def main(config):
saved_data = torch.load(
config.model_fn,
map_location='cpu' if config.gpu_id < 0 else 'cuda:%d' % config.gpu_id)
train_config = saved_data['config']
rnn_best = saved_data['rnn']
cnn_best = saved_data['cnn']
vocab = saved_data['vocab']
classes = saved_data['classes']
vocab_size = len(vocab)
n_classes = len(classes)
text_field, label_field = define_field()
text_field.vocab = vocab
label_field.vocab = classes
lines = read_text(max_length=config.max_length)
with torch.no_grad():
ensemble = []
if rnn_best is not None and not config.drop_rnn:
# Declare model and load pre-trained weights.
model = RNNClassifier(
input_size=vocab_size,
word_vec_size=train_config.word_vec_size,
hidden_size=train_config.hidden_size,
n_classes=n_classes,
n_layers=train_config.n_layers,
dropout_p=train_config.dropout,
)
model.load_state_dict(rnn_best)
ensemble += [model]
if cnn_best is not None and not config.drop_cnn:
# Declare model and load pre-trained weights.
model = CNNClassifier(
input_size=vocab_size,
word_vec_size=train_config.word_vec_size,
n_classes=n_classes,
use_batch_norm=train_config.use_batch_norm,
dropout_p=train_config.dropout,
window_sizes=train_config.window_sizes,
n_filters=train_config.n_filters,
)
model.load_state_dict(cnn_best)
ensemble += [model]
y_hats = []
# Get prediction with iteration on ensemble.
for model in ensemble:
if config.gpu_id >= 0:
model.cuda(config.gpu_id)
# Don't forget turn-on evaluation mode.
model.eval()
y_hat = []
for idx in range(0, len(lines), config.batch_size):
# Converts string to list of index.
x = text_field.numericalize(
text_field.pad(lines[idx:idx + config.batch_size]),
device='cuda:%d' %
config.gpu_id if config.gpu_id >= 0 else 'cpu',
)
y_hat += [model(x).cpu()]
# Concatenate the mini-batch wise result
y_hat = torch.cat(y_hat, dim=0)
# |y_hat| = (len(lines), n_classes)
y_hats += [y_hat]
model.cpu()
# Merge to one tensor for ensemble result and make probability from log-prob.
y_hats = torch.stack(y_hats).exp()
# |y_hats| = (len(ensemble), len(lines), n_classes)
y_hats = y_hats.sum(dim=0) / len(ensemble) # Get average
# |y_hats| = (len(lines), n_classes)
probs, indice = y_hats.topk(config.top_k)
for i in range(len(lines)):
sys.stdout.write('%s\t%s\n' % (' '.join(
[classes.itos[indice[i][j]]
for j in range(config.top_k)]), ' '.join(lines[i])))
def main(config):
saved_data = torch.load(
config.model_fn,
map_location='cpu' if config.gpu_id < 0 else 'cuda:%d' % config.gpu_id
)
train_config = saved_data['config']
rnn_best = saved_data['rnn']
cnn_best = saved_data['cnn']
vocab = saved_data['vocab']
classes = saved_data['classes']
vocab_size = len(vocab)
n_classes = len(classes)
text_field, label_field = define_field()
text_field.vocab = vocab
label_field.vocab = classes
lines, label = read_text(max_length=config.max_length)
with torch.no_grad():
ensemble = []
if rnn_best is not None and not config.drop_rnn:
# Declare model and load pre-trained weights.
model = RNNClassifier(
input_size=vocab_size,
word_vec_size=train_config.word_vec_size,
hidden_size=train_config.hidden_size,
n_classes=n_classes,
n_layers=train_config.n_layers,
dropout_p=train_config.dropout,
)
model.load_state_dict(rnn_best)
ensemble += [model]
if cnn_best is not None and not config.drop_cnn:
# Declare model and load pre-trained weights.
model = CNNClassifier(
input_size=vocab_size,
word_vec_size=train_config.word_vec_size,
n_classes=n_classes,
use_batch_norm=train_config.use_batch_norm,
dropout_p=train_config.dropout,
window_sizes=train_config.window_sizes,
n_filters=train_config.n_filters,
)
model.load_state_dict(cnn_best)
ensemble += [model]
y_hats = []
# Get prediction with iteration on ensemble.
for model in ensemble:
if config.gpu_id >= 0:
model.cuda(config.gpu_id)
# Don't forget turn-on evaluation mode.
model.eval()
y_hat = []
for idx in range(0, len(lines), config.batch_size):
# Converts string to list of index.
x = text_field.numericalize(
text_field.pad(lines[idx:idx + config.batch_size]),
device='cuda:%d' % config.gpu_id if config.gpu_id >= 0 else 'cpu',
)
y_hat += [model(x).cpu()]
# Concatenate the mini-batch wise result
y_hat = torch.cat(y_hat, dim=0)
# |y_hat| = (len(lines), n_classes)
y_hats += [y_hat]
model.cpu()
# Merge to one tensor for ensemble result and make probability from log-prob.
y_hats = torch.stack(y_hats).exp()
# |y_hats| = (len(ensemble), len(lines), n_classes)
y_hats = y_hats.sum(dim=0) / len(ensemble) # Get average
# |y_hats| = (len(lines), n_classes)
probs, indice = y_hats.topk(config.top_k)
'''
for i in range(30):
sys.stdout.write('%s\t%s\n' % (
' '.join([classes.itos[indice[i][j]] for j in range(config.top_k)]),
' '.join(lines[i])
))
'''
#print([indice[i][j] for j in range(config.top_k)], [classes.itos[indice[i][j]] for j in range(config.top_k)], label[i])
'''
correct = 0
total = 0
for i in range(len(lines)):
if [label[i]] == [classes.itos[indice[i][j]] for j in range(config.top_k)]:
correct += 1
print('Accuracy: %d %%' % (100 * correct / len(lines)))
'''
count1 = 0
count2 = 0
count3 = 0
count4 = 0
cnt = Counter()
for i in range(len(lines)):
if [label[i]] == [classes.itos[indice[i][0]]] or [classes.itos[indice[i][1]]] :
if abs(probs[i][0] - probs[i][1]) <= 0.1 :
twoclasses = [ classes.itos[indice[i][0]] , classes.itos[indice[i][1]] ]
diff = float(abs(probs[i][0] - probs[i][1]))
for two in twoclasses :
cnt[two] += 1
print(cnt)
two classes = [ [가설 설정, 이론모형] , [가설 설정, 데이터처리] ,
'''
for t in twoclasses :
if t == [ '가설 설정', '이론/모형' ] :
count1 += 1
if t == [ '가설 설정', '데이터처리'] :
count2 += 1
if t == [ '가설 설정', '대상 데이터' ] :
count3 += 1
if t == [ '가설 설정', '제안 방법'] :
count4 += 1
print('가설 설정, 이론/모형 : ', count1)
print('가설 설정, 데이터처리 : ', count2)
print('가설 설정, 대상 데이터 : ', count3)
print('가설 설정, 제안 방법 : ', count4)
'''
if __name__ == '__main__':
config = define_argparser()
main(config)