def main(_):
print("-" * 80)
if FLAGS.inference_mode:
if not os.path.isdir(FLAGS.checkpoint_dir):
print("Path {} does not exist. need checkpoint dir to inference.".format(FLAGS.output_dir))
inference()
elif FLAGS.test_mode:
test_mode()
else:
if not os.path.isdir(FLAGS.output_dir):
print(FLAGS.output_dir)
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
train()
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
if not FLAGS.structure_path:
exit()
with open(FLAGS.structure_path, 'r') as fp:
lines = fp.readlines()
lines = [eval(line.strip()) for line in lines]
structures = lines
n = len(lines)
# eval the first structure
eva = Eval()
eva.eval(structures[0])
eva.eval(structures[1])
eva.eval(structures[0])
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {0} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {0} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print_user_flags()
hparams = Hparams()
images, labels = read_data(FLAGS.data_path)
g = tf.Graph()
with g.as_default():
ops = get_ops(images, labels)
# count model variables
tf_variables = tf.trainable_variables()
num_params = count_model_params(tf_variables)
print("-" * 80)
print("Starting session")
config = tf.ConfigProto(allow_soft_placement=True)
with tf.train.SingularMonitoredSession(
config=config, checkpoint_dir=FLAGS.output_dir) as sess:
# training loop
print("-" * 80)
print("Starting training")
for step in range(1, hparams.train_steps + 1):
sess.run(ops["train_op"])
if step % FLAGS.log_every == 0:
global_step, train_loss, valid_acc = sess.run([
ops["global_step"],
ops["train_loss"],
ops["valid_acc"],
])
log_string = ""
log_string += "step={0:<6d}".format(step)
log_string += " loss={0:<5.2f}".format(train_loss)
log_string += " val_acc={0:<3d}/{1:<3d}".format(
valid_acc, hparams.eval_batch_size)
print(log_string)
sys.stdout.flush()
# final test
print("-" * 80)
print("Training done. Eval on TEST set")
num_corrects = 0
for _ in range(10000 // hparams.eval_batch_size):
num_corrects += sess.run(ops["test_acc"])
print("test_accuracy: {0:>5d}/10000".format(num_corrects))
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.checkpoint_dir):
print("Path {} does not exist. Can't find checkpoint.".format(
FLAGS.checkpoint_dir))
else:
print("checkpoint exists. at {}".format(FLAGS.checkpoint_dir))
print("-" * 80)
utils.print_user_flags()
evaluate()
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
train(mode="train")
def Eval_NN():
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
'''
# below are for batch evaluation of all arcs defined in the structure_path
if not FLAGS.structure_path:
exit()
with open(FLAGS.structure_path, 'r') as fp:
lines = fp.readlines()
lines = [eval(line.strip()) for line in lines]
structures = []
for line in lines:
row = []
for ele in line:
row += ele
structures.append(row)
n = len(lines)
# eval the first structure
Acc = []
eva = Eval()
eva.eval(structures[0])
eva.eval(structures[1])
acc = eva.eval(structures[0])
print(acc)
pdb.set_trace()
'''
eva = Eval()
return eva
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
model_file = os.path.join(FLAGS.output_dir, "models.csv")
if FLAGS.child_fixed_arc is None:
with open(model_file, 'a+') as f:
headers = ['num_layers', 'accuracy', 'models_arc']
writer = csv.DictWriter(f,
headers,
delimiter=',',
lineterminator='\n')
writer.writeheader()
for i in range(FLAGS.search_from, FLAGS.child_num_layers + 1):
tf.compat.v1.logging.info(
"Searching with constraint, num_layers: %d" % i)
map_task = train(i)
for k, v in map_task.items():
writer.writerow({
'num_layers': i,
'accuracy': k,
'models_arc': v
})
f.flush()
else:
_ = train(FLAGS.child_num_layers)
def main(_):
# Prepare directory
pdb.set_trace()
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
# Redirect stdout1 --------------------------------------------------------------------------------------------
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout1")
if not os.path.exists(log_file):
os.mknod(log_file)
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
utils.print_user_flags()
print('Reserving gpu memory...')
tf.Session()
# Load pickles file
print('Loading pickled file...')
with open('/home/yuwei/projects/vincent/pickleRick/allCrops1.pkl') as p_crop:
allCrops1 = cPickle.load(p_crop)
with open('/home/yuwei/projects/vincent/pickleRick/allCrops2.pkl') as p_crop:
allCrops2 = cPickle.load(p_crop)
with open('/home/yuwei/projects/vincent/pickleRick/labels.pkl','r') as p_crop:
labels1 = cPickle.load(p_crop)
labels2 = cPickle.load(p_crop)
labels3 = cPickle.load(p_crop)
labels1_Brio1 = cPickle.load(p_crop)
labels1_Brio2 = cPickle.load(p_crop)
labels2_Brio1 = cPickle.load(p_crop)
# Prepare and divide data
autoTrainNN = AutoTrain()
combined_1 = zip(allCrops1 + allCrops2, np.concatenate((labels1,labels2)))
autoTrainNN.addLabelledData(combined_1)
# train(autoTrainNN)
# Redirect stdout2 -------------------------------------------------------------------------------------------
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout2")
if not os.path.exists(log_file):
os.mknod(log_file)
print("Logging to {}".format(log_file))
sys.stdout.log = open(log_file, "a") # Change log file
# Load pickles file
print('Loading pickled file...')
with open('/home/yuwei/projects/vincent/pickleRick/allCrops3.pkl') as p_crop:
allCrops3 = cPickle.load(p_crop)
with open('/home/yuwei/projects/vincent/pickleRick/brio1/allCrops1.pkl') as p_crop:
allCrops1_Brio1 = cPickle.load(p_crop)
combined_2 = zip(allCrops3 + allCrops1_Brio1, np.concatenate((labels3,labels1_Brio1)))
autoTrainNN.addLabelledData(combined_2)
# train(autoTrainNN)
# Redirect stdout3 --------------------------------------------------------------------------------------------
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout3")
if not os.path.exists(log_file):
os.mknod(log_file)
print("Logging to {}".format(log_file))
sys.stdout.log = open(log_file, "a") # Change log file
utils.print_user_flags()
# Load pickles file
print('Loading pickled file...')
with open('/home/yuwei/projects/vincent/pickleRick/brio2/allCrops1.pkl') as p_crop:
allCrops1_Brio2 = cPickle.load(p_crop)
with open('/home/yuwei/projects/vincent/pickleRick/brio1/allCrops2.pkl') as p_crop:
allCrops2_Brio1 = cPickle.load(p_crop)
combined_3 = zip(allCrops1_Brio2 + allCrops2_Brio1, np.concatenate((labels1_Brio2,labels2_Brio1)))
autoTrainNN.addLabelledData(combined_3)
train(autoTrainNN)