Commit d7fc8ac4 authored by Andrey Filippov's avatar Andrey Filippov

Modyfying costs, feeding datasets to placeholders instead of constants

parent 47ad3d9f
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...@@ -30,7 +30,7 @@ FILES_PER_SCENE = 5 # number of random offset files for the scene to select f ...@@ -30,7 +30,7 @@ FILES_PER_SCENE = 5 # number of random offset files for the scene to select f
#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from #MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from
#MAX_BATCH_FILES = 10 #maximal number of files to use in a batch #MAX_BATCH_FILES = 10 #maximal number of files to use in a batch
MAX_EPOCH = 500 MAX_EPOCH = 500
LR = 1e-3 # learning rate LR = 1e-4 # learning rate
USE_CONFIDENCE = False USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False ABSOLUTE_DISPARITY = False
DEBUG_PLT_LOSS = True DEBUG_PLT_LOSS = True
...@@ -167,16 +167,21 @@ checkpoint_dir = './attic/result_inmem/' ...@@ -167,16 +167,21 @@ checkpoint_dir = './attic/result_inmem/'
save_freq = 500 save_freq = 500
def lrelu(x): def lrelu(x):
return tf.maximum(x*0.2,x) return tf.maximum(x*0.5,x)
# return tf.nn.relu(x) # return tf.nn.relu(x)
def network(input): def network(input):
# fc1 = slim.fully_connected(input, 512, activation_fn=lrelu,scope='g_fc1') fc1 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc1')
# fc2 = slim.fully_connected(fc1, 512, activation_fn=lrelu,scope='g_fc2') fc2 = slim.fully_connected(fc1, 128, activation_fn=lrelu,scope='g_fc2')
fc3 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3') ## fc3 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
fc4 = slim.fully_connected(fc3, 128, activation_fn=lrelu,scope='g_fc4') ## fc4 = slim.fully_connected(fc3, 128, activation_fn=lrelu,scope='g_fc4')
fc5 = slim.fully_connected(fc4, 64, activation_fn=lrelu,scope='g_fc5') ## fc5 = slim.fully_connected(fc4, 64, activation_fn=lrelu,scope='g_fc5')
fc3 = slim.fully_connected(fc2, 64, activation_fn=lrelu,scope='g_fc3')
fc4 = slim.fully_connected(fc3, 20, activation_fn=lrelu,scope='g_fc4')
fc5 = slim.fully_connected(fc4, 16, activation_fn=lrelu,scope='g_fc5')
if USE_CONFIDENCE: if USE_CONFIDENCE:
fc6 = slim.fully_connected(fc5, 2, activation_fn=lrelu,scope='g_fc6') fc6 = slim.fully_connected(fc5, 2, activation_fn=lrelu,scope='g_fc6')
else: else:
...@@ -318,7 +323,7 @@ with tf.Session() as sess: ...@@ -318,7 +323,7 @@ with tf.Session() as sess:
for epoch in range(EPOCHS_TO_RUN): for epoch in range(EPOCHS_TO_RUN):
if SHUFFLE_EPOCH: # if SHUFFLE_EPOCH:
dataset_train = dataset_train.shuffle(buffer_size=10000) dataset_train = dataset_train.shuffle(buffer_size=10000)
sess.run(iterator_train.initializer) sess.run(iterator_train.initializer)
...@@ -331,6 +336,8 @@ with tf.Session() as sess: ...@@ -331,6 +336,8 @@ with tf.Session() as sess:
# Train run # Train run
if i<START_TEST: if i<START_TEST:
if (epoch <50) or (epoch > 100) :
try: try:
# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run( # _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_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( 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(
...@@ -347,6 +354,33 @@ with tf.Session() as sess: ...@@ -347,6 +354,33 @@ with tf.Session() as sess:
_cost1, _cost1,
corr2d325, corr2d325,
# target_disparity, # target_disparity,
# gt_ds
],
feed_dict={lr:LR})
# save all for now as a test
#train_writer.add_summary(summary, i)
#train_writer.add_summary(train_summary, i)
except tf.errors.OutOfRangeError:
break
else:
try:
# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_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(
[ merged,
# G_opt,
G_loss,
out,
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
corr2d325,
# target_disparity,
# gt_ds # gt_ds
], ],
feed_dict={lr:LR}) feed_dict={lr:LR})
......
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