def train(dataroot, classifier_name='cnn'):
balance = get_balancing_technique()
K = 10
fold_prefix = str(K) + 'bal_fold_{}.csv' if balance == 'explicit' else str(
K) + 'r_fold_{}.csv'
class_weight = get_class_weights(dataroot)
classifier_args, config = get_args(classifier_name, class_weight)
pre_fingerprint = join(dataroot, 'c_{}'.format(classifier_name))
fingerprint = join(pre_fingerprint + config, 'K_{}'.format(K))
print(fingerprint)
folds_data = load_folds(dataroot, fold_prefix, K)
for test_index in range(K):
print('-----------{}----------'.format(test_index))
X_train = np.concatenate(
[fold[0] for i, fold in enumerate(folds_data) if i != test_index],
axis=0)
y_train = np.concatenate(
[fold[1] for i, fold in enumerate(folds_data) if i != test_index],
axis=0)
logdir = join(fingerprint, 'log', '{}'.format(test_index))
ensure_dir(logdir)
classifier_args['runs_dir'] = logdir
clf = get_classifier(classifier_args)
clf.fit(X_train, y_train)
modelname = join(classifier_args['runs_dir'], 'model.pkl')
pickle.dump(clf, open(modelname, 'wb'))
def train(dataroot, classifier_name='cnn'):
balance = get_balancing_technique()
K = 10
fold_prefix = '{}bal_fold_{}.csv' if balance == 'explicit' else '{}r_fold_{}.csv'
class_weight = get_class_weights(dataroot)
classifier_args, config = get_args(classifier_name, class_weight)
pre_fingerprint = join(dataroot, 'c_{}'.format(classifier_name))
fingerprint = join(pre_fingerprint + config, 'K_{}'.format(K))
print(fingerprint)
num_epochs = 40
for test_index in range(K):
print('-----------{}----------'.format(test_index))
dev_indices = [i for i in range(K) if i != test_index]
val_index = dev_indices[0]
train_indices = dev_indices[1:]
val_csv = join(dataroot, fold_prefix.format(K, val_index))
list_of_train_csvs = [
join(dataroot, fold_prefix.format(K, i)) for i in train_indices
]
logdir = join(fingerprint, 'log', '{}'.format(test_index))
ensure_dir(logdir)
classifier_args['runs_dir'] = logdir
clf = get_classifier(classifier_args)
clf.fit(list_of_train_csvs, val_csv, num_epochs)
def train(self, epochs=500):
opt = tf.keras.optimizers.Adam()
loss = tf.keras.losses.categorical_crossentropy
X, Y = self.get_dataset()
self.compile(optimizer=opt, loss=loss)
self.fit(
X,
Y,
batch_size=64,
epochs=epochs,
class_weight=get_class_weights(
np.argmax(Y, axis=1), smooth_factor=0.05
) # weight under-represented classes almost equally
)
def main():
parser = options.get_parser('Trainer')
options.add_dataset_args(parser)
options.add_preprocessing_args(parser)
options.add_model_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
args = parser.parse_args()
print(args)
args.cuda = not args.disable_cuda and torch.cuda.is_available()
# checkpoint
checkpoint_dir = os.path.dirname(args.checkpoint)
if not os.path.isdir(checkpoint_dir):
os.mkdir(checkpoint_dir)
# load dataset
train_raw_corpus, val_raw_corpus, test_raw_corpus = utils.load_corpus(args.processed_dir)
assert train_raw_corpus and val_raw_corpus and test_raw_corpus, 'Corpus not found, please run preprocess.py to obtain corpus!'
train_corpus = [(line.sent, line.type, line.p1, line.p2) for line in train_raw_corpus]
val_corpus = [(line.sent, line.type, line.p1, line.p2) for line in val_raw_corpus]
test_corpus = [(line.sent, line.type, line.p1, line.p2) for line in test_raw_corpus]
start_epoch = 0
caseless = args.caseless
batch_size = args.batch_size
num_epoch = args.num_epoch
# preprocessing
sents = [tup[0] for tup in train_corpus + val_corpus]
feature_map = utils.build_vocab(sents, min_count=args.min_count, caseless=caseless)
##
# target_map = {c:i for i, c in enumerate(['null', 'true'])}
target_map = ddi2013.target_map
train_features, train_targets = utils.build_corpus(train_corpus, feature_map, target_map, caseless)
val_features, val_targets = utils.build_corpus(val_corpus, feature_map, target_map, caseless)
test_features, test_targets = utils.build_corpus(test_corpus, feature_map, target_map, caseless)
class_weights = torch.Tensor(utils.get_class_weights(train_targets)) if args.class_weight else None
train_loader = utils.construct_bucket_dataloader(train_features, train_targets, feature_map['PAD'], batch_size, args.position_bound, is_train=True)
val_loader = utils.construct_bucket_dataloader(val_features, val_targets, feature_map['PAD'], batch_size, args.position_bound, is_train=False)
test_loader = utils.construct_bucket_dataloader(test_features, test_targets, feature_map['PAD'], batch_size, args.position_bound, is_train=False)
print('Preprocessing done! Vocab size: {}'.format(len(feature_map)))
# build model
vocab_size = len(feature_map)
tagset_size = len(target_map)
model = utils.build_model(args, vocab_size, tagset_size)
# loss
criterion = utils.build_loss(args, class_weights=class_weights)
# load states
if os.path.isfile(args.load_checkpoint):
print('Loading checkpoint file from {}...'.format(args.load_checkpoint))
checkpoint_file = torch.load(args.load_checkpoint)
start_epoch = checkpoint_file['epoch'] + 1
model.load_state_dict(checkpoint_file['state_dict'])
# optimizer.load_state_dict(checkpoint_file['optimizer'])
else:
print('no checkpoint file found: {}, train from scratch...'.format(args.load_checkpoint))
if not args.rand_embedding:
pretrained_word_embedding, in_doc_word_indices = utils.load_word_embedding(args.emb_file, feature_map, args.embedding_dim)
print(pretrained_word_embedding.size())
print(vocab_size)
model.load_pretrained_embedding(pretrained_word_embedding)
if args.disable_fine_tune:
model.update_part_embedding(in_doc_word_indices) # update only non-pretrained words
model.rand_init(init_embedding=args.rand_embedding)
# trainer
trainer = SeqTrainer(args, model, criterion)
if os.path.isfile(args.load_checkpoint):
dev_prec, dev_rec, dev_f1, _ = evaluate(trainer, val_loader, target_map, cuda=args.cuda)
test_prec, test_rec, test_f1, _ = evaluate(trainer, test_loader, target_map, cuda=args.cuda)
print('checkpoint dev_prec: {:.4f}, dev_rec: {:.4f}, dev_f1: {:.4f}, test_prec: {:.4f}, test_rec: {:.4f}, test_f1: {:.4f}'.format(
dev_prec, dev_rec, dev_f1, test_prec, test_rec, test_f1))
track_list = []
best_f1 = float('-inf')
patience_count = 0
start_time = time.time()
for epoch in range(start_epoch, num_epoch):
epoch_loss = train(train_loader, trainer, epoch)
# update lr
trainer.lr_step()
dev_prec, dev_rec, dev_f1, dev_loss = evaluate(trainer, val_loader, target_map, cuda=args.cuda)
if dev_f1 >= best_f1:
patience_count = 0
best_f1 = dev_f1
test_prec, test_rec, test_f1, _ = evaluate(trainer, test_loader, target_map, cuda=args.cuda)
track_list.append({'epoch': epoch, 'loss': epoch_loss,
'dev_prec': dev_prec, 'dev_rec': dev_rec, 'dev_f1': dev_f1, 'dev_loss': dev_loss,
'test_prec': test_prec, 'test_rec': test_rec, 'test_f1': test_f1})
print('epoch: {}, loss: {:.4f}, dev_f1: {:.4f}, dev_loss: {:.4f}, test_f1: {:.4f}\tsaving...'.format(epoch, epoch_loss, dev_f1, dev_loss, test_f1))
try:
utils.save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
'f_map': feature_map,
't_map': target_map,
}, {'track_list': track_list,
'args': vars(args)
}, args.checkpoint + '_lstm')
except Exception as inst:
print(inst)
else:
patience_count += 1
track_list.append({'epoch': epoch,'loss': epoch_loss, 'dev_prec': dev_prec, 'dev_rec': dev_rec, 'dev_f1': dev_f1, 'dev_loss': dev_loss})
print('epoch: {}, loss: {:.4f}, dev_f1: {:.4f}, dev_loss: {:.4f}'.format(epoch, epoch_loss, dev_f1, dev_loss))
print('epoch: {} in {} take: {} s'.format(epoch, args.num_epoch, time.time() - start_time))
if patience_count >= args.patience:
break
def main():
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--train", required=True, help="Train type")
args = vars(ap.parse_args())
#parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
TRAIN = args["train"]
if (TRAIN not in ["SEG", "CLASS"]):
raise ValueError(f"{TRAIN} not defined")
# base config
base_dir = cp["BASE"].get("base_dir")
# train config
output_dir = cp[TRAIN].get("output_dir")
base_model_name = cp[TRAIN].get("model_name")
use_base_model_weights = cp[TRAIN].getboolean("use_base_model_weights")
use_trained_model_weights = cp[TRAIN].getboolean(
"use_trained_model_weights")
use_best_weights = cp[TRAIN].getboolean("use_best_weights")
output_weights_name = cp[TRAIN].get("output_weights_name")
epochs = cp[TRAIN].getint("epochs")
batch_size = cp[TRAIN].getint("batch_size")
initial_learning_rate = cp[TRAIN].getfloat("initial_learning_rate")
generator_workers = cp[TRAIN].getint("generator_workers")
image_dimension = cp[TRAIN].getint("image_dimension")
train_steps = cp[TRAIN].get("train_steps")
validation_steps = cp[TRAIN].get("validation_steps")
patience_reduce_lr = cp[TRAIN].getint("patience_reduce_lr")
patience_early_stop = cp[TRAIN].getint("patience_early_stop")
min_lr = cp[TRAIN].getfloat("min_lr")
dataset_csv_dir = cp[TRAIN].get("dataset_csv_dir")
show_model_summary = cp[TRAIN].getboolean("show_model_summary")
if (TRAIN == "CLASS"):
positive_weights_multiply = cp[TRAIN].getfloat(
"positive_weights_multiply")
class_names = cp[TRAIN].get("class_names").split(",")
mask_folder = cp[TRAIN].get("mask_folder")
patch_size = cp[TRAIN].getint("patch_size")
N = cp["TEST"].getint("N")
else:
num_classes = cp[TRAIN].getint("num_classes")
class_names = None
current_epoch = 0
# check output_dir, create it if not exists
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
# if previously trained weights is used, never re-split
if use_trained_model_weights:
# resuming mode
print("** use trained model weights **")
# load training status for resuming
training_stats_file = os.path.join(output_dir, ".training_stats.json")
if os.path.isfile(training_stats_file):
training_stats = json.load(open(training_stats_file))
initial_learning_rate = training_stats['lr']
current_epoch = training_stats['epoch']
else:
training_stats = {}
else:
# start over
training_stats = {}
print(f"backup config file to {output_dir}")
shutil.copy(config_file,
os.path.join(output_dir,
os.path.split(config_file)[1]))
datasets = ["train", "val", "test"]
for dataset in datasets:
shutil.copy(os.path.join(dataset_csv_dir, f"{dataset}.csv"),
output_dir)
# get train/dev sample counts
train_counts, train_pos_counts = get_class_counts(
os.path.join(output_dir, "train.csv"), class_names)
val_counts, _ = get_class_counts(os.path.join(output_dir, "val.csv"),
class_names)
# compute steps
if train_steps == "auto":
train_steps = int(train_counts / batch_size)
else:
try:
train_steps = int(train_steps)
except ValueError:
raise ValueError(
f"train_steps: {train_steps} is invalid, please use 'auto' or integer."
)
print(f"** train_steps: {train_steps} **")
if validation_steps == "auto":
validation_steps = int(val_counts / batch_size)
else:
try:
validation_steps = int(validation_steps)
except ValueError:
raise ValueError(
f"validation_steps: {validation_steps} is invalid,please use 'auto' or integer."
)
print(f"** validation_steps: {validation_steps} **")
class_weights = None
if (TRAIN == "CLASS"):
# compute class weights
print("** compute class weights from training data **")
class_weights = get_class_weights(
train_counts,
train_pos_counts,
multiply=positive_weights_multiply,
)
print("** class_weights **")
print(class_weights)
print("** load model **")
if use_trained_model_weights:
if use_best_weights:
model_weights_file = os.path.join(output_dir,
f"best_{output_weights_name}")
else:
model_weights_file = os.path.join(output_dir, output_weights_name)
else:
model_weights_file = None
print("** compile model **")
METRICS = [
TruePositives(name='tp'),
FalsePositives(name='fp'),
TrueNegatives(name='tn'),
FalseNegatives(name='fn'),
Accuracy(name='accuracy'),
Precision(name='precision'),
Recall(name='recall'),
AUC(name='auc'),
]
optimizer = Adam(lr=initial_learning_rate)
if (TRAIN == "CLASS"):
model = Resnet18(input_shape=(N, patch_size, patch_size, 3),
weights_path=model_weights_file,
N=N,
nb_classes=len(class_names))
model.compile(optimizer=optimizer,
loss="categorical_crossentropy",
metrics=METRICS)
checkpoint_monitor = 'val_loss'
else:
model = Densenet103(nb_classes=num_classes - 1,
weights_path=model_weights_file,
input_shape=(image_dimension, image_dimension, 1))
model.compile(optimizer=optimizer, loss="binary_crossentropy")
checkpoint_monitor = 'val_loss'
if show_model_summary:
print(model.summary())
print("** create image generators **")
if (TRAIN == "CLASS"):
train_sequence = classification_gen(
dataset_csv_file=os.path.join(output_dir, "train.csv"),
class_names=class_names,
N=N,
batch_size=batch_size,
normalization_func=imageNet_preprocessing,
target_size=(image_dimension, image_dimension),
patch_size=(patch_size, patch_size),
augmenter=augmenter,
base_dir=base_dir,
mask_folder=mask_folder,
steps=train_steps,
)
validation_sequence = classification_gen(
dataset_csv_file=os.path.join(output_dir, "val.csv"),
class_names=class_names,
N=N,
batch_size=batch_size,
normalization_func=imageNet_preprocessing,
target_size=(image_dimension, image_dimension),
patch_size=(patch_size, patch_size),
augmenter=augmenter,
base_dir=base_dir,
steps=validation_steps,
mask_folder=mask_folder,
shuffle_on_epoch_end=False,
)
else:
train_sequence = segmentation_gen(
dataset_csv_file=os.path.join(output_dir, "train.csv"),
batch_size=batch_size,
target_size=(image_dimension, image_dimension),
augmenter=augmenter,
base_dir=base_dir,
steps=train_steps,
)
validation_sequence = segmentation_gen(
dataset_csv_file=os.path.join(output_dir, "val.csv"),
batch_size=batch_size,
target_size=(image_dimension, image_dimension),
augmenter=augmenter,
base_dir=base_dir,
steps=validation_steps,
shuffle_on_epoch_end=False,
)
output_weights_path = os.path.join(output_dir, output_weights_name)
print(f"** set output weights path to: {output_weights_path} **")
checkpoint = ModelCheckpoint(output_weights_path,
save_weights_only=True,
save_best_only=True,
verbose=1,
monitor=checkpoint_monitor)
if (TRAIN == "CLASS"):
performance_callback = MultipleClassAUROC(
sequence=validation_sequence,
class_names=class_names,
weights_path=output_weights_path,
stats=training_stats,
workers=generator_workers)
else:
performance_callback = Jaccard(sequence=validation_sequence,
weights_path=output_weights_path,
stats=training_stats,
workers=generator_workers)
callbacks = [
checkpoint,
performance_callback,
TensorBoard(log_dir=os.path.join(output_dir, "logs"),
batch_size=batch_size),
ReduceLROnPlateau(monitor='loss',
factor=0.1,
patience=patience_reduce_lr,
verbose=1,
mode="min",
min_lr=min_lr),
EarlyStopping(
monitor="val_loss",
min_delta=0,
patience=patience_early_stop,
verbose=0,
mode="min",
baseline=None,
restore_best_weights=False,
),
]
print("** start training **")
history = model.fit_generator(
generator=train_sequence,
initial_epoch=current_epoch,
epochs=epochs,
class_weight=class_weights,
validation_data=validation_sequence,
callbacks=callbacks,
workers=generator_workers,
shuffle=False,
)
# dump history
print("** dump history **")
with open(os.path.join(output_dir, "history.pkl"), "wb") as f:
pickle.dump({"history": history.history}, f)
print("** done! **")
for layer in trained_net.layers:
if 'l1' in layer.name:
layer.trainable = True
if 'l2' in layer.name:
layer.trainable = False
if 'l3' in layer.name:
layer.trainable = True
else:
for layer in trained_net.layers:
if 'l1' in layer.name:
layer.trainable = False
if 'l2' in layer.name:
layer.trainable = True
if 'l3' in layer.name:
layer.trainable = True
class_weights = get_class_weights(y_t_c)
trained_net.compile(optimizer=tf.keras.optimizers.Adam(LR_TARGET),
loss=losses,
loss_weights=loss_weights,
metrics=['accuracy'])
trained_net.fit(x_t_c, [y_t_c, y_t_c, y_t_c],
batch_size=64,
epochs=1,
verbose=0,
class_weight=class_weights)
# evalulation
res_t = trained_net.evaluate(X_T_VAL, [Y_T_VAL, Y_T_VAL, Y_T_VAL],
verbose=0)
print('\nStep:', step, 'Acc on target domain test data:', res_t[-1],
'Len y_t_p:', len(y_t_p), '\n')
img_file, label_file, h_enc, w_enc, h_dec, w_dec)
# create single head datasets
train_single_ds = filelist_train.shuffle(n_train).map(map_single).cache().batch(batch_size).repeat()
val_single_ds = filelist_val.map(map_single).cache().batch(batch_size).repeat()
test_single_ds = filelist_test.map(map_single).cache().batch(batch_size).repeat()
# create double head datasets
train_double_ds = filelist_train.shuffle(n_train).map(map_double).cache().batch(batch_size).repeat()
val_double_ds = filelist_val.map(map_double).cache().batch(batch_size).repeat()
test_double_ds = filelist_test.map(map_double).cache().batch(batch_size).repeat()
# get class weights
label_filelist = tf.data.Dataset.list_files(label_pattern, shuffle=False)
label_ds = label_filelist.map(lambda x: map_label(x, h_dec, w_dec))
class_weights = get_class_weights(label_ds).tolist()
def train_enet():
Enet = EnetModel(C=12,MultiObjective=True,l2=1e-3)
# Train Encoder
for layer in Enet.layers[-6:]:
layer.trainable = False
n_epochs = 60
adam_optimizer = tf.keras.optimizers.Adam(learning_rate=5e-4)
Enet.compile(optimizer=adam_optimizer,
loss=['sparse_categorical_crossentropy','sparse_categorical_crossentropy'],
metrics=['accuracy','accuracy'],
loss_weights=[1.0,0.0])
def cross_validate(model_params, train_params, feature_type, naming):
# Get trail list
cross_val_splits = utils.get_cross_val_splits()
# Cross-Validation Result
result = []
# Cross Validation
for split_idx, split in enumerate(cross_val_splits):
feature_dir = os.path.join(raw_feature_dir, split['name'])
test_trail_list = split['test']
train_trail_list = split['train']
split_naming = naming + '_split_{}'.format(split_idx + 1)
trained_model_file = utils.get_tcn_model_file(split_naming)
log_dir = utils.get_tcn_log_sub_dir(split_naming)
# Model
model = EncoderDecoderNet(**model_params)
model = model.cuda()
print(model)
n_layers = len(model_params['encoder_params']['layer_sizes'])
# Dataset
train_dataset = RawFeatureDataset(dataset_name,
feature_dir,
train_trail_list,
feature_type=feature_type,
encode_level=n_layers,
sample_rate=sample_rate,
sample_aug=False,
normalization=[None, None])
test_norm = [train_dataset.get_means(), train_dataset.get_stds()]
test_dataset = RawFeatureDataset(dataset_name,
feature_dir,
test_trail_list,
feature_type=feature_type,
encode_level=n_layers,
sample_rate=sample_rate,
sample_aug=False,
normalization=test_norm)
loss_weights = utils.get_class_weights(train_dataset)
#loss_weights = None
if train_params is not None:
train_model(model,
train_dataset,
test_dataset,
**train_params,
loss_weights=loss_weights,
trained_model_file=trained_model_file,
log_dir=log_dir)
#log_dir=None)
model.load_state_dict(torch.load(trained_model_file))
acc, edit, _, f_scores = test_model(model,
test_dataset,
loss_weights=loss_weights,
plot_naming=split_naming)
result.append(
[acc, edit, f_scores[0], f_scores[1], f_scores[2], f_scores[3]])
result = np.array(result)
return result
def train(FLAGS):
# -------------------- Defining the hyperparameters --------------------
batch_size = FLAGS.batch_size #
epochs = FLAGS.epochs #
training_type = FLAGS.training_type #
learning_rate = FLAGS.learning_rate #
save_every = FLAGS.save_every #
num_classes = FLAGS.num_classes #
weight_decay = FLAGS.weight_decay
img_pattern = FLAGS.img_pattern #
label_pattern = FLAGS.label_pattern #
img_pattern_val = FLAGS.img_pattern_val #
label_pattern_val = FLAGS.label_pattern_val #
tb_logs = FLAGS.tensorboard_logs #
img_width = FLAGS.img_width #
img_height = FLAGS.img_height #
save_model = FLAGS.save_model #
cache_train = FLAGS.cache_train #
cache_val = FLAGS.cache_val #
cache_test = FLAGS.cache_test #
print('[INFO]Defined all the hyperparameters successfully!')
# setup tensorboard
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=1)
# encoder and decoder dimensions
h_enc = img_height // 8
w_enc = img_width // 8
h_dec = img_height
w_dec = img_width
# create (img,label) string tensor lists
filelist_train = preprocess_img_label(img_pattern, label_pattern)
filelist_val = preprocess_img_label(img_pattern_val, label_pattern_val)
# training dataset size
n_train = tf.data.experimental.cardinality(filelist_train).numpy()
n_val = tf.data.experimental.cardinality(filelist_val).numpy()
# define mapping functions for single and double head nets
map_single = partial(map_singlehead, h_img=h_dec, w_img=w_dec)
map_double = partial(map_doublehead,
h_enc=h_enc,
w_enc=w_enc,
h_dec=h_dec,
w_dec=w_dec)
# create dataset
if training_type == 0 or training_type == 1:
map_fn = map_double
else:
map_fn = map_single
train_ds = filelist_train.shuffle(n_train).map(map_fn).cache(
cache_train).batch(batch_size).repeat()
val_ds = filelist_val.map(map_fn).cache(cache_val).batch(
batch_size).repeat()
# final training and validation datasets
# -------------------- get the class weights --------------------
print('[INFO]Starting to define the class weights...')
label_filelist = tf.data.Dataset.list_files(label_pattern, shuffle=False)
label_ds = label_filelist.map(lambda x: process_label(x, h_dec, w_dec))
class_weights = get_class_weights(label_ds).tolist()
print('[INFO]Fetched all class weights successfully!')
# -------------------- istantiate model --------------------
if training_type == 0 or training_type == 1:
Enet = EnetModel(C=num_classes, MultiObjective=True, l2=weight_decay)
else:
Enet = EnetModel(C=num_classes, l2=weight_decay)
print('[INFO]Model Instantiated!')
# -------------------- start training --------------------
adam_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
# -- two stages training --
if training_type == 0:
# freeze decoder layers
for layer in Enet.layers[-6:]:
layer.trainable = False
# compile encoder: only the first objective matters
Enet.compile(optimizer=adam_optimizer,
loss=[
'sparse_categorical_crossentropy',
'sparse_categorical_crossentropy'
],
metrics=['accuracy', 'accuracy'],
loss_weights=[1.0, 0.0])
# train encoder
Enet.fit(x=train_ds,
epochs=epochs,
steps_per_epoch=n_train // batch_size,
validation_data=val_ds,
validation_steps=n_val // batch_size // 5,
class_weight=class_weights,
callbacks=[tensorboard_callback])
# freeze encoder and unfreeze decoder
for layer in Enet.layers[-6:]:
layer.trainable = True
for layer in Enet.layers[:-6]:
layer.trainable = False
# compile model: only the second objective matters
Enet.compile(optimizer=adam_optimizer,
loss=[
'sparse_categorical_crossentropy',
'sparse_categorical_crossentropy'
],
metrics=['accuracy', 'accuracy'],
loss_weights=[0.0, 1.0])
# train decoder
enet_hist = Enet.fit(x=train_ds,
epochs=epochs,
steps_per_epoch=n_train // batch_size,
validation_data=val_ds,
validation_steps=n_val // batch_size // 5,
class_weight=class_weights,
callbacks=[tensorboard_callback])
# -- simultaneous double objective trainings --
elif training_type == 1:
# compile model
Enet.compile(optimizer=adam_optimizer,
loss=[
'sparse_categorical_crossentropy',
'sparse_categorical_crossentropy'
],
metrics=['accuracy', 'accuracy'],
loss_weights=[0.5, 0.5])
# fit model
print('train: ', n_train, 'batch: ', batch_size)
enet_hist = Enet.fit(x=train_ds,
epochs=epochs,
steps_per_epoch=n_train // batch_size,
validation_data=val_ds,
validation_steps=n_val // batch_size // 5,
class_weight=class_weights,
callbacks=[tensorboard_callback])
# -- end to end training --
else:
# compile model
Enet.compile(optimizer=adam_optimizer,
loss=['sparse_categorical_crossentropy'],
metrics=['accuracy'])
enet_hist = Enet.fit(x=train_ds,
epochs=epochs,
steps_per_epoch=n_train // batch_size,
validation_data=val_ds,
validation_steps=n_val // batch_size // 5,
class_weight=class_weights,
callbacks=[tensorboard_callback])
# -------------------- save model --------------------
Enet.save_weights(save_model)
def main():
parser = options.get_parser('Generator')
options.add_dataset_args(parser)
options.add_preprocessing_args(parser)
options.add_model_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_generation_args(parser)
args = parser.parse_args()
model_path = args.load_checkpoint + '.model'
args_path = args.load_checkpoint + '.json'
with open(args_path, 'r') as f:
_args = json.load(f)['args']
[setattr(args, k, v) for k, v in _args.items()]
args.cuda = not args.disable_cuda and torch.cuda.is_available()
print(args)
if args.cuda:
torch.backends.cudnn.benchmark = True
# increase recursion depth
sys.setrecursionlimit(10000)
# load dataset
train_raw_corpus, val_raw_corpus, test_raw_corpus = utils.load_corpus(
args.processed_dir, ddi=False)
assert train_raw_corpus and val_raw_corpus and test_raw_corpus, 'Corpus not found, please run preprocess.py to obtain corpus!'
train_corpus = [(line.sent, line.type, line.p1, line.p2)
for line in train_raw_corpus]
val_corpus = [(line.sent, line.type, line.p1, line.p2)
for line in val_raw_corpus]
caseless = args.caseless
batch_size = args.batch_size
# build vocab
sents = [tup[0] for tup in train_corpus + val_corpus]
feature_map = utils.build_vocab(sents,
min_count=args.min_count,
caseless=caseless)
target_map = ddi2013.target_map
# get class weights
_, train_targets = utils.build_corpus(train_corpus, feature_map,
target_map, caseless)
class_weights = torch.Tensor(
utils.get_class_weights(train_targets)) if args.class_weight else None
# load dataets
_, _, test_loader = utils.load_datasets(args.processed_dir,
args.train_size,
args,
feature_map,
dataloader=True)
# build model
vocab_size = len(feature_map)
tagset_size = len(target_map)
model = RelationTreeModel(vocab_size, tagset_size, args)
# loss
criterion = utils.build_loss(args, class_weights=class_weights)
# load states
assert os.path.isfile(model_path), "Checkpoint not found!"
print('Loading checkpoint file from {}...'.format(model_path))
checkpoint_file = torch.load(model_path)
model.load_state_dict(checkpoint_file['state_dict'])
# trainer
trainer = TreeTrainer(args, model, criterion)
# predict
y_true, y_pred, treelists, f1_by_len = predict(trainer,
test_loader,
target_map,
cuda=args.cuda)
# assign words to roots
for tup, treelist in zip(test_raw_corpus, treelists):
for t in treelist:
t.idx = tup.sent[t.idx] if t.idx < len(tup.sent) else None
# prediction
print('Predicting...')
# write result: sent_id|e1|e2|ddi|type
with open(args.predict_file, 'w') as f:
for tup, pred in zip(test_raw_corpus, y_pred):
ddi = 0 if pred == 'null' else 1
f.write('|'.join([tup.sent_id, tup.e1, tup.e2, str(ddi), pred]))
f.write('\n')
def print_info(f, tup, target, pred, root):
f.write('{}\n'.format(' '.join(tup.sent)))
f.write('{}\n'.format(' | '.join(
[tup.sent_id, tup.e1, tup.e2, target, pred])))
f.write('{}\n\n'.format(root))
# error analysis
print('Analyzing...')
with open(args.error_file, 'w') as f:
f.write(' | '.join(['sent_id', 'e1', 'e2', 'target', 'pred']))
f.write('\n')
for tup, target, pred, treelist in zip(test_raw_corpus, y_true, y_pred,
treelists):
if target != pred:
print_info(f, tup, target, pred, treelist[-1])
# attention
print('Writing attention scores...')
with open(args.correct_file, 'w') as f:
f.write(' | '.join(['target', 'sent', 'att_weight']))
f.write('\n')
for tup, target, pred, treelist in zip(test_raw_corpus, y_true, y_pred,
treelists):
if target == pred and target != 'null':
print_info(f, tup, target, pred, treelist[-1])
def main():
parser = options.get_parser('Trainer')
options.add_dataset_args(parser)
options.add_preprocessing_args(parser)
options.add_model_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
args = parser.parse_args()
print(args)
args.cuda = not args.disable_cuda and torch.cuda.is_available()
torch.manual_seed(5)
if args.cuda:
torch.backends.cudnn.benchmark = True
# increase recursion depth
sys.setrecursionlimit(10000)
# checkpoint
checkpoint_dir = os.path.dirname(args.checkpoint)
if not os.path.isdir(checkpoint_dir):
os.mkdir(checkpoint_dir)
# load dataset
train_raw_corpus, val_raw_corpus, test_raw_corpus = utils.load_corpus(args.processed_dir, ddi=False)
assert train_raw_corpus and val_raw_corpus and test_raw_corpus, 'Corpus not found, please run preprocess.py to obtain corpus!'
train_corpus = [(line.sent, line.type, line.p1, line.p2) for line in train_raw_corpus]
val_corpus = [(line.sent, line.type, line.p1, line.p2) for line in val_raw_corpus]
start_epoch = 0
caseless = args.caseless
batch_size = args.batch_size
num_epoch = args.num_epoch
# build vocab
sents = [tup[0] for tup in train_corpus + val_corpus]
feature_map = utils.build_vocab(sents, min_count=args.min_count, caseless=caseless)
target_map = ddi2013.target_map
# get class weights
_, train_targets = utils.build_corpus(train_corpus, feature_map, target_map, caseless)
class_weights = torch.Tensor(utils.get_class_weights(train_targets)) if args.class_weight else None
train_loader, val_loader, test_loader = utils.load_datasets(args.processed_dir, args.train_size, args, feature_map, dataloader=True)
# build model
vocab_size = len(feature_map)
tagset_size = len(target_map)
model = RelationTreeModel(vocab_size, tagset_size, args)
# loss
criterion = utils.build_loss(args, class_weights=class_weights)
# load states
if os.path.isfile(args.load_checkpoint):
print('Loading checkpoint file from {}...'.format(args.load_checkpoint))
checkpoint_file = torch.load(args.load_checkpoint)
start_epoch = checkpoint_file['epoch'] + 1
model.load_state_dict(checkpoint_file['state_dict'])
# optimizer.load_state_dict(checkpoint_file['optimizer'])
else:
print('no checkpoint file found: {}, train from scratch...'.format(args.load_checkpoint))
if not args.rand_embedding:
pretrained_word_embedding, in_doc_word_indices = utils.load_word_embedding(args.emb_file, feature_map, args.embedding_dim)
print(pretrained_word_embedding.size())
print(vocab_size)
model.load_pretrained_embedding(pretrained_word_embedding)
if args.disable_fine_tune:
model.update_part_embedding(in_doc_word_indices) # update only non-pretrained words
model.rand_init(init_embedding=args.rand_embedding)
# trainer
trainer = TreeTrainer(args, model, criterion)
best_f1 = float('-inf')
if os.path.isfile(args.load_checkpoint):
dev_prec, dev_rec, dev_f1, _ = evaluate(trainer, val_loader, target_map, cuda=args.cuda)
test_prec, test_rec, test_f1, _ = evaluate(trainer, test_loader, target_map, cuda=args.cuda)
best_f1 = dev_f1
print('checkpoint dev_prec: {:.4f}, dev_rec: {:.4f}, dev_f1: {:.4f}, test_prec: {:.4f}, test_rec: {:.4f}, test_f1: {:.4f}'.format(
dev_prec, dev_rec, dev_f1, test_prec, test_rec, test_f1))
track_list = []
patience_count = 0
start_time = time.time()
q = mp.Queue()
# set start methods
try:
mp.set_start_method('spawn')
except RuntimeError:
pass
for epoch in range(start_epoch, num_epoch):
epoch_loss = train(train_loader, trainer, epoch)
# processes = []
# for rank in range(args.num_processes):
# p = mp.Process(target=train, args=(train_loader, trainer, epoch, q))
# p.start()
# processes.append(p)
# for p in processes:
# p.join()
#
# epoch_loss = q.get()
# update lr
trainer.lr_step(epoch_loss)
dev_prec, dev_rec, dev_f1, dev_loss = evaluate(trainer, val_loader, target_map, cuda=args.cuda)
test_prec, test_rec, test_f1, _ = evaluate(trainer, test_loader, target_map, cuda=args.cuda)
if dev_f1 >= best_f1:
patience_count = 0
best_f1 = dev_f1
track_list.append({'epoch': epoch, 'loss': epoch_loss,
'dev_prec': dev_prec, 'dev_rec': dev_rec, 'dev_f1': dev_f1, 'dev_loss': dev_loss,
'test_prec': test_prec, 'test_rec': test_rec, 'test_f1': test_f1})
print('epoch: {}, loss: {:.4f}, dev_f1: {:.4f}, dev_loss: {:.4f}, test_f1: {:.4f}\tsaving...'.format(epoch, epoch_loss, dev_f1, dev_loss, test_f1))
try:
utils.save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': trainer.optimizer.state_dict(),
'f_map': feature_map,
't_map': target_map,
}, {'track_list': track_list,
'args': vars(args)
}, args.checkpoint)
except Exception as inst:
print(inst)
else:
patience_count += 1
track_list.append({'epoch': epoch,'loss': epoch_loss, 'dev_prec': dev_prec, 'dev_rec': dev_rec, 'dev_f1': dev_f1, 'dev_loss': dev_loss})
print('epoch: {}, loss: {:.4f}, dev_f1: {:.4f}, dev_loss: {:.4f}, test_f1: {:.4f}'.format(epoch, epoch_loss, dev_f1, dev_loss, test_f1))
print('epoch: {} in {} take: {} s'.format(epoch, args.num_epoch, time.time() - start_time))
if patience_count >= args.patience:
break
FLAGS.setDefaults()
model_factory = ModelFactory()
# load training and test set file names
train_generator = get_generator(FLAGS.train_csv, FLAGS, augmenter)
test_generator = get_generator(FLAGS.test_csv, FLAGS)
class_weights = None
if FLAGS.use_class_balancing:
if FLAGS.multi_label_classification:
class_weights = get_multilabel_class_weights(
train_generator.y, FLAGS.positive_weights_multiply)
else:
class_weights = get_class_weights(train_generator.get_class_counts(),
FLAGS.positive_weights_multiply)
# load classifier from saved weights or get a new one
training_stats = {}
learning_rate = FLAGS.learning_rate
if FLAGS.load_model_path != '' and FLAGS.load_model_path is not None:
visual_model = load_model(FLAGS.load_model_path)
if FLAGS.show_model_summary:
visual_model.summary()
training_stats_file = os.path.join(FLAGS.save_model_path,
".training_stats.json")
if os.path.isfile(training_stats_file):
training_stats = json.load(open(training_stats_file))
learning_rate = training_stats['lr']
print("Will continue from learning rate: {}".format(learning_rate))
if args.name != '':
config['model']['name'] = args.name
if args.max_iter > 0:
config['train']['max_iter'] = args.max_iter
if args.batch_size > 0:
config['train']['batch_size'] = args.batch_size
num_classes = int(config['model']['num_classes'])
batch_size = int(config['train']['batch_size'])
train_data = VFDataset([1, 3, 5, 8])
valid_data = VFDataset([4, 6])
test_data = VFDataset([2, 7])
class_weights = get_class_weights(num_classes=num_classes,
train_data=train_data,
valid_data=valid_data)
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(valid_data, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)
device = torch.device('cuda')
Model = model_dict[args.model]
model = Model(n_classes=num_classes).to(device)
model = nn.DataParallel(model)
trainer = Trainer(model, config, train_loader, valid_loader, class_weights,
device)
trainer.train(vis_data=test_data)
accuracy, precisions, recalls, IoUs = trainer.eval(test_loader)
def classification(variant='1_hour',
structureless=False,
batch_size=8,
x_size=12,
h_size=8,
emo_size=8,
top_sizes=(16, 16),
p_drop=0.1,
verbose=1,
bi=True,
deep=True,
lr_tree=0.05,
lr_top=0.01,
decay_tree=0.003,
decay_top=0.006,
epochs=60,
cuda_id=-1):
data_dir = '../data/'
out_dir = '../results/'
graphs_dir = data_dir + 'graphs_all/'
cascade_size_file = data_dir + 'cascade_size.csv'
device = set_device(cuda_id)
if structureless:
x_size = x_size - 2
train_ids = np.array([
ID.split('_')[0] for ID in os.listdir(graphs_dir)
if variant in ID and 'test' not in ID
])
test_ids = np.unique([
ID.split('_')[0] for ID in os.listdir(graphs_dir)
if variant + '_test' in ID
])
train_set = CascadeData(train_ids,
graphs_dir,
cascade_size_file,
variant=variant,
categorical=True,
structureless=structureless)
test_set = CascadeData(test_ids,
graphs_dir,
cascade_size_file,
test=True,
variant=variant,
categorical=True,
structureless=structureless)
_, weights_all = get_class_weights(train_set)
weighted_sampler = WeightedRandomSampler(weights_all, len(weights_all))
train_generator = DataLoader(train_set,
collate_fn=cascade_batcher(device),
batch_size=batch_size,
num_workers=8,
sampler=weighted_sampler)
test_generator = DataLoader(test_set,
collate_fn=cascade_batcher(device),
batch_size=batch_size,
num_workers=8)
deep_tree = DeepTreeLSTMClassifier(x_size,
4,
emo_size,
h_size=h_size,
top_sizes=top_sizes,
bi=bi,
deep=deep,
pd=p_drop)
criterion = nn.CrossEntropyLoss()
optimizer_tree = th.optim.Adam(deep_tree.bottom_net.parameters(),
lr=lr_tree,
weight_decay=decay_tree)
optimizer_top = th.optim.Adam(deep_tree.top_net.parameters(),
lr=lr_top,
weight_decay=decay_top)
scheduler_tree = th.optim.lr_scheduler.StepLR(optimizer_tree,
step_size=10,
gamma=0.8)
scheduler_top = th.optim.lr_scheduler.StepLR(optimizer_top,
step_size=10,
gamma=0.8)
callbacks = [
EarlyStopping(patience=10),
ExperimentLogger(out_dir, filename='logs_classification.csv')
]
model_trainer = DeepTreeTrainer(deep_tree)
model_trainer.compile(optimizer_tree,
optimizer_top,
criterion,
scheduler_tree=scheduler_tree,
scheduler_top=scheduler_top,
callbacks=callbacks,
metrics=['mul_acc'])
model_trainer.fit(train_generator,
test_generator,
epochs,
cuda_id,
verbose=verbose)
return deep_tree
train_do_ds = tf_dataset_generator(train_path,
process_path_double_obj,
batch_size=8)
val_do_ds = tf_dataset_generator(val_path,
process_path_double_obj,
batch_size=8)
test_do_ds = tf_dataset_generator(test_path,
process_path_double_obj,
batch_size=8)
list_ds = tf.data.Dataset.list_files(train_path + '/*')
data_set = list_ds.map(process_path_double_obj,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
# get class weights
class_weights = get_class_weights(
tf_dataset_generator(train_path, process_path, train=False, cache=False))
# define single headed model
EnetSingle = EnetModel(C=12)
for img, iml in train_ds.take(1):
img_test = img
iml_test = iml
img_out = EnetSingle(img_test)
# define double headed model
EnetDouble = EnetModel(C=12, MultiObjective=True)
for img, iml in train_do_ds.take(1):
img_do_test = img
iml_do_test = iml
img_do_out = EnetDouble(img_do_test)
def train_model(model,
train_dl,
val_dl,
epochs: int = 10,
lr: float = 3e-4,
name: str = 'no_name',
mcat_ratio: float = 0.1,
ema: float = 0.99,
pbar_width: int = None,
use_wandb: bool = True,
overwrite_model: bool = True):
''' Train a given model.
INPUT
model: torch.nn.Module
The model we would like to train
train_dl: torch.utils.data.DataLoader
A dataloader containing the training set
val_dl : torch.utils.data.DataLoader
A dataloader containing the validation set
epochs: int = 10
The amount of epochs to train
lr: float = 3e-4
The learning rate used
name: str = 'no_name'
The name of the training run, used for wandb purposes
mcat_ratio: float = 0.1
How much the master category loss is prioritised over the
category loss
ema: float = 0.99
The fact used in computing the exponential moving averages of
the loss and sample-average F1 scores. Roughly corresponds to
taking the average of the previous 1 / (1 - ema) many batches
pbar_width: int = None
The width of the progress bar. If running in a Jupyter notebook
then this should be set to ~1000
use_wandb: bool = True
Whether to use the Weights & Biases online performance recording
overwrite_model: bool = True
Whether to overwrite existing models when saving
OUTPUT
The trained model
'''
from sklearn.metrics import f1_score
import warnings
from pathlib import Path
print(f'Training on {len(train_dl) * train_dl.batch_size:,d} samples '\
f'and validating on {len(val_dl) * val_dl.batch_size:,d} samples.')
print(f'Number of trainable parameters: {model.trainable_params():,d}')
# Sign into wandb and log metrics from model
if use_wandb:
import wandb
config = {
'name': name,
'mcat_ratio': mcat_ratio,
'epochs': epochs,
'lr': lr,
'batch_size': train_dl.batch_size,
'ema': ema,
'vectors': train_dl.vectors,
'dropout': model.params['dropout'],
'nlayers': model.params['nlayers'],
'dim': model.params['dim'],
'boom_dim': model.params['boom_dim'],
'emb_dim': model.params['vocab'].vectors.shape[1],
}
wandb.init(project='scholarly', config=config)
wandb.watch(model)
weights = get_class_weights(train_dl,
pbar_width=model.pbar_width,
data_dir=model.data_dir)
criterion = NestedBCELoss(**weights,
mcat_ratio=mcat_ratio,
data_dir=model.data_dir)
optimizer = optim.Adam(model.parameters(), lr=lr)
mcat_masks = get_mcat_masks(data_dir=model.data_dir)
if model.is_cuda():
mcat_masks = mcat_masks.cuda()
criterion = criterion.cuda()
avg_loss, avg_cat_f1, avg_mcat_f1, best_score = 0, 0, 0, 0
for epoch in range(epochs):
with tqdm(total=len(train_dl) * train_dl.batch_size,
ncols=model.pbar_width) as pbar:
model.train()
for idx, (x_train, y_train) in enumerate(train_dl):
optimizer.zero_grad()
if model.is_cuda():
x_train = x_train.cuda()
y_train = y_train.cuda()
# Get cat predictions
y_hat = model(x_train)
preds = torch.sigmoid(y_hat)
# Get master cat predictions
my_hat, my_train = cats2mcats(y_hat,
y_train,
masks=mcat_masks,
data_dir=model.data_dir)
mpreds = torch.sigmoid(my_hat)
# Calculate loss and perform backprop
loss = criterion(y_hat, y_train)
loss.backward()
optimizer.step()
# Compute f1 scores
with warnings.catch_warnings():
warnings.simplefilter('ignore')
cat_f1 = f1_score(preds.cpu() > 0.5,
y_train.cpu(),
average='samples')
mcat_f1 = f1_score(mpreds.cpu() > 0.5,
my_train.cpu(),
average='samples')
# Keep track of the current iteration index
iteration = epoch * len(train_dl) * train_dl.batch_size
iteration += idx * train_dl.batch_size
# Exponentially moving average of loss and f1 scores
avg_loss = ema * avg_loss + (1 - ema) * float(loss)
avg_loss /= 1 - ema**(iteration / (1 - ema) + 1)
avg_cat_f1 = ema * avg_cat_f1 + (1 - ema) * float(cat_f1)
avg_cat_f1 /= 1 - ema**(iteration / (1 - ema) + 1)
avg_mcat_f1 = ema * avg_mcat_f1 + (1 - ema) * float(mcat_f1)
avg_mcat_f1 /= 1 - ema**(iteration / (1 - ema) + 1)
# Log wandb
if use_wandb:
wandb.log({
'loss': avg_loss,
'cat f1': avg_cat_f1,
'mcat f1': avg_mcat_f1
})
# Update the progress bar
desc = f'Epoch {epoch:2d} - '\
f'loss {avg_loss:.4f} - '\
f'cat f1 {avg_cat_f1:.4f} - '\
f'mcat f1 {avg_mcat_f1:.4f}'
pbar.set_description(desc)
pbar.update(train_dl.batch_size)
# Compute validation scores
with torch.no_grad():
model.eval()
val_loss, val_cat_f1, val_mcat_f1 = 0, 0, 0
y_vals, y_hats = [], []
for x_val, y_val in val_dl:
if model.is_cuda():
x_val = x_val.cuda()
y_val = y_val.cuda()
# Get cat predictions
y_hat = model(x_val)
preds = torch.sigmoid(y_hat)
# Get mcat predictions
my_hat, my_val = cats2mcats(y_hat,
y_val,
masks=mcat_masks,
data_dir=model.data_dir)
mpreds = torch.sigmoid(my_hat)
# Collect the true and predicted labels
y_vals.append(y_val)
y_hats.append(preds > 0.5)
# Accumulate loss
val_loss += float(criterion(y_hat, y_val, weighted=False))
# Accumulate f1 scores
with warnings.catch_warnings():
warnings.simplefilter('ignore')
val_cat_f1 += f1_score(preds.cpu() > 0.5,
y_val.cpu(),
average='samples')
val_mcat_f1 += f1_score(mpreds.cpu() > 0.5,
my_val.cpu(),
average='samples')
# Concatenate the true and predicted labels
y_val = torch.cat(y_vals, dim=0)
y_hat = torch.cat(y_hats, dim=0)
# Compute the average loss and f1 scores
val_loss /= len(val_dl)
val_cat_f1 /= len(val_dl)
val_mcat_f1 /= len(val_dl)
# Log wandb
if use_wandb:
wandb.log({
'val loss': val_loss,
'val cat f1': val_cat_f1,
'val mcat f1': val_mcat_f1
})
# If the current cat f1 score is the best so far, then
# replace the stored model with the current one
if val_cat_f1 > best_score:
model_fname = f'model_{val_cat_f1 * 100:.2f}.pt'
best_score = val_cat_f1
data = {
'params': model.params,
'state_dict': model.state_dict(),
'scores': model.evaluate(val_dl, output_dict=True)
}
if overwrite_model:
for f in get_path(model.data_dir).glob(f'model*.pt'):
f.unlink()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
path = get_path(model.data_dir) / model_fname
torch.save(data, path)
# Save the model's state dict to wandb directory
if use_wandb:
if overwrite_model:
for f in Path(wandb.run.dir).glob(f'model*.pt'):
f.unlink()
torch.save(data, Path(wandb.run.dir) / model_fname)
wandb.save(model_fname)
# Update progress bar
desc = f'Epoch {epoch:2d} - '\
f'loss {avg_loss:.4f} - '\
f'cat f1 {avg_cat_f1:.4f} - '\
f'mcat f1 {avg_mcat_f1:.4f} - '\
f'val_loss {val_loss:.4f} - '\
f'val cat f1 {val_cat_f1:.4f} - '\
f'val mcat f1 {val_mcat_f1:.4f}'
pbar.set_description(desc)
return model
mode='test')
c_test.validate_corpus()
# Sentences, Labels, POS Tags - I could just use better variable names
x_test, y_test, z_pos = features.generate_input_and_labels(c_test.sentences,
Vectors=embeddings)
# POS Tags to numerical sequences
pos_tokenizer = Tokenizer()
pos_tokenizer.fit_on_texts(z_pos)
pos_sequences = pos_tokenizer.texts_to_sequences(z_pos)
z_test = to_categorical(pos_sequences)
# TODO: Needs to come from the train I assume
class_weights = list(
utils.get_class_weights(c_test.label_list, WEIGHT_SMOOTHING).values())
print('loss_weight {}'.format(class_weights))
# Load model and Embeddings
model = load_model('naacl_metaphor.h5',
custom_objects={
'loss':
utils.weighted_categorical_crossentropy(class_weights),
'f1':
utils.f1,
'precision':
utils.precision,
'recall':
utils.recall
})
def train_with_erm(classifier, train_loader, tune_loader, num_epochs, logger=None, save_file=None):
''' Train the provided classifier on the data from train_loader, evaluating the performance
along the way with the data from tune_loader
Arguments
---------
classifier: pytorch.nn.Module
The model to train
train_loader: pytorch.utils.data.DataLoader
The DataLoader containing training data
tune_loader: pytorch.utils.data.DataLoader
The DataLoader containing tuning data
num_epochs: int
The number of times the model should be trained on all the data
logger: logging.logger
The python logger object to handle printing logs
save_file: string or Path object
The file to save the model to. If save_file is None, the model won't be saved
Returns
-------
results: dict
A dictionary containing lists tracking different loss metrics across epochs
'''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier = classifier.to(device)
optimizer = optim.Adam(classifier.parameters(), lr=1e-5)
class_weights = utils.get_class_weights(train_loader)
# Calculate baseline tune set prediction accuracy (just pick the largest class)
tune_label_counts, _ = utils.get_value_counts(tune_loader)
baseline = max(list(tune_label_counts.values())) / len(tune_dataset)
results = {'train_loss': [], 'tune_loss': [], 'train_acc': [], 'tune_acc': [],
'baseline': baseline}
try:
best_tune_loss = None
for epoch in tqdm_notebook(range(num_epochs)):
train_loss = 0
train_correct = 0
# Set training mode
classifier = classifier.train()
for batch in train_loader:
expression, labels, ids = batch
expression = expression.float().to(device)
labels = labels.float().to(device)
# Get weights to handle the class imbalance
batch_weights = [class_weights[int(label)] for label in labels]
batch_weights = torch.FloatTensor(batch_weights).to(device)
loss_function = nn.BCEWithLogitsLoss(weight=batch_weights)
# Standard update step
optimizer.zero_grad()
output = classifier(expression)
loss = loss_function(output, labels)
loss.backward()
optimizer.step()
train_loss += loss.item()
train_correct += utils.count_correct(output, labels)
# Disable the gradient and switch into model evaluation mode
with torch.no_grad():
classifier = classifier.eval()
tune_loss = 0
tune_correct = 0
for tune_batch in tune_loader:
expression, labels, ids = tune_batch
expression = expression.float().to(device)
tune_labels = labels.float().to(device)
batch_weights = [class_weights[int(label)] for label in labels]
batch_weights = torch.FloatTensor(batch_weights).to(device)
loss_function = nn.BCEWithLogitsLoss(weight=batch_weights)
tune_output = classifier(expression)
loss = loss_function(tune_output, tune_labels)
tune_loss += loss.item()
tune_correct += utils.count_correct(tune_output, tune_labels)
# Save the model
if save_file is not None:
if best_tune_loss is None or tune_loss < best_tune_loss:
best_tune_loss = tune_loss
torch.save(classifier, save_file)
train_accuracy = train_correct / len(train_dataset)
tune_accuracy = tune_correct / len(tune_dataset)
if logger is not None:
logger.info('Epoch {}'.format(epoch))
logger.info('Train loss: {}'.format(train_loss / len(train_dataset)))
logger.info('Tune loss: {}'.format(tune_loss / len(tune_dataset)))
logger.info('Train accuracy: {}'.format(train_accuracy))
logger.info('Tune accuracy: {}'.format(tune_accuracy))
logger.info('Baseline accuracy: {}'.format(baseline))
results['train_loss'].append(train_loss / len(train_dataset))
results['tune_loss'].append(tune_loss / len(tune_dataset))
results['train_acc'].append(train_accuracy)
results['tune_acc'].append(tune_accuracy)
except Exception as e:
# Print error
logger.error(e, exc_info=True)
finally:
results = utils.add_study_ids_to_results(results, train_dirs, tune_dirs)
return results