def main(argv=None):
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE_HEIGHT, IMAGE_SIZE_WIDTH, 3], name="input_image")
annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE_HEIGHT, IMAGE_SIZE_WIDTH, 2], name="annotation")
pred_annotation, logits = inference(image, keep_probability) # build the FCN graph
prob = tf.nn.softmax(logits)
logits_ = tf.reshape(logits, [1, IMAGE_SIZE_HEIGHT*IMAGE_SIZE_WIDTH, 2])
annotation_ = tf.reshape(annotation, [1, IMAGE_SIZE_HEIGHT*IMAGE_SIZE_WIDTH, 2])
loss = utils.focal_loss(logits_, annotation_)
#loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=tf.squeeze(annotation, axis=3),
# name="entropy")))
#tf.summary.image('image', image)
#tf.summary.image('water_gt', tf.cast(annotation, tf.float32))
#tf.summary.image('water_pred', tf.expand_dims(tf.cast(pred_annotation, tf.float32), axis=3))
#tf.summary.scalar('loss', loss)
#merged = tf.summary.merge_all()
#summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, graph=tf.get_default_graph())
trainable_var = tf.trainable_variables()
train_op = train(loss, trainable_var)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.Session(config = config)
print("Setting up Saver...")
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path and (FLAGS.mode=="visualize" or FLAGS.mode == "train-continue"):
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
if FLAGS.mode == "train" or FLAGS.mode == "train-continue" :
print("Loading data")
global p;
p = load_training_dataset_path()
#train_imgs, gt_imgs = load_data(FLAGS.data_dir)
#gt_imgs = np.expand_dims(gt_imgs, axis=3)
print("Start Training...")
for itr in xrange(MAX_ITERATION):
print('Setp: %d'%(itr))
p1 = next_batch(1, itr)
# print("p1:\n",p1)
x_train, y_train, y_train2 = load_data(p1, itr)
y_train = np.expand_dims(y_train, axis=3)
y_train2 = np.expand_dims(y_train2, axis=3)
label_in = np.concatenate((y_train2, y_train), axis=3)
#x_train, y_train = next_batch(train_imgs, gt_imgs, FLAGS.batch_size)
feed_dict = {image: x_train, annotation: label_in, keep_probability: 0.85}
print("train feed_dict done!")
start_time = time.time()
sess.run(train_op, feed_dict = feed_dict)
dur = time.time() - start_time
print("dur : %f"% dur)
if itr % 1 == 0:
train_loss = sess.run(loss, feed_dict = feed_dict)
print('KITTI Step: %d, Train_loss:%g'%(itr, train_loss))
#if itr % 100 == 0:
# summary = sess.run(merged, feed_dict = feed_dict)
# summary_writer.add_summary(summary, itr)
if itr % 1000 == 0:
print('Save Net Model...')
saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
if itr % 1000 == 0 and itr >= 20000:
FLAGS.learning_rate = FLAGS.learning_rate / 2
elif FLAGS.mode == "visualize":
p = np.genfromtxt('../Dataset/on_road_test.txt', dtype='str')
for idx in range(0,p.shape[0]):
test_images1 = load_test_data(p[idx,0])
start_time = time.time()
likelyhood, pred = sess.run([prob, pred_annotation], feed_dict={image:test_images1, keep_probability: 1.0})
dur = time.time() - start_time
print("dur = ",dur)
#np.save('./likelyhood/test/likelyhood_%06d'%(idx), likelyhood)
#print(pred.shape)
for itr in range(pred.shape[0]):
utils.save_image(pred[itr].astype(np.float32), FLAGS.output_dir, name="pred_test_bin_%06d"%(idx))
print("Saved image :%d"%(idx))
def main(argv=None):
keep_probability = tf.compat.v1.placeholder(tf.float32,
name="keep_probabilty")
image = tf.compat.v1.placeholder(
tf.float32,
shape=[None, IMAGE_SIZE_HEIGHT, IMAGE_SIZE_WIDTH, 3],
name="input_image")
annotation = tf.compat.v1.placeholder(
tf.float32,
shape=[None, IMAGE_SIZE_HEIGHT, IMAGE_SIZE_WIDTH, 2],
name="annotation")
pred_annotation, logits = inference(
image, keep_probability) # build the FCN graph
prob = tf.nn.softmax(logits)
logits_ = tf.reshape(logits, [1, IMAGE_SIZE_HEIGHT * IMAGE_SIZE_WIDTH, 2])
annotation_ = tf.reshape(annotation,
[1, IMAGE_SIZE_HEIGHT * IMAGE_SIZE_WIDTH, 2])
loss = utils.focal_loss(logits_, annotation_)
#loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=tf.squeeze(annotation, axis=3),
# name="entropy")))
#tf.summary.image('image', image)
#tf.summary.image('water_gt', tf.cast(annotation, tf.float32))
#tf.summary.image('water_pred', tf.expand_dims(tf.cast(pred_annotation, tf.float32), axis=3))
#tf.summary.scalar('loss', loss)
#merged = tf.summary.merge_all()
#summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, graph=tf.get_default_graph())
trainable_var = tf.trainable_variables()
train_op = train(loss, trainable_var)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.Session(config=config)
print("Setting up Saver...")
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
print("TungNV_FLAGS.logs_dir: ", FLAGS.logs_dir)
ckpt = load_model(saver, sess)
if FLAGS.mode == "train":
print("Loading data")
global p
p = load_training_dataset_path()
#train_imgs, gt_imgs = load_data(FLAGS.data_dir)
#gt_imgs = np.expand_dims(gt_imgs, axis=3)
step = check_step_train(ckpt.model_checkpoint_path)
print("Start Training...")
for itr in xrange(step, MAX_ITERATION):
print('Step: %d' % (itr))
p1 = next_batch(1, itr)
print("p1:\n", p1)
x_train, y_train, y_train2 = load_data(p1, itr)
y_train = np.expand_dims(y_train, axis=3)
y_train2 = np.expand_dims(y_train2, axis=3)
label_in = np.concatenate((y_train2, y_train), axis=3)
#x_train, y_train = next_batch(train_imgs, gt_imgs, FLAGS.batch_size)
feed_dict = {
image: x_train,
annotation: label_in,
keep_probability: 0.85
}
print("train feed_dict done!")
start_time = time.time()
print("Run...")
sess.run(train_op, feed_dict=feed_dict)
dur = time.time() - start_time
print("Time to run : %f" % dur)
if itr % 1 == 0:
train_loss = sess.run(loss, feed_dict=feed_dict)
print('KITTI Step: %d, Train_loss:%g' % (itr, train_loss))
#if itr % 100 == 0:
# summary = sess.run(merged, feed_dict = feed_dict)
# summary_writer.add_summary(summary, itr)
if itr % 5000 == 0:
print('Save Net Model...')
saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
if itr % 5000 == 0 and itr >= 20000:
FLAGS.learning_rate = FLAGS.learning_rate / 2
elif FLAGS.mode == "test":
# test_image_from_dataset(PATH_DATASET_TEST, keep_probability, image, pred_annotation, prob, sess)
test_image_from_folder(PATH_IMAGE, keep_probability, image,
pred_annotation, prob, sess)
