making pydev happy

nn_ds_neibs16.py

@@ -55,7 +55,22 @@ Temporarily for backward compatibility
 """
 if not "SLOSS_CLIP" in parameters:
     parameters['SLOSS_CLIP'] = 0.5
-    print ("Old config, setting SLOSS_CLIP=",SLOSS_CLIP)
+    print ("Old config, setting SLOSS_CLIP=", parameters['SLOSS_CLIP'])
+    
+"""
+Defined in config file
+"""
+TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
+ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA,  SLOSS_LAMBDA, SLOSS_CLIP  = [None]*5
+SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE  = [None]*5
+CLUSTER_RADIUS = None
+PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP,  BATCH_WEIGHTS, ONLY_TILE = [None] * 5  
+USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
+LR600,LR400,LR200,LR100,LR = [None]*5
+SHUFFLE_FILES, EPOCHS_FULL_TEST, SAVE_TIFFS = [None] * 3
+
+
+
 globals().update(parameters)
 
 
@@ -178,7 +193,7 @@ def debug_gt_variance(
         gt_ds_batch # [?:9:2]
         ):
     with tf.name_scope("Debug_GT_Variance"):
-        tf_num_tiles =  tf.shape(gt_ds_batch)[0]
+#        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")
@@ -401,7 +416,8 @@ with tf.Session()  as sess:
     img_gain_test9 =  1.0
     
     num_train_variants = len(datasets_train)
-    thr=None;
+    thr=None
+    thr_result = 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):
         """

qcstereo_functions.py

@@ -10,7 +10,8 @@ import tensorflow as tf
 import xml.etree.ElementTree as ET
 import time
 import imagej_tiffwriter
-
+TIME_LAST = 0
+TIME_START = 0
 
 class bcolors:
     HEADER = '\033[95m'
@@ -32,8 +33,6 @@ def print_time(txt="",end="\n"):
 def parseXmlConfig(conf_file, root_dir):
     tree = ET.parse(conf_file)
     root = tree.getroot()
-    directories = root.find('directories')
-    files =       root.find('files')
     parameters = {}
     for p in root.find('parameters'):
         parameters[p.tag]=eval(p.text.strip())
@@ -141,7 +140,7 @@ def getMoreFiles(fpaths,rslt, cluster_radius, hor_flip, tile_layers, tile_side):
         rslt.append(dataset)
 
 #from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
-def read_and_decode(filename_queue):
+def read_and_decode(filename_queue, featrures_per_tile):
     reader = tf.TFRecordReader()
     _, serialized_example = reader.read(filename_queue)
 
@@ -149,7 +148,7 @@ def read_and_decode(filename_queue):
       serialized_example,
       # Defaults are not specified since both keys are required.
       features={
-        'corr2d':           tf.FixedLenFeature([FEATURES_PER_TILE],tf.float32), #string),
+        'corr2d':           tf.FixedLenFeature([featrures_per_tile],tf.float32), #string),
         'target_disparity': tf.FixedLenFeature([1],   tf.float32), #.string),
         'gt_ds':            tf.FixedLenFeature([2],  tf.float32)  #.string)
         })
@@ -488,19 +487,28 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
     """
     tiff_path = npy_path.replace('.npy','.tiff')
     data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
-         
+    nn_out =            0
+    target_disparity =  1     
+    gt_disparity =      2     
+    gt_strength =       3     
     if not absolute:
         if fix_nan:
-            data[...,0] +=  np.nan_to_num(data[...,1], copy=True)
+            data[...,nn_out] +=  np.nan_to_num(data[...,1], copy=True)
         else:
-            data[...,0] +=  data[...,1]
+            data[...,nn_out] +=  data[...,1]
     if insert_deltas:
-        data = np.concatenate([data[...,0:4],data[...,0:2],data[...,4:]], axis = 2)
-        data[...,4] -= data[...,2] 
-        data[...,5] -= data[...,2]
-        np.nan_to_num(data[...,3], copy=False)
-        data[...,4] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,4]])
-        data[...,5] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,5]])
+        np.nan_to_num(data[...,gt_strength], copy=False)
+        data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2)
+        data[...,6] -= data[...,gt_disparity] 
+        data[...,7] -= data[...,gt_disparity]
+        for l in [4,5,6,7]:
+            data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])
+        # All other layers - mast too
+        for l in range(8,data.shape[2]):
+            data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])
+            
+#        data[...,4] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,4]])
+#       data[...,5] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,5]])
             
     data = data.transpose(2,0,1)
     print("Saving results to TIFF: "+tiff_path)