def save_error(self, site, error):
print('Error', site, error, "\n\n")
self.processed_sites.append(
dict(
success=False,
donor=site,
error=error,
))
save_pickle(self.tmp_file_name, self.processed_sites)
def train_val_split(self, X: list, y: list, valid_size: float,
data_name=None, data_dir=None, save=True):
logger.info('split train data into train and valid')
Xy = []
for i in range(len(X)):
Xy.append((X[i], y[i]))
train, valid = train_test_split(
Xy, test_size=valid_size, random_state=42)
if save:
train_path = data_dir / "{}.train.pkl".format(data_name)
valid_path = data_dir / "{}.valid.pkl".format(data_name)
save_pickle(data=train, file_path=train_path)
save_pickle(data=valid, file_path=valid_path)
return train, valid
def create_mask(manifest_path, output_path, max_distance, n_jobs, xy):
mask = Mask.load_manifest(manifest_path=manifest_path)
print_("Manifest loaded.")
mask.split_manifest(XY=xy)
print_("Data prepared.")
pairs_collector = mask.find_pairs(n_jobs=n_jobs, search_range=max_distance)
print_("Pairs located.")
extracted_mask = mask.extract(pairs_collector)
print_("Pairs extracted.")
save_pickle(output_path, extracted_mask)
print_("Done.")
def save_data():
global processed_links, links_to_process, links_with_comments_form, processed_domains
stage = {
'processed_links': processed_links,
'links_to_process': links_to_process,
'links_with_comments_form': links_with_comments_form,
'processed_domains': processed_domains,
}
print(
"Saved Data, links_with_comments_form: {}, processed_links: {}, links_to_process: {}, processed_domains: {}"
.format(len(links_with_comments_form), len(processed_links),
len(links_to_process), len(processed_domains)))
save_pickle(tmp_file, stage)
with open('domains.txt', 'w') as file:
for link in processed_domains:
file.write("{}\n".format(link))
def save_traintest(self):
graphs = self.data[GRAPH] # load all the graphs
# labels = self.labels
# graphs_names = self.graphs_names
# debug purposes: reshuffle all the data before the splitting
random_indices = list(range(len(graphs)))
random.shuffle(random_indices)
graphs = [graphs[i] for i in random_indices]
labels = self.labels[random_indices]
graphs_names = [self.graphs_names[i] for i in random_indices]
if True:
train_list_file = '/media/tunguyen/TuTu_Passport/MTAAV/HAN-sec-new/__save_results/reverse__TuTu__vocabtutu__iapi__tfidf__topk=3/9691/train_list.txt'
test_list_file = '/media/tunguyen/TuTu_Passport/MTAAV/HAN-sec-new/__save_results/reverse__TuTu__vocabtutu__iapi__tfidf__topk=3/9691/test_list.txt'
train_list_file = '/media/tunguyen/TuTu_Passport/MTAAV/HAN-sec-new/data/TuTu_train_list.txt'
test_list_file = '/media/tunguyen/TuTu_Passport/MTAAV/HAN-sec-new/data/TuTu_test_list.txt'
train_files = []
test_files = []
g_train = []
l_train = []
n_train = []
g_test = []
l_test = []
n_test = []
with open(train_list_file, 'r') as f:
train_files = [l.strip() for l in f.readlines()]
with open(test_list_file, 'r') as f:
test_files = [l.strip() for l in f.readlines()]
for i in range(len(labels)):
graph_jsonpath = graphs_names[i]
# print(graph_jsonpath)
if graph_jsonpath in train_files:
g_train.append(graphs[i])
l_train.append(labels[i])
n_train.append(graphs_names[i])
if graph_jsonpath in test_files:
g_test.append(graphs[i])
l_test.append(labels[i])
n_test.append(graphs_names[i])
l_train = torch.Tensor(l_train).type(torch.LongTensor)
l_test = torch.Tensor(l_test).type(torch.LongTensor)
if self.is_cuda is True:
l_train = l_train.cuda()
l_test = l_test.cuda()
print('[app][save_traintest] len labels', len(labels))
print('[app][save_traintest] len l_test', len(l_test))
print('[app][save_traintest] len l_train', len(l_train))
tot_bgn = (labels == self.mapping['benign']).sum().item()
tot_mal = (labels == self.mapping['malware']).sum().item()
print('[app][save_traintest] tot_bgn', tot_bgn, 'tot_mal', tot_mal)
if not os.path.isdir(self.odir):
os.makedirs(self.odir)
save_pickle(g_train, os.path.join(self.odir, 'train'))
save_pickle(l_train, os.path.join(self.odir, 'train_labels'))
save_pickle(g_test, os.path.join(self.odir, 'test'))
save_pickle(l_test, os.path.join(self.odir, 'test_labels'))
def train(self,
save_path='',
k_fold=10,
train_list_file=None,
test_list_file=None):
if self.pretrained_weight is not None:
self.model = load_checkpoint(self.model, self.pretrained_weight,
self.is_cuda)
save_dir = save_path.split('/checkpoint')[0]
loss_fcn = torch.nn.CrossEntropyLoss()
# initialize graphs
self.accuracies = np.zeros(k_fold)
graphs = self.data[GRAPH] # load all the graphs
# debug purposes: reshuffle all the data before the splitting
random_indices = list(range(len(graphs)))
random.shuffle(random_indices)
graphs = [graphs[i] for i in random_indices]
labels = self.labels[random_indices]
graphs_names = [self.graphs_names[i] for i in random_indices]
split_train_test = True if train_list_file is None and test_list_file is None else False
print('[app][train] split_train_test', split_train_test)
'''
if split_train_test is True:
print('[app][train] train_list_file', train_list_file)
print('[app][train] test_list_file', test_list_file)
#############################
# Create new train/test set
# Split train and test
#############################
train_size = int(self.TRAIN_SIZE * len(graphs))
g_train = graphs[:train_size]
l_train = labels[:train_size]
n_train = graphs_names[:train_size]
g_test = graphs[train_size:]
l_test = labels[train_size:]
n_test = graphs_names[train_size:]
else:
#############################
# Load train and test graphs from list
#############################
train_files = []
test_files = []
g_train = []
l_train = []
n_train = []
g_test = []
l_test = []
n_test = []
with open(train_list_file, 'r') as f:
train_files = [l.strip() for l in f.readlines()]
with open(test_list_file, 'r') as f:
test_files = [l.strip() for l in f.readlines()]
for i in range(len(labels)):
graph_jsonpath = graphs_names[i]
# print(graph_jsonpath)
if graph_jsonpath in train_files:
g_train.append(graphs[i])
l_train.append(labels[i])
n_train.append(graphs_names[i])
if graph_jsonpath in test_files:
g_test.append(graphs[i])
l_test.append(labels[i])
n_test.append(graphs_names[i])
l_train = torch.Tensor(l_train).type(torch.LongTensor)
l_test = torch.Tensor(l_test).type(torch.LongTensor)
if self.is_cuda is True:
l_train = l_train.cuda()
l_test = l_test.cuda()
'''
print('[app][train] len labels', len(labels))
print('[app][train] len g_train', len(g_train))
# print('[app][train] g_train', g_train)
if not os.path.isdir(self.odir):
os.makedirs(self.odir)
save_pickle(g_train, os.path.join(self.odir, 'train'))
save_pickle(l_train, os.path.join(self.odir, 'train_labels'))
save_pickle(g_test, os.path.join(self.odir, 'test'))
save_pickle(l_test, os.path.join(self.odir, 'test_labels'))
# save graph name list to txt file
save_txt(n_train, os.path.join(self.odir, 'train_list.txt'))
save_txt(n_test, os.path.join(self.odir, 'test_list.txt'))
K = k_fold
for k in range(K):
self.model = self.ModelObj(g=self.data_graph[0],
config_params=self.model_config,
n_classes=self.data_nclasses,
n_rels=self.data_nrels,
n_entities=self.data_nentities,
is_cuda=self.is_cuda,
batch_size=1,
model_src_path=self.model_src_path)
print('*** [app][__init__] Model layers ***')
for name, param in self.model.named_parameters():
if param.requires_grad:
print('\t', name, param.data.type())
print('>>> [app][__init__] self.model.fc.weight.type',
self.model.fc.weight.type())
optimizer = torch.optim.Adam(
self.model.parameters(),
lr=self.learning_config['lr'],
weight_decay=self.learning_config['weight_decay'])
start = int(len(g_train) / K) * k
end = int(len(g_train) / K) * (k + 1)
print('\n\n\n[app][train] Process new k=' + str(k) + ' | ' +
str(start) + '-' + str(end))
# training batch
train_batch_graphs = g_train[:start] + g_train[end:]
train_batch_labels = l_train[list(range(0, start)) +
list(range(end + 1, len(g_train)))]
train_batch_samples = list(
map(list, zip(train_batch_graphs, train_batch_labels)))
train_batches = DataLoader(
train_batch_samples,
batch_size=self.learning_config['batch_size'],
shuffle=True,
collate_fn=collate)
# testing batch
val_batch_graphs = g_train[start:end]
val_batch_labels = l_train[start:end]
# print('[app][train] val_batch_graphs', val_batch_graphs)
print('[app][train] len val_batch_graphs', len(val_batch_graphs))
print('[app][train] val_batch_graphs[0].number_of_nodes()',
val_batch_graphs[0].number_of_nodes())
print('[app][train] val_batch_graphs[-1].number_of_nodes()',
val_batch_graphs[-1].number_of_nodes())
val_batch = dgl.batch(val_batch_graphs)
print('[app][train] train_batches size: ', len(train_batches))
print('[app][train] train_batch_graphs size: ',
len(train_batch_graphs))
print('[app][train] val_batch_graphs size: ',
len(val_batch_graphs))
print('[app][train] train_batches', train_batches)
print('[app][train] val_batch_labels', val_batch_labels)
dur = []
for epoch in range(self.learning_config['epochs']):
self.model.train()
if epoch >= 3:
t0 = time.time()
losses = []
training_accuracies = []
for iter_idx, (bg, label) in enumerate(train_batches):
# print('~~~ [app][train] bg', bg)
logits = self.model(bg)
if self.learning_config['cuda']:
label = label.cuda()
loss = loss_fcn(logits, label)
losses.append(loss.item())
_, indices = torch.max(logits, dim=1)
# print('~~~~ logits', logits)
# print('------------------')
print('\t [app][train] indices', indices)
# print('\t label', label)
correct = torch.sum(indices == label)
training_accuracies.append(correct.item() * 1.0 /
len(label))
optimizer.zero_grad()
loss.backward(retain_graph=True)
# loss.backward()
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
val_acc, val_loss, _ = self.model.eval_graph_classification(
val_batch_labels, val_batch)
print(
"[app][train] Epoch {:05d} | Time(s) {:.4f} | train_acc {:.4f} | train_loss {:.4f} | val_acc {:.4f} | val_loss {:.4f}"
.format(epoch,
np.mean(dur) if dur else 0,
np.mean(training_accuracies), np.mean(losses),
val_acc, val_loss))
is_better = self.early_stopping(val_loss, self.model,
save_path)
if is_better:
self.accuracies[k] = val_acc
if self.early_stopping.early_stop:
# Print model's state_dict
# print("*** Model's state_dict:")
# for param_tensor in self.model.state_dict():
# print(param_tensor, "\t", self.model.state_dict()[param_tensor].size())
# # Print optimizer's state_dict
# print("*** Optimizer's state_dict:")
# for var_name in optimizer.state_dict():
# print(var_name, "\t", optimizer.state_dict()[var_name])
# Save state dict
# torch.save(self.model.state_dict(), save_dir+'/model_state.pt')
# Save model
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': self.model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'val_loss': val_loss,
# }, save_dir+'/saved')
print("[app][train] Early stopping")
break
self.early_stopping.reset()
edge_df.to_pickle(f"../data/edge_angle/{molecule}.pkl")
if __name__ == "__main__":
with utils.timer("make_feature_per_molecule"):
for mode in ["train", "test"]:
meta_df = pd.read_pickle(f"../pickle/{mode}.pkl").set_index("id")
molecules = meta_df["molecule_name"].unique().tolist()
st_df = pd.read_pickle("../pickle/structures.pkl")
## train or validのstructureに絞る
st_df = st_df[st_df.molecule_name.isin(molecules)]\
[["molecule_name","atom_index","atom","x","y","z"]]
# 分子単位に処理
st_gr = st_df.groupby("molecule_name")
st_dict = {}
for molecule in tqdm(molecules):
st_dict[molecule] = st_gr.get_group(molecule)
all_file_num = len(molecules)
with Pool(4) as p:
res = p.map(make_per_molecule, molecules)
with utils.timer("concatenate_molecules_feature"):
for mode in ["train", "test"]:
meta_df = pd.read_pickle(f"../pickle/{mode}.pkl").set_index("id")
molecules = meta_df["molecule_name"].unique().tolist()
df_list = []
for molecule in tqdm(molecules):
df_list.append(
utils.load_pickle(f"../data/edge_angle/{molecule}.pkl"))
all_df = pd.concat(df_list).reset_index(drop=True)
utils.save_pickle(all_df, f"../pickle/{mode}_edge_angle.pkl")
def donors_loop(self, donors):
Comment = SeleniumChecker()
total = len(donors)
count = 0
for donor in donors:
count += 1
if not donor: continue
if donor in self.processed_donors: continue
try:
tries = 1
print(getpid(), '{} of {}'.format(count, total), donor)
Comment.get(donor)
if not Comment.find_form():
self.save_error(donor, 'Form not found')
else:
comment = random.choice(self.comments)
author = random.choice(self.usernames)
email = random.choice(self.emails)
acceptor = next(self.acceptors)
posted_data, screenshot_before, screenshot_after = self.post(
Comment, donor, acceptor, comment, author, email)
#login to google account, wp, facebook
# press button in iframe
# https://stackoverflow.com/questions/27793187/unable-to-click-on-a-button-inside-an-iframe
#http://jkuat.ac.ke/campuses/nairobicbd/student-email-portal-e-learning/
#https://brownsenglish.edu.au/blog/english-student-testimonial-kim/
#https://georgelakoff.com/2017/07/28/time-to-solve-the-student-debt-crisis/
while Comment.check_text(
'You are being asked to login because') != -1:
tries += 1
if tries > 7: break
Comment.wait()
Comment.get(donor)
Comment.find_form()
comment = random.choice(self.comments)
author = random.choice(self.usernames)
email = random.choice(self.emails)
posted_data, screenshot_before, screenshot_after = self.post(
Comment, donor, acceptor, comment, author, email)
Comment.unwait()
while Comment.check_text(
'Duplicate comment detected') != -1:
tries += 1
if tries > 7: break
Comment.wait()
Comment.get(donor)
Comment.find_form()
comment = random.choice(self.comments)
author = random.choice(self.usernames)
email = random.choice(self.emails)
acceptor = next(self.acceptors)
posted_data, screenshot_before, screenshot_after = self.post(
Comment, donor, acceptor, comment, author, email)
Comment.unwait()
while Comment.check_text(
'Forbidden. Sender blacklisted.') != -1:
tries += 1
if tries > 7: break
Comment.wait()
Comment.get(donor)
Comment.find_form()
comment = random.choice(self.comments)
author = random.choice(self.usernames)
email = random.choice(self.emails)
acceptor = next(self.acceptors)
posted_data, screenshot_before, screenshot_after = self.post(
Comment, donor, acceptor, comment, author, email)
Comment.unwait()
self.processed_donors.append(donor)
self.processed_sites.append(
dict(
success=True,
donor=donor,
acceptor=acceptor,
comment=comment,
author=author,
email=email,
posted_data=posted_data,
screenshot_before=screenshot_before,
screenshot_after=screenshot_after,
))
save_pickle(self.tmp_file_name, self.processed_sites)
except Exception as e:
self.save_error(donor, str(e))
def _save_feature(self, train_df, test_df):
new_cols = self._get_new_cols(train_df, test_df)
self.train_path.parent.mkdir(exist_ok=True)
self.test_path.parent.mkdir(exist_ok=True)
utils.save_pickle(train_df[new_cols], self.train_path)
utils.save_pickle(test_df[new_cols], self.test_path)
right_on="atom_index",
how="left")
del df["atom_index"]
df = df.merge(atom_df,
left_on="atom_index_1",
right_on="atom_index",
how="left")
del df["atom_index"]
df.rename(columns={"atom_x": "atom_0", "atom_y": "atom_1"}, inplace=True)
df["bond_atom"] = df["atom_0"].map(atom_dict) + df["atom_1"].map(atom_dict)
df["bond_atom"] = df["bond_atom"].map(map_func)
df["molecule_name"] = molecule_name
return df
atom_dict = {0: "H", 1: "C", 2: "N", 3: "O", 4: "F"}
#mode = "train"
mode = "test"
meta_df = pd.read_pickle(f"../pickle/{mode}.pkl")
molecules = meta_df["molecule_name"].unique()
with Pool(4) as p:
res = p.map(make_bond, molecules)
all_df = pd.concat(res, axis=0).reset_index(drop=True)
utils.save_pickle(all_df, f"../pickle/{mode}_bond_v2.pkl")
# save only connect info
only_bond_df = all_df.loc[all_df.bond_type != -1,
["molecule_name", "atom_index_0", "atom_index_1"]]
utils.save_pickle(only_bond_df, f"../pickle/{mode}_bond_v2_only_bond.pkl")
def train(self,
save_path='',
k_fold=10,
train_list_file=None,
test_list_file=None):
if self.pretrained_weight is not None:
self.model = load_checkpoint(self.model, self.pretrained_weight)
loss_fcn = torch.nn.CrossEntropyLoss()
# initialize graphs
self.accuracies = np.zeros(k_fold)
graphs = self.data[GRAPH] # load all the graphs
# debug purposes: reshuffle all the data before the splitting
random_indices = list(range(len(graphs)))
random.shuffle(random_indices)
graphs = [graphs[i] for i in random_indices]
labels = self.labels[random_indices]
graphs_names = [self.graphs_names[i] for i in random_indices]
split_train_test = True if train_list_file is None and test_list_file is None else False
print('split_train_test', split_train_test)
print('train_list_file', train_list_file)
print('test_list_file', test_list_file)
if split_train_test is True:
#############################
# Create new train/test set
# Split train and test
#############################
train_size = int(self.TRAIN_SIZE * len(graphs))
g_train = graphs[:train_size]
l_train = labels[:train_size]
n_train = graphs_names[:train_size]
g_test = graphs[train_size:]
l_test = labels[train_size:]
n_test = graphs_names[train_size:]
else:
#############################
# Load train and test graphs from list
#############################
train_files = []
test_files = []
g_train = []
l_train = []
n_train = []
g_test = []
l_test = []
n_test = []
with open(train_list_file, 'r') as f:
train_files = [l.strip() for l in f.readlines()]
with open(test_list_file, 'r') as f:
test_files = [l.strip() for l in f.readlines()]
for i in range(len(labels)):
graph_jsonpath = graphs_names[i]
# print(graph_jsonpath)
if graph_jsonpath in train_files:
g_train.append(graphs[i])
l_train.append(labels[i])
n_train.append(graphs_names[i])
if graph_jsonpath in test_files:
g_test.append(graphs[i])
l_test.append(labels[i])
n_test.append(graphs_names[i])
l_train = torch.Tensor(l_train).type(torch.LongTensor)
l_test = torch.Tensor(l_test).type(torch.LongTensor)
if self.is_cuda is True:
l_train = l_train.cuda()
l_test = l_test.cuda()
# print('len g_train', len(g_train))
# print('g_train', g_train)
if not os.path.isdir(self.odir):
os.makedirs(self.odir)
save_pickle(g_train, os.path.join(self.odir, 'train'))
save_pickle(l_train, os.path.join(self.odir, 'train_labels'))
save_pickle(g_test, os.path.join(self.odir, 'test'))
save_pickle(l_test, os.path.join(self.odir, 'test_labels'))
# save graph name list to txt file
save_txt(n_train, os.path.join(self.odir, 'train_list.txt'))
save_txt(n_test, os.path.join(self.odir, 'test_list.txt'))
K = k_fold
for k in range(K): # K-fold cross validation
# create GNN model
# self.model = Model(g=self.data[GRAPH],
# config_params=self.model_config,
# n_classes=self.data[N_CLASSES],
# n_rels=self.data[N_RELS] if N_RELS in self.data else None,
# n_entities=self.data[N_ENTITIES] if N_ENTITIES in self.data else None,
# is_cuda=self.learning_config['cuda'],
# seq_dim=self.seq_max_length,
# batch_size=1)
optimizer = torch.optim.Adam(
self.model.parameters(),
lr=self.learning_config['lr'],
weight_decay=self.learning_config['weight_decay'])
if self.learning_config['cuda']:
self.model.cuda()
start = int(len(g_train) / K) * k
end = int(len(g_train) / K) * (k + 1)
print('\n\n\nProcess new k=' + str(k) + ' | ' + str(start) + '-' +
str(end))
# testing batch
val_batch_graphs = g_train[start:end]
val_batch_labels = l_train[start:end]
val_batch = dgl.batch(val_batch_graphs)
# training batch
train_batch_graphs = g_train[:start] + g_train[end:]
train_batch_labels = l_train[list(range(0, start)) +
list(range(end + 1, len(g_train)))]
train_batch_samples = list(
map(list, zip(train_batch_graphs, train_batch_labels)))
train_batches = DataLoader(
train_batch_samples,
batch_size=self.learning_config['batch_size'],
shuffle=True,
collate_fn=collate)
print('train_batches size: ', len(train_batches))
print('train_batch_graphs size: ', len(train_batch_graphs))
print('val_batch_graphs size: ', len(val_batch_graphs))
print('train_batches', train_batches)
print('val_batch_labels', val_batch_labels)
dur = []
for epoch in range(self.learning_config['epochs']):
self.model.train()
if epoch >= 3:
t0 = time.time()
losses = []
training_accuracies = []
for iter_idx, (bg, label) in enumerate(train_batches):
logits = self.model(bg)
if self.learning_config['cuda']:
label = label.cuda()
loss = loss_fcn(logits, label)
losses.append(loss.item())
_, indices = torch.max(logits, dim=1)
correct = torch.sum(indices == label)
training_accuracies.append(correct.item() * 1.0 /
len(label))
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
if epoch >= 3:
dur.append(time.time() - t0)
val_acc, val_loss, _ = self.model.eval_graph_classification(
val_batch_labels, val_batch)
print(
"Epoch {:05d} | Time(s) {:.4f} | train_acc {:.4f} | train_loss {:.4f} | val_acc {:.4f} | val_loss {:.4f}"
.format(epoch,
np.mean(dur) if dur else 0,
np.mean(training_accuracies), np.mean(losses),
val_acc, val_loss))
is_better = self.early_stopping(val_loss, self.model,
save_path)
if is_better:
self.accuracies[k] = val_acc
if self.early_stopping.early_stop:
print("Early stopping")
break
self.early_stopping.reset()
def gen_tokenizer(save_path,
pretrained_model='google/electra-small-discriminator'):
tokenizer = ElectraTokenizer.from_pretrained(pretrained_model)
tokenizer.add_tokens(['[E]', '[/E]'])
save_pickle(save_path, data=tokenizer)
def __init__(self,
data,
model_config,
learning_config,
pretrained_weight,
early_stopping=True,
patience=100,
json_path=None,
pickle_folder=None,
vocab_path=None,
mapping_path=None,
odir=None,
model_src_path=None):
if model_src_path is not None:
sys.path.insert(0, model_src_path + 'model.py')
print('*** model_src_path', model_src_path)
from model_edgnn import Model
else:
from models.model import Model
self.data = data
self.model_config = model_config
# max length of a sequence (max nodes among graphs)
self.learning_config = learning_config
self.pretrained_weight = pretrained_weight
self.is_cuda = learning_config['cuda']
# with open(vocab_path+'/../mapping.json', 'r') as f:
with open(mapping_path, 'r') as f:
self.mapping = json.load(f)
self.labels = self.data[LABELS]
self.graphs_names = self.data[GNAMES]
self.data_graph = self.data[GRAPH]
# save nid and eid to nodes & edges
# print('self.data_graph[0]', self.data_graph[0])
if 'nid' not in self.data_graph[0].ndata:
# if True:
for k, g in enumerate(self.data_graph):
g = self.write_nid_eid(g)
self.data_graph[k] = g
# print('self.data_graph', self.data_graph)
save_pickle(self.data_graph, os.path.join(pickle_folder, GRAPH))
data_nclasses = self.data[N_CLASSES]
if N_RELS in self.data:
data_nrels = self.data[N_RELS]
else:
data_nrels = None
if N_ENTITIES in self.data:
data_nentities = self.data[N_ENTITIES]
else:
data_nentities = None
self.model = Model(g=self.data_graph[0],
config_params=model_config,
n_classes=data_nclasses,
n_rels=data_nrels,
n_entities=data_nentities,
is_cuda=self.is_cuda,
batch_size=1,
json_path=json_path,
vocab_path=vocab_path,
model_src_path=model_src_path)
if self.is_cuda is True:
# self.model.cuda()
print('Use cuda')
self.model.to(torch.device('cuda'))
print('*** Model parameters ***')
pp = 0
for p in list(self.model.parameters()):
nn = 1
for s in list(p.size()):
# print('p', p)
print('\t s, nn, nn*s', s, nn, nn * s)
nn = nn * s
pp += nn
print('Total params', pp)
if early_stopping:
self.early_stopping = EarlyStopping(patience=patience,
verbose=True)
# Output folder to save train / test data
if odir is None:
odir = 'output/' + time.strftime("%Y-%m-%d_%H-%M-%S")
self.odir = odir
def __init__(self,
data,
model_config,
learning_config,
pretrained_weight,
early_stopping=True,
patience=100,
json_path=None,
pickle_folder=None,
vocab_path=None,
mapping_path=None,
odir=None,
model_src_path=None,
append_nid_eid=False,
gdot_path=None):
if model_src_path is not None:
sys.path.insert(1, model_src_path)
print('*** model_src_path', model_src_path)
from model_edgnn_o import Model
else:
from models.model_edgnn_o import Model
self.data = data
self.model_config = model_config
# max length of a sequence (max nodes among graphs)
self.learning_config = learning_config
self.pretrained_weight = pretrained_weight
self.is_cuda = learning_config['cuda']
# with open(vocab_path+'/../mapping.json', 'r') as f:
with open(mapping_path, 'r') as f:
self.mapping = json.load(f)
# print('[App][__init__] GNAMES', GNAMES)
# print('[App][__init__] self.data', self.data)
self.graphs_names = self.data[GNAMES]
# print('[App][__init__] self.graphs_names', self.graphs_names)
self.data_graph = self.data[GRAPH]
# save nid and eid to nodes & edges
if append_nid_eid is True:
print('self.data_graph[0]', self.data_graph[0])
if 'nid' not in self.data_graph[0].ndata:
print('*** Not found nid. Appending...')
# if True:
for k, g in enumerate(self.data_graph):
g = self.write_nid_eid(g)
self.data_graph[k] = g
# print('self.data_graph', self.data_graph)
save_pickle(self.data_graph, os.path.join(pickle_folder, GRAPH))
# data_nclasses = self.data[N_CLASSES]
data_nclasses = 2
if N_RELS in self.data:
data_nrels = self.data[N_RELS]
else:
data_nrels = None
if N_ENTITIES in self.data:
data_nentities = self.data[N_ENTITIES]
else:
data_nentities = None
self.model = Model(g=self.data_graph[0],
config_params=self.model_config,
n_classes=data_nclasses,
n_rels=data_nrels,
n_entities=data_nentities,
is_cuda=self.is_cuda,
batch_size=1,
model_src_path=model_src_path,
gdot_path=gdot_path)
if self.is_cuda is True:
# self.model.cuda()
print('* Use cuda')
self.model.to(torch.device('cuda'))
# print('*** Model parameters ***')
# pp = 0
# print('self.model', self.model)
# # self.e_src_attn_layer = self.model.edgnn_layers[1].e_src_attn_fc
# # self.e_group_attn_layer = self.model.edgnn_layers[1].e_group_attn_fc
# # # self.dst_attn_layer = self.model.edgnn_layers[1].dst_attn_fc
# # print('self.e_group_attn_layer', self.e_group_attn_layer)
# # print('self.e_src_attn_layer', self.e_src_attn_layer)
# # # print('self.dst_attn_layer', self.dst_attn_layer)
# # self.e_group_attn_layer.register_forward_hook(self.hook)
# # self.e_src_attn_layer.register_forward_hook(self.hook2)
# # self.dst_attn_layer.register_forward_hook(self.hook)
# for p in list(self.model.parameters()):
# nn = 1
# for s in list(p.size()):
# # print('p', p)
# # print('\t s, nn, nn*s', s, nn, nn*s)
# nn = nn*s
# pp += nn
# print('Total params', pp)
if early_stopping:
self.early_stopping = EarlyStopping(patience=patience,
verbose=True)
# Output folder to save train / test data
if odir is None:
odir = 'output/' + time.strftime("%Y-%m-%d_%H-%M-%S")
self.odir = odir
