Commit 17304955 authored by Andrey Filippov's avatar Andrey Filippov
Browse files

making pydev happy

parent 92d4bc4e

nn_ds_neibs16.py

+19 −3
Original line number Diff line number Diff line
@@ -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

+22 −14
Original line number Diff line number Diff line
@@ -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)