def getdata_matching(task, type, batch=4):
Task, vocab, ind, index, Devset, Testset = preprocess(task, type)
Train = []
global Test
j = 0
for i in range(len(Task)):
vocab.index_dataset(Task[i], field_name='words', new_field_name='words')
if i in ind:
list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
slice = random.sample(list, 4)
another = [x for x in list if x not in slice]
train = Task[i][another]
support = Task[i][slice]
else:
length = len(Task[i])
list = [x - 1 for x in range(length)]
slice = random.sample(list, 20)
another = [x for x in list if x not in slice]
train, support = Task[i][another], Task[i][slice]
if i == index:
Test = Pair(Testset, support)
Dev = Pair(Devset, support)
Train.append(Pair(train, support))
for i in range(len(Train)):
Train[i].batch.set_input('words')
Train[i].support.set_input('words')
Train[i].batch.set_target('onehot')
Train[i].support.set_target('onehot')
Train[i].batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Train[i].support.apply(lambda x: len(x['words']), new_field_name='seq_len')
Test.batch.set_input('words')
Test.support.set_input('words')
Test.batch.set_target('onehot')
Test.support.set_target('onehot')
Test.batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Test.support.apply(lambda x: len(x['words']), new_field_name='seq_len')
Dev.batch.set_input('words')
Dev.support.set_input('words')
Dev.batch.set_target('onehot')
Dev.support.set_target('onehot')
Dev.batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Dev.support.apply(lambda x: len(x['words']), new_field_name='seq_len')
Train_batch = []
for i in range(len(Train)):
if i in ind:
sampler = BucketSampler(num_buckets=1, batch_size=batch, seq_len_field_name='seq_len')
Train_batch.append(Pair(Batch(batch_size=batch, dataset=Train[i].batch, sampler=sampler), Train[i].support))
else:
sampler = BucketSampler(batch_size=batch, seq_len_field_name='seq_len')
Train_batch.append(Pair(Batch(batch_size=batch, dataset=Train[i].batch, sampler=sampler), Train[i].support))
sampler = BucketSampler(batch_size=batch, seq_len_field_name='seq_len')
Test_batch = Pair(Batch(batch_size=batch, dataset=Test.batch, sampler=sampler), Test.support)
Dev_batch = Pair(Batch(batch_size=batch, dataset=Dev.batch, sampler=sampler), Dev.support)
return Train_batch, Dev_batch, Test_batch, len(vocab)
def cnn_train(epoch, data, model, batch_size=32):
device = torch.device("cuda")
optim = torch.optim.Adam(model.parameters(), lr=0.001)
lossfunc = nn.CrossEntropyLoss()
train_sampler = BucketSampler(batch_size=batch_size,
seq_len_field_name='seq_len')
train_batch = Batch(batch_size=batch_size,
dataset=data,
sampler=train_sampler)
for i in range(epoch):
loss_list = []
cnt = 0
for batch_x, batch_y in train_batch:
optim.zero_grad()
batch_x['words'] = batch_x['words'].long().contiguous().to(device)
batch_y['target'] = batch_y['target'].long().contiguous().to(
device)
output = model(batch_x['words'])
loss = lossfunc(output['pred'], batch_y['target'])
loss.backward()
optim.step()
loss_list.append(loss.item())
info_str = '[info] Epoch {:d} Iteration {:d} Loss : {:.2f}'.format(
i, cnt, loss_list[-1])
print(info_str)
with open('./cnn_rec.txt', 'a') as fp:
fp.write(info_str)
fp.write('\n')
cnt += 1
loss_list.clear()
torch.save(model.state_dict(), './cnn_state.pth')
def cnn_train(epoch, data, model, batch_size=20):
device = torch.device("cuda")
optim = torch.optim.Adam(model.parameters(), lr=0.002)
lossfunc = nn.CrossEntropyLoss()
train_sampler = BucketSampler(batch_size=batch_size,
seq_len_field_name='seq_len')
train_batch = Batch(batch_size=batch_size,
dataset=data,
sampler=train_sampler)
for i in range(epoch):
loss_list = []
cnt = 0
for batch_x, batch_y in train_batch:
batch_x['words'] = batch_x['words'].long().contiguous().to(device)
batch_y['target'] = batch_y['target'].long().contiguous().to(
device)
optim.zero_grad()
output = model(batch_x['words'])
loss = lossfunc(output['pred'], batch_y['target'])
loss.backward()
optim.step()
loss_list.append(loss.item())
print('[info] Epoch %d Iteration %d Loss : %f' %
(i, cnt, loss_list[-1]))
cnt += 1
loss_list.clear()
torch.save(model.state_dict(), './cnn_state.pth')
def _get_trainer(self, models_folder):
optimizer = optim.SGD(self.parameters(),
lr=self.config['lr'],
momentum=0.9)
callbacks = []
clip_callback = GradientClipCallback(clip_type='value', clip_value=5)
evaluate_callback = EvaluateCallback(
self.data_bundle.get_dataset('test'))
if self.config['warmup_steps'] > 0:
warmup_callback = WarmupCallback(self.config['warmup_steps'],
schedule='linear')
callbacks.append(warmup_callback)
callbacks.extend([clip_callback, evaluate_callback])
return Trainer(self.data_bundle.get_dataset('train'),
self,
optimizer,
batch_size=self.config['batch_size'],
sampler=BucketSampler(),
num_workers=2,
n_epochs=100,
dev_data=self.data_bundle.get_dataset('dev'),
metrics=SpanFPreRecMetric(
tag_vocab=self.data_bundle.get_vocab('target'),
encoding_type=self.config['encoding_type']),
dev_batch_size=self.config['batch_size'] * 5,
callbacks=callbacks,
device=self.config['device'],
test_use_tqdm=False,
use_tqdm=True,
print_every=300,
save_path=models_folder)
def test_BucketSampler(self):
sampler = BucketSampler(num_buckets=3,
batch_size=16,
seq_len_field_name="seq_len")
data_set = DataSet({
"x": [[0] * random.randint(1, 10)] * 10,
"y": [[5, 6]] * 10
})
data_set.apply(lambda ins: len(ins["x"]), new_field_name="seq_len")
indices = sampler(data_set)
self.assertEqual(len(indices), 10)
def preprocess(batch=16):
raw_data1 = []
raw_data2 = []
for i in range(len(traindata.data)):
raw_data1.append(
Instance(sentence=traindata.data[i],
label=int(traindata.target[i])))
trainset = DataSet(raw_data1)
trainset.apply(lambda x: pre(x['sentence']), new_field_name='words')
for i in range(len(testdata.data)):
raw_data2.append(
Instance(sentence=testdata.data[i], label=int(testdata.target[i])))
testset = DataSet(raw_data2)
testset.apply(lambda x: pre(x['sentence']), new_field_name='words')
global vocab
vocab = Vocabulary(min_freq=1).from_dataset(trainset,
testset,
field_name='words')
vocab.index_dataset(trainset,
testset,
field_name='words',
new_field_name='words')
trainset.set_input('words')
testset.set_input('words')
trainset.apply(lambda x: int(x['label']),
new_field_name='target',
is_target=True)
testset.apply(lambda x: int(x['label']),
new_field_name='target',
is_target=True)
trainset.apply(lambda x: len(x['words']), new_field_name='seq_len')
testset.apply(lambda x: len(x['words']), new_field_name='seq_len')
global vocabsize
vocabsize = len(vocab)
sampler = BucketSampler(batch_size=batch, seq_len_field_name='seq_len')
train_batch = Batch(batch_size=batch, dataset=trainset, sampler=sampler)
test_batch = Batch(batch_size=batch, dataset=testset, sampler=sampler)
return train_batch, test_batch, vocabsize
def run_train(config):
train_dir, model_dir = initial_dir('train', config)
config.train_path = train_dir
config.model_path = model_dir
print_config(config, train_dir)
datainfo = set_up_data('train', config)
train_sampler = BucketSampler(batch_size=config.batch_size, seq_len_field_name='enc_len')
criterion = MyLoss(config=config, padding_idx=datainfo.vocabs["train"].to_index(PAD_TOKEN))
model = Model(vocab=datainfo.vocabs["train"], config=config)
params = list(model.encoder.parameters()) + list(model.decoder.parameters()) + \
list(model.reduce_state.parameters())
initial_lr = config.lr_coverage if config.is_coverage else config.lr
optimizer = Adagrad(params, lr=initial_lr, initial_accumulator_value=config.adagrad_init_acc)
train_loader = datainfo.datasets["train"]
valid_loader = datainfo.datasets["dev"]
summary_writer = tf.compat.v1.summary.FileWriter(train_dir)
trainer = Trainer(model=model, train_data=train_loader, optimizer=optimizer, loss=criterion,
batch_size=config.batch_size, check_code_level=-1,
n_epochs=config.n_epochs, print_every=100, dev_data=valid_loader,
metrics=FastRougeMetric(pred='prediction', art_oovs='article_oovs',
abstract_sentences='abstract_sentences', config=config,
vocab=datainfo.vocabs["train"]),
metric_key="rouge-l-f", validate_every=-1, save_path=model_dir,
callbacks=[TrainCallback(config, summary_writer, patience=10)], use_tqdm=False,
device=config.visible_gpu)
logger.info("-" * 5 + "start training" + "-" * 5)
traininfo = trainer.train(load_best_model=True)
logger.info(' | end of Train | time: {:5.2f}s | '.format(traininfo["seconds"]))
logger.info('[INFO] best eval model in epoch %d and iter %d', traininfo["best_epoch"], traininfo["best_step"])
logger.info(traininfo["best_eval"])
bestmodel_save_path = os.path.join(config.model_path,
'bestmodel.pkl') # this is where checkpoints of best models are saved
state = {
'encoder_state_dict': model.encoder.state_dict(),
'decoder_state_dict': model.decoder.state_dict(),
'reduce_state_dict': model.reduce_state.state_dict()
}
torch.save(state, bestmodel_save_path)
# 不是作为形参传入到Trainer里面的么，怎么里面的model变化会影响到外面的？
logger.info('[INFO] Saving eval best model to %s', bestmodel_save_path)
metrics.append(LossMetric(loss=Const.LOSS))
optimizer = Adam(model.parameters(), lr=ops.lr, weight_decay=0)
scheduler = LRScheduler(
LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)))
callbacks.append(scheduler)
# callbacks.append(LRScheduler(CosineAnnealingLR(optimizer, 15)))
# optimizer = SWATS(model.parameters(), verbose=True)
# optimizer = Adam(model.parameters(), lr=0.005)
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
trainer = Trainer(train_data=data.datasets['train'],
model=model,
optimizer=optimizer,
sampler=BucketSampler(num_buckets=50,
batch_size=ops.batch_size),
device=device,
dev_data=data.datasets['dev'],
batch_size=ops.batch_size,
metrics=metrics,
check_code_level=-1,
callbacks=callbacks,
num_workers=2,
n_epochs=ops.num_epochs)
trainer.train()
torch.save(model, 'idcnn.pt')
tester = Tester(data=data.datasets['test'],
model=model,
metrics=metrics,
'dev':"NER/corpus/CoNLL-2003/eng.testa"}
data = Conll2003NERPipe(encoding_type=encoding_type).process_from_file(paths)
return data
data = load_data()
print(data)
char_embed = CNNCharEmbedding(vocab=data.get_vocab('words'), embed_size=30, char_emb_size=30, filter_nums=[30],
kernel_sizes=[3], word_dropout=0, dropout=0.5)
word_embed = StaticEmbedding(vocab=data.get_vocab('words'),
model_dir_or_name='en-glove-6b-100d',
requires_grad=True, lower=True, word_dropout=0.01, dropout=0.5)
word_embed.embedding.weight.data = word_embed.embedding.weight.data/word_embed.embedding.weight.data.std()
embed = StackEmbedding([word_embed, char_embed])
model = CNNBiLSTMCRF(embed, hidden_size=200, num_layers=1, tag_vocab=data.vocabs[Const.TARGET],
encoding_type=encoding_type)
callbacks = [
GradientClipCallback(clip_type='value', clip_value=5),
EvaluateCallback(data=data.get_dataset('test')) # 额外对test上的数据进行性能评测
]
optimizer = SGD(model.parameters(), lr=0.008, momentum=0.9)
scheduler = LRScheduler(LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)))
callbacks.append(scheduler)
trainer = Trainer(train_data=data.get_dataset('train'), model=model, optimizer=optimizer, sampler=BucketSampler(),
device=0, dev_data=data.get_dataset('dev'), batch_size=20,
metrics=SpanFPreRecMetric(tag_vocab=data.vocabs[Const.TARGET], encoding_type=encoding_type),
callbacks=callbacks, num_workers=2, n_epochs=100, dev_batch_size=512)
trainer.train()
CWS_dataset = DataSet()
for key in task_list:
if key.startswith('CWS'):
for ins in all_data[target][key]:
CWS_dataset.append(ins)
del all_data[target][key]
CWS_dataset.set_input('chars', 'target', 'seq_len', 'task_class')
CWS_dataset.set_target('target', 'seq_len')
all_data[target]['CWS-all'] = CWS_dataset
train_data = dict()
train_data['POS-ctb9'] = all_data['train']['POS-ctb9']
train_data['CWS-all'] = all_data['train']['CWS-all']
train_data = MultiTaskIter(all_data['train'],
batch_size=batch_size,
sampler=BucketSampler(batch_size=batch_size))
#del pos
trainer = Trainer(train_data=train_data,
model=model,
optimizer=optimizer,
device=device,
dev_data=all_data['dev']['POS-ctb9'],
batch_size=batch_size,
metrics=metric3,
loss=None,
n_epochs=n_epochs,
check_code_level=-1,
update_every=update_every,
test_use_tqdm=True,
callbacks=callbacks)
app_index=char_labels_vocab['APP'],
pre_chars_embed=pre_chars_embed,
pre_bigrams_embed=pre_bigrams_embed,
pre_trigrams_embed=pre_trigrams_embed)
metric1 = SegAppCharParseF1Metric(char_labels_vocab['APP'])
metric2 = CWSMetric(char_labels_vocab['APP'])
metrics = [metric1, metric2]
optimizer = optim.Adam(
[param for param in model.parameters() if param.requires_grad],
lr=lr,
weight_decay=weight_decay,
betas=[0.9, 0.9])
sampler = BucketSampler(seq_len_field_name='seq_lens')
callbacks = []
# scheduler = LambdaLR(optimizer, lr_lambda=lambda step:(0.75)**(step//5000))
scheduler = StepLR(optimizer, step_size=18, gamma=0.75)
# optim_callback = OptimizerCallback(optimizer, scheduler, update_every)
# callbacks.append(optim_callback)
scheduler_callback = LRScheduler(scheduler)
callbacks.append(scheduler_callback)
callbacks.append(GradientClipCallback(clip_type='value', clip_value=5))
tester = Tester(data=data.datasets['test'],
model=model,
metrics=metrics,
batch_size=64,
device=device,
verbose=0)
hidden_size=200,
num_layers=1,
tag_vocab=data.vocabs[Const.TARGET],
encoding_type=encoding_type)
callbacks = [
GradientClipCallback(clip_type='value', clip_value=5),
FitlogCallback({'test': data.datasets['test']}, verbose=1),
# SaveModelCallback('save_models/', top=3, only_param=False, save_on_exception=True)
]
# optimizer = Adam(model.parameters(), lr=0.001)
# optimizer = SWATS(model.parameters(), verbose=True)
optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
scheduler = LRScheduler(
LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)))
callbacks.append(scheduler)
trainer = Trainer(train_data=data.datasets['train'],
model=model,
optimizer=optimizer,
sampler=BucketSampler(batch_size=20),
device=1,
dev_data=data.datasets['dev'],
batch_size=20,
metrics=SpanFPreRecMetric(
tag_vocab=data.vocabs[Const.TARGET],
encoding_type=encoding_type),
callbacks=callbacks,
num_workers=2,
n_epochs=100)
trainer.train()
@cache_results('caches/conll2003.pkl', _refresh=False)
def load_data():
# 替换路径
paths = 'data/conll2003'
data = Conll2003NERPipe(encoding_type=encoding_type).process_from_file(paths)
return data
data = load_data()
print(data)
embed = BertEmbedding(data.get_vocab(Const.INPUT), model_dir_or_name='en-base-cased',
pool_method='max', requires_grad=True, layers='11', include_cls_sep=False, dropout=0.5,
word_dropout=0.01)
callbacks = [
GradientClipCallback(clip_type='norm', clip_value=1),
WarmupCallback(warmup=0.1, schedule='linear'),
EvaluateCallback(data.get_dataset('test'))
]
model = BertCRF(embed, tag_vocab=data.get_vocab('target'), encoding_type=encoding_type)
optimizer = AdamW(model.parameters(), lr=2e-5)
trainer = Trainer(train_data=data.datasets['train'], model=model, optimizer=optimizer, sampler=BucketSampler(),
device=0, dev_data=data.datasets['dev'], batch_size=6,
metrics=SpanFPreRecMetric(tag_vocab=data.vocabs[Const.TARGET], encoding_type=encoding_type),
loss=None, callbacks=callbacks, num_workers=2, n_epochs=5,
check_code_level=0, update_every=3, test_use_tqdm=False)
trainer.train()
from fastNLP.core.losses import CMRC2018Loss
from fastNLP.core.metrics import CMRC2018Metric
from fastNLP.io.pipe.qa import CMRC2018BertPipe
from fastNLP import Trainer, BucketSampler
from fastNLP import WarmupCallback, GradientClipCallback
from fastNLP.core.optimizer import AdamW
data_bundle = CMRC2018BertPipe().process_from_file()
data_bundle.rename_field('chars', 'words')
print(data_bundle)
embed = BertEmbedding(data_bundle.get_vocab('words'), model_dir_or_name='cn', requires_grad=True, include_cls_sep=False, auto_truncate=True,
dropout=0.5, word_dropout=0.01)
model = BertForQuestionAnswering(embed)
loss = CMRC2018Loss()
metric = CMRC2018Metric()
wm_callback = WarmupCallback(schedule='linear')
gc_callback = GradientClipCallback(clip_value=1, clip_type='norm')
callbacks = [wm_callback, gc_callback]
optimizer = AdamW(model.parameters(), lr=5e-5)
trainer = Trainer(data_bundle.get_dataset('train'), model, loss=loss, optimizer=optimizer,
sampler=BucketSampler(seq_len_field_name='context_len'),
dev_data=data_bundle.get_dataset('dev'), metrics=metric,
callbacks=callbacks, device=0, batch_size=6, num_workers=2, n_epochs=2, print_every=1,
test_use_tqdm=False, update_every=10)
trainer.train(load_best_model=False)
bi_embed=bi_embed,bert_embed=bert_embed,
fc_dropout=fc_dropout,
pos_embed=pos_embed,
scale=attn_type == 'transformer')
# model = BERT_TENER(tag_vocab=data_bundle.get_vocab('target'), embed=embed, num_layers=num_layers,
# d_model=d_model, n_head=n_heads,
# feedforward_dim=dim_feedforward, dropout=dropout,
# after_norm=after_norm, attn_type=attn_type,
# bi_embed=bi_embed, bert_embed=bert_embed,
# fc_dropout=fc_dropout,
# pos_embed=pos_embed,
# scale=attn_type == 'transformer')
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
callbacks = []
clip_callback = GradientClipCallback(clip_type='value', clip_value=5)
evaluate_callback = EvaluateCallback(data_bundle.get_dataset('test'))
if warmup_steps > 0:
warmup_callback = WarmupCallback(warmup_steps, schedule='linear')
callbacks.append(warmup_callback)
callbacks.extend([clip_callback, evaluate_callback])
trainer = Trainer(data_bundle.get_dataset('train'), model, optimizer, batch_size=batch_size, sampler=BucketSampler(),
num_workers=2, n_epochs=n_epochs, dev_data=data_bundle.get_dataset('dev'),
metrics=SpanFPreRecMetric(tag_vocab=data_bundle.get_vocab('target'), encoding_type=encoding_type),
dev_batch_size=batch_size, callbacks=callbacks, device=device, test_use_tqdm=False,
use_tqdm=True, print_every=300, save_path=None)
trainer.train(load_best_model=False)
for name, para in model.named_parameters():
if name.find("task_embed") == -1:
para.requires_grad = False
else:
para.requires_grad = True
print(name)
optimizer = optm.NoamOpt(
options.d_model, options.factor, 4000,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
optimizer._step = options.step
best_model_file_name = "{}/model.bin".format(root_dir)
train_sampler = BucketSampler(batch_size=options.batch_size,
seq_len_field_name='seq_len')
dev_sampler = SequentialSampler()
i2t = utils.to_id_list(tag_vocab.word2idx)
i2task = utils.to_id_list(task_vocab.word2idx)
dev_set.set_input("ori_words")
test_set.set_input("ori_words")
word_dic = pickle.load(open("dict.pkl", "rb"))
def tester(model, test_batch, write_out=False):
res = []
prf = utils.CWSEvaluator(i2t)
prf_dataset = {}
oov_dataset = {}
bigram_embed_opt = EmbeddingOption(embed_filepath=bigram_embed_path)
data_name = os.path.basename(file_dir)
cache_fp = 'caches/{}.pkl'.format(data_name)
data = prepare_data(_cache_fp=cache_fp, _refresh=False)
model = ShiftRelayCWSModel(char_embed=data.embeddings['chars'],
bigram_embed=data.embeddings['bigrams'],
hidden_size=hidden_size,
num_layers=num_layers,
L=L,
num_bigram_per_char=1,
drop_p=drop_p)
sampler = BucketSampler(batch_size=32)
optimizer = Adam(model.parameters(), lr=lr)
clipper = GradientClipCallback(clip_value=5, clip_type='value')
callbacks = [clipper]
# if pretrain:
# fixer = FixEmbedding([model.char_embedding, model.bigram_embedding], fix_until=fix_until)
# callbacks.append(fixer)
trainer = Trainer(data.datasets['train'],
model,
optimizer=optimizer,
loss=None,
batch_size=32,
sampler=sampler,
update_every=5,
n_epochs=3,
print_every=5,
hidden_size=1200,
num_layers=1,
tag_vocab=data.vocabs[Const.TARGET],
encoding_type=encoding_type,
dropout=dropout)
callbacks = [
GradientClipCallback(clip_value=5, clip_type='value'),
EvaluateCallback(data.datasets['test'])
]
optimizer = SGD(model.parameters(), lr=lr, momentum=0.9)
scheduler = LRScheduler(
LambdaLR(optimizer, lr_lambda=lambda epoch: 1 / (1 + 0.05 * epoch)))
callbacks.append(scheduler)
trainer = Trainer(train_data=data.get_dataset('train'),
model=model,
optimizer=optimizer,
sampler=BucketSampler(num_buckets=100),
device=0,
dev_data=data.get_dataset('dev'),
batch_size=batch_size,
metrics=SpanFPreRecMetric(
tag_vocab=data.vocabs[Const.TARGET],
encoding_type=encoding_type),
callbacks=callbacks,
num_workers=1,
n_epochs=100,
dev_batch_size=256)
trainer.train()
def getdata_proto(task, type, batch=4):
Task, vocab, ind, index, testset, devset = preprocess(task, type)
Train = []
global Test
for i in range(len(Task)):
vocab.index_dataset(Task[i], field_name='words', new_field_name='words')
if i in ind:
list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
rawsupport0 = []
rawsupport1 = []
while (len(rawsupport0) == 0 or len(rawsupport1) == 0):
slice = random.sample(list, 4)
another = [x for x in list if x not in slice]
train = Task[i][another]
for inn in slice:
if Task[i][inn]['label'] == -1:
rawsupport0.append(inn)
else:
rawsupport1.append(inn)
support0 = Task[i][rawsupport0]
support1 = Task[i][rawsupport1]
else:
length = len(Task[i])
list = [x - 1 for x in range(length)]
rawsupport0 = []
rawsupport1 = []
while (len(rawsupport0) == 0 or len(rawsupport1) == 0):
slice = random.sample(list, 20)
another = [x for x in list if x not in slice]
train = Task[i][another]
for inn in slice:
if Task[i][inn]['label'] == -1:
rawsupport0.append(inn)
else:
rawsupport1.append(inn)
support0 = Task[i][rawsupport0]
support1 = Task[i][rawsupport1]
if i == index:
Test = Triple(testset, support0, support1)
Dev = Triple(devset, support0, support1)
Train.append(Triple(train, support0, support1))
for i in range(len(Train)):
Train[i].batch.set_input('words')
Train[i].support0.set_input('words')
Train[i].support1.set_input('words')
Train[i].batch.set_target('onehot')
Train[i].support0.set_target('onehot')
Train[i].support1.set_target('onehot')
Train[i].batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Train[i].support0.apply(lambda x: len(x['words']), new_field_name='seq_len')
Train[i].support1.apply(lambda x: len(x['words']), new_field_name='seq_len')
Test.batch.set_input('words')
Test.support0.set_input('words')
Test.support1.set_input('words')
Test.batch.set_target('onehot')
Test.support0.set_target('onehot')
Test.support1.set_target('onehot')
Test.batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Test.support0.apply(lambda x: len(x['words']), new_field_name='seq_len')
Test.support1.apply(lambda x: len(x['words']), new_field_name='seq_len')
Dev.batch.set_input('words')
Dev.support0.set_input('words')
Dev.support1.set_input('words')
Dev.batch.set_target('onehot')
Dev.support0.set_target('onehot')
Dev.support1.set_target('onehot')
Dev.batch.apply(lambda x: len(x['words']), new_field_name='seq_len')
Dev.support0.apply(lambda x: len(x['words']), new_field_name='seq_len')
Dev.support1.apply(lambda x: len(x['words']), new_field_name='seq_len')
Train_batch = []
for i in range(len(Train)):
if i in ind:
sampler = BucketSampler(num_buckets=1, batch_size=batch, seq_len_field_name='seq_len')
Train_batch.append(Triple(Batch(batch_size=batch, dataset=Train[i].batch, sampler=sampler), Train[i].support0, Train[i].support1))
else:
sampler = BucketSampler(batch_size=batch, seq_len_field_name='seq_len')
Train_batch.append(Triple(Batch(batch_size=batch, dataset=Train[i].batch, sampler=sampler), Train[i].support0, Train[i].support1))
sampler = BucketSampler(batch_size=batch, seq_len_field_name='seq_len')
Test_batch = Triple(Batch(batch_size=batch, dataset=Test.batch, sampler=sampler), Test.support0, Test.support1)
Dev_batch = Triple(Batch(batch_size=batch, dataset=Dev.batch, sampler=sampler), Dev.support0, Dev.support1)
return Train_batch, Dev_batch, Test_batch, len(vocab)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
callbacks = []
clip_callback = GradientClipCallback(clip_type='value', clip_value=5)
evaluate_callback = EvaluateCallback(data_bundle.get_dataset('test'))
if warmup_steps > 0:
warmup_callback = WarmupCallback(warmup_steps, schedule='linear')
callbacks.append(warmup_callback)
callbacks.extend([clip_callback, evaluate_callback])
trainer = Trainer(data_bundle.get_dataset('train'),
model,
optimizer,
batch_size=batch_size,
sampler=BucketSampler(),
num_workers=2,
n_epochs=n_epochs,
dev_data=data_bundle.get_dataset('dev'),
metrics=SpanFPreRecMetric(
tag_vocab=data_bundle.get_vocab('target'),
encoding_type=encoding_type),
dev_batch_size=batch_size,
callbacks=callbacks,
device=device,
test_use_tqdm=False,
use_tqdm=True,
print_every=300,
save_path=None)
trainer.train(load_best_model=False)
def main():
if args.do_eval:
torch.multiprocessing.set_start_method('spawn', force=True)
if args.model == 'bert':
model = BertCRF(embed, [data_bundle.get_vocab('target')],
encoding_type='bioes')
else:
model = StackedTransformersCRF(
tag_vocabs=[data_bundle.get_vocab('target')],
embed=embed,
num_layers=num_layers,
d_model=d_model,
n_head=n_heads,
feedforward_dim=dim_feedforward,
dropout=trans_dropout,
after_norm=after_norm,
attn_type=attn_type,
bi_embed=None,
fc_dropout=fc_dropout,
pos_embed=pos_embed,
scale=attn_type == 'transformer')
model = torch.nn.DataParallel(model)
if args.do_eval:
if os.path.exists(os.path.expanduser(args.saved_model)):
print("Load checkpoint from {}".format(
os.path.expanduser(args.saved_model)))
model = torch.load(args.saved_model)
model.to('cuda')
print('model to CUDA')
optimizer = AdamW(model.parameters(), lr=lr, eps=1e-8)
callbacks = []
clip_callback = GradientClipCallback(clip_type='value', clip_value=5)
evaluate_callback = EvaluateCallback(data_bundle.get_dataset('test'))
checkpoint_callback = CheckPointCallback(os.path.join(
directory, 'model.pth'),
delete_when_train_finish=False,
recovery_fitlog=True)
if warmup_steps > 0:
warmup_callback = WarmupCallback(warmup_steps, schedule='linear')
callbacks.append(warmup_callback)
callbacks.extend([clip_callback, checkpoint_callback, evaluate_callback])
if not args.do_eval:
trainer = Trainer(data_bundle.get_dataset('train'),
model,
optimizer,
batch_size=batch_size,
sampler=BucketSampler(),
num_workers=no_cpu,
n_epochs=args.n_epochs,
dev_data=data_bundle.get_dataset('dev'),
metrics=SpanFPreRecMetric(
tag_vocab=data_bundle.get_vocab('target'),
encoding_type=encoding_type),
dev_batch_size=batch_size,
callbacks=callbacks,
device=args.device,
test_use_tqdm=True,
use_tqdm=True,
print_every=300,
save_path=os.path.join(directory, 'best'))
trainer.train(load_best_model=True)
predictor = Predictor(model)
predict(os.path.join(directory, 'predictions_dev.tsv'), data_bundle,
predictor, 'dev')
predict(os.path.join(directory, 'predictions_test.tsv'), data_bundle,
predictor, 'test')
else:
print('Predicting')
# predictions of multiple files
torch.multiprocessing.freeze_support()
model.share_memory()
predictor = Predictor(model)
if len(files) > multiprocessing.cpu_count():
with torch.multiprocessing.Pool(processes=no_cpu) as p:
with tqdm(total=len(files)) as pbar:
for i, _ in enumerate(
p.imap_unordered(
partial(predict,
data_bundle=data_bundle,
predictor=predictor,
predict_on='train',
do_eval=args.do_eval), files)):
pbar.update()
else:
for file in tqdm(files):
predict(file, data_bundle, predictor, 'train', args.do_eval)