def evaluate(sess, epoch_num, labels, logits, loss, data_size, name):
print('\nTest performance:')
loss_avg = 0
batch_size = FLAGS.batch_size
print('testsize = ', data_size)
assert data_size % batch_size == 0
num_batches = data_size // batch_size
start_time = time.time()
conf_mat = np.zeros((FLAGS.num_classes, FLAGS.num_classes),
dtype=np.uint64)
for step in range(1, num_batches + 1):
if (step % 5 == 0):
print('evaluation: batch %d / %d ' % (step, num_batches))
out_logits, loss_value, batch_labels = sess.run([logits, loss, labels])
loss_avg += loss_value
collect_confusion(out_logits, batch_labels, conf_mat)
print('Evaluation {} in epoch {} lasted {}'.format(
name, epoch_num, train_helper.get_expired_time(start_time)))
print('')
(acc, iou, prec, rec, _) = eval_helper.compute_errors(conf_mat,
name,
class_names,
verbose=True)
loss_avg /= num_batches
print('IoU=%.2f Acc=%.2f Prec=%.2f Rec=%.2f' % (iou, acc, prec, rec))
return acc, iou, prec, rec, loss_avg
def end_epoch(train_data):
pixacc, iou, _, _, _ = eval_helper.compute_errors(
train_conf_mat, 'Train', train_dataset.class_info)
is_best = False
if len(train_data['iou']) > 0 and iou > max(train_data['iou']):
is_best = True
train_data['iou'].append(iou)
train_data['acc'].append(pixacc)
train_loss_val = np.mean(train_loss_arr)
train_data['loss'].append(train_loss_val)
return is_best
def train(model, resume_path=None):
with tf.Graph().as_default():
# configure the training session
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=config)
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
num_batches_per_epoch = (FLAGS.train_size // FLAGS.batch_size)
decay_steps = int(num_batches_per_epoch * FLAGS.num_epochs_per_decay)
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(FLAGS.initial_learning_rate,
global_step,
decay_steps,
FLAGS.learning_rate_decay_factor,
staircase=True)
train_data, train_labels, train_names, train_weights = reader.inputs(
shuffle=True,
num_epochs=FLAGS.max_epochs,
dataset_partition='train')
val_data, val_labels, val_names, val_weights = reader.inputs(
shuffle=False,
num_epochs=FLAGS.max_epochs,
dataset_partition='val')
with tf.variable_scope('model'):
logits, loss, init_op, init_feed = model.build(
train_data, train_labels, train_weights)
with tf.variable_scope('model', reuse=True):
logits_val, loss_val = model.build(val_data,
val_labels,
val_weights,
is_training=False)
tf.summary.scalar('learning_rate', lr)
opt = tf.train.AdamOptimizer(lr)
grads = opt.compute_gradients(loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
# Add histograms for gradients.
grad_tensors = []
for grad, var in grads:
grad_tensors += [grad]
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
variables_averages_op = variable_averages.apply(
tf.trainable_variables())
with tf.control_dependencies(
[apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=FLAGS.max_epochs,
sharded=False)
if len(FLAGS.resume_path) > 0:
print('\nRestoring params from:', FLAGS.resume_path)
saver.restore(sess, FLAGS.resume_path)
sess.run(tf.local_variables_initializer())
else:
sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables())
sess.run(init_op, feed_dict=init_feed)
summary_op = tf.summary.merge_all()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph=sess.graph)
num_batches = FLAGS.train_size // FLAGS.batch_size
plot_data = {}
plot_data['train_loss'] = []
plot_data['valid_loss'] = []
plot_data['train_iou'] = []
plot_data['valid_iou'] = []
plot_data['train_acc'] = []
plot_data['valid_acc'] = []
plot_data['train_prec'] = []
plot_data['valid_prec'] = []
plot_data['train_rec'] = []
plot_data['valid_rec'] = []
plot_data['lr'] = []
ex_start_time = time.time()
global_step_value = 0
visualize_dir = os.path.join(FLAGS.train_dir, 'visualize')
for epoch_num in range(1, FLAGS.max_epochs + 1):
print('\ntensorboard --logdir=' + FLAGS.train_dir + '\n')
confusion_matrix = np.zeros((FLAGS.num_classes, FLAGS.num_classes),
dtype=np.uint64)
avg_train_loss = 0
for step in range(1, num_batches + 1):
start_time = time.time()
run_ops = [train_op, logits, loss, global_step, train_labels]
if global_step_value % 50 == 0:
run_ops += [summary_op]
ret_val = sess.run(run_ops)
(_, logits_value, loss_value, global_step_value,
batch_labels, summary_str) = ret_val
summary_writer.add_summary(summary_str, global_step_value)
else:
ret_val = sess.run(run_ops)
(_, logits_value, loss_value, global_step_value,
batch_labels) = ret_val
duration = time.time() - start_time
avg_train_loss += loss_value
if step >= num_batches * 4 // 5:
collect_confusion(logits_value, batch_labels,
confusion_matrix)
acc, iou, rec, prec, size = eval_helper.compute_errors(
confusion_matrix, 'train', class_names, verbose=False)
if step % 5 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = '%s: epoch %d, batch %d / %d,\n' \
'batch loss= %.2f\n' \
'avg loss = %.2f\n' \
'avg_acc= %.2f\n' \
'avg_iou= %.2f\n' \
'avg prec=%.2f\n' \
'avg rec=%.2f\n \
(%.1f examples/sec; %.3f sec/batch)'
print(format_str %
(train_helper.get_expired_time(ex_start_time),
epoch_num, step, num_batches + 1, loss_value,
avg_train_loss / step, acc, iou, prec, rec,
examples_per_sec, sec_per_batch))
else:
if step % 5 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = '%s: epoch %d, batch %d / %d,\n' \
'batch loss= %.2f\n'\
'avg loss = %.2f\n \
(%.1f examples/sec; %.3f sec/batch)'
print(format_str %
(train_helper.get_expired_time(ex_start_time),
epoch_num, step, num_batches + 1, loss_value,
avg_train_loss / step, examples_per_sec,
sec_per_batch))
train_loss = avg_train_loss / num_batches
valid_acc, valid_iou, valid_prec, valid_rec, valid_loss = evaluate(
sess,
epoch_num,
val_labels,
logits_val,
loss_val,
data_size=FLAGS.val_size,
name='validation')
plot_data['train_loss'] += [train_loss]
plot_data['valid_loss'] += [valid_loss]
plot_data['train_iou'] += [iou]
plot_data['valid_iou'] += [valid_iou]
plot_data['train_acc'] += [acc]
plot_data['valid_acc'] += [valid_acc]
plot_data['train_prec'] += [prec]
plot_data['valid_prec'] += [valid_prec]
plot_data['train_rec'] += [rec]
plot_data['valid_rec'] += [valid_rec]
plot_data['lr'] += [lr.eval(session=sess)]
eval_helper.plot_training_progress(visualize_dir, plot_data)
if valid_iou >= max(plot_data['valid_iou']):
print('Saving model...')
t = time.time()
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path)
print('Model is saved! t={}'.format(
train_helper.get_expired_time(t)))
coord.request_stop()
coord.join(threads)
sess.close()