def train():
seed = set_rng_seeds(1234)
print('RNG SEED {}'.format(seed))
print('loading data')
ds_list, word_v, tag_v = g_datasets['{}-{}'.format(
g_args.ds, g_args.task)]()
print(ds_list[0][:2])
embed = load_pretrain_emb(word_v, lang='zh' if g_args.ds == 'ctb' else 'en')
g_model_cfg['num_cls'] = len(tag_v)
print(g_model_cfg)
g_model_cfg['init_embed'] = embed
model = g_model_select[g_args.task.lower()](**g_model_cfg)
def init_model(model):
for p in model.parameters():
if p.size(0) != len(word_v):
nn.init.normal_(p, 0.0, 0.05)
init_model(model)
train_data = ds_list[0]
dev_data = ds_list[2]
test_data = ds_list[1]
print(tag_v.word2idx)
if g_args.task in ['pos', 'ner']:
padding_idx = tag_v.padding_idx
else:
padding_idx = -100
print('padding_idx ', padding_idx)
loss = FN.CrossEntropyLoss(padding_idx=padding_idx)
metrics = {
'pos': (None, FN.AccuracyMetric()),
'ner': ('f', FN.core.metrics.SpanFPreRecMetric(
tag_vocab=tag_v, encoding_type='bmeso', ignore_labels=[''], )),
'cls': (None, FN.AccuracyMetric()),
'nli': (None, FN.AccuracyMetric()),
}
metric_key, metric = metrics[g_args.task]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ex_param = [x for x in model.parameters(
) if x.requires_grad and x.size(0) != len(word_v)]
optim_cfg = [{'params': model.enc.embedding.parameters(), 'lr': g_args.lr*0.1},
{'params': ex_param, 'lr': g_args.lr, 'weight_decay': g_args.w_decay}, ]
trainer = FN.Trainer(model=model, train_data=train_data, dev_data=dev_data,
loss=loss, metrics=metric, metric_key=metric_key,
optimizer=torch.optim.Adam(optim_cfg),
n_epochs=g_args.ep, batch_size=g_args.bsz, print_every=10, validate_every=3000,
device=device,
use_tqdm=False, prefetch=False,
save_path=g_args.log,
callbacks=[MyCallback()])
trainer.train()
tester = FN.Tester(data=test_data, model=model, metrics=metric,
batch_size=128, device=device)
tester.test()
def test_train(self):
ds, v1, v2, v3 = init_data()
model = BiaffineParser(word_vocab_size=len(v1),
word_emb_dim=30,
pos_vocab_size=len(v2),
pos_emb_dim=30,
num_label=len(v3))
trainer = fastNLP.Trainer(model=model,
train_data=ds,
dev_data=ds,
loss=ParserLoss(),
metrics=ParserMetric(),
metric_key='UAS',
n_epochs=10,
use_cuda=False,
use_tqdm=False)
trainer.train(load_best_model=False)
def train():
print('loading data')
ds_list, word_v, tag_v = g_datasets['{}-{}'.format(g_args.ds,
g_args.task)]()
print(ds_list[0][:2])
print(len(ds_list[0]), len(ds_list[1]), len(ds_list[2]))
embed = load_pretrain_emb(word_v,
lang='zh' if g_args.ds == 'ctb' else 'en')
g_model_cfg['num_cls'] = len(tag_v)
print(g_model_cfg)
g_model_cfg['init_embed'] = embed
model = g_model_select[g_args.task.lower()](**g_model_cfg)
def init_model(model):
for p in model.parameters():
if p.size(0) != len(word_v):
if len(p.size()) < 2:
nn.init.constant_(p, 0.0)
else:
nn.init.normal_(p, 0.0, 0.05)
init_model(model)
train_data = ds_list[0]
dev_data = ds_list[1]
test_data = ds_list[2]
print(tag_v.word2idx)
if g_args.task in ['pos', 'ner']:
padding_idx = tag_v.padding_idx
else:
padding_idx = -100
print('padding_idx ', padding_idx)
loss = FN.CrossEntropyLoss(padding_idx=padding_idx)
metrics = {
'pos': (None, FN.AccuracyMetric()),
'ner': ('f',
FN.core.metrics.SpanFPreRecMetric(
tag_vocab=tag_v,
encoding_type='bmeso',
ignore_labels=[''],
)),
'cls': (None, FN.AccuracyMetric()),
'nli': (None, FN.AccuracyMetric()),
}
metric_key, metric = metrics[g_args.task]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
params = [(x, y) for x, y in list(model.named_parameters())
if y.requires_grad and y.size(0) != len(word_v)]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
print([n for n, p in params])
optim_cfg = [
#{'params': model.enc.embedding.parameters(), 'lr': g_args.lr*0.1},
{
'params':
[p for n, p in params if not any(nd in n for nd in no_decay)],
'lr': g_args.lr,
'weight_decay': 1.0 * g_args.w_decay
},
{
'params':
[p for n, p in params if any(nd in n for nd in no_decay)],
'lr': g_args.lr,
'weight_decay': 0.0 * g_args.w_decay
}
]
print(model)
trainer = FN.Trainer(model=model,
train_data=train_data,
dev_data=dev_data,
loss=loss,
metrics=metric,
metric_key=metric_key,
optimizer=torch.optim.Adam(optim_cfg),
n_epochs=g_args.ep,
batch_size=g_args.bsz,
print_every=100,
validate_every=1000,
device=device,
use_tqdm=False,
prefetch=False,
save_path=g_args.log,
sampler=FN.BucketSampler(100, g_args.bsz,
C.INPUT_LEN),
callbacks=[MyCallback()])
print(trainer.train())
tester = FN.Tester(data=test_data,
model=model,
metrics=metric,
batch_size=128,
device=device)
print(tester.test())
def RUN(data, label, split, modelfunc, classnum=2, epochs=10):
assert (len(data) == len(label))
if split == None:
dataset = fastNLP.DataSet({'raw_sentence': data, 'label_str': label})
else:
dataset = fastNLP.DataSet({
'raw_sentence': data,
'label_str': label,
'split': split
})
dataset.drop(lambda x: len(x['raw_sentence']) == 0)
#[dataset.append(fastNLP.DataSet({'raw_sentence': data[x], 'label': label[x]})) for x in range(len(data))]
dataset.apply(lambda x: int(float(x['label_str'])),
new_field_name='label',
is_target=True)
dataset.apply(lambda x: x['raw_sentence'].split(),
new_field_name='word_str')
vocab = fastNLP.Vocabulary(min_freq=1)
dataset.apply(lambda x: [vocab.add(word) for word in x['word_str']])
if split == None:
traindata, testdata = dataset.split(0.1)
#print(len(traindata), len(testdata))
else:
traindata = dataset[:]
testdata = dataset[:]
traindata.drop(lambda x: x['split'] != 'train')
testdata.drop(lambda x: x['split'] != 'test')
#print(len(traindata), len(testdata))
traindata.apply(lambda x: [vocab.to_index(word) for word in x['word_str']],
new_field_name='word_seq',
is_input=True)
testdata.apply(lambda x: [vocab.to_index(word) for word in x['word_str']],
new_field_name='word_seq',
is_input=True)
model = modelfunc(embed_num=len(vocab),
embed_dim=100,
num_classes=classnum,
kernel_nums=(3, 4, 5),
kernel_sizes=(3, 4, 5),
padding=0,
dropout=0)
model.embed.dropout = torch.nn.Dropout(0.5)
gloveemb = np.random.rand(len(vocab), 100)
for i in range(len(vocab)):
word = vocab.to_word(i)
try:
#print(word)
#print(len(glove), word)
#print(glove[word])
#input()
emb = glove[word]
gloveemb[i, :] = emb
except:
pass
model.addembed(gloveemb)
trainer = fastNLP.Trainer(model=model,
train_data=traindata,
dev_data=testdata,
loss=fastNLP.CrossEntropyLoss(),
metrics=fastNLP.AccuracyMetric(),
use_cuda=True,
n_epochs=epochs,
check_code_level=-1)
trainer.train()