def tc_init(self, model_dir):
self.tc_model = tclstm()
self.X_input = tf.placeholder("float", [None, tcopts['time_steps'], tcopts['lstm_num_input']])
self.maps = tf.placeholder("float", [None, 19, 19, 1])
self.map_logits = self.tc_model.map_net(self.maps)
self.Inputs = tf.concat((self.X_input, self.map_logits), axis=2)
self.logits, _ = self.tc_model.net(self.Inputs)
variables_to_restore = [var for var in tf.global_variables() if
(var.name.startswith('tclstm') or var.name.startswith('mapnet'))]
saver = tf.train.Saver(var_list=variables_to_restore)
if self.p.checkpoint is None:
checkpoint = tf.train.latest_checkpoint(os.path.join(base_path, 'DiMP_LTMU/meta_updater', model_dir))
else:
checkpoint = os.path.join(base_path, 'DiMP_LTMU/meta_updater/' + self.p.model_dir + '/lstm_model.ckpt-' + str(self.p.checkpoint))
saver.restore(self.sess, checkpoint)
def tc_init(self, model_dir):
self.tc_model = tclstm()
self.tc_input = tf.placeholder(
"float", [None, tcopts['time_steps'], tcopts['lstm_num_input']])
# self.tc_label = tf.placeholder("float", [None, tcopts['lstm_num_classes']])
self.tclogits = self.tc_model.net(self.tc_input)
variables_to_restore = [
var for var in tf.global_variables()
if (var.name.startswith('tclstm'))
]
saver = tf.train.Saver(var_list=variables_to_restore)
checkpoint = tf.train.latest_checkpoint(
os.path.join('./meta_updater', model_dir))
# if checkpoint is not None:
saver.restore(self.sess, checkpoint)
def tc_init(self, model_dir):
self.tc_model = tclstm()
self.X_input = tf.placeholder(
"float", [None, tcopts['time_steps'], tcopts['lstm_num_input']])
self.maps = tf.placeholder("float", [None, 19, 19, 1])
self.map_logits = self.tc_model.map_net(self.maps)
self.Inputs = tf.concat((self.X_input, self.map_logits), axis=2)
self.logits, _ = self.tc_model.net(self.Inputs)
variables_to_restore = [
var for var in tf.global_variables()
if (var.name.startswith('tclstm') or var.name.startswith('mapnet'))
]
saver = tf.train.Saver(var_list=variables_to_restore)
checkpoint = tf.train.latest_checkpoint(
os.path.join('./tracker_controller', model_dir))
# if checkpoint is not None:
saver.restore(self.sess, checkpoint)
def train():
model = tclstm()
training_steps = 50000
display_step = 200
with tf.device('cpu:0'):
global_step = slim.create_global_step()
# tf Graph input
X = tf.placeholder(
"float", [None, tcopts['time_steps'], tcopts['lstm_num_input']])
Y = tf.placeholder("float", [None, tcopts['lstm_num_classes']])
lrOp = tf.train.exponential_decay(tcopts['lstm_initial_lr'],
global_step,
tcopts['lstm_decay_steps'],
tcopts['lr_decay_factor'],
staircase=True)
logits = model.net(X)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=lrOp)
optimizer = tf.train.MomentumOptimizer(learning_rate=lrOp,
momentum=0.9)
loss_op = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
prediction = tf.nn.softmax(logits)
grads = optimizer.compute_gradients(loss_op)
train_op = optimizer.apply_gradients(grads, global_step=global_step)
# Evaluate model (with test logits, for dropout to be disabled)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
test_accuracy = accuracy
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
pos_data, neg_data = load_training_data('pos_data.npy', 'neg_data.npy')
test_pos_data, test_neg_data = load_training_data(
'test_pos_data.npy', 'test_neg_data.npy')
test_pos_num, test_neg_num, test_batch_input, test_labels = get_test_batch_input(
test_pos_data, test_neg_data)
test_accuracy_pos = tf.reduce_mean(
tf.cast(correct_pred[:test_pos_num], tf.float32))
test_accuracy_neg = tf.reduce_mean(
tf.cast(correct_pred[test_pos_num:], tf.float32))
accuracy_pos = tf.reduce_mean(
tf.cast(correct_pred[:tcopts['batch_size']], tf.float32))
accuracy_neg = tf.reduce_mean(
tf.cast(correct_pred[tcopts['batch_size']:], tf.float32))
saver = tf.train.Saver(keep_checkpoint_every_n_hours=tcopts[
'keep_checkpoint_every_n_hours'])
# add summary
tf.summary.scalar('learning_rate', lrOp)
tf.summary.scalar('loss', loss_op)
tf.summary.scalar('training_accuracy', accuracy)
tf.summary.scalar('training_accuracy_pos', accuracy_pos)
tf.summary.scalar('training_accuracy_neg', accuracy_neg)
# grads
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
# # trainable var
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
summary_op = tf.summary.merge_all()
tf.summary.scalar('testing_accuracy_pos', test_accuracy_pos)
tf.summary.scalar('testing_accuracy_neg', test_accuracy_neg)
tf.summary.scalar('testing_accuracy', test_accuracy)
test_merge_summary = tf.summary.merge(
[tf.get_collection(tf.GraphKeys.SUMMARIES, 'testing_accuracy')])
summary_writer = tf.summary.FileWriter(tcopts['lstm_train_dir'],
graph=tf.get_default_graph())
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.2
# Start training
with tf.Session(config=tfconfig) as sess:
# Run the initializer
sess.run(init)
checkpoint = tf.train.latest_checkpoint(tcopts['lstm_train_dir'])
if checkpoint is not None:
saver.restore(sess, checkpoint)
while True:
batch_input, labels = get_batch_input(pos_data, neg_data, 64)
# Reshape data to get 28 seq of 28 elements
# Run optimization op (backprop)
_, g_step = sess.run([train_op, global_step],
feed_dict={
X: batch_input[:, :,
tcopts['lstm_input']],
Y: labels
})
if g_step % display_step == 0:
# Calculate batch loss and accuracy
loss, acc, acc_pos, acc_neg, summary_str = sess.run(
[
loss_op, accuracy, accuracy_pos, accuracy_neg,
summary_op
],
feed_dict={
X: batch_input[:, :, tcopts['lstm_input']],
Y: labels
})
print("Step " + str(g_step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + \
"{:.3f}".format(acc) + ", Accuracy pos= " + "{:.3f}".format(
acc_pos) + ", Accuracy neg= " + "{:.3f}".format(acc_neg))
if g_step % tcopts['model_save_interval'] == 0:
checkpoint_path = os.path.join(tcopts['lstm_train_dir'],
'lstm_model.ckpt')
saver.save(sess, checkpoint_path, global_step=g_step)
print('Save model, global step: %d' % g_step)
if g_step % tcopts['eval_interval'] == 0:
test_acc, test_acc_pos, test_acc_neg, test_summary_str = sess.run(
[
test_accuracy, test_accuracy_pos,
test_accuracy_neg, test_merge_summary
],
feed_dict={
X: test_batch_input[:, :, tcopts['lstm_input']],
Y: test_labels
})
summary_writer.add_summary(test_summary_str, g_step)
print("test accuracy:" + "{:.4f}".format(test_acc) +
" test accuracy pos:" +
"{:.4f}".format(test_acc_pos) +
" test accuracy neg:" +
"{:.4f}".format(test_acc_neg))
if test_acc_pos > tcopts[
'save_pos_thr'] and test_acc_neg > tcopts[
'save_neg_thr']:
checkpoint_path = os.path.join(
tcopts['lstm_train_dir'], 'lstm_model.ckpt')
saver.save(sess, checkpoint_path, global_step=g_step)
print('Save model, global step: %d' % g_step)
if g_step % tcopts['summary_save_interval'] == 0:
summary_writer.add_summary(summary_str, g_step)
print('Save summary, global step: %d' % g_step)
