Commit c64289c7 authored by Andrey Filippov's avatar Andrey Filippov
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parent 1fb4f69a

explore_data.py

+6 −5
Original line number Diff line number Diff line
@@ -396,6 +396,7 @@ class ExploreData: 
            for i in range (self.hist_to_batch.max()+1):

                lst.append([])

#            bb1d = bb[findx].reshape(self.num_tiles)

            if use_neibs:    

                disp_var_tiles =   disp_var[findx].reshape(self.num_tiles)

                disp_neibs_tiles = disp_neibs[findx].reshape(self.num_tiles)

            for n, indx in enumerate(bb[findx].reshape(self.num_tiles)):
@@ -718,10 +719,10 @@ if __name__ == "__main__": 
      ml_subdir =   "ml"



  #Parameters to generate neighbors data. Set radius to 0 to generate single-tile     

  RADIUS = 1

  RADIUS = 0

  MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9

  VARIANCE_THRESHOLD = 1.5

  NUM_TRAIN_SETS = 2

  NUM_TRAIN_SETS = 6

 

  if RADIUS == 0:

    BATCH_DISP_BINS = 20

nn_ds_inmem4.py

+18 −9
Original line number Diff line number Diff line
@@ -36,6 +36,7 @@ ABSOLUTE_DISPARITY = False # True # False 
DEBUG_PLT_LOSS =     True

FEATURES_PER_TILE =  324

EPOCHS_TO_RUN =     10000 #0

EPOCHS_SAME_FILE =   20

RUN_TOT_AVG =       100 # last batches to average. Epoch is 307 training  batches  

BATCH_SIZE =       1000 # Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches

SHUFFLE_EPOCH =    True
@@ -115,12 +116,13 @@ def read_and_decode(filename_queue): 
try:

    train_filenameTFR =  sys.argv[1]

except IndexError:

    train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train.tfrecords"

    train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_00.tfrecords"

try:

    test_filenameTFR =  sys.argv[2]

except IndexError:

    test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"

#FILES_PER_SCENE

train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"



import tensorflow as tf

import tensorflow.contrib.slim as slim
@@ -128,6 +130,13 @@ import tensorflow.contrib.slim as slim 
print_time("Importing training data... ", end="")

corr2d_train, target_disparity_train, gt_ds_train = readTFRewcordsEpoch(train_filenameTFR)

print_time("  Done")

print_time("Importing second training data... ", end="")

corr2d_train1, target_disparity_train1, gt_ds_train1 = readTFRewcordsEpoch(train_filenameTFR1)

print_time("  Done")



corr2d_trains =           [corr2d_train,            corr2d_train1] 

target_disparity_trains = [target_disparity_train,  target_disparity_train1] 

gt_ds_trains =            [gt_ds_train,             gt_ds_train1] 



corr2d_train_placeholder =           tf.placeholder(corr2d_train.dtype,           (None,324)) # corr2d_train.shape)

target_disparity_train_placeholder = tf.placeholder(target_disparity_train.dtype, (None,1))  #target_disparity_train.shape)
@@ -382,14 +391,14 @@ with tf.Session() as sess: 
    train2_avg = 0.0

    test_avg = 0.0     

    test2_avg = 0.0

    

    for epoch in range (EPOCHS_TO_RUN):

        

#        file_index = (epoch // 20) % 2 

        file_index = (epoch // 1) % 2 

    #       if SHUFFLE_EPOCH:

    #        dataset_train = dataset_train.shuffle(buffer_size=10000)

        sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_train,

                                                        target_disparity_train_placeholder: target_disparity_train,

                                                        gt_ds_train_placeholder: gt_ds_train})

        sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[file_index],

                                                        target_disparity_train_placeholder: target_disparity_trains[file_index],

                                                        gt_ds_train_placeholder: gt_ds_trains[file_index]})

        for i in range(dataset_train_size):

            try:

                train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out  = sess.run(