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python3-imagej-tiff
Commit 6363bf5b authored by Andrey Filippov's avatar Andrey Filippov
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just changing dataset file lists

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nn_ds_neibs.py
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...@@ -168,24 +168,28 @@ except IndexError: ...@@ -168,24 +168,28 @@ except IndexError:
#FILES_PER_SCENE #FILES_PER_SCENE
train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords" train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"
files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords"] files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_LE_1.5.tfrecords",
""" "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-003_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-004_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-005_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-006_R1_LE_1.5.tfrecords", ]
"/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-007_R1_LE_1.5.tfrecords"] files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train000_R1_GT_1.5.tfrecords",
""" "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train001_R1_GT_1.5.tfrecords",
#files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-000_R1_LE_1.5.tfrecords", "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train002_R1_GT_1.5.tfrecords",
# "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-001_R1_LE_1.5.tfrecords"] "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train003_R1_GT_1.5.tfrecords",
files_train_hvar = [] "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train004_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train005_R1_GT_1.5.tfrecords",
#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3/test-TEST_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords" "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train006_R1_GT_1.5.tfrecords",
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords" "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/train007_R1_GT_1.5.tfrecords",
file_test_hvar= None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords" ]
#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_LE_1.5.tfrecords"
file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3b/testTEST_R1_GT_1.5.tfrecords" # None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
weight_hvar = 0.13 weight_hvar = 0.13
weight_lvar = 1.0 - weight_hvar weight_lvar = 1.0 - weight_hvar
...@@ -254,16 +258,27 @@ if (file_test_hvar): ...@@ -254,16 +258,27 @@ if (file_test_hvar):
print_time(" Done") print_time(" Done")
pass pass
#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 = []
if (file_test_lvar):
datasets_test.append(dataset_test_lvar)
datasets_weights_test.append(weight_lvar)
if (file_test_hvar):
datasets_test.append(dataset_test_hvar)
datasets_weights_test.append(weight_hvar)
"""
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,FEATURES_PER_TILE)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(target_disparity_train.dtype, (None,1)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(gt_ds_train.dtype, (None,2)) #gt_ds_train.shape)
"""
corr2d_train_placeholder = tf.placeholder(datasets_train_lvar[0]['corr2d'].dtype, (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape) corr2d_train_placeholder = tf.placeholder(datasets_train_lvar[0]['corr2d'].dtype, (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(datasets_train_lvar[0]['target_disparity'].dtype, (None,1 * cluster_size)) #target_disparity_train.shape) target_disparity_train_placeholder = tf.placeholder(datasets_train_lvar[0]['target_disparity'].dtype, (None,1 * cluster_size)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(datasets_train_lvar[0]['gt_ds'].dtype, (None,2 * cluster_size)) #gt_ds_train.shape) gt_ds_train_placeholder = tf.placeholder(datasets_train_lvar[0]['gt_ds'].dtype, (None,2 * cluster_size)) #gt_ds_train.shape)
...@@ -461,24 +476,13 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -461,24 +476,13 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum") iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum")
w_norm = tf.multiply (w_all, iw_sum, name = "w_norm") w_norm = tf.multiply (w_all, iw_sum, name = "w_norm")
# disp_slice = tf.slice(out_batch,[0,0],[-1,1], name = "disp_slice")
# d_gt_slice = tf.slice(gt_ds_batch,[0,0],[-1,1], name = "d_gt_slice")
disp_slice = tf.reshape(out_batch[:,0],[-1], name = "disp_slice") 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") d_gt_slice = tf.reshape(gt_ds_batch[:,0],[-1], name = "d_gt_slice")
"""
if absolute_disparity:
out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
else:
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp")
out_diff = tf.subtract(disp_slice, residual_disp, name = "out_diff")
"""
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat") td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
if absolute_disparity: if absolute_disparity:
adisp = disp_slice adisp = disp_slice
else: else:
# td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
adisp = tf.add(disp_slice, td_flat, name = "adisp") adisp = tf.add(disp_slice, td_flat, name = "adisp")
out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff") out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff")
...@@ -610,21 +614,16 @@ with tf.Session() as sess: ...@@ -610,21 +614,16 @@ with tf.Session() as sess:
gtvar_train_avg = 0.0 gtvar_train_avg = 0.0
gtvar_test_avg = 0.0 gtvar_test_avg = 0.0
num_train_variants = len(files_train_lvar) # num_train_variants = len(files_train_lvar)
num_train_variants = len(datasets_train)
for epoch in range (EPOCHS_TO_RUN): for epoch in range (EPOCHS_TO_RUN):
# file_index = (epoch // 20) % 2 # file_index = (epoch // 20) % 2
file_index = epoch % num_train_variants file_index = epoch % num_train_variants
learning_rate = [LR,LR100][epoch >=100] learning_rate = [LR,LR100][epoch >=100]
# if SHUFFLE_EPOCH:
# dataset_tt = dataset_tt.shuffle(buffer_size=10000) 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'],
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[file_index], gt_ds_train_placeholder: datasets_train[file_index]['gt_ds']})
target_disparity_train_placeholder: target_disparity_trains[file_index],
gt_ds_train_placeholder: gt_ds_trains[file_index]})
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train_lvar[file_index]['corr2d'],
target_disparity_train_placeholder: datasets_train_lvar[file_index]['target_disparity'],
gt_ds_train_placeholder: datasets_train_lvar[file_index]['gt_ds']})
for i in range(dataset_train_size): for i in range(dataset_train_size):
try: 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( # 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(
...@@ -658,21 +657,13 @@ with tf.Session() as sess: ...@@ -658,21 +657,13 @@ with tf.Session() as sess:
train2_avg = np.average(loss2_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) gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg}) for dataset_test in datasets_test:
#tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg') sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test['corr2d'],
#tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg') target_disparity_train_placeholder: dataset_test['target_disparity'],
""" gt_ds_train_placeholder: dataset_test['gt_ds']})
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: corr2d_test,
target_disparity_train_placeholder: target_disparity_test,
gt_ds_train_placeholder: gt_ds_test})
"""
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test_lvar['corr2d'],
target_disparity_train_placeholder: dataset_test_lvar['target_disparity'],
gt_ds_train_placeholder: dataset_test_lvar['gt_ds']})
for i in range(dataset_test_size): for i in range(dataset_test_size):
try: try:
# test_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = 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_summary, G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[merged, [merged,
G_loss, G_loss,
...@@ -685,7 +676,6 @@ with tf.Session() as sess: ...@@ -685,7 +676,6 @@ with tf.Session() as sess:
_out_wdiff2, _out_wdiff2,
_cost1, _cost1,
GT_variance GT_variance
# corr2d325,
], ],
feed_dict={lr:learning_rate,tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg, tf_gtvar_diff:gtvar_test_avg}) # previous value of *_avg feed_dict={lr:learning_rate,tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg, tf_gtvar_diff:gtvar_test_avg}) # previous value of *_avg
loss_test_hist[i] = G_loss_tested loss_test_hist[i] = G_loss_tested
...@@ -695,7 +685,6 @@ with tf.Session() as sess: ...@@ -695,7 +685,6 @@ with tf.Session() as sess:
print("test done at step %d"%(i)) print("test done at step %d"%(i))
break break
# print_time("%d:%d -> %f"%(epoch,i,G_current))
test_avg = np.average(loss_test_hist).astype(np.float32) test_avg = np.average(loss_test_hist).astype(np.float32)
test2_avg = np.average(loss2_test_hist).astype(np.float32) test2_avg = np.average(loss2_test_hist).astype(np.float32)
gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32) gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
......
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