loss.backward()
optimizer.step()
score = validate_random(model, valid_dl, n_tta=10)
if score > best_score:
best_score = score
model_state = {
"model_name": 'freesound',
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"state_dict": model.state_dict()
}
torch.save(model_state, model_state_path)
scheduler.step(score)
print(epoch, score)
earlystopper.step(score)
if earlystopper.stop:
break
print(best_score)
model_state = torch.load(model_state_path)
model.load_state_dict(model_state["state_dict"])
score, predictions = validate_search(model, train_meta.iloc[train_splits[fold][1]], train_cache)
print('score of loaded model: {}'.format(score))
sub.iloc[:, 1:] = predictions
sub.to_csv('train_focus_fold_{}_gpu_{}.csv'.format(fold, args.gpu), index=False)
if fold == 0:
full_predictions = 1 / predictions
else:
full_predictions += 1 / predictions
with torch.no_grad():
for batch_idx, (img, mask, _) in enumerate(valid_loader):
img = img.cuda()
mask = mask.cuda()
pred = model(img)
loss = criterion(pred, mask)
valid_loss.append(loss.item())
print('[EPOCH {}/{}] Train Loss: {:.4f}; Valid Loss: {:.4f}'.format(
epoch, args.epochs, np.mean(train_loss), np.mean(valid_loss)
))
flag, best, bad_epochs = es.step(torch.Tensor([np.mean(valid_loss)]))
if flag:
print('Early stopping criterion met')
break
else:
if bad_epochs == 0:
save_nets(nets, 'model')
print('Saving current best model')
print('Current Valid loss: {:.4f}; Current best: {:.4f}; Bad epochs: {}'.format(
np.mean(valid_loss), best.item(), bad_epochs
))
print('Training done... start evaluation')
with torch.no_grad():
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)
if epoch % 50 == 0:
valid_loss, valid_incorrect = forward_pass(valid_loader, elisa_net,
None, None, 'VALIDATE',
epoch, args)
valid_loss = np.mean(valid_loss)
valid_accuracy = 1. - (sum(valid_incorrect) / len(valid_set))
print('[EPOCH {}/{}][VALIDATE AVG] Loss: {:.4f}; ACCURACY: {:.4f}'.
format(epoch, args.epochs, valid_loss, valid_accuracy))
writer.add_scalars('Valid Loss', {'ELISANET': valid_loss}, epoch)
writer.add_scalars('Valid ACCURACY', {'ELISANET': valid_accuracy},
epoch)
flag, best, bad_epochs = es.step(torch.Tensor([valid_loss]))
if flag:
print('Early stopping criterion met')
break
else:
if bad_epochs == 0:
save_nets([elisa_net], solver, epoch, args)
best_epoch = epoch
print('Saving best net weights')
elif bad_epochs % (args.patience // 2) == 0:
scheduler.step()
print(
'[EPOCH {}] Current Valid Loss: {:.6f}; Best: {:.6f}; Bad epochs: {}; Best epoch: {}'
.format(epoch, valid_loss, best.item(), bad_epochs, best_epoch))
