combining low/high disparity cluster variance into the same batch

#BATCH_SIZE =       1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches

BATCH_SIZE =       2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches

SHUFFLE_EPOCH =    True

NET_ARCH1 =          0 # 4 # 3 # overwrite with argv?

NET_ARCH2 =          0 # 3 # overwrite with argv?

NET_ARCH1 =          6 #0 # 4 # 3 # overwrite with argv?

NET_ARCH2 =          6 # 0 # 3 # overwrite with argv?

ONLY_TILE =          None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)

              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]

              4:[0,   0,   0,   0,   16,  16],

              5:[0,   0,  64,  32,   32,  16],

              6:[0,   0,  32,  16,   16,  16],

              }

NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]

NN_LAYOUT2 = NN_LAYOUTS[NET_ARCH2]

ROOT_PATH  = './attic/nn_ds_neibs_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)

with tf.Session()  as sess:

    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_train_hist=  np.empty(dataset_train_size, dtype=np.float32)

    loss_test_hist=   np.empty(dataset_test_size, dtype=np.float32)

    loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)

        train2_avg =      np.average(loss2_train_hist).astype(np.float32)

        gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)

        for dataset_test in datasets_test:

        test_summaries = [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_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(

#                    test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(

                    test_summaries[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,

                         out,

#        _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})

        train_writer.add_summary(train_summary, epoch)

        test_writer.add_summary(test_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) dbg:%f %f"%(epoch,i,train_avg, test_avg,train2_avg, test2_avg, gtvar_train_avg, gtvar_test_avg))

     # 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")