def main():
logger.info("Reading and creating arguments")
args = read_arguments()
logger.info("Reading Data")
datasets = {}
for split in args.splits:
dataset = prepare_data.read_data_to_variable(args.data_paths[split],
args.alphabets,
args.device,
symbolic_root=True)
datasets[split] = dataset
if args.set_num_training_samples is not None:
print('Setting train and dev to %d samples' %
args.set_num_training_samples)
datasets = rearrange_splits.rearranging_splits(
datasets, args.set_num_training_samples)
logger.info("Creating Networks")
num_data = sum(datasets['train'][1])
model, optimizer, dev_eval_dict, test_eval_dict, start_epoch = build_model_and_optimizer(
args)
best_model = deepcopy(model)
best_optimizer = deepcopy(optimizer)
logger.info('Training INFO of in domain %s' % args.domain)
logger.info('Training on Dependecy Parsing')
logger.info("train: gamma: %f, batch: %d, clip: %.2f, unk replace: %.2f" %
(args.gamma, args.batch_size, args.clip, args.unk_replace))
logger.info('number of training samples for %s is: %d' %
(args.domain, num_data))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(args.p_in, args.p_out, args.p_rnn))
logger.info("num_epochs: %d" % (args.num_epochs))
print('\n')
if not args.eval_mode:
logger.info("Training")
num_batches = prepare_data.calc_num_batches(datasets['train'],
args.batch_size)
lr = args.learning_rate
patient = 0
decay = 0
for epoch in range(start_epoch + 1, args.num_epochs + 1):
print(
'Epoch %d (Training: rnn mode: %s, optimizer: %s, learning rate=%.6f, eps=%.1e, decay rate=%.2f (schedule=%d, decay=%d)): '
% (epoch, args.rnn_mode, args.opt, lr, args.epsilon,
args.decay_rate, args.schedule, decay))
model.train()
total_loss = 0.0
total_arc_loss = 0.0
total_arc_tag_loss = 0.0
total_train_inst = 0.0
train_iter = prepare_data.iterate_batch_rand_bucket_choosing(
datasets['train'],
args.batch_size,
args.device,
unk_replace=args.unk_replace)
start_time = time.time()
batch_num = 0
for batch_num, batch in enumerate(train_iter):
batch_num = batch_num + 1
optimizer.zero_grad()
# compute loss of main task
word, char, pos, ner_tags, heads, arc_tags, auto_label, masks, lengths = batch
out_arc, out_arc_tag, masks, lengths = model.forward(
word, char, pos, mask=masks, length=lengths)
loss_arc, loss_arc_tag = model.loss(out_arc,
out_arc_tag,
heads,
arc_tags,
mask=masks,
length=lengths)
loss = loss_arc + loss_arc_tag
# update losses
num_insts = masks.data.sum() - word.size(0)
total_arc_loss += loss_arc.item() * num_insts
total_arc_tag_loss += loss_arc_tag.item() * num_insts
total_loss += loss.item() * num_insts
total_train_inst += num_insts
# optimize parameters
loss.backward()
clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
time_ave = (time.time() - start_time) / batch_num
time_left = (num_batches - batch_num) * time_ave
# update log
if batch_num % 50 == 0:
log_info = 'train: %d/%d, domain: %s, total loss: %.2f, arc_loss: %.2f, arc_tag_loss: %.2f, time left: %.2fs' % \
(batch_num, num_batches, args.domain, total_loss / total_train_inst, total_arc_loss / total_train_inst,
total_arc_tag_loss / total_train_inst, time_left)
sys.stdout.write(log_info)
sys.stdout.write('\n')
sys.stdout.flush()
print('\n')
print(
'train: %d/%d, domain: %s, total_loss: %.2f, arc_loss: %.2f, arc_tag_loss: %.2f, time: %.2fs'
% (batch_num, num_batches, args.domain,
total_loss / total_train_inst, total_arc_loss /
total_train_inst, total_arc_tag_loss / total_train_inst,
time.time() - start_time))
dev_eval_dict, test_eval_dict, best_model, best_optimizer, patient = in_domain_evaluation(
args, datasets, model, optimizer, dev_eval_dict,
test_eval_dict, epoch, best_model, best_optimizer, patient)
if patient >= args.schedule:
lr = args.learning_rate / (1.0 + epoch * args.decay_rate)
optimizer = generate_optimizer(args, lr, model.parameters())
print('updated learning rate to %.6f' % lr)
patient = 0
print_results(test_eval_dict['in_domain'], 'test', args.domain,
'best_results')
print('\n')
for split in datasets.keys():
evaluation(args, datasets[split], split, best_model, args.domain,
epoch, 'best_results')
else:
logger.info("Evaluating")
epoch = start_epoch
#for split in ['train', 'dev', 'test']:
for split in ['test']:
eval_dict = evaluation(args, datasets[split], split, model,
args.domain, epoch, 'best_results')
write_results(args, datasets[split], args.domain, split, model,
args.domain, eval_dict)
def main():
logger.info("Reading and creating arguments")
args = read_arguments()
logger.info("Reading Data")
datasets = {}
for split in args.splits:
dataset = prepare_data.read_data_to_variable(args.data_paths[split],
args.alphabets,
args.device,
symbolic_root=True)
datasets[split] = dataset
if args.set_num_training_samples is not None:
print('Note the change here')
print('dev set in not touched similar to test set')
# print('Setting train and dev to %d samples' % args.set_num_training_samples)
datasets = rearrange_splits.rearranging_splits(
datasets, args.set_num_training_samples)
logger.info("Creating Networks")
num_data = sum(datasets['train'][1])
#########################################################
# Here constraints need to be added.
model, optimizer, dev_eval_dict, test_eval_dict, start_epoch = build_model_and_optimizer(
args)
best_model = deepcopy(model)
best_optimizer = deepcopy(optimizer)
#########################################################
logger.info('Training INFO of in domain %s' % args.domain)
logger.info('Training on Dependecy Parsing')
logger.info("train: gamma: %f, batch: %d, clip: %.2f, unk replace: %.2f" %
(args.gamma, args.batch_size, args.clip, args.unk_replace))
logger.info('number of training samples for %s is: %d' %
(args.domain, num_data))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(args.p_in, args.p_out, args.p_rnn))
logger.info("num_epochs: %d" % (args.num_epochs))
print('\n')
if not args.eval_mode:
logger.info("Training")
num_batches = prepare_data.calc_num_batches(datasets['train'],
args.batch_size)
lr = args.learning_rate
patient = 0
decay = 0
for epoch in range(start_epoch + 1, args.num_epochs + 1):
print(
'Epoch %d (Training: rnn mode: %s, optimizer: %s, learning rate=%.6f, eps=%.1e, decay rate=%.2f (schedule=%d, decay=%d)): '
% (epoch, args.rnn_mode, args.opt, lr, args.epsilon,
args.decay_rate, args.schedule, decay))
model.train()
total_loss = 0.0
total_arc_loss = 0.0
total_arc_tag_loss = 0.0
total_train_inst = 0.0
train_iter = prepare_data.iterate_batch_rand_bucket_choosing(
datasets['train'],
args.batch_size,
args.device,
unk_replace=args.unk_replace)
start_time = time.time()
batch_num = 0
for batch_num, batch in enumerate(train_iter):
batch_num = batch_num + 1
optimizer.zero_grad()
# compute loss of main task
# word,pos,heads,ner_tags,masks,auto_label,heads: [16,25]
# char: [16,25,29]
# lengths: [16]
# Why do we need auto_label
word, char, pos, ner_tags, heads, arc_tags, auto_label, masks, lengths = batch
out_arc, out_arc_tag, masks, lengths = model.forward(
word, char, pos, mask=masks, length=lengths)
# The decoder outputs a score s_ij , indicating the model belief that the latter should be the head of the former
# out_arc:[16, 24, 24]
#out_arc_tag_h: torch.Size([16, 24, 128])
#out_arc_tag_c: torch.Size([16, 24, 128])
# out_arc_tag =(out_arc_tag_h,out_arc_tag_c)
loss_arc, loss_arc_tag = model.loss(out_arc,
out_arc_tag,
heads,
arc_tags,
mask=masks,
length=lengths)
loss = loss_arc + loss_arc_tag
# update losses
num_insts = masks.data.sum() - word.size(0)
total_arc_loss += loss_arc.item() * num_insts
total_arc_tag_loss += loss_arc_tag.item() * num_insts
total_loss += loss.item() * num_insts
total_train_inst += num_insts
# optimize parameters
loss.backward()
clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
time_ave = (time.time() - start_time) / batch_num
time_left = (num_batches - batch_num) * time_ave
# update log
if batch_num % 50 == 0:
log_info = 'train: %d/%d, domain: %s, total loss: %.2f, arc_loss: %.2f, arc_tag_loss: %.2f, time left: %.2fs' % \
(batch_num, num_batches, args.domain, total_loss / total_train_inst, total_arc_loss / total_train_inst,
total_arc_tag_loss / total_train_inst, time_left)
sys.stdout.write(log_info)
sys.stdout.write('\n')
sys.stdout.flush()
print('\n')
print(
'train: %d/%d, domain: %s, total_loss: %.2f, arc_loss: %.2f, arc_tag_loss: %.2f, time: %.2fs'
% (batch_num, num_batches, args.domain,
total_loss / total_train_inst, total_arc_loss /
total_train_inst, total_arc_tag_loss / total_train_inst,
time.time() - start_time))
#
dev_eval_dict, test_eval_dict, best_model, best_optimizer, patient, curr_dev_eval_dict = in_domain_evaluation(
args, datasets, model, optimizer, dev_eval_dict,
test_eval_dict, epoch, best_model, best_optimizer, patient)
store ={'total_loss':str(total_loss.cpu().numpy() / total_train_inst.cpu().numpy())\
,'arc_loss': str(total_arc_loss.cpu().numpy() / total_train_inst.cpu().numpy()), \
'arc_tag_loss': str(total_arc_tag_loss.cpu().numpy() / total_train_inst.cpu().numpy()),'eval':curr_dev_eval_dict }
#############################################
str_file = args.full_model_name + '_' + 'all_epochs'
with open(str_file, 'a') as f:
f.write(str(store) + '\n')
###############################################
if patient >= args.schedule:
lr = args.learning_rate / (1.0 + epoch * args.decay_rate)
optimizer = generate_optimizer(args, lr, model.parameters())
print('updated learning rate to %.6f' % lr)
patient = 0
print_results(test_eval_dict['in_domain'], 'test', args.domain,
'best_results')
print('\n')
for split in datasets.keys():
flag = False
eval_dict = evaluation(args, datasets[split], split, best_model,
args.domain, epoch, flag, 'best_results')
else:
logger.info("Evaluating")
epoch = start_epoch
# epoch = 70
# print('Start epoch is',start_epoch)
for split in ['train', 'dev', 'test']:
if split == 'dev':
flag = False
else:
flag = False
eval_dict = evaluation(args, datasets[split], split, model,
args.domain, epoch, flag, 'best_results')
# print_results(eval_dict, split, args.domain, 'results')
print('\n')