for i in range(num_tasks):
with codecs.open(parse_args.out_files[i], 'r', 'utf-8') as f:
test_lines0 = f.readlines()
test_lines.append(test_lines0)
for i in range(num_tasks):
test_word0 = utils.read_features_sentences(test_lines[i])
print(test_word0[0])
print("Number of docs : " + str(len(test_word0)))
test_word.append(test_word0)
test_word = reorder_list(test_word, reorder_index)
#for i in range(num_files):
for i in reorder_index:
dataset, forw_corp, back_corp = utils.load_data_pkl_file(file_prefix + 'train_' + str(i) + ".pkl")
dev_dataset, forw_dev, back_dev = utils.load_data_pkl_file(file_prefix + 'dev_' + str(i) + ".pkl")
test_dataset, forw_test, back_test = utils.load_data_pkl_file(file_prefix + 'test_' + str(i) + ".pkl")
new_dataset_loader.append([torch.utils.data.DataLoader(tup, parse_args.batch_size, shuffle=True, drop_last=False, num_workers=40) for tup in dataset])
new_dev_dataset_loader.append([torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in dev_dataset])
new_test_dataset_loader.append([torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in test_dataset])
print('Loading data dictionary from file ' + save_filename)
with open(save_filename, 'rb') as fp:
d = pickle.load(fp)
args = d['args']
args.gpu = 0
label_maps = d['label_maps']
def get_multi_task_model(
prefix='multi_task_tagger/data/pkl_files/num_gen_cas_tense_lem/50k_',
args_file='multi_task_tagger/data/pkl_files/num_gen_cas_tense_lem/50k_all_data.pkl',
order_pkl_file='num-gen-cas-tense-lem',
order='num-gen-cas-tense-lem',
num_tasks=5,
output_directory='test_data/poetry/',
eva_matrix='a',
word_dim=200,
char_dim=30,
char_layers=1,
word_layers=1,
small_crf=True,
checkpoint='multi_task_tagger/checkpoints_new/layered_num_gen_cas_tense_lem_15_May/',
word_hidden=256,
batch_size=16,
char_hidden=100,
load_check_point=True,
do_eval=True,
checkpoint_file='multi_task_tagger/checkpoints_new/layered_num_gen_cas_tense_lem_15_May/cwlm_lstm_crf_cas_2.model',
out_files=None):
#out_files = out_files
assert len(order_pkl_file.split('-')) == len(order.split('-'))
pkl_order_dict = {}
pkl_orders = order_pkl_file.split('-')
for i, o in enumerate(pkl_orders):
pkl_order_dict[o] = i
order_list = order.split('-')
reorder_index = []
for o in order_list:
reorder_index.append(pkl_order_dict[o])
print("Re-ordering list is ")
print(reorder_index)
output_directory = output_directory
save_filename = args_file
args_file = args_file
num_tasks = num_tasks
num_files = num_tasks
print("Number of tasks : " + str(num_tasks))
print("Order of the tasks is " + order)
print("CRF type -- " + str(small_crf))
file_prefix = prefix
new_dataset_loader = []
new_dev_dataset_loader = []
new_test_dataset_loader = []
test_word = []
test_lines = []
#print('Loading --out files for annotation....')
#for i in range(num_tasks):
# with codecs.open(out_files[i], 'r', 'utf-8') as f:
# test_lines0 = f.readlines()
# test_lines.append(test_lines0)
#for i in range(num_tasks):
# test_word0 = utils.read_features_sentences(test_lines[i])
# print(test_word0[0])
# print("Number of docs : " + str(len(test_word0)))
# test_word.append(test_word0)
#test_word = reorder_list(test_word, reorder_index)
#for i in range(num_files):
for i in reorder_index:
dataset, forw_corp, back_corp = utils.load_data_pkl_file(file_prefix +
'train_' +
str(i) +
".pkl")
dev_dataset, forw_dev, back_dev = utils.load_data_pkl_file(
file_prefix + 'dev_' + str(i) + ".pkl")
test_dataset, forw_test, back_test = utils.load_data_pkl_file(
file_prefix + 'test_' + str(i) + ".pkl")
new_dataset_loader.append([
torch.utils.data.DataLoader(tup,
batch_size,
shuffle=True,
drop_last=False,
num_workers=40) for tup in dataset
])
new_dev_dataset_loader.append([
torch.utils.data.DataLoader(tup,
50,
shuffle=False,
drop_last=False) for tup in dev_dataset
])
new_test_dataset_loader.append([
torch.utils.data.DataLoader(tup,
50,
shuffle=False,
drop_last=False)
for tup in test_dataset
])
print('Loading data dictionary from file ' + save_filename)
with open(save_filename, 'rb') as fp:
d = pickle.load(fp)
args = d['args']
args.gpu = 0
label_maps = d['label_maps']
char_map = d['char_map']
f_map = d['f_map']
file_num = d['file_num']
in_doc_words = d['in_doc_words']
embedding_tensor = d['embedding_tensor']
dataset_loader = d['dataset_loader']
dev_dataset_loader = d['dev_dataset_loader']
test_dataset_loader = d['test_dataset_loader']
forw_corp = d['forw_corp']
back_corp = d['back_corp']
forw_dev = d['forw_dev']
back_dev = d['back_dev']
forw_test = d['forw_test']
back_test = d['back_test']
file_num = num_tasks
print('Shape of Embedding Tensor')
print(embedding_tensor.shape)
# Reorder label_maps
label_maps = reorder_list(label_maps, reorder_index)
args.checkpoint = checkpoint
# Set args
args.word_hidden = word_hidden
args.char_hidden = char_hidden
args.word_dim = word_dim
args.char_dim = char_dim
args.char_layers = char_layers
args.word_layers = word_layers
args.small_crf = small_crf
args.eva_matrix = eva_matrix
args.load_check_point = load_check_point
args.do_eval = do_eval
args.checkpoint_file = checkpoint_file
print(args.word_hidden)
print("Will save checkpoint in " + str(args.checkpoint))
inv_f_map = {}
for k, v in f_map.items():
inv_f_map[v] = k
#print(inv_f_map[6430])
print(f_map['<unk>'])
args.output_annotation = True
print("Number of files : " + str(file_num))
dataset_loader = new_dataset_loader
dev_dataset_loader = new_dev_dataset_loader
test_dataset_loader = new_test_dataset_loader
if args.gpu >= 0:
torch.cuda.set_device(args.gpu)
print(args)
args.batch_size = batch_size
# build model
print('building model')
print(label_maps)
label_maps_sizes = [len(lmap) for lmap in label_maps]
print(label_maps_sizes)
print('File_num' + str(file_num))
ner_model = LM_LSTM_CRF(label_maps_sizes,
len(char_map),
args.char_dim,
args.char_hidden,
args.char_layers,
args.word_dim,
args.word_hidden,
args.word_layers,
len(f_map),
args.drop_out,
file_num,
large_CRF=args.small_crf,
if_highway=args.high_way,
in_doc_words=in_doc_words,
highway_layers=args.highway_layers)
if args.load_check_point:
print(args.checkpoint_file)
if os.path.isfile(args.checkpoint_file):
print("loading checkpoint: '{}'".format(args.checkpoint_file))
checkpoint_file = torch.load(args.checkpoint_file)
else:
raise FileNotFoundError('File not found')
ner_model.load_state_dict(checkpoint_file['state_dict'])
else:
if not args.rand_embedding:
ner_model.load_pretrained_word_embedding(embedding_tensor)
ner_model.rand_init(init_word_embedding=args.rand_embedding)
if args.update == 'sgd':
optimizer = optim.SGD(ner_model.parameters(),
lr=args.lr,
momentum=args.momentum)
elif args.update == 'adam':
optimizer = optim.Adam(ner_model.parameters(), lr=args.lr)
if args.load_check_point and args.load_opt:
optimizer.load_state_dict(checkpoint_file['optimizer'])
crit_lm = nn.CrossEntropyLoss()
crit_ner_list = nn.ModuleList()
for i in range(file_num):
ith_label_map = label_maps[i]
crit_ner = CRFLoss_vb(len(ith_label_map), ith_label_map['<start>'],
ith_label_map['<pad>'])
crit_ner_list.append(crit_ner)
if args.gpu >= 0:
if_cuda = True
print('device: ' + str(args.gpu))
torch.cuda.set_device(args.gpu)
crit_lm.cuda()
for i in range(file_num):
crit_ner_list[i].cuda()
ner_model.cuda()
packer_list = []
for i in range(file_num):
packer = CRFRepack_WC(len(label_maps[i]), True)
packer_list.append(packer)
else:
if_cuda = False
packer_list = []
for i in range(file_num):
packer = CRFRepack_WC(len(label_maps[i]), False)
packer_list.append(packer)
tot_length = sum(map(lambda t: len(t), dataset_loader))
best_f1 = []
for i in range(file_num):
best_f1.append(float('-inf'))
best_pre = []
for i in range(file_num):
best_pre.append(float('-inf'))
best_rec = []
for i in range(file_num):
best_rec.append(float('-inf'))
best_acc = []
for i in range(file_num):
best_acc.append(float('-inf'))
track_list = list()
start_time = time.time()
print('Num of epochs : ' + str(args.epoch))
epoch_list = range(args.start_epoch, args.start_epoch + args.epoch)
patience_count = 0
evaluator_list = []
predictor_list = []
for i in range(file_num):
evaluator = eval_wc(packer_list[i], label_maps[i], args.eva_matrix)
predictor = predict_wc(if_cuda, f_map, char_map, label_maps[i],
f_map['<eof>'], char_map['\n'],
label_maps[i]['<pad>'],
label_maps[i]['<start>'], True, args.batch_size,
args.caseless) #NEW
evaluator_list.append(evaluator)
predictor_list.append(predictor)
return predictor_list, ner_model