def load_datasets(self):
if self.mode == 'voc':
self.num_classes = 21
train_pascal_12 = tfds.load('voc/2012',
data_dir=self.data_dir,
split='train')
valid_train_12 = tfds.load('voc/2012',
data_dir=self.data_dir,
split='validation')
train_pascal_07 = tfds.load("voc",
data_dir=self.data_dir,
split='train')
valid_train_07 = tfds.load("voc",
data_dir=self.data_dir,
split='validation')
train_data = train_pascal_07.concatenate(valid_train_07).\
concatenate(train_pascal_12).concatenate(valid_train_12)
test_data = tfds.load("voc", data_dir=self.data_dir, split='test')
else:
self.num_classes = 81
train_data = tfds.load('coco/2017',
data_dir=self.data_dir,
split='train')
train_data = train_data.filter(lambda x: tf.reduce_all(
tf.not_equal(tf.size(x['objects']['bbox']), 0)))
train_data = train_data.filter(lambda x: tf.reduce_all(
tf.not_equal(tf.size(x['objects']['label']), 0)))
test_data = tfds.load('coco/2017',
data_dir=self.data_dir,
split='validation')
test_data = test_data.filter(lambda x: tf.reduce_all(
tf.not_equal(tf.size(x['objects']['bbox']), 0)))
test_data = test_data.filter(lambda x: tf.reduce_all(
tf.not_equal(tf.size(x['objects']['label']), 0)))
self.number_train = train_data.reduce(0, lambda x, _: x + 1).numpy()
self.number_test = test_data.reduce(0, lambda x, _: x + 1).numpy()
self.training_dataset = prepare_dataset(train_data,
self.image_size,
self.batch_size,
self.target,
self.num_classes,
train=True)
self.validation_dataset = prepare_dataset(test_data,
self.image_size,
self.batch_size,
self.target,
self.num_classes,
train=False)
def run_gru():
train_file = 'Quora_question_pair_partition/train.tsv'
dev_file = 'Quora_question_pair_partition/dev.tsv'
test_file = 'Quora_question_pair_partition/test.tsv'
# Prepare datasets
q1_train, q2_train, y_train, emb_matrix = preprocessing.prepare_dataset(
train_file, 1)
q1_dev, q2_dev, y_dev = preprocessing.prepare_dataset(dev_file)
q1_test, q2_test, y_test = preprocessing.prepare_dataset(test_file)
net = create_model(emb_matrix)
# Start training
hist = net.fit(
[q1_train, q2_train],
y_train,
validation_data=([q1_dev, q2_dev], y_dev),
batch_size=4,
nb_epoch=20,
shuffle=True,
)
# compute final accuracy on training and test sets
scores = net.evaluate([q1_test, q2_test], y_test)
print("\n%s: %.2f%%" % (net.metrics_names[2], scores[2] * 100))
def run_gru():
train_file = 'Quora_question_pair_partition/train.tsv'
dev_file = 'Quora_question_pair_partition/dev.tsv'
test_file = 'Quora_question_pair_partition/test.tsv'
# Prepare datasets
q1_train, q2_train, y_train, emb_matrix = preprocessing.prepare_dataset(train_file, 1)
q1_dev, q2_dev, y_dev = preprocessing.prepare_dataset(dev_file)
q1_test, q2_test, y_test = preprocessing.prepare_dataset(test_file)
net = create_model(emb_matrix)
# Start training
hist = net.fit([q1_train, q2_train], y_train,
validation_data=([q1_dev, q2_dev], y_dev),
batch_size=4, nb_epoch=20, shuffle=True, )
# compute final accuracy on training and test sets
scores = net.evaluate([q1_test, q2_test], y_test)
print("\n%s: %.2f%%" % (net.metrics_names[2], scores[2] * 100))
dense = Dense(dense_dim, activation=activation)(dense)
dense = Dropout(dense_dropout)(dense)
# out_ = Dense(3, activation='sigmoid')(dense)
out_ = Dense(1, activation='sigmoid')(dense)
model = Model(inputs=model_input, outputs=out_)
model.compile(optimizer=Adam(lr=lr), loss='binary_crossentropy',
metrics=['binary_crossentropy', 'accuracy', model_utils.f1])
return model
if __name__ == "__main__":
train_file = 'Quora_question_pair_partition/train.tsv'
dev_file = 'Quora_question_pair_partition/dev.tsv'
test_file = 'Quora_question_pair_partition/test.tsv'
# Prepare datasets
q1_train, q2_train, y_train, word_embedding_matrix = preprocessing.prepare_dataset(train_file, 1)
q1_dev, q2_dev, y_dev = preprocessing.prepare_dataset(dev_file)
q1_test, q2_test, y_test = preprocessing.prepare_dataset(test_file)
net = create_model(word_embedding_matrix)
# Start training
hist = net.fit([q1_train, q2_train], y_train,
validation_data=([q1_dev, q2_dev], y_dev),
batch_size=4, nb_epoch=20, shuffle=True, )
# compute final accuracy on training and test sets
scores = net.evaluate([q1_test, q2_test], y_test)
print("\n%s: %.2f%%" % (net.metrics_names[2], scores[2] * 100))
specs = set_priorBox(MODEL_NAME)
priors = create_priors_boxes(specs, IMAGE_SIZE[0])
target_transform = MatchingPriors(priors, center_variance, size_variance,
iou_threshold)
if TRAIN_MODE == 'voc':
test_data = tfds.load('voc', data_dir=DATASET_DIR, split='test')
number_test = test_data.reduce(0, lambda x, _: x + 1).numpy()
print("테스트 데이터 개수:", number_test)
CLASSES_NUM = 21
with open('./pascal_labels.txt') as f:
CLASSES = f.read().splitlines()
validation_dataset = prepare_dataset(test_data,
IMAGE_SIZE,
BATCH_SIZE,
target_transform,
CLASSES_NUM,
train=False)
else:
if EVAL_TESTDEV:
test_data, test_info = tfds.load('coco/2017',
data_dir=DATASET_DIR,
split='validation',
with_info=True)
# test_data = test_data.filter(lambda x: tf.reduce_all(tf.not_equal(tf.size(x['objects']['bbox']), 0)))
# test_data = test_data.filter(lambda x: tf.reduce_all(tf.not_equal(tf.size(x['objects']['label']), 0)))
else:
test_dev_idList = []
with open('datasets/image_info_test-dev2017.json', 'r') as f:
def run(FLAGS):
# quora_full_dataset = FLAGS.full_dataset
train_file = FLAGS.train_path
dev_file = FLAGS.dev_path
test_file = FLAGS.test_path
embeddings = FLAGS.embeddings
model = FLAGS.model
mode = FLAGS.mode
maxlen = FLAGS.max_sent_length
max_nb_words = FLAGS.max_nb_words
experiment = FLAGS.experiment
dataset = FLAGS.task
sent_embed = FLAGS.sent_embed
if embeddings == 'elmo':
init_embeddings = 0
else:
init_embeddings = 1
word_index = vocab.prepare_vocab(train_file, embeddings)
# Prepare datasets
if init_embeddings == 1:
q1_train, q2_train, y_train, qid_train, raw1_train, raw2_train, word_embedding_matrix = preprocessing.prepare_dataset(
train_file, maxlen, max_nb_words, experiment, dataset, embeddings,
init_embeddings)
else:
q1_train, q2_train, y_train, qid_train, raw1_train, raw2_train = preprocessing.prepare_dataset(
train_file, maxlen, max_nb_words, experiment, dataset, embeddings,
init_embeddings)
word_embedding_matrix = np.zeros(1)
q1_dev, q2_dev, y_dev, qid_dev, raw1_dev, raw2_dev = preprocessing.prepare_dataset(
dev_file, maxlen, max_nb_words, experiment, dataset, embeddings)
q1_test, q2_test, y_test, qid_test, raw1_test, raw2_test = preprocessing.prepare_dataset(
test_file, maxlen, max_nb_words, experiment, dataset, embeddings)
if dataset == 'snli':
y_train = to_categorical(y_train, num_classes=None)
y_dev = to_categorical(y_dev, num_classes=None)
y_test = to_categorical(y_test, num_classes=None)
net = create_model(word_embedding_matrix)
net.summary()
filepath = "models/weights.best.%s.%s.%s.%s.%s.hdf5" % (
FLAGS.task, model, experiment, embeddings, sent_embed)
if mode == "ensemble":
print("Create ensemble of models")
elif mode == "fine-tuning":
model_file = "models/weights.best.quora.dec_att.training_full.glove.no_univ_sent.hdf5"
print("Loading pre-trained model from %s:" % model_file)
net.load_weights(model_file)
net = freeze_layers(net)
net.compile(
optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=['binary_crossentropy', 'accuracy', model_utils.f1])
t0 = time.time()
callbacks = get_callbacks(filepath)
history = net.fit(
[q1_train, q2_train, raw1_train, raw2_train],
y_train,
validation_data=([q1_dev, q2_dev, raw1_dev, raw2_dev], y_dev),
batch_size=FLAGS.batch_size,
nb_epoch=FLAGS.max_epochs,
shuffle=True,
callbacks=callbacks)
pickle_file = "saved_history/history.%s.%s.%s.%s.%s.pickle" % (
FLAGS.task, model, experiment, embeddings, sent_embed)
with open(pickle_file, 'wb') as handle:
pickle.dump(history.history,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
t1 = time.time()
print("Training ended at", datetime.datetime.now())
print("Minutes elapsed: %f" % ((t1 - t0) / 60.))
elif mode == "transfer_learning":
model_file = "models/weights.best.snli.esim.hdf5"
print("Loading pre-trained model from %s:" % model_file)
net.load_weights(model_file)
net = replace_layer(net)
net.compile(
optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=['binary_crossentropy', 'accuracy', model_utils.f1])
t0 = time.time()
callbacks = get_callbacks(filepath)
history = net.fit(
[q1_train, q2_train, raw1_train, raw2_train],
y_train,
validation_data=([q1_dev, q2_dev, raw1_dev, raw2_dev], y_dev),
batch_size=FLAGS.batch_size,
nb_epoch=FLAGS.max_epochs,
shuffle=True,
callbacks=callbacks)
pickle_file = "saved_history/history.%s.%s.%s.%s.%s.pickle" % (
FLAGS.task, model, experiment, embeddings, sent_embed)
with open(pickle_file, 'wb') as handle:
pickle.dump(history.history,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
t1 = time.time()
print("Training ended at", datetime.datetime.now())
print("Minutes elapsed: %f" % ((t1 - t0) / 60.))
elif mode == "load":
print("Loading weights from %s" % filepath)
net.load_weights(filepath)
net.compile(
optimizer=Adam(lr=1e-3),
loss='binary_crossentropy',
metrics=['binary_crossentropy', 'accuracy', model_utils.f1])
elif mode == "training":
# Start training
print("Starting training at", datetime.datetime.now())
t0 = time.time()
callbacks = get_callbacks(filepath)
history = net.fit(
[q1_train, q2_train, raw1_train, raw2_train],
y_train,
validation_data=([q1_dev, q2_dev, raw1_dev, raw2_dev], y_dev),
batch_size=FLAGS.batch_size,
nb_epoch=FLAGS.max_epochs,
shuffle=True,
callbacks=callbacks)
pickle_file = "saved_history/history.%s.%s.%s.%s.%s.pickle" % (
FLAGS.task, model, experiment, embeddings, sent_embed)
with open(pickle_file, 'wb') as handle:
pickle.dump(history.history,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
t1 = time.time()
print("Training ended at", datetime.datetime.now())
print("Minutes elapsed: %f" % ((t1 - t0) / 60.))
max_val_acc, idx = get_best(history)
print('Maximum accuracy at epoch', '{:d}'.format(idx + 1), '=',
'{:.4f}'.format(max_val_acc))
else:
print("------------Unknown mode------------")
test_f1 = get_confusion_matrix(net, q1_test, q2_test, y_test, raw1_test,
raw2_test)
predictions = find_prediction_probability(net, q1_test, q2_test, y_test,
qid_test, raw1_test, raw2_test)
write_predictions(predictions)
# cvscores, loss_scores = evaluate_model(word_embedding_matrix, q1_train, q2_train, y_train
# q1_test, q2_test, y_test)
print("Finished running %s model on %s with %s and %s" %
(model, experiment, embeddings, sent_embed))
# print_crossval(cvscores)
# print("Crossvalidation accuracy result: %.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores)))
# print("Crossvalidation lostt result: %.2f (+/- %.2f)" % (np.mean(loss_scores), np.std(loss_scores)))
if mode != "ensemble":
test_loss, test_acc = evaluate_best_model(net, q1_test, q2_test,
y_test, raw1_test, raw2_test,
filepath)
else:
test_loss = evaluate_error(net, q1_test, q2_test, raw1_test, raw2_test,
y_test)
test_acc = evaluate_accuracy(net, q1_test, q2_test, raw1_test,
raw2_test, y_test)
print(
'Evaluation without crossval: loss = {0:.4f}, accuracy = {1:.4f} F1-score = {2:.4f}'
.format(test_loss, test_acc * 100, test_f1 * 100))
with open("results.txt", "a") as myfile:
myfile.write(
"Finished running %s model on %s with %s and %s in %s mode" %
(model, experiment, embeddings, sent_embed, mode))
myfile.write(
'Evaluation without crossval: loss = {0:.4f}, accuracy = {1:.4f} F1-score = {2:.4f}'
.format(test_loss, test_acc * 100, test_f1 * 100))
myfile.write('\n')
dev_sentences = load_sentences(data_path, lower, "dev")
test_train_sentences = load_sentences(data_path, lower, "train_test")
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), parameters['pre_emb'],
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in dev_sentences]))
if not parameters['all_emb'] else None)
dico_chars, char_to_id, id_to_char = char_mapping(train_sentences)
dico_tags, tag_to_id, id_to_tag = tag_mapping(data_path, "train")
train_data = prepare_dataset(train_sentences, word_to_id, char_to_id,
tag_to_id, data_path, "train", lower)
dev_data = prepare_dataset(dev_sentences, word_to_id, char_to_id, tag_to_id,
data_path, "dev", lower)
test_train_data = prepare_dataset(test_train_sentences, word_to_id, char_to_id,
tag_to_id, data_path, "train_test", lower)
print("%i / %i sentences in train / dev." % (len(train_data), len(dev_data)))
all_word_embeds = {}
for i, line in enumerate(codecs.open(opts.pre_emb, 'r', 'utf-8')):
s = line.strip().split()
if len(s) == parameters['word_dim'] + 1:
all_word_embeds[s[0]] = np.array([float(i) for i in s[1:]])
print('Loaded %i pretrained embeddings.' % len(all_word_embeds))
# compute vocab size
params['label_size'] = len(mappings['id_to_tag'])
params['word_vocab_size'] = len(mappings['id_to_word'])
params['bi_word_vocab_size'] = len(mappings['bi_id_to_word'])
params['char_vocab_size'] = len(mappings['id_to_char'])
params['feat_vocab_size'] = [len(item) for item in mappings['id_to_feat_list']]
print("word vocab size: ", params['word_vocab_size'])
print("bi word vocab size: ", params['bi_word_vocab_size'])
# Index data
dataset = dict()
dataset['train'] = prepare_dataset(train_sentences,
mappings,
zeros,
lower,
is_train=True)
dataset['bi_train'] = prepare_dataset_bi(bi_train_sentences,
mappings,
zeros,
lower,
is_train=True)
dataset['dev'] = prepare_dataset(dev_sentences,
mappings,
zeros,
lower,
is_train=True)
dataset['test'] = prepare_dataset(test_sentences,
mappings,
zeros,
specs = [
SSDSpec(38, 8, SSDBoxSizes(30, 60), [2]),
SSDSpec(19, 16, SSDBoxSizes(60, 111), [2, 3]),
SSDSpec(10, 32, SSDBoxSizes(111, 162), [2, 3]),
SSDSpec(5, 64, SSDBoxSizes(162, 213), [2, 3]),
SSDSpec(3, 100, SSDBoxSizes(213, 264), [2]),
SSDSpec(1, 300, SSDBoxSizes(264, 315), [2])
]
priors = generate_ssd_priors(specs, IMAGE_SIZE[0])
target_transform = MatchPrior(priors, center_variance, size_variance, iou_threshold)
# instantiate the datasets
training_dataset = prepare_dataset(train_data, IMAGE_SIZE, BATCH_SIZE, target_transform, train=True)
validation_dataset = prepare_dataset(test_data, IMAGE_SIZE, BATCH_SIZE, target_transform, train=False)
print("Building SSD Model with EfficientNet{0} backbone..".format(MODEL_NAME))
model = ssd(MODEL_NAME)
steps_per_epoch = number_train // BATCH_SIZE
validation_steps = number_test // BATCH_SIZE
print("Number of Train Batches:", steps_per_epoch)
print("Number of Test Batches:", validation_steps)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=1, min_lr=1e-5, verbose=1)
checkpoint = ModelCheckpoint(checkpoint_filepath, monitor='val_loss', save_best_only=True, save_weights_only=True, verbose=1)
model.compile(
optimizer = tf.keras.optimizers.Adam(learning_rate=base_lr),