def predict_images(test_data_path=None, video=None):
save_dir = os.path.join('saved_model', element)
tf.reset_default_graph()
with tf.Session() as sess:
# Predict the logits
input_image, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(
sess, vgg_path)
nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out,
num_classes)
logits = tf.reshape(nn_last_layer, (-1, num_classes))
saver = tf.train.Saver()
last_checkpoint = helper.get_latest_checkpoint_number(save_dir) - 1
if video is None:
saver.restore(
sess,
os.path.join('saved_model', element,
'model-' + str(last_checkpoint)))
print("Restored the saved Model in save_model")
helper.pred_samples(runs_dir, test_data_path, sess, image_shape,
logits, keep_prob, input_image, crops,
restrict_prediction)
else:
save_dir = os.path.join('saved_model', test_data_path)
last_checkpoint = helper.get_latest_checkpoint_number(save_dir) - 1
saver.restore(
sess,
os.path.join('saved_model', test_data_path,
'model-' + str(last_checkpoint)))
return helper.get_binary_seg(video, sess, image_shape, logits,
keep_prob, input_image, crops)
def opt_predict_images(test_data_path=None, video=None):
frozen_graph = os.path.join('optimised_model', element, 'graph.pb')
# frozen_graph = os.path.join('frozen_model', element, 'saved_model.pb')
tf.reset_default_graph()
with tf.gfile.GFile(frozen_graph, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
input_image = graph.get_tensor_by_name('image_input:0')
keep_prob = graph.get_tensor_by_name('keep_prob:0')
logits = graph.get_tensor_by_name('logits:0')
sess = tf.Session(graph=graph)
# with tf.Session(graph=graph) as sess:
if video is None:
helper.pred_samples(runs_dir, test_data_path, sess, image_shape,
logits, keep_prob, input_image, crops,
restrict_prediction)
else:
return helper.get_binary_seg(video, sess, image_shape, logits,
keep_prob, input_image, crops)
def predict_images(test_data_path, print_speed=False):
num_classes = 2
image_shape = (160, 576)
runs_dir = './outputs'
vgg_path = os.path.join('./data', 'vgg')
with tf.Session() as sess:
input_image, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(
sess, vgg_path)
nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out,
num_classes)
logits = tf.reshape(nn_last_layer, (-1, num_classes))
saver = tf.train.Saver()
saver.restore(sess, model_path)
print("Restored the saved Model in file: %s" % model_path)
helper.pred_samples(runs_dir, test_data_path, sess, image_shape,
logits, keep_prob, input_image, print_speed)
def predict_images(test_data_path, print_speed=False):
num_classes = 2
image_shape = (160, 576)
runs_dir = '/home/shuijing/Desktop/ece498sm_project/runs'
# Path to vgg model
vgg_path = os.path.join('/home/shuijing/Desktop/ece498sm_project/', 'vgg')
with tf.Session() as sess:
# Predict the logits
input_image, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)
nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)
logits = tf.reshape(nn_last_layer, (-1, num_classes))
# Restore the saved model
saver = tf.train.Saver()
saver.restore(sess, model_path)
print("Restored the saved Model in file: %s" % model_path)
# Predict the samples
helper.pred_samples(runs_dir, test_data_path, sess, image_shape, logits, keep_prob, input_image, print_speed)