def run_train(self, model, dataloaders, num_epochs=10, callbacks=None):
if callbacks is None:
callbacks = []
model.to(self.device)
earlystop = EarlyStopping(patience=20,
verbose=True,
checkpoint='checkpoint_effnet_large')
tracker = MetricsTracker()
batch_size = self.opt.batchSize
for epoch in tqdm(range(num_epochs)):
print('Epoch:', epoch)
for phase in ['train', 'valid']:
if phase == ' train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
dataloader = iter(dataloaders[phase])
for batch_idx, batch_data in tqdm(
enumerate(dataloader), leave=False,
total=len(dataloader)):
data, target = batch_data['data'], batch_data['class']
running_corrects_, running_loss_ = self.run_one_step(model,
batch_data,
phase)
accuracy = float(running_corrects) / ((batch_idx + 1) * batch_size)
metrics = dict(accuracy=accuracy,
loss=running_loss_ / data.size(0))
if batch_idx % self.opt.display_count == 0:
for cb in callbacks:
cb.update_metrics(metrics, phase)
running_corrects += running_corrects_
running_loss += running_loss_
epoch_acc = running_corrects.double() / (len(dataloaders[phase]) * batch_size)
epoch_loss = running_loss / (
len(dataloaders[phase]) * batch_size)
if phase == 'valid':
tracker.end_valid_iteration(epoch_loss, epoch_acc.item())
earlystop(epoch_loss, model)
if phase == 'train':
tracker.end_train_iteration(epoch_loss, epoch_acc.item())
if (earlystop.early_stop):
print("Early stopping")
break
print('{} Accuracy: '.format(phase), epoch_acc.item())
tracker.end_epoch()
def train(self, training_data: TrainingData) -> None:
x_train, y_train, x_val, y_val, vocab, class_to_i, i_to_class = preprocess_dataset(training_data, full_question=args.full_question, create_runs=args.create_runs)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
print('Batchifying data')
train_batches = batchify(x_train, y_train, shuffle=True)
val_batches = batchify(x_val, y_val, shuffle=False)
self.model = ElmoModel(len(i_to_class), dropout=self.dropout)
self.model = self.model.to(self.device)
print(f'Model:\n{self.model}')
parameters = list(self.model.classifier.parameters())
for mix in self.model.elmo._scalar_mixes:
parameters.extend(list(mix.parameters()))
self.optimizer = Adam(parameters)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=5, verbose=True, mode='max')
temp_prefix = get_tmp_filename()
self.model_file = f'{temp_prefix}.pt'
print(f'Saving model to: {self.model_file}')
log = get(__name__)
manager = TrainingManager([
BaseLogger(log_func=log.info), TerminateOnNaN(), EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100), ModelCheckpoint(create_save_model(self.model), self.model_file, monitor='test_acc')
])
log.info('Starting training')
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_batches)
random.shuffle(train_batches)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(val_batches, train=False)
stop_training, reasons = manager.instruct(
train_time, train_loss, train_acc,
test_time, test_loss, test_acc
)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
'lr': args.lr,
'dropout': args.dropout
})
save_dir = 'save_model'
result_dir = save_dir + '/result/'
ckpt_dir = save_dir + '/checkpoints/'
makedirs(result_dir)
makedirs(ckpt_dir)
result_fn = result_dir + args.save + '.pkl'
ckpt_fn = ckpt_dir + args.save + '.pt'
# instantiate the early stopping object
patience = 7
early_stopping = EarlyStopping(ckpt_dir,
args.save + '.pt',
patience=patience,
verbose=True)
# Set the random seed manually for reproducibility.
if args.cuda:
if not torch.cuda.is_available():
print("WARNING: No CUDA device detected, switch to cpu device!")
device = torch.device('cpu')
torch.manual_seed(args.seed)
else:
device = torch.device('cuda')
torch.cuda.manual_seed(args.seed)
else:
if torch.cuda.is_available():
print("WARNING: CUDA device detected, continue to use cpu device!")
device = torch.device('cpu')
class NNModel(object):
def __init__(self, sess, args):
self.x = tf.placeholder(tf.float32, [None, args.feature_dim], name="input")
self.y = tf.placeholder(tf.float32, [None, args.output_dim], name="output")
self.y_output = build_model(self.x, args.output_dim)
self.y_prob = tf.nn.softmax(self.y_output, name='prob')
self.y_pred = tf.arg_max(self.y_prob, 1, name='pred')
self.correct_predictions = tf.equal(self.y_pred, tf.math.argmax(self.y, 1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_predictions, "float"), name="accuracy")
self.loss_f = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.y_output, labels=self.y))
self.x_1 = tf.placeholder(tf.float32, [None, args.feature_dim], name="input")
self.y_1_output = build_model(self.x_1, args.output_dim)
self.loss_augment = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.y_1_output, labels=self.y))
self.final_reg = tf.add(args.reg_param_1 * self.loss_y_y1, args.reg_param_2 * self.loss_augment, name="loss_final")
self.loss_final = tf.add(self.final_reg, self.loss_f, name="loss_final")
self.optimizer_final = tf.train.AdamOptimizer(args.lr, name = "opt2").minimize(loss_final)
def restore(self, sess, output_dir, model_name):
model_filename = "{}/models/{}.cpkt".format(output_dir,model_name)
if os.path.isfile(model_filename):
print('restoring model from %s...' % model_filename)
self.saver.restore(sess, model_filename)
def save(self, sess, output_dir,model_name,overwrite=True):
model_filename = "{}/models/{}.cpkt".format(output_dir,model_name)
if not os.path.isfile(model_filename) or overwrite:
print('saving model to %s...' % model_filename)
self.saver.save(sess, model_filename)
# train for the base model
def train_basemodel(self, sess, batch_x, batch_y, update_num):
feed_dict={self.x: batch_x, self.y: batch_y}
_ = sess.run(self.optimizer_f, feed_dict=feed_dict)
def standard_augmentation(self, sess, batch_x, batch_x1, batch_y):
batch_x1= am_util.augment_data_y1_modified(batch_x, S_30)
feed_dict={self.x: batch_x, self.y: batch_y, self.x_1: batch_x1}
_ = sess.run(self.optimizer_final, feed_dict=feed_dict)
# evalute on the validation set, keep the summaries
def val(self, sess, val_x, val_y):
#loss_vector = []
feed_dict = {self.x: val_x, self.y_: val_y}
# get all metrics here
loss = sess.run(self.loss_f, feed_dict=feed_dict)
#loss_vector.append(loss)
print("validation loss = ", "{:.4f} | ".format(loss))
return loss
# test and save the best result
def test(self, sess, test_x, test_y, output_dir):
feed_dict = {self.x: test_x, self.y_: test_y}
loss, accuracy, pred_test_y = sess.run([self.loss_f, self.accuracy, self.pred], feed_dict=feed_dict)
print("test loss is =", "{:.4f} | ".format(loss), "test accuracy is =", "{:.4f} | ".format(accuracy))
np.save(output_dir + '/pred_test_y.npy', pred_test_y)
np.save(output_dir + '/test_y', test_y)
with open(output_dir + '/Final_result.csv', 'a') as newFile:
headers = ['epsilon', 'reg_param1', 'reg_param2', 'test_loss', 'test_accuracy']
newFileWriter = csv.DictWriter(newFile, fieldnames=headers)
newFileWriter.writeheader()
newFileWriter.writerow({'epsilon': args.epsilon, 'reg_param1': args.reg_param1, 'reg_param2': args.reg_param2, 'test_loss': loss, 'test_accuracy': accuracy})
## 7. define the session
from util import EarlyStopping
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
sess = tf.Session(config = config)
# get the model
model = NNModel(sess, args)
# get the early stop
early_stop = EarlyStopping(model)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
update_num = 0
is_early_stop = False
## base model training
for epoch in range(args.epochs):
permutation = np.random.choice(train_num,train_num, replace=False)
if is_early_stop == True:
break
for j in range(batch_num):
update_num = update_num + 1
batch_index = permutation[(j * args.batch_size): (j + 1) * args.batch_size]
batch_x = train_x[batch_index, :]
batch_y = train_y[batch_index, :]
model.train_basemodel(sess, batch_x, batch_y, update_num, model.train_summary_writer, args.display_step)
val_loss = model.val(sess, val_x, val_y, update_num, model.val_summary_writer, args.display_step)
##Early stopping starts after updates
if update_num > args.start_early_stop and update_num % args.display_step == 0:
is_early_stop = early_stop(val_loss, args.stopping_criteria, args.model_name, update_num)
print(is_early_stop)
if is_early_stop:
early_stop.restore(sess, args.model_name, update_num)
#model.test(sess, test_x, test_y, output_dir)
break
shuffle=True,
drop_last=True,
num_workers=4)
train_dl = DataLoader(dataset=train_ds,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=4)
model = densenet161((1, 128, 128))
model.cuda()
criterion = BCEWithLogitsLoss().cuda()
optimizer = Adam(model.parameters(), lr=1e-3)
scheduler = MultiStepLR(optimizer, milestones=[30, 45, 60], gamma=0.1)
earlystopper = EarlyStopping(mode='max',
min_delta=0.0001,
patience=15,
percentage=False)
best_score = 0
model_state_path = os.path.join(
Path.MODEL_DIR, 'densenet161_noisy_gpu_{}.tar'.format(args.gpu))
for epoch in range(MAX_ITERATIONS):
model.train()
for idx, ((x1, y1), (x2, y2)) in enumerate(zip(focus_dl, train_dl)):
optimizer.zero_grad()
x1, y1 = x1.float().cuda(), y1.float().cuda()
x2, y2 = x2.float().cuda(), y2.float().cuda()
x2, y2 = mixup_data(x2, y2, x2, y2, alpha=alpha)
out2 = model(x2)
out1 = model(x1)
loss2 = criterion(out2, y2)
f_1 = f_2= np.arange(28)
f_1, f_2 = np.meshgrid(f_1, f_2)
f_1 = f_1.reshape(784,1)
f_2 = f_2.reshape(784,1)
fsd = np.concatenate((f_2, f_1), axis = 1)
S = euclidean_distances(fsd)
S[S>8]=0
#1.load base model
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
sess = tf.Session(config = config)
# get the model
model = NNModel(sess,args)
# get the early stop
early_stop = EarlyStopping(model)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
update_num = 0
i1s_early_stop = False
update_num = 0
args.batch_size = train_num
batch_num = int(train_num / args.batch_size)
is_early_stop = False
model.restore(sess, args.base_model_path , 'base_model')
##train the final model
model_name = 'final_model'
for epoch in range(args.epochs):
permutation = np.random.choice(train_num,train_num, replace=False)
def __init__(self, conf):
self.conf = conf
self.device = torch.device(f"cuda:{conf.gpu_id}")
self.log = get_logger()
torch.set_printoptions(precision=8)
if conf.runid:
conf.rundir = mkdir(conf.outdir / conf.runid)
if not conf.rundir:
conf.rundir = next_rundir(conf.outdir, log=self.log)
self.rundir = conf.rundir
dump_args(conf, conf.rundir / "conf.json")
set_random_seed(conf.random_seed)
if self.conf.use_bert:
assert self.conf.lang in Bert.supported_langs, self.conf.lang
self.bert = Bert(self.conf.bert_model_name, device=self.device)
else:
self.bert = None
self.data = load_dataset(conf, conf.lang, bert=self.bert)
_data = [self.data]
for d in _data:
self.log.info(
f"{len(d.train_loader)} batches | bs {conf.batch_size}")
self.model = self.get_model()
self.optimizer = get_optim(conf, self.model)
optimum = "min"
if conf.lr_scheduler == "plateau":
self.lr_scheduler = ReduceLROnPlateau(self.optimizer,
factor=0.1,
patience=2,
mode=optimum,
verbose=True)
elif conf.lr_scheduler:
raise ValueError("Unknown lr_scheduler: " + conf.lr_scheduler)
self.losses = LossTrackers.from_names("loss", log=self.log)
if (self.main_lang_data.tag == "ner"
or self.conf.dataset.startswith("sr3de")):
if self.data.is_multilingual:
self.sentence_texts = {
split_name: self.main_lang_data.token_texts(split_name)
for split_name in ["dev", "test"]
}
self.conll_score = {
lang: ConllScore(tag_enc=self.main_lang_data.tag_enc)
for lang in self.data.dev
}
self.score = {
lang: Score("f1",
save_model=False,
log=self.log,
score_func=self.conll_score[lang],
add_mode="append")
for lang in self.data.dev
}
self.avg_score = Score("avg_f1",
log=self.log,
score_func="dummy",
add_mode="append")
else:
self.sentence_texts = {
split_name: self.main_lang_data.token_texts(split_name)
[:conf.max_eval_inst]
for split_name in ["dev", "test"]
}
self.conll_score = ConllScore(
tag_enc=self.main_lang_data.tag_enc)
self.score = Score("f1",
log=self.log,
score_func=self.conll_score,
add_mode="append")
else:
if self.data.is_multilingual:
self.score = {
lang: Score("acc", log=self.log)
for lang in self.data.dev
}
self.avg_score = Score("avg_acc",
log=self.log,
score_func="dummy",
add_mode="append")
else:
self.score = Score("acc", log=self.log)
if conf.early_stop > 0:
score_optimum = ("max" if
(self.conf.dataset.startswith("wikiannmulti")
or self.data.is_multilingual) else
self.score.optimum)
self.early_stop = EarlyStopping(
score_optimum,
min_delta=conf.early_stop_min_delta,
patience=conf.early_stop)
else:
self.early_stop = None
self.epoch = 0
class Trainer():
def __init__(self, conf):
self.conf = conf
self.device = torch.device(f"cuda:{conf.gpu_id}")
self.log = get_logger()
torch.set_printoptions(precision=8)
if conf.runid:
conf.rundir = mkdir(conf.outdir / conf.runid)
if not conf.rundir:
conf.rundir = next_rundir(conf.outdir, log=self.log)
self.rundir = conf.rundir
dump_args(conf, conf.rundir / "conf.json")
set_random_seed(conf.random_seed)
if self.conf.use_bert:
assert self.conf.lang in Bert.supported_langs, self.conf.lang
self.bert = Bert(self.conf.bert_model_name, device=self.device)
else:
self.bert = None
self.data = load_dataset(conf, conf.lang, bert=self.bert)
_data = [self.data]
for d in _data:
self.log.info(
f"{len(d.train_loader)} batches | bs {conf.batch_size}")
self.model = self.get_model()
self.optimizer = get_optim(conf, self.model)
optimum = "min"
if conf.lr_scheduler == "plateau":
self.lr_scheduler = ReduceLROnPlateau(self.optimizer,
factor=0.1,
patience=2,
mode=optimum,
verbose=True)
elif conf.lr_scheduler:
raise ValueError("Unknown lr_scheduler: " + conf.lr_scheduler)
self.losses = LossTrackers.from_names("loss", log=self.log)
if (self.main_lang_data.tag == "ner"
or self.conf.dataset.startswith("sr3de")):
if self.data.is_multilingual:
self.sentence_texts = {
split_name: self.main_lang_data.token_texts(split_name)
for split_name in ["dev", "test"]
}
self.conll_score = {
lang: ConllScore(tag_enc=self.main_lang_data.tag_enc)
for lang in self.data.dev
}
self.score = {
lang: Score("f1",
save_model=False,
log=self.log,
score_func=self.conll_score[lang],
add_mode="append")
for lang in self.data.dev
}
self.avg_score = Score("avg_f1",
log=self.log,
score_func="dummy",
add_mode="append")
else:
self.sentence_texts = {
split_name: self.main_lang_data.token_texts(split_name)
[:conf.max_eval_inst]
for split_name in ["dev", "test"]
}
self.conll_score = ConllScore(
tag_enc=self.main_lang_data.tag_enc)
self.score = Score("f1",
log=self.log,
score_func=self.conll_score,
add_mode="append")
else:
if self.data.is_multilingual:
self.score = {
lang: Score("acc", log=self.log)
for lang in self.data.dev
}
self.avg_score = Score("avg_acc",
log=self.log,
score_func="dummy",
add_mode="append")
else:
self.score = Score("acc", log=self.log)
if conf.early_stop > 0:
score_optimum = ("max" if
(self.conf.dataset.startswith("wikiannmulti")
or self.data.is_multilingual) else
self.score.optimum)
self.early_stop = EarlyStopping(
score_optimum,
min_delta=conf.early_stop_min_delta,
patience=conf.early_stop)
else:
self.early_stop = None
self.epoch = 0
def get_model(self):
ntags = self.data.tag_enc.nlabels
nshapes = self.data.shape_enc.nlabels
nchars = self.data.char_enc.nlabels
bpe_emb = emb_layer(self.data.bpemb.vectors,
trainable=not self.conf.emb_fixed,
use_weights=not self.conf.emb_random_init)
if self.conf.use_fasttext:
fasttext_file = self.conf.fasttext_emb_file.format(
dataset=self.conf.dataset, lang=self.data.lang)
fasttext_emb = emb_layer(load_word2vec_file(fasttext_file,
add_unk=True),
trainable=not self.conf.emb_fixed,
use_weights=not self.conf.emb_random_init)
else:
fasttext_emb = None
model = SequenceTagger(
bpe_emb,
ntags,
self.conf,
nchars=nchars,
nshapes=nshapes,
fasttext_emb=fasttext_emb,
bert=self.bert,
tag_enc=self.main_lang_data.tag_enc,
).to(self.device)
self.log.info(f'model repr dim: {model.repr_dim}')
if self.conf.model_file:
self.log.info(f"loading model {self.conf.model_file}")
model.load_state_dict(torch.load(self.conf.model_file))
self.log.info(f"loaded model {self.conf.model_file}")
return model
def train(self, train_epoch, do_eval, do_test=None, eval_ds_name=None):
try:
for epoch in range(1, self.conf.max_epochs + 1):
self.epoch = epoch
self.model.train()
train_epoch(epoch=epoch)
self.losses.interval_end_log(epoch, ds_name="train")
burnin_done = epoch >= self.conf.first_eval_epoch
if burnin_done and not epoch % self.conf.eval_every:
score = self.do_eval(do_eval,
epoch=epoch,
eval_ds_name=eval_ds_name)
if do_test:
self.do_eval(do_test, epoch=epoch, eval_ds_name="test")
if score is not None and self.early_stop:
if self.early_stop.step(score):
if epoch >= self.conf.min_epochs:
patience = self.early_stop.patience
self.log.info(
f"Early stop after {patience} steps")
break
except KeyboardInterrupt:
self.log.info("Stopping training due to keyboard interrupt")
def do_eval(self, eval_func, epoch=None, eval_ds_name=None):
self.model.eval()
eval_func(epoch=epoch)
self.log_eval(ds_name=eval_ds_name, epoch=epoch)
if self.data.is_multilingual:
return self.avg_score.current
return self.score.current
def log_eval(self, ds_name=None, epoch=None):
self.losses.interval_end(ds_name=ds_name)
if self.data.is_multilingual:
for lang in getattr(self.data, ds_name):
if hasattr(self, "conll_score"):
self.conll_score[lang].sentences = \
self.sentence_texts[ds_name][lang]
fname = f"{epoch}.{ds_name}.{lang}.conll"
self.conll_score[lang].outfile = self.rundir / fname
self.score[lang].update()
avg_score = np.average(
[score.current for score in self.score.values()])
self.avg_score.update_log(model=self.model,
rundir=self.rundir,
epoch=epoch,
score=avg_score)
else:
if hasattr(self, "conll_score"):
self.conll_score.sentences = self.sentence_texts[ds_name]
fname = f"{epoch}.{ds_name}.conll"
self.conll_score.outfile = self.rundir / fname
self.score.update_log(self.model, self.rundir, epoch)
def save_model(self):
model_file = self.rundir / f"model.e{self.epoch}.pt"
save_model(self.model, model_file, self.log)
@property
def main_lang_data(self):
return self.data[0] if isinstance(self.data, list) else self.data
@property
def batch_iter_train_multilang(self):
main_lang_len = len(self.data[0].train_loader)
max_sim_lang_len = int(self.conf.sim_lang_ratio * main_lang_len)
def get_sim_lang_len(i):
sim_lang_len = len(self.data[i].train_loader)
return min(sim_lang_len, max_sim_lang_len)
lang_idxs = [
i for i, data in enumerate(self.data)
for _ in range(main_lang_len if i == 0 else get_sim_lang_len(i))
]
random.shuffle(lang_idxs)
iters = [data.batch_iter_train for data in self.data]
return ((i, next(iters[i])) for i in lang_idxs)
def hp_search_optuna(trial: optuna.Trial):
global gargs
args = gargs
# set config
config = load_config(args)
config['args'] = args
logger.info("%s", config)
# set path
set_path(config)
# create accelerator
accelerator = Accelerator()
config['accelerator'] = accelerator
args.device = accelerator.device
# set search spaces
lr = trial.suggest_float('lr', 1e-5, 1e-3, log=True)
bsz = trial.suggest_categorical('batch_size', [8, 16, 32, 64])
seed = trial.suggest_int('seed', 17, 42)
epochs = trial.suggest_int('epochs', 1, args.epoch)
# prepare train, valid dataset
train_loader, valid_loader = prepare_datasets(config, hp_search_bsz=bsz)
with temp_seed(seed):
# prepare model
model = prepare_model(config)
# create optimizer, scheduler, summary writer
model, optimizer, scheduler, writer = prepare_others(config,
model,
train_loader,
lr=lr)
# create secondary optimizer, scheduler
_, optimizer_2nd, scheduler_2nd, _ = prepare_others(
config, model, train_loader, lr=args.bert_lr_during_freezing)
train_loader = accelerator.prepare(train_loader)
valid_loader = accelerator.prepare(valid_loader)
config['optimizer'] = optimizer
config['scheduler'] = scheduler
config['optimizer_2nd'] = optimizer_2nd
config['scheduler_2nd'] = scheduler_2nd
config['writer'] = writer
total_batch_size = args.batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_loader)}")
logger.info(f" Num Epochs = {args.epoch}")
logger.info(
f" Instantaneous batch size per device = {args.batch_size}")
logger.info(
f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
)
logger.info(
f" Gradient Accumulation steps = {args.gradient_accumulation_steps}"
)
logger.info(f" Total optimization steps = {args.max_train_steps}")
early_stopping = EarlyStopping(logger,
patience=args.patience,
measure='f1',
verbose=1)
best_eval_f1 = -float('inf')
for epoch in range(epochs):
eval_loss, eval_f1, best_eval_f1 = train_epoch(
model, config, train_loader, valid_loader, epoch, best_eval_f1)
# early stopping
if early_stopping.validate(eval_f1, measure='f1'): break
if eval_f1 == best_eval_f1:
early_stopping.reset(best_eval_f1)
early_stopping.status()
trial.report(eval_f1, epoch)
if trial.should_prune():
raise optuna.TrialPruned()
return eval_f1
def train(args):
# set etc
torch.autograd.set_detect_anomaly(False)
# set config
config = load_config(args)
config['args'] = args
logger.info("%s", config)
# set path
set_path(config)
# create accelerator
accelerator = Accelerator()
config['accelerator'] = accelerator
args.device = accelerator.device
# prepare train, valid dataset
train_loader, valid_loader = prepare_datasets(config)
with temp_seed(args.seed):
# prepare model
model = prepare_model(config)
# create optimizer, scheduler, summary writer
model, optimizer, scheduler, writer = prepare_others(
config, model, train_loader)
# create secondary optimizer, scheduler
_, optimizer_2nd, scheduler_2nd, _ = prepare_others(
config, model, train_loader, lr=args.bert_lr_during_freezing)
train_loader = accelerator.prepare(train_loader)
valid_loader = accelerator.prepare(valid_loader)
config['optimizer'] = optimizer
config['scheduler'] = scheduler
config['optimizer_2nd'] = optimizer_2nd
config['scheduler_2nd'] = scheduler_2nd
config['writer'] = writer
total_batch_size = args.batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_loader)}")
logger.info(f" Num Epochs = {args.epoch}")
logger.info(
f" Instantaneous batch size per device = {args.batch_size}")
logger.info(
f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
)
logger.info(
f" Gradient Accumulation steps = {args.gradient_accumulation_steps}"
)
logger.info(f" Total optimization steps = {args.max_train_steps}")
# training
early_stopping = EarlyStopping(logger,
patience=args.patience,
measure='f1',
verbose=1)
local_worse_epoch = 0
best_eval_f1 = -float('inf')
for epoch_i in range(args.epoch):
epoch_st_time = time.time()
eval_loss, eval_f1, best_eval_f1 = train_epoch(
model, config, train_loader, valid_loader, epoch_i,
best_eval_f1)
# early stopping
if early_stopping.validate(eval_f1, measure='f1'): break
if eval_f1 == best_eval_f1:
early_stopping.reset(best_eval_f1)
early_stopping.status()
)
eval_loader = data.DataLoader(
dataset=DataFolder('dataset/eval_images_256/', 'dataset/eval_masks_256/', 'evaluate'),
batch_size=args.eval_batch_size,
shuffle=False,
num_workers=2
)
model = UNet(1, shrink=1).cuda()
nets = [model]
params = [{'params': net.parameters()} for net in nets]
solver = optim.Adam(params, lr=args.lr)
criterion = nn.CrossEntropyLoss()
es = EarlyStopping(min_delta=args.min_delta, patience=args.patience)
for epoch in range(1, args.epochs+1):
train_loss = []
valid_loss = []
for batch_idx, (img, mask, _) in enumerate(train_loader):
solver.zero_grad()
img = img.cuda()
mask = mask.cuda()
pred = model(img)
loss = criterion(pred, mask)
# 查看可训练参数
print("Trainable Parameter lists:")
for name, param in model.named_parameters():
if param.requires_grad:
print(name)
loss_func=nn.BCELoss()
learning_rate = 2e-3
# learning_rate = 1e-5
optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate)
# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.5)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[20, 50], gamma=0.1)
early_stopping = EarlyStopping(patience=10, verbose=False, path=model_save_path)
num_epoches = 100
print_every=40
k_value=5
print("Starting Training...")
for epoch in range(num_epoches):
# scheduler.step()
total = 0
count = 0
avg_loss = 0
train_count = 0
train_total = 0
step = 0
patience=5,
verbose=1,
epsilon=1e-8,
cooldown=0,
min_lr=0,
eps=1e-8)
train_monitor = TrainingMonitor(file_dir='pics', arch=param.model_name)
if param.task == 'cue':
model_checkpoint = ModelCheckpoint(
checkpoint_dir=f'D:/Dev/Bert/{param.model_name}',
monitor='val_cue_f1',
mode='max',
arch=param.model_name)
early_stopping = EarlyStopping(patience=param.early_stop_thres,
monitor='val_cue_f1',
mode='max')
trainer = CueTrainer(n_gpu=1,
model=model,
logger=global_logger,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
label2id=cue_label2id,
criterion=criterion,
training_monitor=train_monitor,
model_checkpoint=model_checkpoint,
resume_path=None,
grad_clip=5.0,
gradient_accumulation_steps=1,
early_stopping=early_stopping)
elif param.task == 'scope':
eval_set = ElisaDataset('elisadata/standard', 'EVALUATE')
eval_loader = data.DataLoader(dataset=eval_set,
batch_size=args.eval_batch_size,
shuffle=False,
num_workers=0)
elisa_net = network.ElisaNet(args.c_feat).cuda()
params = [{'params': elisa_net.parameters()}]
solver = optim.Adam(params, lr=args.lr)
lmda = lambda x: 0.5 # TODO: can change this based on bad_epochs
scheduler = LS.MultiplicativeLR(solver, lr_lambda=lmda)
es = EarlyStopping(mode=args.es_mode,
min_delta=args.loss_delta,
patience=args.patience)
epoch = 0
if args.resume_epoch != 0:
load_weights([elisa_net], solver, args.resume_epoch, args)
epoch = args.resume_epoch
solver = lr_resume(solver, args.lr_resume)
print('Loaded weights from epoch {}'.format(args.resume_epoch))
while epoch < args.epochs and not args.eval:
epoch += 1
train_loss, _ = forward_pass(train_loader, elisa_net, solver, scheduler,
'TRAIN', epoch, args)
def train(self, training_data: TrainingData) -> None:
x_train, y_train, x_val, y_val, vocab, class_to_i, i_to_class = preprocess_dataset(training_data, full_question=args.full_question,\
create_runs=args.create_runs, map_pattern=args.map_pattern, wiki_links=args.wiki_links, use_es_highlight=args.use_es_highlight)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.map_pattern = args.map_pattern
self.wiki_links = args.wiki_links
self.use_es_highlight = args.use_es_highlight
self.full_question = args.full_question
self.use_wiki = args.use_wiki
log = get(__name__, "dan.log")
log.info('Batchifying data')
vocab = ['<unk>', '<eos>'] + sorted(vocab)
word_to_i = {x: i for i, x in enumerate(vocab)}
self.word_to_i = word_to_i
log.info('Vocab len: ' + str(len(self.word_to_i)))
train_sampler = RandomSampler(list(zip(x_train, y_train)))
dev_sampler = RandomSampler(list(zip(x_val, y_val)))
dev_loader = DataLoader(list(zip(x_val, y_val)),
batch_size=args.batch_size,
sampler=dev_sampler,
num_workers=0,
collate_fn=self.batchify)
train_loader = DataLoader(list(zip(x_train, y_train)),
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=0,
collate_fn=self.batchify)
self.model = DanModel(len(i_to_class), len(vocab))
self.model = self.model.to(self.device)
log.info(f'Loading GloVe')
glove_word2idx, glove_vectors = load_glove("glove/glove.6B.300d.txt")
for word, emb_index in word_to_i.items():
if word.lower() in glove_word2idx:
glove_index = glove_word2idx[word.lower()]
glove_vec = torch.FloatTensor(glove_vectors[glove_index])
glove_vec = glove_vec.cuda()
self.model.text_embeddings.weight.data[emb_index, :].set_(
glove_vec)
log.info(f'Model:\n{self.model}')
self.optimizer = Adam(self.model.parameters())
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=5,
verbose=True,
mode='max')
temp_prefix = get_tmp_filename()
self.model_file = f'{temp_prefix}.pt'
print(f'Saving model to: {self.model_file}')
log = get(__name__)
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
self.model_file,
monitor='test_acc')
])
log.info('Starting training')
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_loader)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(dev_loader,
train=False)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
