def train():
print(file_io.get_file_length(TEST_TXT) / FLAGS.batch_size)
FLAGS.max_training_iter = file_io.get_file_length(
TEST_TXT) / FLAGS.batch_size
test_batch_data, test_batch_label, test_batch_name = gen_data_label(
TEST_TXT, False)
data_ph = tf.placeholder(tf.float32,
shape=(FLAGS.batch_size, FLAGS.feature_row,
FLAGS.feature_col, FLAGS.feature_cha),
name='feature')
label_ph = tf.placeholder(tf.float32,
shape=(FLAGS.batch_size, FLAGS.label_row,
FLAGS.label_col, FLAGS.label_cha),
name='label')
global_step = tf.Variable(0, name='global_step', trainable=False)
keep_prob_ph = tf.placeholder(tf.float32, name='keep_prob')
train_test_phase_ph = tf.placeholder(tf.bool, name='phase_holder')
#fcn_model.test_infer_size(label_ph)
output_shape = [
FLAGS.batch_size, FLAGS.label_row, FLAGS.label_col, FLAGS.label_cha
]
infer, diff_infer = model.inference(data_ph, output_shape, keep_prob_ph,
train_test_phase_ph)
#loss = model.loss(infer, diff_infer, label_ph)
#train_op = model.train_op(loss, FLAGS.init_learning_rate, global_step)
#test_loss = model.loss(infer, diff_infer, label_ph)
sess = tf.Session()
init_op = tf.initialize_all_variables()
sess.run(init_op)
saver = tf.train.Saver()
sf.restore_model(sess, saver, FLAGS.model_dir, model_name=None)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
result_list = list()
for i in xrange(FLAGS.max_training_iter):
#test_batch_name_v = sess.run(test_batch_name)
test_batch_data_v, test_batch_label_v, test_batch_name_v = sess.run(
[test_batch_data, test_batch_label, test_batch_name])
#test_loss_v, infer_v = sess.run([test_loss, infer], {data_ph:test_batch_data_v, label_ph:test_batch_label_v})
diff_infer_v, infer_v = sess.run([diff_infer, infer], {
data_ph: test_batch_data_v,
label_ph: test_batch_label_v
})
save_results(test_batch_label_v, infer_v, diff_infer_v,
test_batch_name_v, result_list)
file_io.save_file(result_list, FLAGS.result_dir + "/results.txt")
def save_to_file(self, sort_result=True):
if sort_result:
self.file_list.sort()
file_io.save_file(self.file_list, self.result_file_name)
import file_io
import os
data_dir = "/home/geoff/eye_data/2016_11_18_14_17_2_extract/"
train_file_name = "/home/geoff/eye_data/file_list/train_list1.txt"
test_file_name = "/home/geoff/eye_data/file_list/test_list1.txt"
def get_image_list(file_name):
f_list = file_io.read_file(file_name)
return_list = list()
for f in f_list:
f_l = f.split(" ")
b_name = os.path.basename(f_l[0])
st_name = data_dir + b_name
img_name = data_dir + b_name.replace("_st.data", "_1.jpg")
return_list.append(st_name + " " + img_name)
return return_list
train_list = get_image_list(train_file_name)
test_list = get_image_list(test_file_name)
train_save_name = "../file_list/train_list1.txt"
test_save_name = "../file_list/test_list1.txt"
file_io.save_file(train_list, train_save_name)
file_io.save_file(test_list, test_save_name)
import file_io
if __name__ == "__main__":
file_list_dir = "../file_list/"
data_ext = "_resize.jpg"
label_ext = "_resize.desmap"
file_dir = "../data/"
cam_dir_list = file_io.get_dir_list(file_dir)
data_list = list()
for cam_dir in cam_dir_list:
video_list = file_io.get_listfile(cam_dir, ".avi")
for file_name in video_list:
file_dir_name = file_name.replace(".avi", "/")
data_list += file_io.get_listfile(file_dir_name, data_ext)
file_io.save_file(data_list, file_list_dir + 'image_name_list.txt', False)
train_list = list()
test_list = list()
for cam_dir in cam_dir_list:
if cam_dir != "../data/Cam253":
continue
video_list = file_io.get_listfile(cam_dir, ".avi")
data_list = list()
for file_name in video_list:
file_dir_name = file_name.replace(".avi", "/")
data_list += file_io.get_listfile(file_dir_name, data_ext)
partition = 0.7
train_data_len = int(len(data_list) * partition)
random.shuffle(data_list)
train_data = data_list[:train_data_len]
test_data = data_list[train_data_len:]
train_list += [
d + " " + d.replace(data_ext, label_ext) for d in train_data
]
test_list += [
d + " " + d.replace(data_ext, label_ext) for d in test_data
]
train_file_list_name = 'train_list5.txt'
file_io.save_file(train_list, file_list_dir + train_file_list_name, True)
test_file_list_name = 'test_list5.txt'
file_io.save_file(test_list, file_list_dir + test_file_list_name, True)
import file_io
train_file_dir = "../data/toJeff/Train/"
test_file_dir = "../data/toJeff/Test/"
def get_list_file(file_dir):
#train_feature_list = file_io.get_listfile(file_dir + "Resized_images/","resnet_hypercolumn")
train_feature_list = file_io.get_listfile(file_dir + "Resized_images/",
"jpg")
train_label = file_io.get_listfile(file_dir + "Resized_GTdensity/", "npy")
train_list = list()
for tf in train_feature_list:
#tf_label = tf.replace(".resnet_hypercolumn","dots.png.npy").replace("images","GTdensity")
tf_label = tf.replace(".jpg",
"dots.png.npy").replace("images", "GTdensity")
assert (tf_label in train_label)
train_list.append(tf + " " + tf_label)
return train_list
train_list = get_list_file(train_file_dir)
test_list = get_list_file(test_file_dir)
print(len(train_list))
print(len(test_list))
file_io.save_file(train_list, "../file_list/spain_train_list2.txt", True)
file_io.save_file(test_list, "../file_list/spain_test_list2.txt")