse))
+    if USE_CONFIDENCE:
+        fc_out  = slim.fully_connected(fc[-1],     2, activation_fn=lrelu, scope='g_fc_inter_out', reuse = reuse)
+    else:     
+        fc_out  = slim.fully_connected(fc[-1],     1, activation_fn=None, scope='g_fc_inter_out', reuse = reuse)
+        #If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only  
+    return fc_out
+
+def networks_siam(input, # now [?,9,325]-> [?,25,325]
+                 layout1, 
+                 layout2,
+                 sym8 =        False,
+                 only_tile =   None, # just for debugging - feed only data from the center sub-network
+                 partials =    None): 
+    with tf.name_scope("Siam_net"):
+        inp_weights = []
+        num_legs =  input.shape[1] # == 25
+        if partials is None:
+            partials = [[True] * num_legs]
+#        inter_list = []
+        inter_lists = [[] for _ in partials]
+        reuse = False
+        for i in range (num_legs):
+            if ((only_tile is None) or (i == only_tile)) and any([p[i] for p in partials]) :
+                ns, ns_weights = network_sub(input[:,i,:],
+                                          layout= layout1,
+                                          reuse= reuse,
+                                          sym8 = sym8)
+                for n, partial in enumerate(partials):
+                    if partial[i]:
+                        inter_lists[n].append(ns)
+                    else:
+#                        inter_lists[n].append(tf.constant(0.0,dtype=tf.float32, shape=ns.shape))
+#                        inter_lists[n].append(ns * 0.0)
+                        inter_lists[n].append(tf.zeros_like(ns))
+#                inter_list.append(ns)
+                inp_weights += ns_weights
+                reuse = True
+        outs = []         
+        for n, _ in enumerate(partials):
+#            outs.append(network_inter (tf.concat(inter_list, 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
+            outs.append(network_inter (tf.concat(inter_lists[n], 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
+#            inter_tensors.append(tf.concat(inter_list, 1, name='inter_tensor'+str(n)))
+#        inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
+#        return  network_inter (inter_tensor, layout2),  inp_weights 
+        return  outs,  inp_weights 
+
+def debug_gt_variance(
+        indx,        # This tile index (0..8)
+        center_indx, # center tile index
+        gt_ds_batch # [?:9:2]
+        ):
+    with tf.name_scope("Debug_GT_Variance"):
+        tf_num_tiles =       tf.shape(gt_ds_batch)[0]
+        d_gt_this =   tf.reshape(gt_ds_batch[:,2 * indx],[-1],                     name = "d_this")
+        d_gt_center = tf.reshape(gt_ds_batch[:,2 * center_indx],[-1],              name = "d_center")
+        d_gt_diff =   tf.subtract(d_gt_this, d_gt_center,                          name = "d_diff")
+        d_gt_diff2 =  tf.multiply(d_gt_diff, d_gt_diff,                            name = "d_diff2")
+        d_gt_var =    tf.reduce_mean(d_gt_diff2,                                   name = "d_gt_var")
+        return  d_gt_var
+    
+    
+def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
+              target_disparity_batch,      # [batch_size]        tf placeholder
+              gt_ds_batch,                 # [batch_size,2]      tf placeholder
+              absolute_disparity =     True, #when false there should be no activation on disparity output ! 
+              use_confidence =         True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            1.0,
+              error2_offset =          0.0025, # 0.0, # 0.0025, # (0.05^2) ~= coring
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False):  # use calculated disparity for disparity weight boosting (False - use target disparity)
+               
+    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
+        """
+        tf_lambda_conf_avg = tf.constant(lambda_conf_avg, dtype=tf.float32, name="tf_lambda_conf_avg")
+        tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
+        tf_conf_pwr =        tf.constant(conf_pwr,        dtype=tf.float32, name="tf_conf_pwr")
+        tf_gt_conf_offset =  tf.constant(gt_conf_offset,  dtype=tf.float32, name="tf_gt_conf_offset")
+        tf_gt_conf_pwr =     tf.constant(gt_conf_pwr,     dtype=tf.float32, name="tf_gt_conf_pwr")
+        tf_num_tiles =       tf.shape(gt_ds_batch)[0]
+        tf_0f =              tf.constant(0.0,             dtype=tf.float32, name="tf_0f")
+        tf_1f =              tf.constant(1.0,             dtype=tf.float32, name="tf_1f")
+        tf_maxw =            tf.constant(1.0,             dtype=tf.float32, name="tf_maxw")
+        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.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.maximum(w_sub, tf_0f,                              name = "w_clip")
+            if gt_conf_pwr == 1.0:
+                w = w_clip
+            else:
+                w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow")
+    
+        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.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")
+            conf_avg1 =      tf.subtract(conf_avg, tf_1f,                    name = "conf_avg1")
+            conf_avg2 =      tf.square(conf_avg1,                            name = "conf_avg2")
+            cost2 =          tf.multiply (conf_avg2, tf_lambda_conf_avg,     name = "cost2")
+    
+            iconf_avg =      tf.divide(tf_1f, conf_avg,                      name = "iconf_avg")
+            nconf =          tf.multiply (conf_slice, iconf_avg,             name = "nconf") #normalized confidence
+            nconf_pwr =      tf.pow(nconf, conf_pwr,                         name = "nconf_pwr")
+            nconf_pwr_sum =  tf.reduce_sum(nconf_pwr,                        name = "nconf_pwr_sum")
+            nconf_pwr_offs = tf.subtract(nconf_pwr_sum, tf_1f,               name = "nconf_pwr_offs")
+            cost3 =          tf.multiply (conf_avg2, nconf_pwr_offs,         name = "cost3")
+            w_all =          tf.multiply (w, nconf,                          name = "w_all")
+        else:
+            w_all = w
+#            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.reshape(out_batch[:,0],[-1],                 name = "disp_slice")
+        d_gt_slice =         tf.reshape(gt_ds_batch[:,0],[-1],               name = "d_gt_slice")
+        
+        td_flat =        tf.reshape(target_disparity_batch,[-1],         name = "td_flat")
+        if absolute_disparity:
+            adisp =          disp_slice
+        else:
+            adisp =          tf.add(disp_slice, td_flat,                     name = "adisp")
+        out_diff =           tf.subtract(adisp, d_gt_slice,                  name = "out_diff")
+            
+            
+        out_diff2 =          tf.square(out_diff,                             name = "out_diff2")
+        out_wdiff2 =         tf.multiply (out_diff2, w_norm,                 name = "out_wdiff2")
+        
+        cost1 =              tf.reduce_sum(out_wdiff2,                       name = "cost1")
+        
+        out_diff2_offset =   tf.subtract(out_diff2, error2_offset,           name = "out_diff2_offset")
+        out_diff2_biased =   tf.maximum(out_diff2_offset, 0.0,               name = "out_diff2_biased")
+        
+        # calculate disparity-based weight boost
+        if use_out:
+            dispw =          tf.clip_by_value(adisp, disp_wmin, disp_wmax,   name = "dispw")
+        else:
+            dispw =          tf.clip_by_value(td_flat, disp_wmin, disp_wmax, name = "dispw")
+        dispw_boost =        tf.divide(disp_wmax, dispw,                     name = "dispw_boost")
+        dispw_comp =         tf.multiply (dispw_boost, w_norm,               name = "dispw_comp") #HERE??
+#        dispw_comp =         tf.multiply (w_norm, w_norm,               name = "dispw_comp") #HERE??
+        dispw_sum =          tf.reduce_sum(dispw_comp,                       name = "dispw_sum")
+        idispw_sum =         tf.divide(tf_1f, dispw_sum,                     name = "idispw_sum")
+        dispw_norm =         tf.multiply (dispw_comp, idispw_sum,            name = "dispw_norm")
+        
+        out_diff2_wbiased =  tf.multiply(out_diff2_biased, dispw_norm,       name = "out_diff2_wbiased")
+#        out_diff2_wbiased =  tf.multiply(out_diff2_biased, w_norm,       name = "out_diff2_wbiased")
+        cost1b =             tf.reduce_sum(out_diff2_wbiased,                name = "cost1b")
+        
+        if use_confidence:
+            cost12 =         tf.add(cost1b, cost2,                           name = "cost12")
+            cost123 =        tf.add(cost12, cost3,                           name = "cost123")    
+            
+            return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
+        else:
+            return cost1b,  disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
+        
+        
+def weightsLoss(inp_weights):       # [batch_size,(1..2)] tf_result
+#                weights_lambdas):  # single lambda or same length as inp_weights.shape[1]
+    """
+    Enforcing 'smooth' weights for the input 2d correlation tiles
+    @return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
+    """
+    weight_ortho = 1.0
+    weight_diag  = 0.7
+    sw = 4.0 * (weight_ortho + weight_diag)
+    weight_ortho /= sw
+    weight_diag /=  sw
+#    w_neib = tf.const([[weight_diag,  weight_ortho, weight_diag],
+#                       [weight_ortho, -1.0,         weight_ortho],
+#                       [weight_diag,  weight_ortho, weight_diag]])
+    #WBORDERS_ZERO
+    with tf.name_scope("WeightsLoss"):
+        # Adding 1 tile border
+        tf_inp =     tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
+        if WBORDERS_ZERO:
+            tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1,                   tf_inp.shape[3]], name = "tf_zero_col")
+            tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 ,              tf_inp.shape[2] + 2, tf_inp.shape[3]], name = "tf_zero_row")
+            tf_inp_ext_h = tf.concat([tf_zero_col,                 tf_inp,       tf_zero_col                 ], axis = 2, name ="tf_inp_ext_h")
+            tf_inp_ext   = tf.concat([tf_zero_row,                 tf_inp_ext_h, tf_zero_row                 ], axis = 1, name ="tf_inp_ext")
+        else:
+            tf_inp_ext_h = tf.concat([tf_inp       [:, :,  :1, :], tf_inp,       tf_inp      [:,   :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
+            tf_inp_ext   = tf.concat([tf_inp_ext_h [:, :1, :,  :], tf_inp_ext_h, tf_inp_ext_h[:, -1:,   :, :]], axis = 1, name ="tf_inp_ext")
+        
+        s_ortho = tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:,:] + tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:, :] 
+        s_corn =  tf_inp_ext[:, :-2,:-2,:] + tf_inp_ext[:, :-2, 2:,:] + tf_inp_ext[:,2:,  :-2,:] + tf_inp_ext[:,2:  , 2:, :]
+        w_diff =  tf.subtract(tf_inp, s_ortho * weight_ortho + s_corn * weight_diag, name="w_diff") 
+        w_diff2 = tf.multiply(w_diff, w_diff,                                        name="w_diff2") 
+        w_var =   tf.reduce_mean(w_diff2,                                            name="w_var")
+        w2_mean = tf.reduce_mean(inp_weights * inp_weights,                          name="w2_mean")
+        w_rel =   tf.divide(w_var, w2_mean,                                          name= "w_rel")
+        return w_rel # scalar, cost for weights non-smoothness in 2d
+        
+        
+    
+corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
+                          tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
+"""
+out, inp_weights =       network_siam(input=corr2d_Nx325,
+                                      layout1 =   NN_LAYOUT1, 
+                                      layout2 =   NN_LAYOUT2,
+                                      sym8 =      SYM8_SUB,
+                                      only_tile = ONLY_TILE) #Remove/put None for normal operation
+"""                                      
+outs, inp_weights =       networks_siam(input=corr2d_Nx325,
+                                        layout1 =   NN_LAYOUT1, 
+                                        layout2 =   NN_LAYOUT2,
+                                        sym8 =      SYM8_SUB,
+                                        only_tile = ONLY_TILE, #Remove/put None for normal operation
+                                        partials =  partials)                                         
+#            w_slice = tf.reshape(gt_ds_batch[:,1],[-1],                     name = "w_gt_slice")
+
+# Extract target disparity and GT corresponding to the center tile (reshape - just to name)
+#target_disparity_batch_center = tf.reshape(next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1] , [-1,1],  name = "target_center")
+#gt_ds_batch_center =            tf.reshape(next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * center_tile_index+1],    [-1,2],  name = "gt_ds_center")
+tf_partial_weights = tf.constant(PARTIALS_WEIGHTS,dtype=tf.float32,name="partial_weights")
+G_losses = [0.0]*len(partials)
+target_disparity_batch=  next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1]
+gt_ds_batch =            next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)]
+G_losses[0], _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch =         outs[0],        # [batch_size,(1..2)] tf_result
+              target_disparity_batch=  target_disparity_batch, # next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d,   # [batch_size]        tf placeholder
+              gt_ds_batch =            gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)],  # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt,         # [batch_size,2]      tf placeholder
+              absolute_disparity =     ABSOLUTE_DISPARITY,
+              use_confidence =         USE_CONFIDENCE, # True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            2.0,
+              error2_offset =          0, # 0.0025, # (0.05^2)
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False)  # use calculated disparity for disparity weight boosting (False - use target disparity)
+
+G_loss = G_losses[0]
+for n in range (1,len(partials)):
+    G_losses[n], _, _, _, _, _, _, _ = batchLoss(out_batch =         outs[n],        # [batch_size,(1..2)] tf_result
+              target_disparity_batch=  target_disparity_batch, #next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d,   # [batch_size]        tf placeholder
+              gt_ds_batch =            gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)],  # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt,         # [batch_size,2]      tf placeholder
+              absolute_disparity =     ABSOLUTE_DISPARITY,
+              use_confidence =         USE_CONFIDENCE, # True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            2.0,
+              error2_offset =          0, # 0.0025, # (0.05^2)
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False)  # use calculated disparity for disparity weight boosting (False - use target disparity)
+#    G_loss +=  Glosses[n]*PARTIALS_WEIGHTS[n]
+#tf_partial_weights
+tf_wlosses = tf.multiply(G_losses, tf_partial_weights, name =  "tf_wlosses")
+G_losses_sum = tf.reduce_sum(tf_wlosses, name = "G_losses_sum")
+if WLOSS_LAMBDA > 0.0:   
+    W_loss =     weightsLoss(inp_weights[0]) #    inp_weights - list of tensors, currently - just [0]
+#    GW_loss =    tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
+    GW_loss =    tf.add(G_losses_sum, WLOSS_LAMBDA * W_loss, name = "GW_loss")
+else:
+    GW_loss = G_losses_sum # G_loss
+    W_loss =     tf.constant(0.0, dtype=tf.float32,name = "W_loss")
+#debug
+GT_variance =  debug_gt_variance(indx = 0,        # This tile index (0..8)
+                                 center_indx = 4, # center tile index
+                                 gt_ds_batch = next_element_tt['gt_ds'])# [?:18]
+              
+tf_ph_G_loss =    tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
+tf_ph_G_losses =  tf.placeholder(tf.float32,shape=[len(partials)],name='G_losses_avg')
+tf_ph_W_loss =  tf.placeholder(tf.float32,shape=None,name='W_loss_avg')
+tf_ph_GW_loss = tf.placeholder(tf.float32,shape=None,name='GW_loss_avg')
+tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
+tf_gtvar_diff = tf.placeholder(tf.float32,shape=None,name='gtvar_diff')
+tf_img_test0 =  tf.placeholder(tf.float32,shape=None,name='img_test0')
+tf_img_test9 =  tf.placeholder(tf.float32,shape=None,name='img_test9')
+with tf.name_scope('sample'):
+    tf.summary.scalar("GW_loss",      GW_loss)
+    tf.summary.scalar("G_loss",       G_loss)
+    tf.summary.scalar("W_loss",       W_loss)
+    tf.summary.scalar("sq_diff",      _cost1)
+    tf.summary.scalar("gtvar_diff",   GT_variance)
+    
+with tf.name_scope('epoch_average'):
+#    for i, tl in enumerate(tf_ph_G_losses):
+#       tf.summary.scalar("GW_loss_epoch_"+str(i), tl)
+    for i in range(tf_ph_G_losses.shape[0]):
+        tf.summary.scalar("G_loss_epoch_"+str(i), tf_ph_G_losses[i])
+        
+    tf.summary.scalar("GW_loss_epoch", tf_ph_GW_loss)
+    tf.summary.scalar("G_loss_epoch",  tf_ph_G_loss)
+    tf.summary.scalar("W_loss_epoch",  tf_ph_W_loss)
+    tf.summary.scalar("sq_diff_epoch", tf_ph_sq_diff)
+    tf.summary.scalar("gtvar_diff",    tf_gtvar_diff)
+    
+    tf.summary.scalar("img_test0",     tf_img_test0)
+    tf.summary.scalar("img_test9",     tf_img_test9)
+
+t_vars=tf.trainable_variables()
+lr=tf.placeholder(tf.float32)
+G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
+
+saver=tf.train.Saver()
+
+ROOT_PATH  = './attic/nn_ds_neibs10_graph'+SUFFIX+"/"
+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)
+
+WIDTH=324
+HEIGHT=242
+
+with tf.Session()  as sess:
+    
+    sess.run(tf.global_variables_initializer())
+    sess.run(tf.local_variables_initializer())
+    
+    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_gw_train_hist=  np.empty(dataset_train_size, dtype=np.float32)
+    loss_g_train_hist=   np.empty(dataset_train_size, dtype=np.float32)
+    
+    loss_g_train_hists=   [np.empty(dataset_train_size, dtype=np.float32) for p in partials]
+    
+    
+    loss_w_train_hist=   np.empty(dataset_train_size, dtype=np.float32)
+    
+    loss_gw_test_hist=  np.empty(dataset_test_size, dtype=np.float32)
+    loss_g_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    loss_g_test_hists=   [np.empty(dataset_test_size, dtype=np.float32) for p in partials]
+    
+    loss_w_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    
+    loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)
+    loss2_test_hist=  np.empty(dataset_test_size, dtype=np.float32)
+    train_gw_avg = 0.0
+    train_g_avg =  0.0
+    train_g_avgs =  [0.0]*len(partials)
+    
+    train_w_avg =  0.0
+    test_gw_avg =  0.0     
+    test_g_avg =   0.0     
+    test_g_avgs =  [0.0]*len(partials)
+    test_w_avg =   0.0     
+
+    train2_avg = 0.0
+    test2_avg = 0.0
+    gtvar_train_hist=  np.empty(dataset_train_size, dtype=np.float32)
+    gtvar_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    gtvar_train = 0.0
+    gtvar_test = 0.0
+    gtvar_train_avg = 0.0
+    gtvar_test_avg =  0.0
+    img_gain_test0 =  1.0
+    img_gain_test9 =  1.0
+    
+    num_train_variants = len(datasets_train)
+    thr=None;
+    trains_to_update = [train_next[n_train]['files'] > train_next[n_train]['slots'] for n_train in range(len(train_next))]
+    for epoch in range (EPOCHS_TO_RUN):
+        """
+        update files after each epoch, all 4.
+        Convert to threads after testing
+        """
+        if (FILE_UPDATE_EPOCHS > 0) and (epoch % FILE_UPDATE_EPOCHS == 0):
+            if not thr is None:
+                if thr.is_alive():
+                    print_time("Waiting until tfrecord gets loaded", end=" ")
+                else:
+                    print_time("tfrecord is already loaded loaded", end=" ")
+        
+                thr.join()
+                print_time("Done")
+                print_time("Inserting new data", end=" ")
+                for n_train in range(len(trains_to_update)):
+                    if trains_to_update[n_train]:
+                        replaceNextDataset(datasets_train,
+                                           thr_result[n_train],
+                                           train_next= train_next[n_train],
+                                           nset=n_train,
+                                           period=len(train_next))
+                        _nextFileSlot(train_next[n_train])
+                print_time("Done")
+            thr_result = []
+            fpaths = []
+            for n_train in range(len(train_next)):
+                if train_next[n_train]['files'] > train_next[n_train]['slots']:
+                    fpaths.append(files_train[n_train][train_next[n_train]['file']])
+                    print_time("Will read in background: "+fpaths[-1])
+            thr = Thread(target=getMoreFiles, args=(fpaths,thr_result))            
+            thr.start()        
+        file_index = epoch  % num_train_variants
+        if   epoch >=600:
+            learning_rate = LR600
+        elif epoch >=400:
+            learning_rate = LR400
+        elif epoch >=200:
+            learning_rate = LR200
+        elif epoch >=100:
+            learning_rate = LR100
+        else:
+            learning_rate = LR
+#        print ("sr1",file=sys.stderr,end=" ")
+        if (file_index == 0) and SHUFFLE_FILES:
+            num_sets = len(datasets_train_all)
+            print_time("Shuffling how datasets datasets_train_lvar and datasets_train_hvar are zipped together", end="")
+            for i in range(num_sets):
+                shuffle_in_place (datasets_train, i, num_sets)
+            print_time("  Done")
+            print_time("Shuffling tile chunks ", end="")
+            shuffle_chunks_in_place (datasets_train, 1)
+            print_time("  Done")
+            
+        sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:           datasets_train[file_index]['corr2d'],
+                                                     target_disparity_train_placeholder: datasets_train[file_index]['target_disparity'],
+                                                     gt_ds_train_placeholder:            datasets_train[file_index]['gt_ds']})
+        for i in range(dataset_train_size):
+            try:
+#                train_summary,_, GW_loss_trained,  G_loss_trained,  W_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
+                train_summary,_, GW_loss_trained,  G_losses_trained,  W_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
+                    [   merged,
+                        G_opt,
+                        GW_loss,
+#                        G_loss,
+                        G_losses,
+                        W_loss,
+                        outs[0],
+                        _disp_slice,
+                        _d_gt_slice,
+                        _out_diff,
+                        _out_diff2,
+                        _w_norm,
+                        _out_wdiff2,
+                        _cost1,
+                        GT_variance
+                    ],
+                    feed_dict={lr:            learning_rate,
+                               tf_ph_GW_loss: train_gw_avg,
+                               tf_ph_G_loss:  train_g_avgs[0], #train_g_avg,
+                               tf_ph_G_losses:  train_g_avgs,
+                               tf_ph_W_loss:  train_w_avg,
+                               tf_ph_sq_diff: train2_avg,
+                               tf_gtvar_diff: gtvar_train_avg,
+                               tf_img_test0:  img_gain_test0,
+                               tf_img_test9:  img_gain_test9}) # previous value of *_avg #Fetch argument 0.0 has invalid type <class 'float'>, must be a string or Tensor. (Can not convert a float into a Tensor or Operation.)
+                
+                loss_gw_train_hist[i] = GW_loss_trained
+#                loss_g_train_hist[i] =  G_loss_trained
+                for nn, gl  in enumerate(G_losses_trained):
+                    loss_g_train_hists[nn][i] =  gl
+                loss_w_train_hist[i] =  W_loss_trained
+                loss2_train_hist[i] = out_cost1
+                gtvar_train_hist[i] = gt_variance
+            except tf.errors.OutOfRangeError:
+                print("train done at step %d"%(i))
+                break
+
+        train_gw_avg =      np.average(loss_gw_train_hist).astype(np.float32)     
+        train_g_avg =       np.average(loss_g_train_hist).astype(np.float32) 
+        for nn, lgth  in enumerate(loss_g_train_hists):
+            train_g_avgs[nn] =       np.average(lgth).astype(np.float32)
+###############        
+        train_w_avg =       np.average(loss_w_train_hist).astype(np.float32)     
+        train2_avg =      np.average(loss2_train_hist).astype(np.float32)
+        gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
+        
+        test_summaries = [0.0]*len(datasets_test)
+        tst_avg =        [0.0]*len(datasets_test)
+        tst2_avg =       [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_summaries[ntest], GW_loss_tested, G_loss_tested, W_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
+                        [merged,
+                         GW_loss,
+                         G_loss,
+                         W_loss,
+                         outs[0],
+                         _disp_slice,
+                         _d_gt_slice,
+                         _out_diff,
+                         _out_diff2,
+                         _w_norm,
+                         _out_wdiff2,
+                         _cost1,
+                         GT_variance
+                         ],
+                         feed_dict={lr:            learning_rate,
+                                    tf_ph_GW_loss:  test_gw_avg,
+                                    tf_ph_G_loss:   test_g_avg,
+                                    tf_ph_G_losses:  train_g_avgs, # temporary, there is o data fro test
+                                    tf_ph_W_loss:   test_w_avg,
+                                    tf_ph_sq_diff:  test2_avg,
+                                    tf_gtvar_diff:  gtvar_test_avg,
+                                    tf_img_test0:   img_gain_test0,
+                                    tf_img_test9:   img_gain_test9})  # previous value of *_avg
+                    loss_gw_test_hist[i] =  GW_loss_tested
+                    loss_g_test_hist[i] =   G_loss_tested
+                    loss_w_test_hist[i] =   W_loss_tested
+                    loss2_test_hist[i] = out_cost1
+                    gtvar_test_hist[i] = gt_variance
+                except tf.errors.OutOfRangeError:
+                    print("test done at step %d"%(i))
+                    break
+                    
+            test_gw_avg =  np.average(loss_gw_test_hist).astype(np.float32)
+            test_g_avg =  np.average(loss_g_test_hist).astype(np.float32)
+            test_w_avg =  np.average(loss_w_test_hist).astype(np.float32)
+            tst_avg[ntest] =  test_gw_avg   
+            test2_avg = np.average(loss2_test_hist).astype(np.float32)
+            tst2_avg[ntest] =  test2_avg   
+            gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
+             
+        train_writer.add_summary(train_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 %f %f) dbg:%f %f"%(epoch,i,train_gw_avg, tst_avg[0], tst_avg[1], train2_avg, tst2_avg[0], tst2_avg[1], gtvar_train_avg, gtvar_test_avg))
+        if ((epoch + 1) == EPOCHS_TO_RUN) or (((epoch + 1) % EPOCHS_FULL_TEST) == 0):
+###################################################
+# Read the full image
+################################################### 
+            test_summaries_img = [0.0]*len(datasets_img)
+        #    disp_out=  np.empty((dataset_img_size * BATCH_SIZE), dtype=np.float32)
+            disp_out=  np.empty((WIDTH*HEIGHT), dtype=np.float32)
+            
+            for ntest,dataset_img in enumerate(datasets_img):
+                sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:      dataset_img['corr2d'],
+                                                        target_disparity_train_placeholder: dataset_img['target_disparity'],
+                                                        gt_ds_train_placeholder:            dataset_img['gt_ds']})
+                for start_offs in range(0,disp_out.shape[0],BATCH_SIZE):
+                    end_offs = min(start_offs+BATCH_SIZE,disp_out.shape[0])
+                    
+                    try:
+                        test_summaries_img[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,
+                             outs[0],
+                             _disp_slice,
+                             _d_gt_slice,
+                             _out_diff,
+                             _out_diff2,
+                             _w_norm,
+                             _out_wdiff2,
+                             _cost1,
+                             GT_variance
+                             ],
+                             feed_dict={lr:            learning_rate,
+                                        tf_ph_GW_loss:  test_gw_avg,
+                                        tf_ph_G_loss:   test_g_avg,
+                                        tf_ph_G_losses:  train_g_avgs, # temporary, there is o data fro test
+                                        tf_ph_W_loss:   test_w_avg,
+                                        tf_ph_sq_diff: test2_avg,
+                                        tf_gtvar_diff: gtvar_test_avg,
+                                        tf_img_test0:  img_gain_test0,
+                                        tf_img_test9:  img_gain_test9})  # previous value of *_avg
+                    except tf.errors.OutOfRangeError:
+                        print("test done at step %d"%(i))
+                        break
+                    try:
+                        disp_out[start_offs:end_offs] = output.flatten()
+                    except ValueError:
+                        print("dataset_img_size= %d, i=%d, output.shape[0]=%d "%(dataset_img_size, i, output.shape[0]))
+                        break;    
+                    pass
+                result_file = result_files[ntest]
+                try:
+                    os.makedirs(os.path.dirname(result_file))
+                except:
+                    pass     
+
+                rslt = np.concatenate([disp_out.reshape(-1,1), t_disp, gtruth],1)
+                np.save(result_file,           rslt.reshape(HEIGHT,WIDTH,-1))
+                
+                rslt = eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)                
+                img_gain_test0 = rslt[0][0]/rslt[0][1]   
+                img_gain_test9 = rslt[9][0]/rslt[9][1]   
+     
+     # 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")
+exit (0)

nn_ds_neibs11.py

+#!/usr/bin/env python3
+from numpy import float64
+from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
+from debian.deb822 import PdiffIndex
+
+__copyright__ = "Copyright 2018, Elphel, Inc."
+__license__   = "GPL-3.0+"
+__email__     = "andrey@elphel.com"
+
+
+from PIL import Image
+
+import os
+import sys
+import glob
+
+import numpy as np
+import itertools
+
+import time
+
+import matplotlib.pyplot as plt
+
+import shutil
+import sys
+from threading import Thread
+
+TIME_START = time.time()
+TIME_LAST  = TIME_START
+DEBUG_LEVEL= 1
+DISP_BATCH_BINS =   20 # Number of batch disparity bins
+STR_BATCH_BINS =    10 # Number of batch strength bins
+FILES_PER_SCENE =    5 # number of random offset files for the scene to select from (0 - use all available)
+#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from 
+#MAX_BATCH_FILES =   10 #maximal number of files to use in a batch
+#MAX_EPOCH =        500
+LR =               1e-3 # learning rate
+LR100 =            3e-4 #LR    # 1e-4
+LR200 =            1e-4 #LR100 # 3e-5
+LR400 =            3e-5 #LR200 # 1e-5
+LR600 =            1e-5 #LR400 # 3e-6
+USE_CONFIDENCE =     False
+ABSOLUTE_DISPARITY = False # True # False # True # False
+DEBUG_PLT_LOSS =     True
+TILE_LAYERS =        4
+FILE_TILE_SIDE =     9
+TILE_SIDE =          9 # 7
+TILE_SIZE =         TILE_SIDE* TILE_SIDE # == 81
+FEATURES_PER_TILE =  TILE_LAYERS * TILE_SIZE# == 324
+EPOCHS_TO_RUN =     3000#0 #0
+EPOCHS_FULL_TEST =   5 # 10 # 25# repeat full image test after this number of epochs
+#EPOCHS_SAME_FILE =   20
+RUN_TOT_AVG =       100 # last batches to average. Epoch is 307 training  batches  
+#BATCH_SIZE =       2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
+TWO_TRAINS =       True # use 2 train sets         
+BATCH_SIZE =       ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
+SHUFFLE_EPOCH =    True
+NET_ARCH1 =       8 # 0 # 0 # 0 # 4 # #4 # 8 # 4 # 0 #3 # 0 #  0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?
+NET_ARCH2 =       3 # 10 # 9 # 0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 #  0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?
+SYM8_SUB =        True # False # True #False #  True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
+ONLY_TILE =        None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
+ZIP_LHVAR =        True # combine _lvar and _hvar as odd/even elements 
+#DEBUG_PACK_TILES = True
+# CLUSTER_RADIUS should match input data
+CLUSTER_RADIUS =     2 # 1 # 1 - 3x3, 2 - 5x5 tiles
+SHUFFLE_FILES  =     True
+WLOSS_LAMBDA =       5.0 # 10.0 # 5.0 # 2.0 # 1.0 # 0.5 #3.0 # 1.0 # 0.3 # 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
+WBORDERS_ZERO =      True # Border conditions for first layer weights: False - free, True - tied to 0
+MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8
+FILE_UPDATE_EPOCHS =  2 # update train files each this many epochs. 0 - do not update
+PARTIALS_WEIGHTS = [1.0,1.0,1.0] # weight of full 5x5, center 3x3 and center 1x1. len(PARTIALS_WEIGHTS) == CLUSTER_RADIUS + 1. Set to None
+
+SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY]) +(["NS","S8"][SYM8_SUB])+"LMBD"+str(WLOSS_LAMBDA)
+
+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],
+              5:[0,   0,  64,   32,  32,  16],
+              6:[0,   0,  32,   16,  16,  16],
+              7:[0,   0,  64,   16,  16,  16],
+              8:[0,   0,   0,   64,  20,  16],
+              9:[0,   0,  256,  64,  32,  16],
+             10:[0, 256,  128,  64,  32,  16],
+              }
+NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
+NN_LAYOUT2 = NN_LAYOUTS[NET_ARCH2]
+USE_PARTIALS =      not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets
+
+#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
+class bcolors:
+    HEADER = '\033[95m'
+    OKBLUE = '\033[94m'
+    OKGREEN = '\033[92m'
+    WARNING = '\033[38;5;214m'
+    FAIL = '\033[91m'
+    ENDC = '\033[0m'
+    BOLD = '\033[1m'
+    BOLDWHITE = '\033[1;37m'
+    UNDERLINE = '\033[4m'
+def print_time(txt="",end="\n"):
+    global TIME_LAST
+    t = time.time()
+    if txt:
+        txt +=" "
+    print(("%s"+bcolors.BOLDWHITE+"at %.4fs (+%.4fs)"+bcolors.ENDC)%(txt,t-TIME_START,t-TIME_LAST), end = end, flush=True)
+    TIME_LAST = t
+#reading to memory (testing)
+train_next = [{'file':0, 'slot':0, 'files':0, 'slots':0},
+              {'file':0, 'slot':0, 'files':0, 'slots':0}]
+
+if TWO_TRAINS:
+    train_next +=  [{'file':0, 'slot':0, 'files':0, 'slots':0},
+                    {'file':0, 'slot':0, 'files':0, 'slots':0}]
+def readTFRewcordsEpoch(train_filename):
+#    filenames = [train_filename]
+#    dataset = tf.data.TFRecorDataset(filenames)
+    if not  '.tfrecords' in train_filename:
+        train_filename += '.tfrecords'
+    npy_dir_name = "npy"
+    dirname = os.path.dirname(train_filename) 
+    npy_dir = os.path.join(dirname, npy_dir_name)
+    filebasename, file_extension = os.path.splitext(train_filename)
+    filebasename = os.path.basename(filebasename)
+    file_corr2d =           os.path.join(npy_dir,filebasename + '_corr2d.npy')
+    file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
+    file_gt_ds =            os.path.join(npy_dir,filebasename + '_gt_ds.npy')
+    if (os.path.exists(file_corr2d) and
+        os.path.exists(file_target_disparity) and
+        os.path.exists(file_gt_ds)):
+        corr2d=            np.load (file_corr2d)
+        target_disparity = np.load(file_target_disparity)
+        gt_ds =            np.load(file_gt_ds)
+        pass
+    else:     
+        record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
+        corr2d_list=[]
+        target_disparity_list=[]
+        gt_ds_list = []
+        for string_record in record_iterator:
+            example = tf.train.Example()
+            example.ParseFromString(string_record)
+            corr2d_list.append           (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32))
+            target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32))
+            gt_ds_list.append            (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32))
+            pass
+        corr2d=            np.array(corr2d_list)
+        target_disparity = np.array(target_disparity_list)
+        gt_ds =            np.array(gt_ds_list)
+        try:
+            os.makedirs(os.path.dirname(file_corr2d))
+        except:
+            pass     
+
+        np.save(file_corr2d,           corr2d)
+        np.save(file_target_disparity, target_disparity)
+        np.save(file_gt_ds,            gt_ds)
+    return corr2d, target_disparity, gt_ds
+
+def getMoreFiles(fpaths,rslt):
+    for fpath in fpaths:
+        corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+        dataset = {"corr2d":           corr2d,
+                     "target_disparity": target_disparity,
+                     "gt_ds":            gt_ds}
+        if FILE_TILE_SIDE > TILE_SIDE:
+            reduce_tile_size([dataset],   TILE_LAYERS, TILE_SIDE)
+        reformat_to_clusters([dataset])
+        rslt.append(dataset)
+
+   
+
+#from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
+def read_and_decode(filename_queue):
+    reader = tf.TFRecordReader()
+    _, serialized_example = reader.read(filename_queue)
+
+    features = tf.parse_single_example(
+      serialized_example,
+      # Defaults are not specified since both keys are required.
+      features={
+        'corr2d':           tf.FixedLenFeature([FEATURES_PER_TILE],tf.float32), #string),
+        'target_disparity': tf.FixedLenFeature([1],   tf.float32), #.string),
+        'gt_ds':            tf.FixedLenFeature([2],  tf.float32)  #.string)
+        })
+    corr2d =           features['corr2d'] # tf.decode_raw(features['corr2d'], tf.float32)
+    target_disparity = features['target_disparity'] # tf.decode_raw(features['target_disparity'], tf.float32)
+    gt_ds =            tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], tf.float32)
+    in_features = tf.concat([corr2d,target_disparity],0)
+    # still some nan-s in correlation data?
+#    in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features)     
+#    corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features_clean, target_disparity, gt_ds],
+    corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, target_disparity, gt_ds],
+                                                 batch_size=1000, # 2,
+                                                 capacity=30,
+                                                 num_threads=2,
+                                                 min_after_dequeue=10)
+    return corr2d_out, target_disparity_out, gt_ds_out
+#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
+def add_margins(npa,radius, val = np.nan):
+    npa_ext = np.empty((npa.shape[0]+2*radius, npa.shape[1]+2*radius, npa.shape[2]), dtype = npa.dtype)
+    npa_ext[radius:radius + npa.shape[0],radius:radius + npa.shape[1]] = npa
+    npa_ext[0:radius,:,:] = val  
+    npa_ext[radius + npa.shape[0]:,:,:] = val  
+    npa_ext[:,0:radius,:] = val  
+    npa_ext[:, radius + npa.shape[1]:,:] = val
+    return npa_ext   
+
+def add_neibs(npa_ext,radius):
+    height = npa_ext.shape[0]-2*radius
+    width =  npa_ext.shape[1]-2*radius
+    side = 2 * radius + 1
+    size = side * side
+    npa_neib = np.empty((height,  width, side, side, npa_ext.shape[2]), dtype = npa_ext.dtype)
+    for dy in range (side):
+        for dx in range (side):
+            npa_neib[:,:,dy, dx,:]= npa_ext[dy:dy+height, dx:dx+width]
+    return npa_neib.reshape(height, width, -1)    
+    
+def extend_img_to_clusters(datasets_img,radius):
+    side = 2 * radius + 1
+    size = side * side
+    width =  324
+    if len(datasets_img) ==0:
+        return
+    num_tiles = datasets_img[0]['corr2d'].shape[0]
+    height = num_tiles // width 
+    for rec in datasets_img:
+        rec['corr2d'] =           add_neibs(add_margins(rec['corr2d'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1)) 
+        rec['target_disparity'] = add_neibs(add_margins(rec['target_disparity'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1)) 
+        rec['gt_ds'] =            add_neibs(add_margins(rec['gt_ds'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
+        pass
+
+
+
+def reformat_to_clusters(datasets_data):
+    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    for rec in datasets_data:
+        rec['corr2d'] =           rec['corr2d'].reshape(          (rec['corr2d'].shape[0]//cluster_size,           rec['corr2d'].shape[1] * cluster_size)) 
+        rec['target_disparity'] = rec['target_disparity'].reshape((rec['target_disparity'].shape[0]//cluster_size, rec['target_disparity'].shape[1] * cluster_size)) 
+        rec['gt_ds'] =            rec['gt_ds'].reshape(           (rec['gt_ds'].shape[0]//cluster_size,            rec['gt_ds'].shape[1] * cluster_size))
+
+def replace_nan(datasets_data):
+    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    for rec in datasets_data:
+        np.nan_to_num(rec['corr2d'],           copy = False) 
+        np.nan_to_num(rec['target_disparity'], copy = False) 
+        np.nan_to_num(rec['gt_ds'],            copy = False)
+
+def permute_to_swaps(perm):
+    pairs = []
+    for i in range(len(perm)):
+        w = np.where(perm == i)[0][0]
+        if w != i:
+            pairs.append([i,w])
+            perm[w] = perm[i]
+            perm[i] = i
+    return pairs        
+        
+def shuffle_in_place(datasets_data, indx, period):
+    swaps = permute_to_swaps(np.random.permutation(len(datasets_data)))
+    num_entries = datasets_data[0]['corr2d'].shape[0] // period
+    for swp in swaps:
+        ds0 = datasets_data[swp[0]]
+        ds1 = datasets_data[swp[1]]
+        tmp = ds0['corr2d'][indx::period].copy() 
+        ds0['corr2d'][indx::period] = ds1['corr2d'][indx::period]
+        ds1['corr2d'][indx::period] = tmp
+
+        tmp = ds0['target_disparity'][indx::period].copy() 
+        ds0['target_disparity'][indx::period] = ds1['target_disparity'][indx::period]
+        ds1['target_disparity'][indx::period] = tmp
+
+        tmp = ds0['gt_ds'][indx::period].copy() 
+        ds0['gt_ds'][indx::period] = ds1['gt_ds'][indx::period]
+        ds1['gt_ds'][indx::period] = tmp
+    
+def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
+    """
+    Improve shuffling by preserving indices inside batches (0 <->0, ... 39 <->39 for 40 tile group batches)
+    """
+    num_files = len(datasets_data)
+    #chunks_per_file = datasets_data[0]['target_disparity']
+    for nf, ds in enumerate(datasets_data):
+        groups_per_file = ds['corr2d'].shape[0]
+        chunks_per_file = groups_per_file//tiles_groups_per_chunk
+        permut = np.random.permutation(chunks_per_file)
+        ds['corr2d'] =           ds['corr2d'].          reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+        ds['target_disparity'] = ds['target_disparity'].reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+        ds['gt_ds'] =            ds['gt_ds'].           reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
+
+
+def _setFileSlot(train_next,files):
+    train_next['files'] = files
+    train_next['slots'] = min(train_next['files'], MAX_FILES_PER_GROUP)
+     
+def _nextFileSlot(train_next):
+    train_next['file'] = (train_next['file'] + 1) % train_next['files'] 
+    train_next['slot'] = (train_next['slot'] + 1) % train_next['slots'] 
+
+    
+def replaceNextDataset(datasets_data, new_dataset, train_next, nset,period):
+    replaceDataset(datasets_data, new_dataset, nset, period, findx = train_next['slot'])
+#    _nextFileSlot(train_next[nset])
+
+        
+def replaceDataset(datasets_data, new_dataset, nset, period, findx):
+    """
+    Replace one file in the dataset
+    """
+    datasets_data[findx]['corr2d']          [nset::period] =  new_dataset['corr2d'] 
+    datasets_data[findx]['target_disparity'][nset::period] =  new_dataset['target_disparity'] 
+    datasets_data[findx]['gt_ds']           [nset::period] =  new_dataset['gt_ds'] 
+       
+    
+
+def zip_lvar_hvar(datasets_all_data, del_src = True):
+#    cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+# Reformat input data
+    num_sets_to_combine = len(datasets_all_data)
+    datasets_data = []
+    if num_sets_to_combine:
+        for nrec in range(len(datasets_all_data[0])):
+            recs = [[] for _ in range(num_sets_to_combine)]
+            for nset, datasets in enumerate(datasets_all_data):
+                recs[nset] = datasets[nrec]
+                
+            rec = {'corr2d':           np.empty((recs[0]['corr2d'].shape[0]*num_sets_to_combine,          recs[0]['corr2d'].shape[1]),dtype=np.float32),
+                   'target_disparity': np.empty((recs[0]['target_disparity'].shape[0]*num_sets_to_combine,recs[0]['target_disparity'].shape[1]),dtype=np.float32),
+                   'gt_ds':            np.empty((recs[0]['gt_ds'].shape[0]*num_sets_to_combine,           recs[0]['gt_ds'].shape[1]),dtype=np.float32)}
+            
+            for nset, reci in enumerate(recs):
+                rec['corr2d']          [nset::num_sets_to_combine] =  recs[nset]['corr2d'] 
+                rec['target_disparity'][nset::num_sets_to_combine] =  recs[nset]['target_disparity'] 
+                rec['gt_ds']           [nset::num_sets_to_combine] =  recs[nset]['gt_ds'] 
+            if del_src:
+                for nset in range(num_sets_to_combine):
+                    datasets_all_data[nset][nrec] = None
+            datasets_data.append(rec)
+    return datasets_data    
+         
+
+# list of dictionaries  
+def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side):
+    if (not datasets_data is None) and (len (datasets_data) > 0): 
+        tsz = (datasets_data[0]['corr2d'].shape[1])// num_tile_layers # 81 # list index out of range
+        tss = int(np.sqrt(tsz)+0.5)
+        offs = (tss - reduced_tile_side) // 2
+        for rec in datasets_data:
+            rec['corr2d'] =  (rec['corr2d'].reshape((-1, num_tile_layers,  tss, tss))
+                                 [..., offs:offs+reduced_tile_side, offs:offs+reduced_tile_side].
+                                 reshape(-1,num_tile_layers*reduced_tile_side*reduced_tile_side))
+
+def eval_results(rslt_path, absolute,
+                 min_disp =       -0.1, #minimal GT disparity
+                 max_disp =       20.0, # maximal GT disparity
+                 max_ofst_target = 1.0,
+                 max_ofst_result = 1.0,
+                 str_pow =         2.0,
+                 radius =          0):
+#    for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in [
+    variants = [[         -0.1,         5.0,              0.5,            0.5,          1.0],           
+                [         -0.1,         5.0,              0.5,            0.5,          2.0],
+                [         -0.1,         5.0,              0.2,            0.2,          1.0],
+                [         -0.1,         5.0,              0.2,            0.2,          2.0],
+                [         -0.1,        20.0,              0.5,            0.5,          1.0],           
+                [         -0.1,        20.0,              0.5,            0.5,          2.0],
+                [         -0.1,        20.0,              0.2,            0.2,          1.0],
+                [         -0.1,        20.0,              0.2,            0.2,          2.0],
+                [         -0.1,        20.0,              1.0,            1.0,          1.0],
+                [min_disp, max_disp, max_ofst_target, max_ofst_result, str_pow]]
+    
+
+    rslt = np.load(result_file)
+    not_nan  =  ~np.isnan(rslt[...,0])
+    not_nan &=  ~np.isnan(rslt[...,1])
+    not_nan &=  ~np.isnan(rslt[...,2])
+    not_nan &=  ~np.isnan(rslt[...,3])
+    not_nan_ext = np.zeros((rslt.shape[0] + 2*radius,rslt.shape[1] + 2 * radius),dtype=np.bool) 
+    not_nan_ext[radius:-radius,radius:-radius] = not_nan
+    for dy in range(2*radius+1):
+        for dx in range(2*radius+1):
+            not_nan_ext[dy:dy+not_nan.shape[0], dx:dx+not_nan.shape[1]] &= not_nan
+    not_nan = not_nan_ext[radius:-radius,radius:-radius]         
+        
+    if  not absolute:
+        rslt[...,0] +=  rslt[...,1]
+    nn_disparity =     np.nan_to_num(rslt[...,0], copy = False)
+    target_disparity = np.nan_to_num(rslt[...,1], copy = False)
+    gt_disparity =     np.nan_to_num(rslt[...,2], copy = False)
+    gt_strength =      np.nan_to_num(rslt[...,3], copy = False)
+    rslt = []
+    for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in variants:
+        good_tiles = not_nan.copy();
+        good_tiles &= (gt_disparity >= min_disparity)
+        good_tiles &= (gt_disparity <= max_disparity)
+        good_tiles &= (target_disparity != gt_disparity)
+        good_tiles &= (np.abs(target_disparity - gt_disparity) <= max_offset_target)
+        good_tiles &= (np.abs(target_disparity - nn_disparity) <= max_offset_result)
+        gt_w =  gt_strength * good_tiles
+        gt_w = np.power(gt_w,strength_pow)
+        sw = gt_w.sum() 
+        diff0 = target_disparity - gt_disparity
+        diff1 = nn_disparity -     gt_disparity
+        diff0_2w = gt_w*diff0*diff0
+        diff1_2w = gt_w*diff1*diff1
+        rms0 = np.sqrt(diff0_2w.sum()/sw) 
+        rms1 = np.sqrt(diff1_2w.sum()/sw)
+        print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
+            min_disparity, max_disparity, max_offset_target,  max_offset_result, strength_pow, rms0, rms1, rms0/rms1, good_tiles.sum() ))
+        rslt.append([rms0,rms1])
+    return rslt 
+
+def concentricSquares(radius):
+    side = 2 * radius + 1
+    return [[((i // side) >= var) and
+             ((i // side) < (side - var)) and
+             ((i % side)  >= var) and
+             ((i % side)  < (side - var))  for i in range (side*side) ] for var in range(radius+1)]    
+                         
+
+"""
+Start of the main code
+"""
+"""
+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_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"
+"""
+data_dir =  "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" 
+data_dir1 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_4" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+###data_dir1 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+ 
+#img_dir =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+img_dir =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
+
+dir_train_lvar =  data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_hvar =  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_test_lvar =   data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+#dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_img =         os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
+dir_result =      os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"
+
+files_train_lvar = ["train000_R2_LE_1.5.tfrecords",
+                    "train001_R2_LE_1.5.tfrecords",
+                    "train002_R2_LE_1.5.tfrecords",
+                    "train003_R2_LE_1.5.tfrecords",
+                    "train004_R2_LE_1.5.tfrecords",
+                    "train005_R2_LE_1.5.tfrecords",
+                    "train006_R2_LE_1.5.tfrecords",
+                    "train007_R2_LE_1.5.tfrecords",
+                    "train008_R2_LE_1.5.tfrecords",
+                    "train009_R2_LE_1.5.tfrecords",
+                    "train010_R2_LE_1.5.tfrecords",
+                    "train011_R2_LE_1.5.tfrecords",
+                    "train012_R2_LE_1.5.tfrecords",
+                    "train013_R2_LE_1.5.tfrecords",
+                    "train014_R2_LE_1.5.tfrecords",
+                    "train015_R2_LE_1.5.tfrecords",
+                    "train016_R2_LE_1.5.tfrecords",
+                    "train017_R2_LE_1.5.tfrecords",
+                    "train018_R2_LE_1.5.tfrecords",
+                    "train019_R2_LE_1.5.tfrecords",
+                    "train020_R2_LE_1.5.tfrecords",
+                    "train021_R2_LE_1.5.tfrecords",
+                    "train022_R2_LE_1.5.tfrecords",
+                    "train023_R2_LE_1.5.tfrecords",
+                    "train024_R2_LE_1.5.tfrecords",
+                    "train025_R2_LE_1.5.tfrecords",
+                    "train026_R2_LE_1.5.tfrecords",
+                    "train027_R2_LE_1.5.tfrecords",
+                    "train028_R2_LE_1.5.tfrecords",
+                    "train029_R2_LE_1.5.tfrecords",
+                    "train030_R2_LE_1.5.tfrecords",
+                    "train031_R2_LE_1.5.tfrecords",
+                    ]
+files_train_hvar = ["train000_R2_GT_1.5.tfrecords",
+                    "train001_R2_GT_1.5.tfrecords",
+                    "train002_R2_GT_1.5.tfrecords",
+                    "train003_R2_GT_1.5.tfrecords",
+                    "train004_R2_GT_1.5.tfrecords",
+                    "train005_R2_GT_1.5.tfrecords",
+                    "train006_R2_GT_1.5.tfrecords",
+                    "train007_R2_GT_1.5.tfrecords",
+                    "train008_R2_GT_1.5.tfrecords",
+                    "train009_R2_GT_1.5.tfrecords",
+                    "train010_R2_GT_1.5.tfrecords",
+                    "train011_R2_GT_1.5.tfrecords",
+                    "train012_R2_GT_1.5.tfrecords",
+                    "train013_R2_GT_1.5.tfrecords",
+                    "train014_R2_GT_1.5.tfrecords",
+                    "train015_R2_GT_1.5.tfrecords",
+                    "train016_R2_GT_1.5.tfrecords",
+                    "train017_R2_GT_1.5.tfrecords",
+                    "train018_R2_GT_1.5.tfrecords",
+                    "train019_R2_GT_1.5.tfrecords",
+                    "train020_R2_GT_1.5.tfrecords",
+                    "train021_R2_GT_1.5.tfrecords",
+                    "train022_R2_GT_1.5.tfrecords",
+                    "train023_R2_GT_1.5.tfrecords",
+                    "train024_R2_GT_1.5.tfrecords",
+                    "train025_R2_GT_1.5.tfrecords",
+                    "train026_R2_GT_1.5.tfrecords",
+                    "train027_R2_GT_1.5.tfrecords",
+                    "train028_R2_GT_1.5.tfrecords",
+                    "train029_R2_GT_1.5.tfrecords",
+                    "train030_R2_GT_1.5.tfrecords",
+                    "train031_R2_GT_1.5.tfrecords",
+]
+
+files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
+                    "train001_R2_LE_1.5.tfrecords",
+                    "train002_R2_LE_1.5.tfrecords",
+                    "train003_R2_LE_1.5.tfrecords",
+                    "train004_R2_LE_1.5.tfrecords",
+                    "train005_R2_LE_1.5.tfrecords",
+                    "train006_R2_LE_1.5.tfrecords",
+                    "train007_R2_LE_1.5.tfrecords",
+                    "train008_R2_LE_1.5.tfrecords",
+                    "train009_R2_LE_1.5.tfrecords",
+                    "train010_R2_LE_1.5.tfrecords",
+                    "train011_R2_LE_1.5.tfrecords",
+                    "train012_R2_LE_1.5.tfrecords",
+                    "train013_R2_LE_1.5.tfrecords",
+                    "train014_R2_LE_1.5.tfrecords",
+                    "train015_R2_LE_1.5.tfrecords",
+                    "train016_R2_LE_1.5.tfrecords",
+                    "train017_R2_LE_1.5.tfrecords",
+                    "train018_R2_LE_1.5.tfrecords",
+                    "train019_R2_LE_1.5.tfrecords",
+                    "train020_R2_LE_1.5.tfrecords",
+                    "train021_R2_LE_1.5.tfrecords",
+                    "train022_R2_LE_1.5.tfrecords",
+                    "train023_R2_LE_1.5.tfrecords",
+                    ]
+files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
+                    "train001_R2_GT_1.5.tfrecords",
+                    "train002_R2_GT_1.5.tfrecords",
+                    "train003_R2_GT_1.5.tfrecords",
+                    "train004_R2_GT_1.5.tfrecords",
+                    "train005_R2_GT_1.5.tfrecords",
+                    "train006_R2_GT_1.5.tfrecords",
+                    "train007_R2_GT_1.5.tfrecords",
+                    "train008_R2_GT_1.5.tfrecords",
+                    "train009_R2_GT_1.5.tfrecords",
+                    "train010_R2_GT_1.5.tfrecords",
+                    "train011_R2_GT_1.5.tfrecords",
+                    "train012_R2_GT_1.5.tfrecords",
+                    "train013_R2_GT_1.5.tfrecords",
+                    "train014_R2_GT_1.5.tfrecords",
+                    "train015_R2_GT_1.5.tfrecords",
+                    "train016_R2_GT_1.5.tfrecords",
+                    "train017_R2_GT_1.5.tfrecords",
+                    "train018_R2_GT_1.5.tfrecords",
+                    "train019_R2_GT_1.5.tfrecords",
+                    "train020_R2_GT_1.5.tfrecords",
+                    "train021_R2_GT_1.5.tfrecords",
+                    "train022_R2_GT_1.5.tfrecords",
+                    "train023_R2_GT_1.5.tfrecords",
+]
+"""
+files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
+                    "train001_R2_LE_1.5.tfrecords",
+                    "train002_R2_LE_1.5.tfrecords",
+                    "train003_R2_LE_1.5.tfrecords",
+                    "train004_R2_LE_1.5.tfrecords",
+                    "train005_R2_LE_1.5.tfrecords",
+                    "train006_R2_LE_1.5.tfrecords",
+                    "train007_R2_LE_1.5.tfrecords",
+                    ]
+files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
+                    "train001_R2_GT_1.5.tfrecords",
+                    "train002_R2_GT_1.5.tfrecords",
+                    "train003_R2_GT_1.5.tfrecords",
+                    "train004_R2_GT_1.5.tfrecords",
+                    "train005_R2_GT_1.5.tfrecords",
+                    "train006_R2_GT_1.5.tfrecords",
+                    "train007_R2_GT_1.5.tfrecords",
+]
+"""
+
+
+"""
+Try again - all hvar training, train than different hvar testing.
+tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-bothtrain-test-hvar" --port=7069
+Seems that even different (but used) hvar perfectly match each other, but training for both lvar and hvar never get
+match for either of hvar/lvar, even those that were used for training
+tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-19-tested_with_same_hvar_lvar_as_trained" --port=7070
+
+try same with higher LR - will they eventually converge?
+
+Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as test)
+"""
+#just testing:
+#files_train_lvar = files_train_hvar
+files_test_lvar =  ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]
+files_test_hvar =  ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
+files_img =        ['1527257933_150165-v04'] # overlook
+#files_img =        ['1527256858_150165-v01'] # State Street
+#files_img =        ['1527256816_150165-v02'] # State Street
+#files_img =        ['1527182802_096892-v02'] # plane near
+##files_img =        ['1527182805_096892-v02'] # plane midrange used up to -49
+#files_img =        ['1527182810_096892-v02'] # plane far
+#MAX_FILES_PER_GROUP
+for i, path in enumerate(files_train_lvar):
+    files_train_lvar[i]=os.path.join(dir_train_lvar, path)
+    
+for i, path in enumerate(files_train_hvar):
+    files_train_hvar[i]=os.path.join(dir_train_hvar, path)
+
+# Second set of files
+
+for i, path in enumerate(files_train_lvar1):
+    files_train_lvar1[i]=os.path.join(dir_train_lvar1, path)
+    
+for i, path in enumerate(files_train_hvar1):
+    files_train_hvar1[i]=os.path.join(dir_train_hvar1, path)
+    
+for i, path in enumerate(files_test_lvar):
+    files_test_lvar[i]=os.path.join(dir_test_lvar, path)
+    
+for i, path in enumerate(files_test_hvar):
+    files_test_hvar[i]=os.path.join(dir_test_hvar, path)
+
+result_files=[]
+for i, path in enumerate(files_img):
+    files_img[i] =      os.path.join(dir_img,    path+'.tfrecords')
+    result_files.append(os.path.join(dir_result, path+"_"+SUFFIX+'.npy'))
+
+files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
+
+#file_test_hvar=  None
+weight_hvar = 0.26
+weight_lvar = 1.0 - weight_hvar 
+partials = None
+partials = concentricSquares(CLUSTER_RADIUS)
+PARTIALS_WEIGHTS = [1.0*pw/sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS]
+if not USE_PARTIALS:
+    partials = partials[0:1]
+    PARTIALS_WEIGHTS = [1.0]
+
+
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+
+for result_file in result_files:
+    try:
+        eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
+    #exit(0)
+    except:
+        pass
+
+datasets_img = []
+for fpath in files_img:
+    print_time("Importing test image data from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_img.append(       {"corr2d":           corr2d,
+                                "target_disparity": target_disparity,
+                                "gt_ds":            gt_ds})
+    print_time("  Done")
+gtruth =   datasets_img[0]['gt_ds'].copy()
+t_disp =   datasets_img[0]['target_disparity'].reshape([-1,1]).copy()
+extend_img_to_clusters(datasets_img, radius = CLUSTER_RADIUS)
+#reformat_to_clusters(datasets_img) already this format   
+replace_nan(datasets_img)
+pass
+pass
+
+
+
+datasets_train_lvar =  []
+datasets_train_hvar =  []
+datasets_train_lvar1 = []
+datasets_train_hvar1 = []
+
+datasets_train_all = [[],[],[],[]]
+#files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
+
+for n_train, f_train in enumerate(files_train):
+    if len(f_train) and ((n_train<2) or TWO_TRAINS):
+        _setFileSlot(train_next[n_train], len(f_train))
+        for i, fpath in enumerate(f_train):
+            if i >= MAX_FILES_PER_GROUP:
+                break
+            print_time("Importing train data "+(["low variance","high variance", "low variance1","high variance1"][n_train]) +" from "+fpath, end="")
+            corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+            datasets_train_all[n_train].append({"corr2d":corr2d,
+                                        "target_disparity":target_disparity,
+                                        "gt_ds":gt_ds})
+            _nextFileSlot(train_next[n_train])
+            print_time("  Done")
+
+datasets_test_lvar = []
+for fpath in files_test_lvar:
+    print_time("Importing test data (low variance) from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_test_lvar.append({"corr2d":corr2d,
+                                "target_disparity":target_disparity,
+                                "gt_ds":gt_ds})
+    print_time("  Done")
+
+datasets_test_hvar = []
+for fpath in files_test_hvar:
+    print_time("Importing test data (high variance) from "+fpath, end="")
+    corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
+    datasets_test_hvar.append({"corr2d":corr2d,
+                                "target_disparity":target_disparity,
+                                "gt_ds":gt_ds})
+    print_time("  Done")
+    
+    
+    
+    
+#CLUSTER_RADIUS
+cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
+center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
+# Reformat input data
+if FILE_TILE_SIDE > TILE_SIDE:
+    print_time("Reducing correlation tile size from %d to %d"%(FILE_TILE_SIDE, TILE_SIDE), end="")
+    for d_train in datasets_train_all:
+        reduce_tile_size(d_train,  TILE_LAYERS, TILE_SIDE)
+    reduce_tile_size(datasets_test_lvar,   TILE_LAYERS, TILE_SIDE)
+    reduce_tile_size(datasets_test_hvar,   TILE_LAYERS, TILE_SIDE)
+    print_time("  Done")
+pass
+
+# Reformat to 1/9/25 tile clusters
+for n_train, d_train in enumerate(datasets_train_all):
+    print_time("Reshaping train data ("+(["low variance","high variance", "low variance1","high variance1"][n_train])+") ", end="")
+    reformat_to_clusters(d_train)
+    print_time("  Done")
+
+print_time("Reshaping test data (low variance)", end="")
+reformat_to_clusters(datasets_test_lvar)
+print_time("  Done")
+print_time("Reshaping test data (high variance)", end="")
+reformat_to_clusters(datasets_test_hvar)
+print_time("  Done")
+pass    
+
+"""
+datasets_train_lvar & datasets_train_hvar ( that will increase batch size and placeholders twice
+test has to have even original, batches will not zip - just use two batches for one big one
+"""
+
+if ZIP_LHVAR:
+    print_time("Zipping together datasets datasets_train_lvar and datasets_train_hvar", end="")
+    datasets_train = zip_lvar_hvar(datasets_train_all, del_src = True) # no shuffle, delete src
+    print_time("  Done")
+else:
+    #Alternate lvar/hvar
+    datasets_train = []
+    datasets_weights_train = []
+    for indx in range(max(len(datasets_train_lvar),len(datasets_train_hvar))):
+        if (indx < len(datasets_train_lvar)):
+            datasets_train.append(datasets_train_lvar[indx])
+            datasets_weights_train.append(weight_lvar)
+        if (indx < len(datasets_train_hvar)):
+            datasets_train.append(datasets_train_hvar[indx])
+            datasets_weights_train.append(weight_hvar)
+
+datasets_test = []
+datasets_weights_test = []
+for dataset_test_lvar in datasets_test_lvar:
+    datasets_test.append(dataset_test_lvar)
+    datasets_weights_test.append(weight_lvar)
+for dataset_test_hvar in datasets_test_hvar:
+    datasets_test.append(dataset_test_hvar)
+    datasets_weights_test.append(weight_hvar)
+
+    
+corr2d_train_placeholder =           tf.placeholder(datasets_train[0]['corr2d'].dtype,           (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
+target_disparity_train_placeholder = tf.placeholder(datasets_train[0]['target_disparity'].dtype, (None,1 *   cluster_size))  #target_disparity_train.shape)
+gt_ds_train_placeholder =            tf.placeholder(datasets_train[0]['gt_ds'].dtype,            (None,2 *   cluster_size)) #gt_ds_train.shape)
+#dataset_tt    TensorSliceDataset: <TensorSliceDataset shapes: {gt_ds: (50,), corr2d: (8100,), target_disparity: (25,)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>    
+dataset_tt = tf.data.Dataset.from_tensor_slices({
+    "corr2d":corr2d_train_placeholder,
+    "target_disparity": target_disparity_train_placeholder,
+    "gt_ds": gt_ds_train_placeholder})
+
+
+#dataset_train_size = len(corr2d_train)
+
+#dataset_train_size = len(datasets_train_lvar[0]['corr2d'])
+dataset_train_size = len(datasets_train[0]['corr2d'])
+dataset_train_size //= BATCH_SIZE
+dataset_test_size = len(datasets_test_lvar[0]['corr2d'])
+dataset_test_size //= BATCH_SIZE
+dataset_img_size = len(datasets_img[0]['corr2d'])
+dataset_img_size //= BATCH_SIZE
+
+#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
+dataset_tt = dataset_tt.batch(BATCH_SIZE)
+dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
+iterator_tt = dataset_tt.make_initializable_iterator()
+next_element_tt = iterator_tt.get_next()
+#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
+#BatchDataset: <BatchDataset shapes: {gt_ds: (?, 50), corr2d: (?, 8100), target_disparity: (?, 25)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
+"""
+next_element_tt    dict: {'gt_ds': <tf.Tensor 'IteratorGetNext:1' shape=(?, 50) dtype=float32>, 'corr2d': <tf.Tensor 'IteratorGetNext:0' shape=(?, 8100) dtype=float32>, 'target_disparity': <tf.Tensor 'IteratorGetNext:2' shape=(?, 25) dtype=float32>}    
+    'corr2d' (140405473715624)    Tensor: Tensor("IteratorGetNext:0", shape=(?, 8100), dtype=float32)    
+    'gt_ds' (140405473715680)    Tensor: Tensor("IteratorGetNext:1", shape=(?, 50), dtype=float32)    
+    'target_disparity' (140405501995888)    Tensor: Tensor("IteratorGetNext:2", shape=(?, 25), dtype=float32)    
+"""
+
+#https://www.tensorflow.org/versions/r1.5/programmers_guide/datasets
+result_dir = './attic/result_neibs_'+     SUFFIX+'/'
+checkpoint_dir = './attic/result_neibs_'+ SUFFIX+'/'
+save_freq = 500
+def lrelu(x):
+    return tf.maximum(x*0.2,x)
+#    return tf.nn.relu(x)
+
+def sym_inputs8(inp):
+    """
+    get input vector [?:4*9*9+1] (last being target_disparity) and reorder for horizontal flip,
+    vertical flip and transpose (8 variants, mode + 1 - hor, +2 - vert, +4 - transpose)
+    return same lengh, reordered
+    """
+    with tf.name_scope("sym_inputs8"):
+        td =           inp[:,-1:] # tf.reshape(inp,[-1], name = "td")[-1]
+        inp_corr =     tf.reshape(inp[:,:-1],[-1,4,TILE_SIDE,TILE_SIDE], name = "inp_corr")
+        inp_corr_h =   tf.stack([-inp_corr  [:,0,:,-1::-1], inp_corr  [:,1,:,-1::-1], -inp_corr  [:,3,:,-1::-1], -inp_corr  [:,2,:,-1::-1]], axis=1, name = "inp_corr_h")
+        inp_corr_v =   tf.stack([ inp_corr  [:,0,-1::-1,:],-inp_corr  [:,1,-1::-1,:],  inp_corr  [:,3,-1::-1,:],  inp_corr  [:,2,-1::-1,:]], axis=1, name = "inp_corr_v")
+        inp_corr_hv =  tf.stack([ inp_corr_h[:,0,-1::-1,:],-inp_corr_h[:,1,-1::-1,:],  inp_corr_h[:,3,-1::-1,:],  inp_corr_h[:,2,-1::-1,:]], axis=1, name = "inp_corr_hv")
+        inp_corr_t =   tf.stack([tf.transpose(inp_corr   [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr   [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr   [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr   [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_t")
+        inp_corr_ht =  tf.stack([tf.transpose(inp_corr_h [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_h [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_h [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_h [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_ht")
+        inp_corr_vt =  tf.stack([tf.transpose(inp_corr_v [:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_v [:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_v [:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_v [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_vt")
+        inp_corr_hvt = tf.stack([tf.transpose(inp_corr_hv[:,1], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_hv[:,0], perm=[0,2,1]),
+                                 tf.transpose(inp_corr_hv[:,2], perm=[0,2,1]),
+                                -tf.transpose(inp_corr_hv[:,3], perm=[0,2,1])], axis=1, name = "inp_corr_hvt")
+#        return td, [inp_corr, inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
+        """
+        return [tf.concat([tf.reshape(inp_corr,    [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr"),
+                tf.concat([tf.reshape(inp_corr_h,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_h"),
+                tf.concat([tf.reshape(inp_corr_v,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_v"),
+                tf.concat([tf.reshape(inp_corr_hv, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hv"),
+                tf.concat([tf.reshape(inp_corr_t,  [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_t"),
+                tf.concat([tf.reshape(inp_corr_ht, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_ht"),
+                tf.concat([tf.reshape(inp_corr_vt, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_vt"),
+                tf.concat([tf.reshape(inp_corr_hvt,[inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hvt")]
+        """
+        cl = 4 * TILE_SIDE * TILE_SIDE
+        return [tf.concat([tf.reshape(inp_corr,    [-1,cl]),td], axis=1,name = "out_corr"),
+                tf.concat([tf.reshape(inp_corr_h,  [-1,cl]),td], axis=1,name = "out_corr_h"),
+                tf.concat([tf.reshape(inp_corr_v,  [-1,cl]),td], axis=1,name = "out_corr_v"),
+                tf.concat([tf.reshape(inp_corr_hv, [-1,cl]),td], axis=1,name = "out_corr_hv"),
+                tf.concat([tf.reshape(inp_corr_t,  [-1,cl]),td], axis=1,name = "out_corr_t"),
+                tf.concat([tf.reshape(inp_corr_ht, [-1,cl]),td], axis=1,name = "out_corr_ht"),
+                tf.concat([tf.reshape(inp_corr_vt, [-1,cl]),td], axis=1,name = "out_corr_vt"),
+                tf.concat([tf.reshape(inp_corr_hvt,[-1,cl]),td], axis=1,name = "out_corr_hvt")]
+#                           inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
+    
+
+def network_sub(input, layout, reuse, sym8 = False):
+    last_indx = None;
+    fc = []
+    inp_weights = []
+    for i, num_outs in enumerate (layout):
+        if num_outs:
+           if fc:
+               inp = fc[-1]
+               fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
+           else:
+               inp = input
+               if sym8:
+                   inp8 = sym_inputs8(inp)
+                   num_non_sum = num_outs %  len(inp8) # if number of first layer outputs is not multiple of 8
+                   num_sym8 =    num_outs // len(inp8) # number of symmetrical groups
+                   fc_sym = []
+                   for j in range (len(inp8)): # ==8
+                       reuse_this = reuse | (j > 0)
+                       scp = 'g_fc_sub'+str(i)
+                       fc_sym.append(slim.fully_connected(inp8[j],    num_sym8, activation_fn=lrelu, scope= scp,     reuse = reuse_this))
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+                   if num_non_sum > 0:
+                       reuse_this = reuse
+                       scp = 'g_fc_sub'+str(i)+"r"
+                       fc_sym.append(slim.fully_connected(inp,     num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))    
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+                   fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
+               else:
+                   scp = 'g_fc_sub'+str(i)
+                   fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
+                   if not reuse:
+                       with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
+                          inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs])) 
+           
+    return fc[-1], inp_weights
+
+def network_inter(input, layout, reuse=False):
+    last_indx = None;
+    fc = []
+    for i, num_outs in enumerate (layout):
+        if num_outs:
+           if fc:
+               inp = fc[-1]
+           else:
+               inp = input
+           fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_inter'+str(i), reuse = reuse))
+    if USE_CONFIDENCE:
+        fc_out  = slim.fully_connected(fc[-1],     2, activation_fn=lrelu, scope='g_fc_inter_out', reuse = reuse)
+    else:     
+        fc_out  = slim.fully_connected(fc[-1],     1, activation_fn=None, scope='g_fc_inter_out', reuse = reuse)
+        #If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only  
+    return fc_out
+
+def networks_siam(input, # now [?,9,325]-> [?,25,325]
+                 layout1, 
+                 layout2,
+                 sym8 =        False,
+                 only_tile =   None, # just for debugging - feed only data from the center sub-network
+                 partials =    None): 
+    with tf.name_scope("Siam_net"):
+        inp_weights = []
+        num_legs =  input.shape[1] # == 25
+        if partials is None:
+            partials = [[True] * num_legs]
+#        inter_list = []
+        inter_lists = [[] for _ in partials]
+        reuse = False
+        for i in range (num_legs):
+            if ((only_tile is None) or (i == only_tile)) and any([p[i] for p in partials]) :
+                ns, ns_weights = network_sub(input[:,i,:],
+                                          layout= layout1,
+                                          reuse= reuse,
+                                          sym8 = sym8)
+                for n, partial in enumerate(partials):
+                    if partial[i]:
+                        inter_lists[n].append(ns)
+                    else:
+#                        inter_lists[n].append(tf.constant(0.0,dtype=tf.float32, shape=ns.shape))
+#                        inter_lists[n].append(ns * 0.0)
+                        inter_lists[n].append(tf.zeros_like(ns))
+#                inter_list.append(ns)
+                inp_weights += ns_weights
+                reuse = True
+        outs = []         
+        for n, _ in enumerate(partials):
+#            outs.append(network_inter (tf.concat(inter_list, 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
+            outs.append(network_inter (tf.concat(inter_lists[n], 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
+#            inter_tensors.append(tf.concat(inter_list, 1, name='inter_tensor'+str(n)))
+#        inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
+#        return  network_inter (inter_tensor, layout2),  inp_weights 
+        return  outs,  inp_weights 
+
+def debug_gt_variance(
+        indx,        # This tile index (0..8)
+        center_indx, # center tile index
+        gt_ds_batch # [?:9:2]
+        ):
+    with tf.name_scope("Debug_GT_Variance"):
+        tf_num_tiles =       tf.shape(gt_ds_batch)[0]
+        d_gt_this =   tf.reshape(gt_ds_batch[:,2 * indx],[-1],                     name = "d_this")
+        d_gt_center = tf.reshape(gt_ds_batch[:,2 * center_indx],[-1],              name = "d_center")
+        d_gt_diff =   tf.subtract(d_gt_this, d_gt_center,                          name = "d_diff")
+        d_gt_diff2 =  tf.multiply(d_gt_diff, d_gt_diff,                            name = "d_diff2")
+        d_gt_var =    tf.reduce_mean(d_gt_diff2,                                   name = "d_gt_var")
+        return  d_gt_var
+    
+    
+def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
+              target_disparity_batch,      # [batch_size]        tf placeholder
+              gt_ds_batch,                 # [batch_size,2]      tf placeholder
+              absolute_disparity =     True, #when false there should be no activation on disparity output ! 
+              use_confidence =         True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            1.0,
+              error2_offset =          0.0025, # 0.0, # 0.0025, # (0.05^2) ~= coring
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False):  # use calculated disparity for disparity weight boosting (False - use target disparity)
+               
+    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
+        """
+        tf_lambda_conf_avg = tf.constant(lambda_conf_avg, dtype=tf.float32, name="tf_lambda_conf_avg")
+        tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
+        tf_conf_pwr =        tf.constant(conf_pwr,        dtype=tf.float32, name="tf_conf_pwr")
+        tf_gt_conf_offset =  tf.constant(gt_conf_offset,  dtype=tf.float32, name="tf_gt_conf_offset")
+        tf_gt_conf_pwr =     tf.constant(gt_conf_pwr,     dtype=tf.float32, name="tf_gt_conf_pwr")
+        tf_num_tiles =       tf.shape(gt_ds_batch)[0]
+        tf_0f =              tf.constant(0.0,             dtype=tf.float32, name="tf_0f")
+        tf_1f =              tf.constant(1.0,             dtype=tf.float32, name="tf_1f")
+        tf_maxw =            tf.constant(1.0,             dtype=tf.float32, name="tf_maxw")
+        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.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.maximum(w_sub, tf_0f,                              name = "w_clip")
+            if gt_conf_pwr == 1.0:
+                w = w_clip
+            else:
+                w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow")
+    
+        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.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")
+            conf_avg1 =      tf.subtract(conf_avg, tf_1f,                    name = "conf_avg1")
+            conf_avg2 =      tf.square(conf_avg1,                            name = "conf_avg2")
+            cost2 =          tf.multiply (conf_avg2, tf_lambda_conf_avg,     name = "cost2")
+    
+            iconf_avg =      tf.divide(tf_1f, conf_avg,                      name = "iconf_avg")
+            nconf =          tf.multiply (conf_slice, iconf_avg,             name = "nconf") #normalized confidence
+            nconf_pwr =      tf.pow(nconf, conf_pwr,                         name = "nconf_pwr")
+            nconf_pwr_sum =  tf.reduce_sum(nconf_pwr,                        name = "nconf_pwr_sum")
+            nconf_pwr_offs = tf.subtract(nconf_pwr_sum, tf_1f,               name = "nconf_pwr_offs")
+            cost3 =          tf.multiply (conf_avg2, nconf_pwr_offs,         name = "cost3")
+            w_all =          tf.multiply (w, nconf,                          name = "w_all")
+        else:
+            w_all = w
+#            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.reshape(out_batch[:,0],[-1],                 name = "disp_slice")
+        d_gt_slice =         tf.reshape(gt_ds_batch[:,0],[-1],               name = "d_gt_slice")
+        
+        td_flat =        tf.reshape(target_disparity_batch,[-1],         name = "td_flat")
+        if absolute_disparity:
+            adisp =          disp_slice
+        else:
+            adisp =          tf.add(disp_slice, td_flat,                     name = "adisp")
+        out_diff =           tf.subtract(adisp, d_gt_slice,                  name = "out_diff")
+            
+            
+        out_diff2 =          tf.square(out_diff,                             name = "out_diff2")
+        out_wdiff2 =         tf.multiply (out_diff2, w_norm,                 name = "out_wdiff2")
+        
+        cost1 =              tf.reduce_sum(out_wdiff2,                       name = "cost1")
+        
+        out_diff2_offset =   tf.subtract(out_diff2, error2_offset,           name = "out_diff2_offset")
+        out_diff2_biased =   tf.maximum(out_diff2_offset, 0.0,               name = "out_diff2_biased")
+        
+        # calculate disparity-based weight boost
+        if use_out:
+            dispw =          tf.clip_by_value(adisp, disp_wmin, disp_wmax,   name = "dispw")
+        else:
+            dispw =          tf.clip_by_value(td_flat, disp_wmin, disp_wmax, name = "dispw")
+        dispw_boost =        tf.divide(disp_wmax, dispw,                     name = "dispw_boost")
+        dispw_comp =         tf.multiply (dispw_boost, w_norm,               name = "dispw_comp") #HERE??
+#        dispw_comp =         tf.multiply (w_norm, w_norm,               name = "dispw_comp") #HERE??
+        dispw_sum =          tf.reduce_sum(dispw_comp,                       name = "dispw_sum")
+        idispw_sum =         tf.divide(tf_1f, dispw_sum,                     name = "idispw_sum")
+        dispw_norm =         tf.multiply (dispw_comp, idispw_sum,            name = "dispw_norm")
+        
+        out_diff2_wbiased =  tf.multiply(out_diff2_biased, dispw_norm,       name = "out_diff2_wbiased")
+#        out_diff2_wbiased =  tf.multiply(out_diff2_biased, w_norm,       name = "out_diff2_wbiased")
+        cost1b =             tf.reduce_sum(out_diff2_wbiased,                name = "cost1b")
+        
+        if use_confidence:
+            cost12 =         tf.add(cost1b, cost2,                           name = "cost12")
+            cost123 =        tf.add(cost12, cost3,                           name = "cost123")    
+            
+            return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
+        else:
+            return cost1b,  disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
+        
+        
+def weightsLoss(inp_weights):       # [batch_size,(1..2)] tf_result
+#                weights_lambdas):  # single lambda or same length as inp_weights.shape[1]
+    """
+    Enforcing 'smooth' weights for the input 2d correlation tiles
+    @return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
+    """
+    weight_ortho = 1.0
+    weight_diag  = 0.7
+    sw = 4.0 * (weight_ortho + weight_diag)
+    weight_ortho /= sw
+    weight_diag /=  sw
+#    w_neib = tf.const([[weight_diag,  weight_ortho, weight_diag],
+#                       [weight_ortho, -1.0,         weight_ortho],
+#                       [weight_diag,  weight_ortho, weight_diag]])
+    #WBORDERS_ZERO
+    with tf.name_scope("WeightsLoss"):
+        # Adding 1 tile border
+        tf_inp =     tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
+        if WBORDERS_ZERO:
+            tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1,                   tf_inp.shape[3]], name = "tf_zero_col")
+            tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 ,              tf_inp.shape[2] + 2, tf_inp.shape[3]], name = "tf_zero_row")
+            tf_inp_ext_h = tf.concat([tf_zero_col,                 tf_inp,       tf_zero_col                 ], axis = 2, name ="tf_inp_ext_h")
+            tf_inp_ext   = tf.concat([tf_zero_row,                 tf_inp_ext_h, tf_zero_row                 ], axis = 1, name ="tf_inp_ext")
+        else:
+            tf_inp_ext_h = tf.concat([tf_inp       [:, :,  :1, :], tf_inp,       tf_inp      [:,   :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
+            tf_inp_ext   = tf.concat([tf_inp_ext_h [:, :1, :,  :], tf_inp_ext_h, tf_inp_ext_h[:, -1:,   :, :]], axis = 1, name ="tf_inp_ext")
+        
+        s_ortho = tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:,:] + tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:, :] 
+        s_corn =  tf_inp_ext[:, :-2,:-2,:] + tf_inp_ext[:, :-2, 2:,:] + tf_inp_ext[:,2:,  :-2,:] + tf_inp_ext[:,2:  , 2:, :]
+        w_diff =  tf.subtract(tf_inp, s_ortho * weight_ortho + s_corn * weight_diag, name="w_diff") 
+        w_diff2 = tf.multiply(w_diff, w_diff,                                        name="w_diff2") 
+        w_var =   tf.reduce_mean(w_diff2,                                            name="w_var")
+        w2_mean = tf.reduce_mean(inp_weights * inp_weights,                          name="w2_mean")
+        w_rel =   tf.divide(w_var, w2_mean,                                          name= "w_rel")
+        return w_rel # scalar, cost for weights non-smoothness in 2d
+        
+        
+    
+corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
+                          tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
+"""
+out, inp_weights =       network_siam(input=corr2d_Nx325,
+                                      layout1 =   NN_LAYOUT1, 
+                                      layout2 =   NN_LAYOUT2,
+                                      sym8 =      SYM8_SUB,
+                                      only_tile = ONLY_TILE) #Remove/put None for normal operation
+"""                                      
+outs, inp_weights =       networks_siam(input=corr2d_Nx325,
+                                        layout1 =   NN_LAYOUT1, 
+                                        layout2 =   NN_LAYOUT2,
+                                        sym8 =      SYM8_SUB,
+                                        only_tile = ONLY_TILE, #Remove/put None for normal operation
+                                        partials =  partials)                                         
+#            w_slice = tf.reshape(gt_ds_batch[:,1],[-1],                     name = "w_gt_slice")
+
+# Extract target disparity and GT corresponding to the center tile (reshape - just to name)
+#target_disparity_batch_center = tf.reshape(next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1] , [-1,1],  name = "target_center")
+#gt_ds_batch_center =            tf.reshape(next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * center_tile_index+1],    [-1,2],  name = "gt_ds_center")
+tf_partial_weights = tf.constant(PARTIALS_WEIGHTS,dtype=tf.float32,name="partial_weights")
+G_losses = [0.0]*len(partials)
+target_disparity_batch=  next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1]
+gt_ds_batch =            next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)]
+G_losses[0], _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch =         outs[0],        # [batch_size,(1..2)] tf_result
+              target_disparity_batch=  target_disparity_batch, # next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d,   # [batch_size]        tf placeholder
+              gt_ds_batch =            gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)],  # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt,         # [batch_size,2]      tf placeholder
+              absolute_disparity =     ABSOLUTE_DISPARITY,
+              use_confidence =         USE_CONFIDENCE, # True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            2.0,
+              error2_offset =          0, # 0.0025, # (0.05^2)
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False)  # use calculated disparity for disparity weight boosting (False - use target disparity)
+
+G_loss = G_losses[0]
+for n in range (1,len(partials)):
+    G_losses[n], _, _, _, _, _, _, _ = batchLoss(out_batch =         outs[n],        # [batch_size,(1..2)] tf_result
+              target_disparity_batch=  target_disparity_batch, #next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d,   # [batch_size]        tf placeholder
+              gt_ds_batch =            gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)],  # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt,         # [batch_size,2]      tf placeholder
+              absolute_disparity =     ABSOLUTE_DISPARITY,
+              use_confidence =         USE_CONFIDENCE, # True, 
+              lambda_conf_avg =        0.01,
+              lambda_conf_pwr =        0.1,
+              conf_pwr =               2.0,
+              gt_conf_offset =         0.08,
+              gt_conf_pwr =            2.0,
+              error2_offset =          0, # 0.0025, # (0.05^2)
+              disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
+              disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
+              use_out =                False)  # use calculated disparity for disparity weight boosting (False - use target disparity)
+#    G_loss +=  Glosses[n]*PARTIALS_WEIGHTS[n]
+#tf_partial_weights
+tf_wlosses = tf.multiply(G_losses, tf_partial_weights, name =  "tf_wlosses")
+G_losses_sum = tf.reduce_sum(tf_wlosses, name = "G_losses_sum")
+if WLOSS_LAMBDA > 0.0:   
+    W_loss =     weightsLoss(inp_weights[0]) #    inp_weights - list of tensors, currently - just [0]
+#    GW_loss =    tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
+    GW_loss =    tf.add(G_losses_sum, WLOSS_LAMBDA * W_loss, name = "GW_loss")
+else:
+    GW_loss = G_losses_sum # G_loss
+    W_loss =     tf.constant(0.0, dtype=tf.float32,name = "W_loss")
+#debug
+GT_variance =  debug_gt_variance(indx = 0,        # This tile index (0..8)
+                                 center_indx = 4, # center tile index
+                                 gt_ds_batch = next_element_tt['gt_ds'])# [?:18]
+              
+tf_ph_G_loss =    tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
+tf_ph_G_losses =  tf.placeholder(tf.float32,shape=[len(partials)],name='G_losses_avg')
+tf_ph_W_loss =  tf.placeholder(tf.float32,shape=None,name='W_loss_avg')
+tf_ph_GW_loss = tf.placeholder(tf.float32,shape=None,name='GW_loss_avg')
+tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
+tf_gtvar_diff = tf.placeholder(tf.float32,shape=None,name='gtvar_diff')
+tf_img_test0 =  tf.placeholder(tf.float32,shape=None,name='img_test0')
+tf_img_test9 =  tf.placeholder(tf.float32,shape=None,name='img_test9')
+with tf.name_scope('sample'):
+    tf.summary.scalar("GW_loss",      GW_loss)
+    tf.summary.scalar("G_loss",       G_loss)
+    tf.summary.scalar("W_loss",       W_loss)
+    tf.summary.scalar("sq_diff",      _cost1)
+    tf.summary.scalar("gtvar_diff",   GT_variance)
+    
+with tf.name_scope('epoch_average'):
+#    for i, tl in enumerate(tf_ph_G_losses):
+#       tf.summary.scalar("GW_loss_epoch_"+str(i), tl)
+    for i in range(tf_ph_G_losses.shape[0]):
+        tf.summary.scalar("G_loss_epoch_"+str(i), tf_ph_G_losses[i])
+        
+    tf.summary.scalar("GW_loss_epoch", tf_ph_GW_loss)
+    tf.summary.scalar("G_loss_epoch",  tf_ph_G_loss)
+    tf.summary.scalar("W_loss_epoch",  tf_ph_W_loss)
+    tf.summary.scalar("sq_diff_epoch", tf_ph_sq_diff)
+    tf.summary.scalar("gtvar_diff",    tf_gtvar_diff)
+    
+    tf.summary.scalar("img_test0",     tf_img_test0)
+    tf.summary.scalar("img_test9",     tf_img_test9)
+
+t_vars=tf.trainable_variables()
+lr=tf.placeholder(tf.float32)
+G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
+
+
+ROOT_PATH  = './attic/nn_ds_neibs11_graph'+SUFFIX+"/"
+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)
+
+WIDTH=324
+HEIGHT=242
+
+with tf.Session()  as sess:
+    
+    sess.run(tf.global_variables_initializer())
+    sess.run(tf.local_variables_initializer())
+    
+    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_gw_train_hist=  np.empty(dataset_train_size, dtype=np.float32)
+    loss_g_train_hist=   np.empty(dataset_train_size, dtype=np.float32)
+    
+    loss_g_train_hists=   [np.empty(dataset_train_size, dtype=np.float32) for p in partials]
+    
+    
+    loss_w_train_hist=   np.empty(dataset_train_size, dtype=np.float32)
+    
+    loss_gw_test_hist=  np.empty(dataset_test_size, dtype=np.float32)
+#    loss_g_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    loss_g_test_hists=   [np.empty(dataset_test_size, dtype=np.float32) for p in partials]
+    
+    loss_w_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    
+    loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)
+    loss2_test_hist=  np.empty(dataset_test_size, dtype=np.float32)
+    train_gw_avg = 0.0
+    train_g_avg =  0.0
+    train_g_avgs =  [0.0]*len(partials)
+    
+    train_w_avg =  0.0
+    test_gw_avg =  0.0     
+    test_g_avg =   0.0     
+    test_g_avgs =  [0.0]*len(partials)
+    test_w_avg =   0.0     
+
+    train2_avg = 0.0
+    test2_avg = 0.0
+    gtvar_train_hist=  np.empty(dataset_train_size, dtype=np.float32)
+    gtvar_test_hist=   np.empty(dataset_test_size, dtype=np.float32)
+    gtvar_train = 0.0
+    gtvar_test = 0.0
+    gtvar_train_avg = 0.0
+    gtvar_test_avg =  0.0
+    img_gain_test0 =  1.0
+    img_gain_test9 =  1.0
+    
+    num_train_variants = len(datasets_train)
+    thr=None;
+    trains_to_update = [train_next[n_train]['files'] > train_next[n_train]['slots'] for n_train in range(len(train_next))]
+    for epoch in range (EPOCHS_TO_RUN):
+        """
+        update files after each epoch, all 4.
+        Convert to threads after testing
+        """
+        if (FILE_UPDATE_EPOCHS > 0) and (epoch % FILE_UPDATE_EPOCHS == 0):
+            if not thr is None:
+                if thr.is_alive():
+                    print_time("Waiting until tfrecord gets loaded", end=" ")
+                else:
+                    print_time("tfrecord is already loaded loaded", end=" ")
+        
+                thr.join()
+                print_time("Done")
+                print_time("Inserting new data", end=" ")
+                for n_train in range(len(trains_to_update)):
+                    if trains_to_update[n_train]:
+                        replaceNextDataset(datasets_train,
+                                           thr_result[n_train],
+                                           train_next= train_next[n_train],
+                                           nset=n_train,
+                                           period=len(train_next))
+                        _nextFileSlot(train_next[n_train])
+                print_time("Done")
+            thr_result = []
+            fpaths = []
+            for n_train in range(len(train_next)):
+                if train_next[n_train]['files'] > train_next[n_train]['slots']:
+                    fpaths.append(files_train[n_train][train_next[n_train]['file']])
+                    print_time("Will read in background: "+fpaths[-1])
+            thr = Thread(target=getMoreFiles, args=(fpaths,thr_result))            
+            thr.start()        
+        file_index = epoch  % num_train_variants
+        if   epoch >=600:
+            learning_rate = LR600
+        elif epoch >=400:
+            learning_rate = LR400
+        elif epoch >=200:
+            learning_rate = LR200
+        elif epoch >=100:
+            learning_rate = LR100
+        else:
+            learning_rate = LR
+#        print ("sr1",file=sys.stderr,end=" ")
+        if (file_index == 0) and SHUFFLE_FILES:
+            num_sets = len(datasets_train_all)
+            print_time("Shuffling how datasets datasets_train_lvar and datasets_train_hvar are zipped together", end="")
+            for i in range(num_sets):
+                shuffle_in_place (datasets_train, i, num_sets)
+            print_time("  Done")
+            print_time("Shuffling tile chunks ", end="")
+            shuffle_chunks_in_place (datasets_train, 1)
+            print_time("  Done")
+            
+        sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:           datasets_train[file_index]['corr2d'],
+                                                     target_disparity_train_placeholder: datasets_train[file_index]['target_disparity'],
+                                                     gt_ds_train_placeholder:            datasets_train[file_index]['gt_ds']})
+        for i in range(dataset_train_size):
+            try:
+#                train_summary,_, GW_loss_trained,  G_loss_trained,  W_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
+                train_summary,_, GW_loss_trained,  G_losses_trained,  W_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
+                    [   merged,
+                        G_opt,
+                        GW_loss,
+#                        G_loss,
+                        G_losses,
+                        W_loss,
+                        outs[0],
+                        _disp_slice,
+                        _d_gt_slice,
+                        _out_diff,
+                        _out_diff2,
+                        _w_norm,
+                        _out_wdiff2,
+                        _cost1,
+                        GT_variance
+                    ],
+                    feed_dict={lr:            learning_rate,
+                               tf_ph_GW_loss: train_gw_avg,
+                               tf_ph_G_loss:  train_g_avgs[0], #train_g_avg,
+                               tf_ph_G_losses:  train_g_avgs,
+                               tf_ph_W_loss:  train_w_avg,
+                               tf_ph_sq_diff: train2_avg,
+                               tf_gtvar_diff: gtvar_train_avg,
+                               tf_img_test0:  img_gain_test0,
+                               tf_img_test9:  img_gain_test9}) # previous value of *_avg #Fetch argument 0.0 has invalid type <class 'float'>, must be a string or Tensor. (Can not convert a float into a Tensor or Operation.)
+                
+                loss_gw_train_hist[i] = GW_loss_trained
+#                loss_g_train_hist[i] =  G_loss_trained
+                for nn, gl  in enumerate(G_losses_trained):
+                    loss_g_train_hists[nn][i] =  gl
+                loss_w_train_hist[i] =  W_loss_trained
+                loss2_train_hist[i] = out_cost1
+                gtvar_train_hist[i] = gt_variance
+            except tf.errors.OutOfRangeError:
+                print("train done at step %d"%(i))
+                break
+
+        train_gw_avg =      np.average(loss_gw_train_hist).astype(np.float32)     
+        train_g_avg =       np.average(loss_g_train_hist).astype(np.float32) 
+        for nn, lgth  in enumerate(loss_g_train_hists):
+            train_g_avgs[nn] =       np.average(lgth).astype(np.float32)
+###############        
+        train_w_avg =       np.average(loss_w_train_hist).astype(np.float32)     
+        train2_avg =      np.average(loss2_train_hist).astype(np.float32)
+        gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
+        
+        test_summaries = [0.0]*len(datasets_test)
+        tst_avg =        [0.0]*len(datasets_test)
+        tst2_avg =       [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_summaries[ntest], GW_loss_tested, G_losses_tested, W_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
+                        [merged,
+                         GW_loss,
+                         G_losses,
+                         W_loss,
+                         outs[0],
+                         _disp_slice,
+                         _d_gt_slice,
+                         _out_diff,
+                         _out_diff2,
+                         _w_norm,
+                         _out_wdiff2,
+                         _cost1,
+                         GT_variance
+                         ],
+                         feed_dict={lr:            learning_rate,
+                                    tf_ph_GW_loss:  test_gw_avg,
+                                    tf_ph_G_loss:   test_g_avg,
+                                    tf_ph_G_losses:  test_g_avgs, # train_g_avgs, # temporary, there is o data fro test
+                                    tf_ph_W_loss:   test_w_avg,
+                                    tf_ph_sq_diff:  test2_avg,
+                                    tf_gtvar_diff:  gtvar_test_avg,
+                                    tf_img_test0:   img_gain_test0,
+                                    tf_img_test9:   img_gain_test9})  # previous value of *_avg
+                    loss_gw_test_hist[i] =  GW_loss_tested
+                    
+#                    loss_g_test_hist[i] =   G_loss_tested
+                    for nn, gl  in enumerate(G_losses_tested):
+                        loss_g_test_hists[nn][i] =  gl
+
+                    loss_w_test_hist[i] =   W_loss_tested
+                    loss2_test_hist[i] = out_cost1
+                    gtvar_test_hist[i] = gt_variance
+                except tf.errors.OutOfRangeError:
+                    print("test done at step %d"%(i))
+                    break
+                    
+            test_gw_avg =  np.average(loss_gw_test_hist).astype(np.float32)
+            
+#            test_g_avg =  np.average(loss_g_test_hist).astype(np.float32)
+            
+            for nn, lgth  in enumerate(loss_g_test_hists):
+                test_g_avgs[nn] =       np.average(lgth).astype(np.float32)
+            
+            test_w_avg =  np.average(loss_w_test_hist).astype(np.float32)
+            tst_avg[ntest] =  test_gw_avg   
+            test2_avg = np.average(loss2_test_hist).astype(np.float32)
+            tst2_avg[ntest] =  test2_avg   
+            gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
+             
+        train_writer.add_summary(train_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 %f %f) dbg:%f %f"%(epoch,i,train_gw_avg, tst_avg[0], tst_avg[1], train2_avg, tst2_avg[0], tst2_avg[1], gtvar_train_avg, gtvar_test_avg))
+        if ((epoch + 1) == EPOCHS_TO_RUN) or (((epoch + 1) % EPOCHS_FULL_TEST) == 0):
+###################################################
+# Read the full image
+################################################### 
+            test_summaries_img = [0.0]*len(datasets_img)
+        #    disp_out=  np.empty((dataset_img_size * BATCH_SIZE), dtype=np.float32)
+            disp_out=  np.empty((WIDTH*HEIGHT), dtype=np.float32)
+            
+            for ntest,dataset_img in enumerate(datasets_img):
+                sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:      dataset_img['corr2d'],
+                                                        target_disparity_train_placeholder: dataset_img['target_disparity'],
+                                                        gt_ds_train_placeholder:            dataset_img['gt_ds']})
+                for start_offs in range(0,disp_out.shape[0],BATCH_SIZE):
+                    end_offs = min(start_offs+BATCH_SIZE,disp_out.shape[0])
+                    
+                    try:
+                        test_summaries_img[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,
+                             outs[0],
+                             _disp_slice,
+                             _d_gt_slice,
+                             _out_diff,
+                             _out_diff2,
+                             _w_norm,
+                             _out_wdiff2,
+                             _cost1,
+                             GT_variance
+                             ],
+                             feed_dict={lr:            learning_rate,
+                                        tf_ph_GW_loss:  test_gw_avg,
+                                        tf_ph_G_loss:   test_g_avg,
+                                        tf_ph_G_losses:  train_g_avgs, # temporary, there is o data fro test
+                                        tf_ph_W_loss:   test_w_avg,
+                                        tf_ph_sq_diff: test2_avg,
+                                        tf_gtvar_diff: gtvar_test_avg,
+                                        tf_img_test0:  img_gain_test0,
+                                        tf_img_test9:  img_gain_test9})  # previous value of *_avg
+                    except tf.errors.OutOfRangeError:
+                        print("test done at step %d"%(i))
+                        break
+                    try:
+                        disp_out[start_offs:end_offs] = output.flatten()
+                    except ValueError:
+                        print("dataset_img_size= %d, i=%d, output.shape[0]=%d "%(dataset_img_size, i, output.shape[0]))
+                        break;    
+                    pass
+                result_file = result_files[ntest]
+                try:
+                    os.makedirs(os.path.dirname(result_file))
+                except:
+                    pass     
+
+                rslt = np.concatenate([disp_out.reshape(-1,1), t_disp, gtruth],1)
+                np.save(result_file,           rslt.reshape(HEIGHT,WIDTH,-1))
+                
+                rslt = eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)                
+                img_gain_test0 = rslt[0][0]/rslt[0][1]   
+                img_gain_test9 = rslt[9][0]/rslt[9][1]   
+     
+     # 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")
+exit (0)

nn_ds_neibs9.py

@@ -58,13 +58,13 @@ SHUFFLE_EPOCH =    True
 NET_ARCH1 =       0 # 4 # #4 # 8 # 4 # 0 #3 # 0 #  0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?
 NET_ARCH2 =       0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 #  0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?
 SYM8_SUB =        False # True #False #  True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
-ONLY_TILE =        None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
+ONLY_TILE =        12 # None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
 ZIP_LHVAR =        True # combine _lvar and _hvar as odd/even elements 
 #DEBUG_PACK_TILES = True
 # CLUSTER_RADIUS should match input data
 CLUSTER_RADIUS =     2 # 1 # 1 - 3x3, 2 - 5x5 tiles
 SHUFFLE_FILES  =     True
-WLOSS_LAMBDA =       0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
+WLOSS_LAMBDA =       2.0 #3.0 # 1.0 # 0.3 # 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
 WBORDERS_ZERO =      True # Border conditions for first layer weights: False - free, True - tied to 0
 MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8
 FILE_UPDATE_EPOCHS =  2 # update train files each this many epochs. 0 - do not update
@@ -439,7 +439,8 @@ dir_train_hvar =  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
 dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
 dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
 dir_test_lvar =   data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
-dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+#dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
+dir_test_hvar =   "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" #  data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
 dir_img =         os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
 dir_result =      os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"
 
@@ -574,7 +575,7 @@ Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as te
 #just testing:
 #files_train_lvar = files_train_hvar
 files_test_lvar =  ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]
-files_test_hvar =  ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
+files_test_hvar =  ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
 #files_img =        ['1527257933_150165-v04'] # overlook
 #files_img =        ['1527256858_150165-v01'] # State Street
 #files_img =        ['1527256816_150165-v02'] # State Street
@@ -1066,8 +1067,8 @@ def weightsLoss(inp_weights):       # [batch_size,(1..2)] tf_result
         # Adding 1 tile border
         tf_inp =     tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
         if WBORDERS_ZERO:
-            tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1,               tf_inp.shape[3]], name = "tf_zero_col")
-            tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 ,              tf_inp.shape[2], tf_inp.shape[3]], name = "tf_zero_row")
+            tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1,                   tf_inp.shape[3]], name = "tf_zero_col")
+            tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 ,              tf_inp.shape[2] + 2, tf_inp.shape[3]], name = "tf_zero_row")
             tf_inp_ext_h = tf.concat([tf_zero_col,                 tf_inp,       tf_zero_col                 ], axis = 2, name ="tf_inp_ext_h")
             tf_inp_ext   = tf.concat([tf_zero_row,                 tf_inp_ext_h, tf_zero_row                 ], axis = 1, name ="tf_inp_ext")
         else: