def run_test():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
valid_reader = Reader('tiny_val.tfrecord', name='valid_data', min_queue_examples=50000, batch_size=BATCH_SIZE, num_threads=13)
HSIs_valid, labels_valid = valid_reader.feed(train_data = False)
# hsi_pl, labels_pl = placeholder_inputs(BATCH_SIZE)
HSI = HSI_branch()
logits1, outputs1, variables1 = HSI(HSIs_valid, keep_prob=1) #(500, 2432)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
start_time = time.time()
do_eval(sess, HSIs_valid, labels_valid, logits1)
duration = time.time() - start_time
logging.info('>>Take time %.3f(sec)' % duration)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
coord.request_stop()
coord.join(threads)
sess.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
records = glob.glob('../HSI/data/*.tfrecord')
train_readers = []
for record in records:
record_name = 'train_data' + os.path.basename(record).split('.')[0]
train_reader = Reader(record, name=record_name, batch_size=40)
train_readers.append(train_reader)
valid_reader = Reader('tiny_val.tfrecord',
name='valid_data',
batch_size=BATCH_SIZE)
train_imgs_and_labels = [
train_reader_.feed(train_data=True)
for train_reader_ in train_readers
]
HSIs_valid, labels_valid = valid_reader.feed(train_data=False)
hsi_pl, labels_pl = placeholder_inputs(BATCH_SIZE)
HSI = HSI_branch()
logits1, variables1, logits2, variables2, logits, outputs, variables = HSI(
hsi_pl, keep_prob=0.5)
predicts1 = model_predict(logits1)
predicts2 = model_predict(logits2)
predicts = model_predict(logits)
loss1, loss2, loss = model_loss(logits1, logits2, logits, labels_pl)
train_op = model_training(variables1, loss1, variables2, loss2,
variables, loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=50)
# init_op = tf.global_variables_initializer()
sess = tf.Session()
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
# sess.run(init_op)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
max_step = 50000
for step in range(5144, max_step):
start_time = time.time()
images_and_labels = sess.run(train_imgs_and_labels)
HSIs, labels = [], []
for hsi, label in images_and_labels:
HSIs.extend(hsi)
labels.extend(label)
shuffle = np.random.permutation(range(len(labels)))
HSIs = np.array(HSIs)
labels = np.array(labels)
HSIs = HSIs[shuffle][:BATCH_SIZE]
labels = labels[shuffle][:BATCH_SIZE]
_, loss_value1,loss_value2,loss_value, summary_str, predicts_value = sess.run([train_op, loss1,loss2,loss, summary, predicts],\
feed_dict={hsi_pl : HSIs, labels_pl : labels}
)
train_precision1 = np.mean(predicts_value == labels)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
duration = time.time() - start_time
count = (step % 500) or 500
message = (
'>>Step: %d loss1 = %.4f loss2 = %.4f loss = %.4f acc = %.3f(%.3f sec) ETA = %.3f'
% (step, loss_value1, loss_value2, loss_value,
train_precision1, duration, (500 - count) * duration))
view_bar(message, count, 500)
#-------------------------------
if step % 500 == 0:
logging.info('>>%s Saving in %s' %
(datetime.now(), checkpoint_dir))
saver.save(sess, checkpoint_file, global_step=step)
logging.info('Valid Data Eval:')
do_eval(
sess,
step,
HSIs_valid,
labels_valid,
hsi_pl,
labels_pl,
predicts1,
predicts2,
predicts,
)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
saver.save(sess, checkpoint_file, global_step=step)
print('Model saved in file :%s' % checkpoint_dir)
coord.request_stop()
coord.join(threads)
sess.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
test_data = dataset.read_data_sets(
dataset_dir='/home/sw/Documents/rgb-nir2/qd_fang2_9_8/field_2ch.npz')
with tf.Graph().as_default():
train_reader = Reader(
'/home/sw/Documents/rgb-nir2/qd_fang2_9_8/country_2ch.tfrecord',
name='train_data',
batch_size=BATCH_SIZE)
leftIMG, rightIMG, labels_op = train_reader.feed() #[64,128]
images_pl1, images_pl2, labels_pl = placeholder_inputs(BATCH_SIZE)
conv_features1, features1 = model.get_features(images_pl1, reuse=False)
conv_features2, features2 = model.get_features(images_pl2, reuse=True)
predicts = tf.sqrt(
tf.reduce_sum(tf.square(features1 - features2), axis=1))
total_loss = model.caculate_loss(conv_features1, conv_features2,
features1, features2)
tf.summary.scalar('sum_loss', total_loss)
train_op = model.training(total_loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=50)
# init_op = tf.global_variables_initializer()
sess = tf.Session()
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
# sess.run(init_op)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
max_step = 500000
for step in range(390380, max_step):
start_time = time.time()
lefts, rights, batch_labels = sess.run(
[leftIMG, rightIMG, labels_op])
_, summary_str, loss_value = sess.run([train_op, summary, total_loss], \
feed_dict={images_pl1:lefts, images_pl2:rights, labels_pl:batch_labels})
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
duration = time.time() - start_time
if step % 10 == 0:
logging.info(
'>> Step %d run_train: loss = %.4f (%.3f sec)' %
(step, loss_value, duration))
#-------------------------------
if step % 1000 == 0:
logging.info('>> %s Saving in %s' %
(datetime.now(), checkpoint_dir))
saver.save(sess, checkpoint_file, global_step=step)
logging.info('Test Data Eval:')
do_eval(sess,
step,
predicts,
images_pl1,
images_pl2,
labels_pl,
test_data,
name='notredame')
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
saver.save(sess, checkpoint_file, global_step=step)
print('\rModel saved in file :%s' % checkpoint_dir)
coord.request_stop()
coord.join(threads)
sess.close()
def run_train():
"""Train CAPTCHA for a number of steps."""
with tf.Graph().as_default():
train_reader = Reader(
'/home/sw/Documents/rgb-nir2/qd_fang2_9_8/country_2ch.tfrecord',
name='train_data',
batch_size=BATCH_SIZE)
leftIMG, rightIMG, labels_op = train_reader.feed() #[64,128]
conv_features1, features1 = model.get_features(leftIMG, reuse=False)
conv_features2, features2 = model.get_features(rightIMG, reuse=True)
predicts = tf.sqrt(
tf.reduce_sum(tf.square(features1 - features2), axis=1))
total_loss = model.caculate_loss(conv_features1, conv_features2,
features1, features2)
eval_all = model.evaluation(features1, features2, labels_op, 1) #train
tf.summary.scalar('sum_loss', total_loss)
tf.summary.scalar('roc/tp', eval_all['tp'])
tf.summary.scalar('roc/fp', eval_all['fp'])
tf.summary.scalar('roc/tpr',
eval_all['tp'] / (eval_all['tp'] + eval_all['fn']))
tf.summary.scalar('roc/precision', eval_all['precision'])
train_op = model.training(total_loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=50)
init_op = tf.global_variables_initializer()
sess = tf.Session()
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
sess.run(init_op)
# saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
max_step = 100000
for step in range(1, max_step):
start_time = time.time()
_, summary_str, loss_value, predicts_value, f1, f2 = sess.run([
train_op, summary, total_loss, predicts, features1,
features2
])
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
duration = time.time() - start_time
if step % 10 == 0:
logging.info(
'\r>> Step %d run_train: loss = %.4f (%.3f sec)' %
(step, loss_value, duration))
#-------------------------------
if step % 1000 == 0:
logging.info('>> %s Saving in %s' %
(datetime.now(), checkpoint_dir))
saver.save(sess, checkpoint_file, global_step=step)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
saver.save(sess, checkpoint_file, global_step=step)
print('\rModel saved in file :%s' % checkpoint_dir)
coord.request_stop()
coord.join(threads)
sess.close()