def run_test(sess, test_file):
"""Testing the model
"""
test_data = []
test_options = []
X = tf.get_collection('input')[0]
V = tf.get_collection('input')[1]
with open(test_file, "r") as f:
header = f.readline()
reader = csv.reader(f, delimiter=",")
for row in reader:
x_location = int(row[1]) // 15 + 2
test_data.append([int(row[0]), float(row[5]), float(row[6]), float(row[7])]) # Trial, V, Grad, Ref_force
test_options.append([int(row[0]), x_location, float(row[3]), float(row[4]), float(row[5]), float(row[7])]) # Trial, x_location, z_location, velocity, voltage, Ref_force
# Data Transformations
test_seq = np.array(range(FLAGS.seq_length, np.shape(test_data)[0]))
test_idxs = np.array([test_seq - n for n in reversed(range(0, FLAGS.seq_length))]).T
# Test Inputs
test_input = np.array(test_data)[:, (1, 2)] # V, gradV
test_input = np.reshape(test_input[test_idxs], [-1, FLAGS.seq_length, FLAGS.input_dim])
test_V = np.reshape(np.array(test_data)[:, 1][test_seq], [-1, 1])
# Test Run
estimate_loc, estimate_force = sess.run(tf.get_collection('test_ops'), feed_dict={X: test_input, V: test_V})
estimate_force = np.reshape(estimate_force, [-1])
estimate_loc_prob = np.reshape(estimate_loc, (-1, FLAGS.num_class)).tolist()
test_options = np.array(test_options)
test_options = np.reshape(test_options[test_seq], [-1, 6])
test_force_truth = np.array(test_data)[test_seq, 3]
test_loc_truth = test_options[:, 1]
test_loc_truth = np.reshape(test_loc_truth, [-1, 1])
print_result(test_options, estimate_force, test_force_truth, estimate_loc, test_loc_truth, test_seq)
def main():
try:
username, password, tenant_name, keystone_url = \
utils.get_token_config()
glance_url = utils.get_glance_url()
if DEBUG:
print username, password, tenant_name, keystone_url, glance_url
user = keystone.Keystone(username, password,
tenant_name, keystone_url)
token = user.get_token()
# glance_url = 'http://localhost:9292'
glance = glanceclient.Client(endpoint=glance_url, token=token)
image = glance.images.create(name='__nvs_monitor__',
data='a' * 1024,
disk_format='qcow2',
container_format='ovf',
is_public=False)
if image.status != 'active':
print 'create image error. %s' % image
image.delete()
try:
image.get()
except glanceexc.HTTPNotFound:
pass
except Exception:
if DEBUG:
utils.print_traceback()
result = 'failed'
else:
result = 'success'
utils.print_result(result)
args.sample = 4
args.lam = 1.0
args.tem = 0.2
args.alpha = 0.5
args.lr = 0.2
args.bn = True
args.input_dropout = 0.6
args.hidden_dropout = 0.8
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "grand")
def proteins_config(args):
return args
@register_func("collab")
def collab_config(args):
args.degree_feature = True
return args
def run(dataset_name):
args = build_default_args_for_unsupervisde_graph_classification(
dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["mutag", "imdb-b", "imdb-m", "proteins", "collab"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "infograph")
@register_func("proteins")
def proteins_config(args):
return args
@register_func("collab")
def collab_config(args):
args.degree_feature = True
return args
def run(dataset_name):
args = build_default_args_for_graph_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["mutag", "imdb-b", "imdb-m", "proteins", "collab"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "patchy_san")
def train(epochs=20,
batchSize=64,
lr=0.0001,
device='cuda:0',
accumulate=True,
a_step=16,
load_saved=False,
file_path='./saved_best.pt',
use_dtp=False,
pretrained_model='./bert_pretrain_model',
tokenizer_model='bert-base-chinese',
weighted_loss=False):
device = device
tokenizer = load_tokenizer(tokenizer_model)
my_net = torch.load(file_path) if load_saved else Net(
load_pretrained_model(pretrained_model))
my_net.to(device, non_blocking=True)
label_dict = dict()
with open('./tianchi_datasets/label.json') as f:
for line in f:
label_dict = json.loads(line)
break
label_weights_dict = dict()
with open('./tianchi_datasets/label_weights.json') as f:
for line in f:
label_weights_dict = json.loads(line)
break
ocnli_train = dict()
with open('./tianchi_datasets/OCNLI/train.json') as f:
for line in f:
ocnli_train = json.loads(line)
break
ocnli_dev = dict()
with open('./tianchi_datasets/OCNLI/dev.json') as f:
for line in f:
ocnli_dev = json.loads(line)
break
ocemotion_train = dict()
with open('./tianchi_datasets/OCEMOTION/train.json') as f:
for line in f:
ocemotion_train = json.loads(line)
break
ocemotion_dev = dict()
with open('./tianchi_datasets/OCEMOTION/dev.json') as f:
for line in f:
ocemotion_dev = json.loads(line)
break
tnews_train = dict()
with open('./tianchi_datasets/TNEWS/train.json') as f:
for line in f:
tnews_train = json.loads(line)
break
tnews_dev = dict()
with open('./tianchi_datasets/TNEWS/dev.json') as f:
for line in f:
tnews_dev = json.loads(line)
break
train_data_generator = Data_generator(ocnli_train, ocemotion_train,
tnews_train, label_dict, device,
tokenizer)
dev_data_generator = Data_generator(ocnli_dev, ocemotion_dev, tnews_dev,
label_dict, device, tokenizer)
tnews_weights = torch.tensor(label_weights_dict['TNEWS']).to(
device, non_blocking=True)
ocnli_weights = torch.tensor(label_weights_dict['OCNLI']).to(
device, non_blocking=True)
ocemotion_weights = torch.tensor(label_weights_dict['OCEMOTION']).to(
device, non_blocking=True)
loss_object = Calculate_loss(label_dict,
weighted=weighted_loss,
tnews_weights=tnews_weights,
ocnli_weights=ocnli_weights,
ocemotion_weights=ocemotion_weights)
optimizer = torch.optim.Adam(my_net.parameters(), lr=lr)
best_dev_f1 = 0.0
best_epoch = -1
for epoch in range(epochs):
my_net.train()
train_loss = 0.0
train_total = 0
train_correct = 0
train_ocnli_correct = 0
train_ocemotion_correct = 0
train_tnews_correct = 0
train_ocnli_pred_list = []
train_ocnli_gold_list = []
train_ocemotion_pred_list = []
train_ocemotion_gold_list = []
train_tnews_pred_list = []
train_tnews_gold_list = []
cnt_train = 0
while True:
raw_data = train_data_generator.get_next_batch(batchSize)
if raw_data == None:
break
data = dict()
data['input_ids'] = raw_data['input_ids']
data['token_type_ids'] = raw_data['token_type_ids']
data['attention_mask'] = raw_data['attention_mask']
data['ocnli_ids'] = raw_data['ocnli_ids']
data['ocemotion_ids'] = raw_data['ocemotion_ids']
data['tnews_ids'] = raw_data['tnews_ids']
tnews_gold = raw_data['tnews_gold']
ocnli_gold = raw_data['ocnli_gold']
ocemotion_gold = raw_data['ocemotion_gold']
if not accumulate:
optimizer.zero_grad()
ocnli_pred, ocemotion_pred, tnews_pred = my_net(**data)
if use_dtp:
tnews_kpi = 0.1 if len(
train_tnews_pred_list
) == 0 else train_tnews_correct / len(train_tnews_pred_list)
ocnli_kpi = 0.1 if len(
train_ocnli_pred_list
) == 0 else train_ocnli_correct / len(train_ocnli_pred_list)
ocemotion_kpi = 0.1 if len(
train_ocemotion_pred_list
) == 0 else train_ocemotion_correct / len(
train_ocemotion_pred_list)
current_loss = loss_object.compute_dtp(tnews_pred, ocnli_pred,
ocemotion_pred,
tnews_gold, ocnli_gold,
ocemotion_gold,
tnews_kpi, ocnli_kpi,
ocemotion_kpi)
else:
current_loss = loss_object.compute(tnews_pred, ocnli_pred,
ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold)
train_loss += current_loss.item()
current_loss.backward()
if accumulate and (cnt_train + 1) % a_step == 0:
optimizer.step()
optimizer.zero_grad()
if not accumulate:
optimizer.step()
if use_dtp:
good_tnews_nb, good_ocnli_nb, good_ocemotion_nb, total_tnews_nb, total_ocnli_nb, total_ocemotion_nb = loss_object.correct_cnt_each(
tnews_pred, ocnli_pred, ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold)
tmp_good = sum(
[good_tnews_nb, good_ocnli_nb, good_ocemotion_nb])
tmp_total = sum(
[total_tnews_nb, total_ocnli_nb, total_ocemotion_nb])
train_ocemotion_correct += good_ocemotion_nb
train_ocnli_correct += good_ocnli_nb
train_tnews_correct += good_tnews_nb
else:
tmp_good, tmp_total = loss_object.correct_cnt(
tnews_pred, ocnli_pred, ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold)
train_correct += tmp_good
train_total += tmp_total
p, g = loss_object.collect_pred_and_gold(ocnli_pred, ocnli_gold)
train_ocnli_pred_list += p
train_ocnli_gold_list += g
p, g = loss_object.collect_pred_and_gold(ocemotion_pred,
ocemotion_gold)
train_ocemotion_pred_list += p
train_ocemotion_gold_list += g
p, g = loss_object.collect_pred_and_gold(tnews_pred, tnews_gold)
train_tnews_pred_list += p
train_tnews_gold_list += g
cnt_train += 1
#torch.cuda.empty_cache()
if (cnt_train + 1) % 1000 == 0:
print('[', cnt_train + 1, '- th batch : train acc is:',
train_correct / train_total, '; train loss is:',
train_loss / cnt_train, ']')
if accumulate:
optimizer.step()
optimizer.zero_grad()
train_ocnli_f1 = get_f1(train_ocnli_gold_list, train_ocnli_pred_list)
train_ocemotion_f1 = get_f1(train_ocemotion_gold_list,
train_ocemotion_pred_list)
train_tnews_f1 = get_f1(train_tnews_gold_list, train_tnews_pred_list)
train_avg_f1 = (train_ocnli_f1 + train_ocemotion_f1 +
train_tnews_f1) / 3
print(epoch, 'th epoch train average f1 is:', train_avg_f1)
print(epoch, 'th epoch train ocnli is below:')
print_result(train_ocnli_gold_list, train_ocnli_pred_list)
print(epoch, 'th epoch train ocemotion is below:')
print_result(train_ocemotion_gold_list, train_ocemotion_pred_list)
print(epoch, 'th epoch train tnews is below:')
print_result(train_tnews_gold_list, train_tnews_pred_list)
train_data_generator.reset()
my_net.eval()
dev_loss = 0.0
dev_total = 0
dev_correct = 0
dev_ocnli_correct = 0
dev_ocemotion_correct = 0
dev_tnews_correct = 0
dev_ocnli_pred_list = []
dev_ocnli_gold_list = []
dev_ocemotion_pred_list = []
dev_ocemotion_gold_list = []
dev_tnews_pred_list = []
dev_tnews_gold_list = []
cnt_dev = 0
with torch.no_grad():
while True:
raw_data = dev_data_generator.get_next_batch(batchSize)
if raw_data == None:
break
data = dict()
data['input_ids'] = raw_data['input_ids']
data['token_type_ids'] = raw_data['token_type_ids']
data['attention_mask'] = raw_data['attention_mask']
data['ocnli_ids'] = raw_data['ocnli_ids']
data['ocemotion_ids'] = raw_data['ocemotion_ids']
data['tnews_ids'] = raw_data['tnews_ids']
tnews_gold = raw_data['tnews_gold']
ocnli_gold = raw_data['ocnli_gold']
ocemotion_gold = raw_data['ocemotion_gold']
ocnli_pred, ocemotion_pred, tnews_pred = my_net(**data)
if use_dtp:
tnews_kpi = 0.1 if len(
dev_tnews_pred_list
) == 0 else dev_tnews_correct / len(dev_tnews_pred_list)
ocnli_kpi = 0.1 if len(
dev_ocnli_pred_list
) == 0 else dev_ocnli_correct / len(dev_ocnli_pred_list)
ocemotion_kpi = 0.1 if len(
dev_ocemotion_pred_list
) == 0 else dev_ocemotion_correct / len(
dev_ocemotion_pred_list)
current_loss = loss_object.compute_dtp(
tnews_pred, ocnli_pred, ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold, tnews_kpi, ocnli_kpi,
ocemotion_kpi)
else:
current_loss = loss_object.compute(tnews_pred, ocnli_pred,
ocemotion_pred,
tnews_gold, ocnli_gold,
ocemotion_gold)
dev_loss += current_loss.item()
if use_dtp:
good_tnews_nb, good_ocnli_nb, good_ocemotion_nb, total_tnews_nb, total_ocnli_nb, total_ocemotion_nb = loss_object.correct_cnt_each(
tnews_pred, ocnli_pred, ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold)
tmp_good += sum(
[good_tnews_nb, good_ocnli_nb, good_ocemotion_nb])
tmp_total += sum(
[total_tnews_nb, total_ocnli_nb, total_ocemotion_nb])
dev_ocemotion_correct += good_ocemotion_nb
dev_ocnli_correct += good_ocnli_nb
dev_tnews_correct += good_tnews_nb
else:
tmp_good, tmp_total = loss_object.correct_cnt(
tnews_pred, ocnli_pred, ocemotion_pred, tnews_gold,
ocnli_gold, ocemotion_gold)
dev_correct += tmp_good
dev_total += tmp_total
p, g = loss_object.collect_pred_and_gold(
ocnli_pred, ocnli_gold)
dev_ocnli_pred_list += p
dev_ocnli_gold_list += g
p, g = loss_object.collect_pred_and_gold(
ocemotion_pred, ocemotion_gold)
dev_ocemotion_pred_list += p
dev_ocemotion_gold_list += g
p, g = loss_object.collect_pred_and_gold(
tnews_pred, tnews_gold)
dev_tnews_pred_list += p
dev_tnews_gold_list += g
cnt_dev += 1
#torch.cuda.empty_cache()
#if (cnt_dev + 1) % 1000 == 0:
# print('[', cnt_dev + 1, '- th batch : dev acc is:', dev_correct / dev_total, '; dev loss is:', dev_loss / cnt_dev, ']')
dev_ocnli_f1 = get_f1(dev_ocnli_gold_list, dev_ocnli_pred_list)
dev_ocemotion_f1 = get_f1(dev_ocemotion_gold_list,
dev_ocemotion_pred_list)
dev_tnews_f1 = get_f1(dev_tnews_gold_list, dev_tnews_pred_list)
dev_avg_f1 = (dev_ocnli_f1 + dev_ocemotion_f1 + dev_tnews_f1) / 3
print(epoch, 'th epoch dev average f1 is:', dev_avg_f1)
print(epoch, 'th epoch dev ocnli is below:')
print_result(dev_ocnli_gold_list, dev_ocnli_pred_list)
print(epoch, 'th epoch dev ocemotion is below:')
print_result(dev_ocemotion_gold_list, dev_ocemotion_pred_list)
print(epoch, 'th epoch dev tnews is below:')
print_result(dev_tnews_gold_list, dev_tnews_pred_list)
dev_data_generator.reset()
if dev_avg_f1 > best_dev_f1:
best_dev_f1 = dev_avg_f1
best_epoch = epoch
torch.save(my_net, file_path)
print('best epoch is:', best_epoch, '; with best f1 is:',
best_dev_f1)
@register_func("citeseer")
def citeseer_config(args):
return args
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "unet")
if i <= 0:
continue
svm_clf = LinearSVC(C=i)
svm_clf.fit(x_train, y_train)
predicted = svm_clf.predict(x_valid)
svm_acc = np.mean(predicted == y_valid)
acc_dict[i] = svm_acc
print("C {:.2f}: {:g}".format(i, svm_acc))
print(acc_dict)
print("")
x_train = vstack((x_train, x_valid))
y_train = np.concatenate((y_train, y_valid), axis=0)
max_C = max(acc_dict.keys(), key=(lambda key: acc_dict[key]))
print("x_train: {}".format(x_train.shape))
print("y_train: {}".format(y_train.shape))
svm_clf = LinearSVC(C=max_C)
svm_clf.fit(x_train, y_train)
predicted = svm_clf.predict(x_test)
svm_acc = np.mean(predicted == y_test)
utils.print_result(args.dataset,
"linear_svc",
svm_acc,
data_str,
str(int(time.time())),
hyperparams="{{C: {}}}".format(max_C))
if required_args is None:
return {'error': 'Failed to prompt for arguments'}
optional_args = prompt_args( method_info['opts'], lambda arghelp, argname: raw_input("optional: %s ('%s'): " % (arghelp, argname) ))
if optional_args is None:
return {'error': 'Failed to prompt for arguments'}
full_args = [method_info['command']] + required_args + optional_args
try:
args, unknown_args = parser.parse_known_args( args=full_args )
except SystemExit:
# invalid arguments
return {'error': 'Invalid arguments. Please try again.'}
result = method( args, config_path=config_path )
return result
# not found
return {'error': "No such command '%s'" % args.action}
if __name__ == '__main__':
result = run_cli()
if 'error' in result:
exit_with_error(result['error'])
else:
print_result(result)
sys.exit(0)
def citeseer_config(args):
args.weight_decay = 0.001
return args
@register_func("pubmed")
def pubmed_config(args):
args.weight_decay = 0.001
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "gat")
@register_func("citeseer")
def citeseer_config(args):
return args
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "sign")
@register_func("citeseer")
def citeseer_config(args):
return args
@register_func("pubmed")
def pubmed_config(args):
args.cpu = True
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "dgi")
beta_method = "fr"
c1 = 1e-4
c2 = .3
restart = 3
ln_maxiter = 100
#############################
optimizer = NCG(beta_method=beta_method, c1=c1, c2=c2, restart=restart,
ln_maxiter = ln_maxiter, norm_g_eps = ng_eps, l_eps = l_eps)
model_history, opt_history, time = optimize_monk_f(monk=monk,
reg = reg,
seed=seed,
optimizer=optimizer,
max_iter = max_iter,
verbose = verbose)
print(" - NCG FR -")
print_result(f="Monk"+monk, opt=("NCG "+beta_method).upper(), c1=c1, c2=c2, r=restart, m="-", history=model_history, opt_history=opt_history, time=time, latex=True)
print_ls_result(ls_max_iter=ln_maxiter, opt_history=opt_history, latex=True)
f_fr = model_history["loss_mse_reg"]
p_fr = rate(model_history["loss_mse_reg"])
save_csv(path="./experiments/methods_comparisons/results/monk{}/m{}-{}_fr.csv".format(monk, monk, seed), f="loss_mse_reg", model_history=model_history, opt_history=opt_history)
#############################
# NCG PR+
#############################
beta_method = "pr+"
c1 = 1e-4
c2 = .4
restart = None
ln_maxiter = 100
#############################
def run(dataset_name, missing_rate=0, num_layers=40):
args = build_default_args_for_node_classification(
dataset_name, missing_rate=missing_rate, num_layers=num_layers)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
params0 = [("cora", 0, 7), ("citeseer", 0, 3), ("pubmed", 0, 9)]
params20 = [("cora", 20, 7), ("citeseer", 20, 3), ("pubmed", 20, 7)]
params40 = [("cora", 40, 7), ("citeseer", 40, 4), ("pubmed", 40, 60)]
params60 = [("cora", 60, 20), ("citeseer", 60, 5), ("pubmed", 60, 7)]
params80 = [("cora", 80, 25), ("citeseer", 80, 50), ("pubmed", 80, 60)]
params100 = [("cora", 100, 40), ("citeseer", 100, 50), ("pubmed", 100, 40)]
results = []
for param in params0:
results += run(dataset_name=param[0],
missing_rate=param[1],
num_layers=param[2])
print_result(results, datasets, "sgcpn")
computer.output = []
if not current_loc in panels:
panels[current_loc] = 0
i += 1
return panels, current_loc, facing, i
def painted_image(panels):
x_min = min([k for k in panels], key=lambda x: x[0])[0]
y_min = min([k for k in panels], key=lambda x: x[1])[1]
x_max = max([k for k in panels], key=lambda x: x[0])[0]
y_max = max([k for k in panels], key=lambda x: x[1])[1]
x = x_max - x_min
y = y_max - y_min
picture = [['.' for _ in range(x + 1)] for _ in range(y + 1)]
for panel, colour in panels.items():
picture[y_max - panel[1]][panel[0] -
x_min] = '\u2588' if colour else '.'
return '\n'.join(' '.join(p) for p in picture)
program = utils.get_input(11)
program = [int(i) for i in program[0].split(',')]
computer = Intcode(program)
first_star = len(operate_robot(computer, start_colour=0)[0])
computer.reset()
second_star = '\n' + painted_image(operate_robot(computer, start_colour=1)[0])
utils.print_result(first_star, second_star)
@register_func("proteins")
def proteins_config(args):
return args
@register_func("collab")
def collab_config(args):
args.degree_feature = True
return args
def run(dataset_name):
args = build_default_args_for_graph_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["mutag", "imdb-b", "imdb-m", "proteins", "collab"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "sagpool")
@register_func("proteins")
def proteins_config(args):
return args
@register_func("collab")
def collab_config(args):
args.degree_feature = True
return args
def run(dataset_name):
args = build_default_args_for_graph_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["mutag", "imdb-b", "imdb-m", "proteins", "collab"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "hgpsl")
@register_func("proteins")
def proteins_config(args):
return args
@register_func("collab")
def collab_config(args):
args.degree_feature = True
return args
def run(dataset_name):
args = build_default_args_for_graph_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["mutag", "imdb-b", "imdb-m", "proteins", "collab"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "sortpool")
def train(args, train_dataset, model):
tb_writer = SummaryWriter()
result_writer = ResultWriter(args.eval_results_dir)
# Sampling 빈도 설정
if args.sample_criteria is None:
# 긍/부정 상황 & 투구구질 비율대로 Random sampling
sampler = RandomSampler(train_dataset)
else:
# <투구구질> 기준(7 가지)으로 sampling 빈도 동등하게 조절
if args.sample_criteria == "pitcher":
counts = train_dataset.pitch_counts
logger.info(" Counts of each ball type : %s", counts)
pitch_contiguous = [
i for p in train_dataset.origin_pitch for i, j in enumerate(p)
if j == 1
]
weights = [
0 if p == 5 or p == 6 else 1.0 / counts[p]
for p in pitch_contiguous
]
sampler = WeightedRandomSampler(weights,
len(train_dataset),
replacement=True)
# <긍,부정> 기준(2 가지)으로 sampling 빈도 동등하게 조절
elif args.sample_criteria == "batter":
counts = train_dataset.label_counts
logger.info(" Counts of each label type : %s", counts)
weights = [1.0 / counts[l] for l in train_dataset.label]
sampler = WeightedRandomSampler(weights,
len(train_dataset),
replacement=True)
# <투구구질 & 긍,부정> 기준(14 가지)으로 sampling 빈도 동등하게 조절
elif args.sample_criteria == "both":
counts = train_dataset.pitch_and_label_count
logger.info(" Counts of each both type : %s", counts)
pitch_contiguous = [
i for p in train_dataset.origin_pitch for i, j in enumerate(p)
if j == 1
]
weights = [
0 if p == 5 or p == 6 else 1.0 / counts[(p, l)]
for p, l in zip(pitch_contiguous, train_dataset.label)
]
sampler = WeightedRandomSampler(weights,
len(train_dataset),
replacement=True)
else:
sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
sampler=sampler,
)
t_total = len(train_dataloader) * args.num_train_epochs
args.warmup_step = int(args.warmup_percent * t_total)
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = [
"bias",
"layernorm.weight",
] # LayerNorm.weight -> layernorm.weight (model_parameter name)
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0,
},
]
optimizer = optim.Adam(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
if args.warmup_step != 0:
scheduler_cosine = CosineAnnealingLR(optimizer, t_total)
scheduler = GradualWarmupScheduler(optimizer,
1,
args.warmup_step,
after_scheduler=scheduler_cosine)
# scheduler_plateau = ReduceLROnPlateau(optimizer, "min")
# scheduler = GradualWarmupScheduler(
# optimizer, 1, args.warmup_step, after_scheduler=scheduler_plateau
# )
else:
scheduler = CosineAnnealingLR(optimizer, t_total)
# scheduler = ReduceLROnPlateau(optimizer, "min")
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
m = torch.nn.Sigmoid()
loss_fct = torch.nn.BCELoss()
# Train!
logger.info("***** Running Baseball Transformer *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Warmup Steps = %d", args.warmup_step)
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(" Total train batch size = %d", args.train_batch_size)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
epochs_trained = 0
steps_trained_in_current_epoch = 0
tr_loss, logging_loss, logging_val_loss = 0.0, 0.0, 0.0
best_pitch_micro_f1, best_pitch_macro_f1, = 0, 0
best_loss = 1e10
model.zero_grad()
train_iterator = trange(
epochs_trained,
int(args.num_train_epochs),
desc="Epoch",
)
set_seed(args) # Added here for reproducibility
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
(
pitcher_discrete,
pitcher_continuous,
batter_discrete,
batter_continuous,
state_discrete,
state_continuous,
pitch,
hit,
label,
masked_pitch,
origin_pitch,
) = list(map(lambda x: x.to(args.device), batch))
model.train()
# sentiment input
pitching_score = model(
pitcher_discrete,
pitcher_continuous,
batter_discrete,
batter_continuous,
state_discrete,
state_continuous,
label,
args.concat if args.concat else 0,
)
pitching_score = pitching_score.contiguous()
pitch = pitch.contiguous()
# with sigmoid(m)
loss = loss_fct(m(pitching_score), pitch)
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.evaluate_during_training:
results, f1_results, f1_log, cm_pos, cm_neg = evaluate(
args, args.eval_data_file, model)
output_eval_file = os.path.join(args.output_dir,
"eval_results_pos.txt")
print_result(output_eval_file, results, f1_log, cm_pos)
for key, value in results.items():
tb_writer.add_scalar("eval_{}".format(key), value,
global_step)
logging_val_loss = results["loss"]
# scheduler.step(logging_val_loss)
tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
tb_writer.add_scalar("loss", (tr_loss - logging_loss) /
args.logging_steps, global_step)
logging_loss = tr_loss
if best_loss > results["loss"]:
best_pitch_micro_f1 = results["pitch_micro_f1"]
best_pitch_macro_f1 = results["pitch_macro_f1"]
best_loss = results["loss"]
output_dir = os.path.join(args.output_dir, "best_model/")
os.makedirs(output_dir, exist_ok=True)
torch.save(model.state_dict(),
os.path.join(output_dir, "pytorch_model.bin"))
torch.save(args,
os.path.join(output_dir, "training_args.bin"))
logger.info("Saving best model to %s", output_dir)
result_path = os.path.join(output_dir, "best_results.txt")
print_result(result_path,
results,
f1_log,
cm_pos,
off_logger=True)
results.update(dict(f1_results))
result_writer.update(args, **results)
logger.info(" best pitch micro f1 : %s", best_pitch_micro_f1)
logger.info(" best pitch macro f1 : %s", best_pitch_macro_f1)
logger.info(" best loss : %s", best_loss)
if args.save_steps > 0 and global_step % args.save_steps == 0:
checkpoint_prefix = "checkpoint"
# Save model checkpoint
output_dir = os.path.join(
args.output_dir, "{}-{}".format(checkpoint_prefix,
global_step))
os.makedirs(output_dir, exist_ok=True)
torch.save(model.state_dict(),
os.path.join(output_dir, "pytorch_model.bin"))
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
rotate_checkpoints(args, checkpoint_prefix)
torch.save(optimizer.state_dict(),
os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(),
os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s",
output_dir)
tb_writer.close()
return global_step, tr_loss / global_step
if optional_args is None:
return {'error': 'Failed to prompt for arguments'}
full_args = [method_info['command']
] + required_args + optional_args
try:
args, unknown_args = parser.parse_known_args(args=full_args)
except SystemExit:
# invalid arguments
return {'error': 'Invalid arguments. Please try again.'}
result = method(args, config_path=config_path)
return {'status': True, 'result': result, 'pragmas': pragmas}
# not found
return {'error': 'No such command "{}"'.format(args.action)}
if __name__ == '__main__':
result = run_cli()
if 'error' in result:
exit_with_error(result['error'])
else:
if 'raw' in result['pragmas']:
print(result['result'])
else:
print_result(result['result'])
sys.exit(0)
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
unsup = False # unsupervised or supervised node classification
if unsup:
args = build_default_args_for_unsupervised_node_classification(
dataset_name)
else:
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "graphsage")
@register_func("citeseer")
def citeseer_config(args):
args.dropout = 0.6
return args
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "srgcn")
@register_func("reddit")
def reddit_config(args):
return args
@register_func("ogbn-product")
def products_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["pubmed", "reddit"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "pprgo")
# Training
# ==================================================
timestamp = str(int(time.time()))
# Linear Support Vector Classifier
if args.model == "all" or args.model == "linear_svc":
svm_clf = LinearSVC(C=args.C)
start = time.time()
svm_clf.fit(x_train, y_train)
train_time = time.time() - start
predicted = svm_clf.predict(x_test)
svm_acc = np.mean(predicted == y_test)
utils.print_result(args.dataset, "linear_svc", svm_acc, data_str, timestamp,
hyperparams="{{C: {}}}".format(args.C))
print("Time Taken: {:g}".format(train_time))
# Multinomial Naive Bayes Classifier
if args.model == "all" or args.model == "multinomial_nb":
bayes_clf = MultinomialNB(alpha=args.alpha)
start = time.time()
bayes_clf.fit(x_train, y_train)
train_time = time.time() - start
predicted = bayes_clf.predict(x_test)
bayes_acc = np.mean(predicted == y_test)
utils.print_result(args.dataset, "multinomial_nb", bayes_acc, data_str, timestamp,
hyperparams="{{alpha: {}}}".format(args.alpha))
print("Time Taken: {:g}".format(train_time))
# Save models as pickles
def pubmed_config(args):
args.hidden_size = 256
args.proj_hidden_size = 256
args.drop_edge_rates = [0.4, 0.1]
args.drop_feature_rates = [0.0, 0.2]
args.tau = 0.7
args.lr = 0.001
args.weight_decay = 0.00001
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "grace")
sess = tf.Session(config=session_conf)
with sess.as_default():
# Init model
cnn = FCholletCNN(sequence_length=seq_len,
num_classes=len(train.class_names),
vocab_size=len(train.vocab),
embedding_size=embedding_dim,
embeddings=embeddings,
filter_widths=filter_widths,
num_features=num_features,
pooling_sizes=pooling_sizes,
fc_layers=fc_layers,
l2_reg_lambda=l2_reg_lambda)
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(os.path.curdir, "runs", args.dataset, model_name,
timestamp))
# Train and test model
max_accuracy = train_and_test(sess, cnn, x_train, y_train, x_test,
y_test, learning_rate, batch_size,
num_epochs, dropout_keep_prob, out_dir)
# Output for results.csv
hyperparams = "{{filter_widths: {}, num_features: {}, pooling_sizes: {}, fc_layers: {}}}".format(
filter_widths, num_features, pooling_sizes, fc_layers)
utils.print_result(args.dataset, model_name, max_accuracy, data_str,
timestamp, hyperparams, args, args.notes)
def test(self, epoch):
"""
testing
"""
top1_error = np.zeros(len(self.segments))
top5_error = np.zeros(len(self.segments))
top1_loss = np.zeros(len(self.segments))
for i in range(len(self.segments)):
self.segments[i].eval()
self.auxfc[i].eval()
iters = len(self.test_loader)
start_time = time.time()
end_time = start_time
for i, (images, labels) in enumerate(self.test_loader):
start_time = time.time()
data_time = start_time - end_time
# if we use multi-gpu, its more efficient to send input to different gpu,
# instead of send it to the master gpu.
if self.settings.nGPU == 1:
images = images.cuda()
images_var = Variable(images, volatile=True)
labels = labels.cuda()
labels_var = Variable(labels, volatile=True)
# forward
outputs, losses = self.forward(images_var, labels_var)
# print len(outputs), len(losses)
# compute loss and error rate
single_error, single_loss, single5_error = utils.compute_singlecrop(
outputs=outputs,
labels=labels_var,
loss=losses,
top5_flag=True,
mean_flag=True)
top1_loss += single_loss
top1_error += single_error
top5_error += single5_error
end_time = time.time()
iter_time = end_time - start_time
utils.print_result(
epoch,
self.settings.nEpochs,
i + 1,
iters,
self.lr_master.lr,
data_time,
iter_time,
single_error,
single_loss,
mode="Test",
)
top1_loss /= iters
top1_error /= iters
top5_error /= iters
"""
warning: for better comparison, we inverse the index of data
"""
if self.logger is not None:
length = len(top1_error) - 1
for i, item in enumerate(top1_error):
self.logger.scalar_summary("test_top1_error_%d" % (length - i),
item, self.run_count)
self.logger.scalar_summary("test_top5_error_%d" % (length - i),
top5_error[i], self.run_count)
self.logger.scalar_summary("test_loss_%d" % (length - i),
top1_loss[i], self.run_count)
self.run_count += 1
print "|===>Testing Error: %.4f Loss: %.4f" % (top1_error[-1],
top1_loss[-1])
return top1_error, top1_loss, top5_error
@register_func("citeseer")
def citeseer_config(args):
return args
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "mixhop")
@register_func("citeseer")
def citeseer_config(args):
return args
@register_func("pubmed")
def pubmed_config(args):
return args
def run(dataset_name):
args = build_default_args_for_node_classification(dataset_name)
args = DATASET_REGISTRY[dataset_name](args)
dataset, args = get_dataset(args)
results = []
for seed in args.seed:
set_random_seed(seed)
task = build_task(args, dataset=dataset)
result = task.train()
results.append(result)
return results
if __name__ == "__main__":
datasets = ["cora", "citeseer", "pubmed"]
results = []
for x in datasets:
results += run(x)
print_result(results, datasets, "ppnp")
def test(self, epoch):
"""
testing
"""
top1_error = []
top5_error = []
top1_loss = []
num_segments = len(self.segments)
for i in range(num_segments):
self.segments[i].eval()
self.auxfc[i].eval()
top1_error.append(utils.AverageMeter())
top5_error.append(utils.AverageMeter())
top1_loss.append(utils.AverageMeter())
iters = len(self.test_loader)
start_time = time.time()
end_time = start_time
for i, (images, labels) in enumerate(self.test_loader):
start_time = time.time()
data_time = start_time - end_time
# if we use multi-gpu, its more efficient to send input to different gpu,
# instead of send it to the master gpu.
if self.settings.nGPU == 1:
images = images.cuda()
images_var = Variable(images, volatile=True)
labels = labels.cuda()
labels_var = Variable(labels, volatile=True)
# forward
outputs, losses = self.forward(images_var, labels_var)
# print len(outputs), len(losses)
# compute loss and error rate
single_error, single_loss, single5_error = utils.compute_singlecrop(
outputs=outputs,
labels=labels_var,
loss=losses,
top5_flag=True,
mean_flag=True)
for j in range(num_segments):
top1_error[j].update(single_error[j], images.size(0))
top5_error[j].update(single5_error[j], images.size(0))
top1_loss[j].update(single_loss[j], images.size(0))
end_time = time.time()
iter_time = end_time - start_time
utils.print_result(
epoch,
self.settings.nEpochs,
i + 1,
iters,
self.lr_master.lr,
data_time,
iter_time,
single_error,
single_loss,
mode="Test",
)
"""
warning: for better comparison, we inverse the index of data
"""
top1_error_list, top1_loss_list, top5_error_list = self._convert_results(
top1_error=top1_error, top1_loss=top1_loss, top5_error=top5_error)
if self.logger is not None:
length = num_segments - 1
for i in range(num_segments):
self.logger.scalar_summary("test_top1_error_%d" % (length - i),
top1_error[i].avg, self.run_count)
self.logger.scalar_summary("test_top5_error_%d" % (length - i),
top5_error[i].avg, self.run_count)
self.logger.scalar_summary("test_loss_%d" % (length - i),
top1_loss[i].avg, self.run_count)
self.run_count += 1
print "|===>Testing Error: %.4f/%.4f, Loss: %.4f" % (
top1_error[-1].avg, top5_error[-1].avg, top1_loss[-1].avg)
return top1_error_list, top1_loss_list, top5_error_list
#!/usr/bin/env python
#-*- encoding: utf-8 -*-
"""
Get all network info from nova-network through RPC call to make
sure it is health.
"""
import nova_utils
import utils
DEBUG = utils.get_debug()
try:
nova_utils.init_nova()
network_info = nova_utils.rpccall_network('get_all_networks')
assert network_info
utils.log(network_info)
except Exception:
if DEBUG:
utils.print_traceback()
result = 'failed'
else:
result = 'success'
utils.print_result(result)
lr_pretrained=optim_params["lr_pretrained"]),
weight_decay=optim_params["weight_decay"])
elif "SGD" == optim_params["name"]:
optimizer = optim.SGD(net.get_params_lr(
lr_not_pretrained=optim_params["lr_not_pretrained"],
lr_pretrained=optim_params["lr_pretrained"]),
momentum=optim_params["momentum"],
weight_decay=optim_params["weight_decay"])
# 学習
train_net(net,
train_loader,
test_loader,
optimizer=optimizer,
loss_fn=loss_fn,
epochs=params["epochs"],
device=device)
# 推論
y, ypred = eval_net(net, test_loader, probability=True, device=device)
# 正答率とネットワークの重みをリストに追加
ys.append(y.cpu().numpy())
ypreds.append(ypred.cpu().numpy())
recall = recall_score(
ys[-1], ypreds[-1].argmax(1), average=None, zero_division=0) * 100
print("テストの各クラスrecall：\n{}\n平均：{}".format(
np.round(recall, decimals=1), np.round(recall.mean(), decimals=1)))
net_weights.append(net.cpu().state_dict())
utils.print_result(params, ys, ypreds)
utils.save_params(params, net_weights)
if required_args is None:
return {'error': 'Failed to prompt for arguments'}
optional_args = prompt_args( method_info['opts'], lambda arghelp, argname: raw_input("optional: %s ('%s'): " % (arghelp, argname) ))
if optional_args is None:
return {'error': 'Failed to prompt for arguments'}
full_args = [method_info['command']] + required_args + optional_args
try:
args, unknown_args = parser.parse_known_args( args=full_args )
except SystemExit:
# invalid arguments
return {'error': 'Invalid arguments. Please try again.'}
result = method( args, config_path=config_path )
return result
# not found
return {'error': "No such command '%s'" % args.action}
if __name__ == '__main__':
result = run_cli()
if 'error' in result:
exit_with_error(result['error'])
else:
print_result(result)
sys.exit(0)
#!/usr/bin/env python
#-*- encoding: utf-8 -*-
import nova_utils
import utils
try:
nova_utils.init_nova()
host = nova_utils.get_random_host()
utils.log(host)
info = nova_utils.rpccall_scheduler('show_host_resources', host=host)
assert info
utils.log(info)
except Exception:
utils.print_traceback()
result = 'failed'
else:
result = 'success'
utils.print_result(result)
optional_args = prompt_args(method_info['opts'], prompt_func)
if optional_args is None:
return {'error': 'Failed to prompt for arguments'}
full_args = [method_info['command']] + required_args + optional_args
try:
args, unknown_args = parser.parse_known_args(args=full_args)
except SystemExit:
# invalid arguments
return {'error': 'Invalid arguments. Please try again.'}
result = method(args, config_path=config_path)
return {'status': True, 'result': result, 'pragmas': pragmas}
# not found
return {'error': 'No such command "{}"'.format(args.action)}
if __name__ == '__main__':
result = run_cli()
if 'error' in result:
exit_with_error(result['error'])
else:
if 'raw' in result['pragmas']:
print(result['result'])
else:
print_result(result['result'])
sys.exit(0)
