def __init__(self, args: argparse.Namespace):
"""Initialize a models, tokenizer and config."""
super().__init__()
self.args = args
if isinstance(args, argparse.Namespace):
self.save_hyperparameters(args)
self.bert_dir = args.bert_path
self.bert_config = BertConfig.from_pretrained(args.bert_path)
self.model = GlyceBertForMaskedLM(self.bert_config)
self.loss_fn = CrossEntropyLoss(reduction="none")
self.acc = MaskedAccuracy(num_classes=self.bert_config.vocab_size)
def train(self, train_dataset, test_dataset, model):
weights = make_weights_for_balanced_classes(train_dataset.targets)
sampler = WeightedRandomSampler(weights, len(weights))
train_dataloader = DataLoader(
train_dataset,
batch_size=self.batch_size,
sampler=sampler,
num_workers=8,
)
test_dataloader = DataLoader(
test_dataset,
batch_size=self.batch_size,
num_workers=8,
)
criterion_triplet = OnlineTripleLoss(
margin=self.triplet_margin,
sampling_strategy=self.triplet_sampling_strategy,
)
criterion_classifier = CrossEntropyLoss()
optimizer_triplet = Adam(
params=model.feature_extractor.parameters(),
lr=self.learning_rate_triplet,
)
optimizer_classifier = Adam(
params=model.classifier.parameters(),
lr=self.learning_rate_classify,
)
print("Training with Triplet loss")
for i in range(self.epochs_triplet):
self._train_epoch_triplet(
model,
train_dataloader,
optimizer_triplet,
criterion_triplet,
i + 1,
)
save_embedding_umap(model, train_dataloader, test_dataloader,
self.exp_folder, i + 1)
print("Training the classifier")
for i in range(self.epochs_classifier):
self._train_epoch_classify(
model,
train_dataloader,
optimizer_classifier,
criterion_classifier,
i + 1,
)
self._test_epoch_(model, test_dataloader, criterion_classifier,
i + 1)
def __init__(self, args: argparse.Namespace):
"""Initialize a model, tokenizer and config."""
super().__init__()
self.args = args
if isinstance(args, argparse.Namespace):
self.save_hyperparameters(args)
self.bert_dir = args.bert_path
self.model = ExplainableModel(self.bert_dir)
self.tokenizer = RobertaTokenizer.from_pretrained(self.bert_dir)
self.loss_fn = CrossEntropyLoss()
self.train_acc = pl.metrics.Accuracy()
self.valid_acc = pl.metrics.Accuracy()
self.output = []
self.check_data = []
def __init__(
self,
args: argparse.Namespace
):
"""Initialize a model, tokenizer and config."""
super().__init__()
self.args = args
if isinstance(args, argparse.Namespace):
self.save_hyperparameters(args)
self.bert_dir = args.bert_path
self.model = RobertaForSequenceClassification.from_pretrained(self.bert_dir)
self.tokenizer = RobertaTokenizer.from_pretrained(self.bert_dir)
self.loss_fn = CrossEntropyLoss()
self.train_acc = pl.metrics.Accuracy()
self.valid_acc = pl.metrics.Accuracy()
def __init__(self, args: argparse.Namespace):
"""Initialize a models, tokenizer and config."""
super().__init__()
self.args = args
if isinstance(args, argparse.Namespace):
self.save_hyperparameters(args)
self.bert_dir = args.bert_path
self.bert_config = BertConfig.from_pretrained(
self.bert_dir, output_hidden_states=False)
self.model = GlyceBertForSequenceClassification.from_pretrained(
self.bert_dir)
self.loss_fn = CrossEntropyLoss()
self.acc = pl.metrics.Accuracy(num_classes=self.bert_config.num_labels)
gpus_string = self.args.gpus if not self.args.gpus.endswith(
',') else self.args.gpus[:-1]
self.num_gpus = len(gpus_string.split(","))
def __init__(self, args: argparse.Namespace):
"""Initialize a models, tokenizer and config."""
super().__init__()
self.args = args
if isinstance(args, argparse.Namespace):
self.save_hyperparameters(args)
self.bert_dir = args.bert_path
self.bert_config = BertConfig.from_pretrained(args.bert_path)
if self.args.mode == 'glyce':
self.model = GlyceBertForMaskedLM.from_pretrained(self.bert_dir)
else:
self.model = BertForMaskedLM.from_pretrained(self.bert_dir)
self.loss_fn = CrossEntropyLoss(reduction="none")
self.acc = MaskedAccuracy(num_classes=self.bert_config.vocab_size)
gpus_string = self.args.gpus if not self.args.gpus.endswith(
',') else self.args.gpus[:-1]
self.num_gpus = len(gpus_string.split(","))
def compute_loss(self, logits, labels):
if self.loss_type == "ce":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, 2), labels.view(-1))
elif self.loss_type == "focal":
loss_fct = FocalLoss(gamma=self.args.focal_gamma, reduction="mean")
loss = loss_fct(logits.view(-1, 2), labels.view(-1))
elif self.loss_type == "dice":
loss_fct = DiceLoss(with_logits=True,
smooth=self.args.dice_smooth,
ohem_ratio=self.args.dice_ohem,
alpha=self.args.dice_alpha,
square_denominator=self.args.dice_square,
reduction="mean")
loss = loss_fct(logits.view(-1, self.num_classes), labels)
else:
raise ValueError
return loss
def __init__(self):
super(ClassLoss, self).__init__()
self.cross_entropy = CrossEntropyLoss(ignore_index=-100)
def main():
args = get_args()
log.info(f'Parsed arguments: \n{pformat(args.__dict__)}')
assert args.cond_type.lower() in ['none', 'platanios', 'oestling']
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
log.info('Using device {}.'.format(device))
use_apex = False
if torch.cuda.is_available() and args.fp16:
log.info('Loading Nvidia Apex and using AMP')
from apex import amp, optimizers
use_apex = True
else:
log.info('Using FP32')
amp = None
log.info(f'Using time stamp {timestamp} to save models and logs.')
if not args.no_seed:
log.info(f'Setting random seed to {args.seed} for reproducibility.')
torch.manual_seed(args.seed)
random.seed(args.seed)
data = Corpus(args.datadir)
data_splits = [
{
'split': 'train',
'languages': args.dev_langs + args.target_langs,
'invert_include': True,
},
{
'split': 'valid',
'languages': args.dev_langs,
},
{
'split': 'test',
'languages': args.target_langs,
},
]
if args.refine:
data_splits.append({
'split': 'train_100',
'languages': args.target_langs,
'ignore_missing': True,
})
data_splits = data.make_datasets(data_splits, force_rebuild=args.rebuild)
train_set, val_set, test_set = data_splits['train'], data_splits[
'valid'], data_splits['test']
dictionary = data_splits['dictionary']
train_language_distr = get_sampling_probabilities(train_set, 1.0)
train_set = Dataset(train_set,
batchsize=args.batchsize,
bptt=args.bptt,
reset_on_iter=True,
language_probabilities=train_language_distr)
val_set = Dataset(val_set,
make_config=True,
batchsize=args.valid_batchsize,
bptt=args.bptt,
eval=True)
test_set = Dataset(test_set,
make_config=True,
batchsize=args.test_batchsize,
bptt=args.bptt,
eval=True)
train_loader = DataLoader(train_set, num_workers=args.workers)
val_loader = DataLoader(val_set, num_workers=args.workers)
test_loader = DataLoader(test_set, num_workers=args.workers)
if args.refine:
refine_set = dict()
for lang, lang_d in data_splits['train_100'].items():
refine_set[lang] = Dataset({lang: lang_d},
batchsize=args.valid_batchsize,
bptt=args.bptt,
make_config=True)
n_token = len(dictionary.idx2tkn)
# Load and preprocess matrix of typological features
# TODO: implement this, the OEST
# prior_matrix = load_prior(args.prior, corpus.dictionary.lang2idx)
# n_components = min(50, *prior_matrix.shape)
# pca = PCA(n_components=n_components, whiten=True)
# prior_matrix = pca.fit_transform(prior_matrix)
prior = None
model = RNN(args.cond_type,
prior,
n_token,
n_input=args.emsize,
n_hidden=args.nhidden,
n_layers=args.nlayers,
dropout=args.dropouto,
dropoute=args.dropoute,
dropouth=args.dropouth,
dropouti=args.dropouti,
wdrop=args.wdrop,
wdrop_layers=[0, 1, 2],
tie_weights=True).to(device)
if args.opt_level != 'O2':
loss_function = SplitCrossEntropyLoss(args.emsize,
splits=[]).to(device)
else:
loss_function = CrossEntropyLoss().to(
device) # Should be ok to use with a vocabulary of this small size
if use_apex:
optimizer = optimizers.FusedAdam(model.parameters(),
lr=args.lr,
weight_decay=args.wdecay)
else:
params = list(filter(lambda p: p.requires_grad,
model.parameters())) + list(
loss_function.parameters())
optimizer = Adam(params, lr=args.lr, weight_decay=args.wdecay)
if use_apex:
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.opt_level)
parameters = {
'model': model,
'optimizer': optimizer,
'loss_function': loss_function,
'use_apex': use_apex,
'amp': amp if use_apex else None,
'clip': args.clip,
'alpha': args.alpha,
'beta': args.beta,
'bptt': args.bptt,
'device': device,
'prior': args.prior,
}
# Add backward hook for gradient clipping
if args.clip:
if use_apex:
for p in amp.master_params(optimizer):
p.register_hook(
lambda grad: torch.clamp(grad, -args.clip, args.clip))
else:
for p in model.parameters():
p.register_hook(
lambda grad: torch.clamp(grad, -args.clip, args.clip))
if args.prior == 'vi':
prior = VIPrior(model, device=device)
parameters['prior'] = prior
def sample_weights(module: torch.nn.Module, input: torch.Tensor):
prior.sample_weights(module)
sample_weights_hook = model.register_forward_pre_hook(sample_weights)
# Load model checkpoint if available
start_epoch = 1
if args.resume:
if args.checkpoint is None:
log.error(
'No checkpoint passed. Specify it using the --checkpoint flag')
checkpoint = None
else:
log.info('Loading the checkpoint at {}'.format(args.checkpoint))
checkpoint = load_model(args.checkpoint, **parameters)
start_epoch = checkpoint['epoch']
if args.wdrop:
for rnn in model.rnns:
if isinstance(rnn, WeightDrop):
rnn.dropout = args.wdrop
elif rnn.zoneout > 0:
rnn.zoneout = args.wdrop
saved_models = list()
result_str = '| Language {} | test loss {:5.2f} | test ppl {:8.2f} | test bpc {:8.3f}'
def test():
log.info('=' * 89)
log.info('Running test set (zero-shot results)...')
test_loss, avg_loss = evaluate(test_loader, **parameters)
log.info('Test set finished | test loss {} | test bpc {}'.format(
test_loss, test_loss / math.log(2)))
for lang, avg_l_loss in avg_loss.items():
langstr = dictionary.idx2lang[lang]
log.info(
result_str.format(langstr, avg_l_loss, math.exp(avg_l_loss),
avg_l_loss / math.log(2)))
log.info('=' * 89)
if args.train:
f = 1.
stored_loss = 1e32
epochs_no_improve = 0
val_losses = list()
# calculate specific language lr
data_spec_count = sum([len(ds) for l, ds in train_set.data.items()])
data_spec_avg = data_spec_count / len(train_set.data.items())
data_spec_lrweights = dict([(l, data_spec_avg / len(ds))
for l, ds in train_set.data.items()])
# estimate total number of steps
total_steps = sum(
[len(ds) // args.bptt
for l, ds in train_set.data.items()]) * args.no_epochs
steps = 0
try:
pbar = tqdm.trange(start_epoch,
args.no_epochs + 1,
position=1,
dynamic_ncols=True)
for epoch in pbar:
steps = train(train_loader,
lr_weights=data_spec_lrweights,
**parameters,
total_steps=total_steps,
steps=steps,
scaling=args.scaling,
n_samples=args.n_samples,
tb_writer=tb_writer)
val_loss, _ = evaluate(val_loader, **parameters)
pbar.set_description('Epoch {} | Val loss {}'.format(
epoch, val_loss))
# Save model
if args.prior == 'vi':
sample_weights_hook.remove()
filename = path.join(
args.checkpoint_dir, '{}_epoch{}{}_{}.pth'.format(
timestamp, epoch, '_with_apex' if use_apex else '',
args.prior))
torch.save(make_checkpoint(epoch + 1, **parameters), filename)
saved_models.append(filename)
if args.prior == 'vi':
sample_weights_hook = model.register_forward_pre_hook(
sample_weights)
# Early stopping
if val_loss < stored_loss:
epochs_no_improve = 0
stored_loss = val_loss
else:
epochs_no_improve += 1
if epochs_no_improve == args.patience:
log.info('Early stopping at epoch {}'.format(epoch))
break
val_losses.append(val_loss)
# Reduce lr every 1/3 total epochs
if epoch - 1 > f / 3 * args.no_epochs:
log.info('Epoch {}/{}. Dividing LR by 10'.format(
epoch, args.no_epochs))
for g in optimizer.param_groups:
g['lr'] = g['lr'] / 10
f += 1.
test()
except KeyboardInterrupt:
log.info('Registered KeyboardInterrupt. Stopping training.')
log.info('Saving last model to disk')
if args.prior == 'vi':
sample_weights_hook.remove()
torch.save(
make_checkpoint(epoch, **parameters),
path.join(
args.checkpoint_dir, '{}_epoch{}{}_{}.pth'.format(
timestamp, epoch, '_with_apex' if use_apex else '',
args.prior)))
return
elif args.test:
test()
# Only test on existing languages if there are no held out languages
if not args.target_langs:
exit(0)
importance = 1e-5
# If use UNIV, calculate informed prior, else use boring prior
if args.prior == 'laplace':
if not isinstance(
prior,
LaplacePrior): # only calculate matrix if it is not supplied.
log.info('Creating laplace approximation dataset')
laplace_set = Dataset(data_splits['train'],
batchsize=args.batchsize,
bptt=100,
reset_on_iter=True)
laplace_loader = DataLoader(laplace_set, num_workers=args.workers)
log.info('Creating Laplacian prior')
prior = LaplacePrior(model,
loss_function,
laplace_loader,
use_apex=use_apex,
amp=amp,
device=device)
parameters['prior'] = prior
torch.save(
make_checkpoint('fisher_matrix', **parameters),
path.join(
args.checkpoint_dir, '{}_fishers_matrix{}_{}.pth'.format(
timestamp, '_with_apex' if use_apex else '',
args.prior)))
importance = 1e5
elif args.prior == 'ninf':
log.info('Creating non-informative Gaussian prior')
parameters['prior'] = GaussianPrior()
elif args.prior == 'vi':
importance = 1e-5
elif args.prior == 'hmc':
raise NotImplementedError
else:
raise ValueError(
f'Passed prior {args.prior} is not an implemented inference technique.'
)
best_model = saved_models[-1] if not len(
saved_models) == 0 else args.checkpoint
# Remove sampling hook from model
if args.prior == 'vi':
sample_weights_hook.remove()
# Refine on 100 samples on each target
if args.refine:
# reset learning rate
optimizer.param_groups[0]['lr'] = args.lr
loss = 0
results = dict()
# Create individual tests sets
test_sets = dict()
for lang, lang_d in data_splits['test'].items():
test_sets[lang] = DataLoader(Dataset({lang: lang_d},
make_config=True,
batchsize=args.test_batchsize,
bptt=args.bptt,
eval=True),
num_workers=args.workers)
for lang, lang_data in tqdm.tqdm(refine_set.items()):
final_loss = False
refine_dataloader = DataLoader(lang_data, num_workers=args.workers)
load_model(best_model, **parameters)
log.info(f'Refining for language {dictionary.idx2lang[lang]}')
for epoch in range(1, args.refine_epochs + 1):
refine(refine_dataloader, **parameters, importance=importance)
if epoch % 5 == 0:
final_loss = True
loss, avg_loss = evaluate(test_sets[lang],
model,
loss_function,
only_l=lang,
report_all=True,
device=device)
for lang, avg_l_loss in avg_loss.items():
langstr = dictionary.idx2lang[lang]
log.debug(
result_str.format(langstr, avg_l_loss,
math.exp(avg_l_loss),
avg_l_loss / math.log(2)))
if not final_loss:
loss, avg_loss = evaluate(test_sets[lang],
model,
loss_function,
only_l=lang,
report_all=True,
device=device)
for lang, avg_l_loss in avg_loss.items():
langstr = dictionary.idx2lang[lang]
log.info(
result_str.format(langstr, avg_l_loss,
math.exp(avg_l_loss),
avg_l_loss / math.log(2)))
results[lang] = avg_l_loss
log.info('=' * 89)
log.info('FINAL FEW SHOT RESULTS: ')
log.info('=' * 89)
for lang, avg_l_loss in results.items():
langstr = dictionary.idx2lang[lang]
log.info(
result_str.format(langstr, avg_l_loss, math.exp(avg_l_loss),
avg_l_loss / math.log(2)))
log.info('=' * 89)
def train(model, SRC, TRG, MODEL_PATH, FORCE_MAX_LEN=50):
model.train()
optimizer = Adam(model.parameters(), lr=hp.LR, betas=(0.9, 0.98), eps=1e-9)
criterion = CrossEntropyLoss(ignore_index=TRG.vocab.stoi["<pad>"])
for epoch in tqdm(range(hp.EPOCHS)):
for step, batch in enumerate(train_iter):
global_step = epoch * len(train_iter) + step
model.train()
optimizer.zero_grad()
optimizer = custom_lr_optimizer(optimizer, global_step)
src = batch.src.T
trg = batch.trg.T
trg_input = trg[:, :-1]
preds, _, _, _ = model(src, trg_input)
ys = trg[:, 1:]
loss = criterion(preds.permute(0, 2, 1), ys)
loss.mean().backward()
optimizer.step()
if global_step % 50 == 0:
print("#" * 90)
rand_index = random.randrange(hp.BATCH_SIZE)
model.eval()
v = next(iter(val_iter))
v_src, v_trg = v.src.T, v.trg.T
v_trg_inp = v_trg[:, :-1].detach()
v_trg_real = v_trg[:, 1:].detach()
v_predictions, _, _, _ = model(v_src, v_trg_inp)
max_args = v_predictions[rand_index].argmax(-1)
print("For random element in VALIDATION batch (real/pred)...")
print([
TRG.vocab.itos[word_idx]
for word_idx in v_trg_real[rand_index, :]
])
print([TRG.vocab.itos[word_idx] for word_idx in max_args])
print("Length til first <PAD> (real -> pred)...")
try:
pred_PAD_idx = max_args.tolist().index(3)
except:
pred_PAD_idx = None
print(v_trg_real[rand_index, :].tolist().index(3), " ---> ",
pred_PAD_idx)
val_loss = criterion(v_predictions.permute(0, 2, 1),
v_trg_real)
print("TRAINING LOSS:", loss.mean().item())
print("VALIDATION LOSS:", val_loss.mean().item())
print("#" * 90)
writer.add_scalar("Training Loss",
loss.mean().detach().item(), global_step)
writer.add_scalar("Validation Loss",
val_loss.mean().detach().item(), global_step)
torch.save(model, MODEL_PATH)
def __init__(self):
super(Model, self).__init__(optimizer_class=optim.SGD,
optimizer_kwargs=dict(lr=0.01,
momentum=0.5),
criterion=CrossEntropyLoss())
min_freq=2,
init_token='<sos>',
eos_token='<eos>')
train_iter, valid_iter, test_iter = iterator_construction(
train=train,
valid=valid,
test=test,
batch_sizes=(hp.BATCH_SIZE, hp.BATCH_SIZE, hp.BATCH_SIZE),
device=device)
transformer_model = Transformer(len(src_field.vocab),
len(trg_field.vocab),
D=hp.D_MODEL,
num_heads=hp.HEADS,
D_ff=hp.D_FF,
dropout=hp.P_DROP)
criterion = CrossEntropyLoss(ignore_index=trg_field.vocab.stoi['<pad>'])
# ignore_index specifies a target value that is ignored and does not contribute to the input gradient.
training(
transformer_model,
epochs=hp.EPOCHS,
train_iterator=train_iter,
valid_iterator=valid_iter,
optimizer=Adam(transformer_model.parameters(),
lr=hp.LR,
betas=(0.9, 0.98),
eps=1e-9), # NEED TO CHANGE
loss_fn=criterion,
device=device,
log_interval=3,
save_model=False,
model_path=
# train_loader = DataLoader(dataset=abideData_train, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)
# test_loader = DataLoader(dataset=abideData_test, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)
train_loader = DataLoader(dataset=abideData_train,
batch_size=batch_size,
shuffle=True)
test_loader = DataLoader(dataset=abideData_test,
batch_size=batch_size,
shuffle=True)
# 创建LSTM模型
model = RNNModel(train_x[0].shape[1],
lstm_hidden_num,
lstm_output_num,
lstm_layers_num,
bidirectional=bidirectional).to(device)
criterion = CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# 开启训练
model.train()
total_step = len(train_loader)
# 初始化Hidden和Cell
(hidden, cell) = model.init_hidden_cell(batch_size)
for epoch in range(EPOCHS):
for i, (data_x, data_y) in enumerate(train_loader):
if data_x.shape[0] != batch_size:
continue
# 设置到GPU
data_x = data_x.requires_grad_().to(device)
data_y = data_y.to(device)
test_df = pd.read_csv('../data/test_dataset.csv')
train_dataset = MyDataset(train_df,
image_folder_path='../data/images',
augmentations=get_training_augmentation())
test_dataset = MyDataset(test_df,
image_folder_path='../data/images',
augmentations=get_validation_augmentation())
train_dataloader = DataLoader(train_dataset, batch_size=config.batch_size)
test_dataloader = DataLoader(test_dataset, batch_size=1)
optimizer = Adam(model.parameters(), lr=config.learning_rate)
loss_function = CrossEntropyLoss()
for epoch in range(config.epochs):
print(f'EPOCH {epoch}')
print(f'-------------')
""" TRAIN """
print(f'TRAINING LOOP')
losses = []
model.train()
for batch in tqdm(train_dataloader):
images = batch['image'].to(device)
targets = batch['class_id'].type(torch.int64).to(device)
