if torch.cuda.is_available():
model.cuda()
optimizer = optim.AdamW(model.parameters(), lr=lr)
data = {}
for name in ['seen', 'unseen', 'desc']:
data[name] = data_bundle.get_dataset(name)
callbacks = [GradientClipCallback(clip_type='value', clip_value=5), WarmupCallback(warmup=0.01, schedule='linear')]
callbacks.append(FitlogCallback(data=data, verbose=1))
train_data = data_bundle.get_dataset('train')
train_data.add_seq_len('input')
# from collections import Counter
# print(Counter(train_data.get_field('seq_len').content))
# exit(0)
sampler = BucketSampler()
clip_max_length(train_data, data_bundle)
trainer = Trainer(train_data=train_data, model=model,
optimizer=optimizer, loss=CrossEntropyLoss(),
batch_size=batch_size, sampler=sampler, drop_last=False, update_every=1,
num_workers=1, n_epochs=n_epochs, print_every=5,
dev_data=data_bundle.get_dataset('dev'), metrics=MonoMetric(),
metric_key='t10',
validate_every=-1, save_path='save_models/', use_tqdm=True, device=None,
callbacks=callbacks, check_code_level=0)
trainer.train(load_best_model=False)
fitlog.add_other(trainer.start_time, name='start_time')
lg_dict = getattr(data_bundle, 'lg_dict')
lg_shifts = getattr(data_bundle, 'lg_shift')
train_lg_shifts = getattr(data_bundle, 'train_lg_shift')
train_data = DataSet()
for name, ds in data_bundle.iter_datasets():
if 'train' in name:
for ins in ds:
train_data.append(ins)
train_data.add_seq_len('input')
train_data.set_input('input', 'language_ids')
train_data.set_target('target')
train_data.set_pad_val('input', pad_id)
clip_max_length(train_data, data_bundle, max_sent_len=50)
model = JointBertReverseDict(pre_name,
train_word2bpes,
target_word2bpes,
pad_id=pad_id,
num_languages=3)
if torch.cuda.is_available():
model.cuda()
optimizer = optim.AdamW(model.parameters(), lr=lr)
data = {}
summary_ms = []
for name, ds in data_bundle.iter_datasets():