def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
# Get model
log.info('Building model...')
model = BiDAF_3(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer,
lambda epoch: 0.8**(epoch // 5)) # Constant LR
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
(log_p1, log_p2), Q = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(
log_p2, y2) + (Q * _QUANTIZATION_FACTOR)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(epoch)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
# Get model
log.info('Building model...')
model, step = get_model(word_vectors, char_vectors, log, args)
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
#optimizer = get_opt(model,args)
#optimizer = optim.Adam(model.parameters(),lr=1,betas=[0.8,0.999],eps=1e-7,weight_decay=args.l2_wd)
#warmup=args.warmup
# cooldown=args.hidden_size*100
#scheduler=sched.LambdaLR(optimizer,lambda step: min(0.001,0.001*(step/warmup)**0.5) if step<=cooldown else max(1e-4 ,1e-3*(1-min(1,(step-cooldown)/cooldown))**0.5))
#scheduler=sched.LambdaLR(optimizer,lambda step: min(0.001,0.001*(step/warmup)**0.5))
'''
cr = 1.0 / math.log(warmup)
scheduler = sched.LambdaLR(
optimizer,
lr_lambda=lambda ee: cr * math.log(ee + 1)
if ee < warmup else 1)
'''
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda step: 1)
# Get data loader
log.info('Building dataset...')
if (args.model_name == "KnowGQA"):
train_dataset = SQUADwithGraph(args.train_record_file_graph,
args.use_squad_v2)
dev_dataset = SQUADwithGraph(args.dev_record_file_graph,
args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn_graph)
dev_loader = data.DataLoader(dev_dataset,
batch_size=4,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn_graph)
else:
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
if (args.model_name == "KnowGQA"):
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, co_idxs, adjs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qw_idxs = qw_idxs.to(device)
qc_idxs = qc_idxs.to(device)
co_idxs = co_idxs.to(device)
adjs = adjs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs,
co_idxs, adjs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, "KnowGQA",
dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name],
device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
elif args.model_name == "BiDAF_nochar":
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
log_p1, log_p2 = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, "BiDAF_nochar",
dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name],
device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
else:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qw_idxs = qw_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, "BiDAF",
dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name],
device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def main(args):
# ===========================================================
torch.cuda.empty_cache()
# ===========================================================
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
# Get model
log.info('Building model...')
# ==============================================================================
# char_vectors = util.torch_from_json(args.char_emb_file)
# char_vocab_size, _ = tuple(char_vectors.size())
char_vocab_size = 1376 # 节省内存
model = QANet(
word_vectors=word_vectors,
char_vocab_size=char_vocab_size,
char_dim=args.char_dim,
d_model=args.d_model,
drop_prob=args.drop_prob,
num_heads=args.num_heads,
num_mod_blocks=args.
num_mod_blocks, # 节省内存=============================
maximum_context_length=args.maximum_context_length)
# ==============================================================================
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
# optimizer = optim.Adadelta(model.parameters(),
# args.lr,
# eps=1e-6,
# weight_decay=args.l2_wd)
# scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# QANet-02================================================================================================================================
# optimizer = optim.Adam(lr=1,
# betas=(args.beta1, args.beta2),
# eps=args.adam_eps,
# weight_decay=args.l2_wd,
# params=model.parameters())
# cr = args.lr / math.log2(args.warm_up)
# scheduler = optim.lr_scheduler.LambdaLR(
# optimizer,
# lr_lambda=lambda ee: cr * math.log2(ee + 1)
# if ee < args.warm_up else args.lr)
# ========================================================================================================================================
# QANet-03-05=============================================================================================================================
# optimizer = optim.Adam(lr=1,
# betas=(args.beta1, args.beta2),
# eps=args.adam_eps,
# weight_decay=args.l2_wd,
# params=model.parameters())
# cr = args.lr / math.log2(args.warm_up)
# scheduler = optim.lr_scheduler.LambdaLR(
# optimizer,
# lr_lambda=lambda ee: cr * math.log2(ee + 1)
# if ee < args.warm_up else args.lr)
# ========================================================================================================================================
# QANet-06 ===============================================================================================================================
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(),
args.lr,
eps=1e-6,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# ========================================================================================================================================
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader: # 08/09 可见作者已经贴心地预留了cc_idxs...
# Setup for forward
cw_idxs = cw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qw_idxs = qw_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
# 8/12 当seq_len > max_context_length = 400 时raise ValueError 并跳过
try:
log_p1, log_p2 = model(
cw_idxs, cc_idxs, qw_idxs,
qc_idxs) # 08/09 增加cc_idxs 和 qc_idxs
except ValueError:
continue
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def main(args):
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
### start our code:
# Get char-embeddings
log.info('Loading char-embeddings...')
char_vectors = util.torch_from_json(args.char_emb_file)
### end our code
# Get model
log.info('Building model...')
model = BiDAF(word_vectors=word_vectors,
hidden_size=args.hidden_size,
char_vectors=char_vectors)
model = nn.DataParallel(model, gpu_ids)
log.info('Loading checkpoint from {}...'.format(args.load_path))
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)['{}_record_file'.format(args.split)]
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Evaluate
log.info('Evaluating on {} split...'.format(args.split))
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions for TensorBoard
sub_dict = {} # Predictions for submission
eval_file = vars(args)['{}_eval_file'.format(args.split)]
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
### start our code:
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
### end our code
# Forward
# log_p1, log_p2 = model(cw_idxs, qw_idxs)
### start our code:
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs)
### end our code
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, args.max_ans_len,
args.use_squad_v2)
# Log info
progress_bar.update(batch_size)
if args.split != 'test':
# No labels for the test set, so NLL would be invalid
progress_bar.set_postfix(NLL=nll_meter.avg)
idx2pred, uuid2pred = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(),
ends.tolist(),
args.use_squad_v2)
pred_dict.update(idx2pred)
sub_dict.update(uuid2pred)
# Log results (except for test set, since it does not come with labels)
if args.split != 'test':
results = util.eval_dicts(gold_dict, pred_dict, args.use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if args.use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('{} {}'.format(args.split.title(), results_str))
# Log to TensorBoard
tbx = SummaryWriter(args.save_dir)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=eval_file,
step=0,
split=args.split,
num_visuals=args.num_visuals)
# Write submission file
sub_path = join(args.save_dir, args.split + '_' + args.sub_file)
log.info('Writing submission file to {}...'.format(sub_path))
with open(sub_path, 'w') as csv_fh:
csv_writer = csv.writer(csv_fh, delimiter=',')
csv_writer.writerow(['Id', 'Predicted'])
for uuid in sorted(sub_dict):
csv_writer.writerow([uuid, sub_dict[uuid]])
def main(args):
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
#Load Word2Idx
log.info('Loading word2Idx...')
word2Idx = json.loads(open(args.word2idx_file).read())
idx2Word = {v: k for (k, v) in word2Idx.items()}
vocab_size = word_vectors.size(0)
print(f"Vocab size: {vocab_size}")
def getWords(idxList):
words = []
for i in idxList:
words.append(idx2Word[i])
return words
def create_new_model():
if args.model_type == "seq2seq":
return Seq2Seq(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device,
num_layers=args.num_layers)
elif args.model_type == "seq2seq_attn":
return Seq2SeqAttn(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device,
num_layers=args.num_layers)
# Get model
log.info('Building model...')
model = create_new_model()
model = nn.DataParallel(model, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path}...')
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)[f'{args.split}_record_file']
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Evaluate
log.info(f'Evaluating on {args.split} split...')
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions by Beam Search
eval_file = vars(args)[f'{args.split}_eval_file']
candidates_corpus = []
references_corpus = []
context_file = os.path.join(args.save_dir, args.context_file)
question_file = os.path.join(args.save_dir, args.question_file)
prediction_file = os.path.join(args.save_dir, args.prediction_file)
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, re_cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
re_cw_idxs = re_cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
cw_list = []
qw_list = []
# Forward
for re_cw_idx, qw_idx in zip(
torch.split(re_cw_idxs, split_size_or_sections=1, dim=0),
torch.split(qw_idxs, split_size_or_sections=1, dim=0)):
cw_list.append(re_cw_idx)
qw_list.append(qw_idx)
#y = F.one_hot(qw_idx, num_classes=len(word_vectors))
#print(getWords(cw_idx.squeeze().tolist()))
#print(getWords(qw_idx.squeeze().tolist()))
#util.TeacherForce(model, word2Idx, Idx2Word, cw_idx, qw_idx, device)
#util.evaluateRandomly(model, word2Idx, Idx2Word, cw_idx, device)
'''
hypotheses = util.beamSearch(model, word2Idx, Idx2Word, cw_idx, device)
loss = 0.
pred_dict[cw_idx] = []
for hyp in hypotheses:
loss = loss + hyp.score
pred_dict[cw_idx].append(hyp.value)
nll_meter.update(loss, batch_size)
#wait = input("Sab chill hai.. press to continue")
cw_list.append(cw_idx)
qw_list.append(qw_idx)
'''
candidates, references = util.find_candidates_and_references_for_bleu(
model, word2Idx, idx2Word, cw_list, qw_list, device,
context_file, question_file, prediction_file)
candidates_corpus += candidates
references_corpus += references
progress_bar.update(batch_size)
# Log info
#if args.split != 'test':
# No labels for the test set, so NLL would be invalid
# progress_bar.set_postfix(NLL=nll_meter.avg)
print("Calculating corpus BLEU...")
util.compute_corpus_level_bleu_score(model, args.split,
candidates_corpus,
references_corpus, device)
def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
# setup_args = get_setup_args()
with open(args.char2idx_file, "r") as f:
char2idx = json_load(f)
# Get model
log.info('Building model...')
model = QANet(word_vectors=word_vectors, char2idx=char2idx)
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(args.load_path))
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
# optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.8, 0.999))
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
dataset1 = np.load(args.train_record_file)
outfile = '/content/dataset/train_light.npz'
n_row = 75000
np.savez(outfile,
context_idxs=dataset1['context_idxs'][:n_row],
context_char_idxs=dataset1['context_char_idxs'][:n_row],
ques_idxs=dataset1['ques_idxs'][:n_row],
ques_char_idxs=dataset1['ques_char_idxs'][:n_row],
y1s=dataset1['y1s'][:n_row],
y2s=dataset1['y2s'][:n_row],
ids=dataset1['ids'][:n_row])
# train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_dataset = SQuAD(outfile, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info('Starting epoch {}...'.format(epoch))
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
if step % 10000 == 0:
print('loss val: {}'.format(loss_val))
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(k), v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
from util import collate_fn, SQuAD
from pickle import load
split = 'test'
args = get_test_args()
args.split = split
eval_file = vars(args)[f'{args.split}_eval_file']
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
#forgot to take the id of the question, normally ok to do it afterwards
#as the dataset is not shuffle in test and here
record_file = vars(args)[f'{args.split}_record_file']
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn)
id_quest = []
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
id_quest += [ids]
p_start = []
p_end = []
save_dir_bidaf = "../save/test/" + split + "/bidaf_original_char_embed-01/"
def main(args):
# Set up logging and devices
startime = datetime.now()
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
time_log = args.log_time
if time_log > 0:
log.info(f'Start training at: {startime.strftime("%H:%M:%S")}')
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
model_type = args.model
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# check this
#useCharEmbeddings = args.model == 'BiDAFplus'
# Get embeddings
log.info('Loading embeddings...')
print(f'{args.word_emb_file}')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
if time_log > 0:
log.info(f'Loaded embeddings: {(datetime.now()-startime).seconds}')
# load_char_vectors
# Get model
log.info('Building model...')
if model_type == 'BiDAFplus': #
model = BiDAFplus(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
elif model_type == 'BiDAFbase':
model = BiDAFbase(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
elif model_type == "Transformer":
model = TransformerModel(word_vectors=word_vectors,
char_vectors=char_vectors,
params=get_params(model_type, args.params))
elif model_type == 'BiDAF':
model = BiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
model = nn.DataParallel(model, args.gpu_ids)
if time_log > 0:
log.info(f'Built model: {(datetime.now()-startime).seconds}')
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
if args.mode != 'quick_eval':
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
else:
loaded_data = quick_eval_data_loader()
train_loader = [loaded_data for _ in range(5)]
dev_loader = [quick_eval_data_loader(dev=True)]
train_dataset = train_loader
dev_dataset = dev_loader
log.info('Built dataset: {}:{}'.format(*divmod((datetime.now() -
startime).seconds, 60)))
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
if time_log > 0:
traintime = datetime.now()
total_iterations = 0
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
if time_log > 0:
epochtime = datetime.now()
if args.mode != 'quick_eval':
progress_len = len(train_loader.dataset)
else:
progress_len = len(train_loader)
with torch.enable_grad(), \
tqdm(total=progress_len) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
#quick_eval_data_saver(cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids)
#########
if time_log > 0:
itertime = datetime.now()
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
if model_type == 'BiDAF' or model_type == "Transformer":
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
log_p1, log_p2 = model(cc_idxs, qc_idxs, cw_idxs, qw_idxs)
# Forward
elif model_type == 'BiDAFbase':
log_p1, log_p2 = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
if time_log > 2:
forwardtime = datetime.now()
log.info('Forward time {}:{}'.format(
*divmod((forwardtime - itertime).seconds, 60)))
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
if time_log > 2:
backwardtime = datetime.now()
log.info('Backward time {}:{}'.format(
*divmod((backwardtime - forwardtime).seconds, 60)))
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
if time_log > 0:
enditertime = datetime.now()
#log.info('Iteration {} {}:{}'.format(total_iterations,
# *divmod((enditertime-itertime).seconds, 60)))
steps_till_eval -= batch_size
if steps_till_eval <= 0 or args.mode == 'quick_eval':
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(
model,
dev_loader,
device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2,
model_type,
quick_eval=args.mode == 'quick_eval')
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
if time_log > 1:
log.info('Eval time {}:{}'.format(
*divmod((datetime.now() -
enditertime).seconds, 60)))
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
total_iterations += 1
if ((time_log == 2) and (total_iterations % 10 == 0)) or (
(time_log == 1) and (total_iterations % 100 == 0)):
log.info('Mean iteration time {}:{}'.format(
*divmod((enditertime - traintime).seconds /
total_iterations, 60)))
if time_log > 0:
endepochtime = datetime.now()
log.info('Epoch time {}:{}'.format(
*divmod((endepochtime - epochtime).seconds, 60)))
def main(args):
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
#Load Word2Idx
log.info('Loading word2Idx...')
word2Idx = json.loads(open(args.word2idx_file).read())
Idx2Word = {v: k for (k, v) in word2Idx.items()}
vocab_size = len(word2Idx)
def getWords(idxList):
words = []
for i in idxList:
words.append(Idx2Word[i])
return words
def create_new_model():
if args.model_type == "seq2seq":
return Seq2Seq(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device)
elif args.model_type == "seq2seq_attn":
return Seq2SeqAttn(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device)
elif args.model_type == "transformer":
return TransformerModel(vocab_size,
device,
num_encoder_layers=2,
num_decoder_layers=2,
dropout=0.0)
# Get model
log.info('Building model...')
model = create_new_model()
model = nn.DataParallel(model, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path}...')
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)[f'{args.split}_record_file']
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Evaluate
log.info(f'Evaluating on {args.split} split...')
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions by Beam Search
eval_file = vars(args)[f'{args.split}_eval_file']
cw_list = []
qw_list = []
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
# Setup for forward
src_idxs = cw_idxs
src_idxs = torch.cat(
(torch.zeros((batch_size, 1), device=device,
dtype=torch.long), src_idxs,
torch.zeros(
(batch_size, 1), device=device, dtype=torch.long)),
dim=-1)
src_idxs[:, 0] = SOS
src_idxs[:, -1] = EOS
tgt_idxs = qw_idxs[:, :-1]
tgt_idxs_y = qw_idxs[:, 1:]
# Forward
for src_idx, tgt_idx in zip(
torch.split(src_idxs, split_size_or_sections=1, dim=0),
torch.split(tgt_idxs, split_size_or_sections=1, dim=0)):
src_mask = src_idx == PAD
print("Context Words:")
print(getWords(src_idx[0].tolist()))
#print(getWords(qw_idxs[batch_size-1].squeeze().tolist()))
#util.evaluateRandomly(model, word2Idx, Idx2Word, re_cw_idxs[batch_size-1].unsqueeze(0), device)
print("Question Words:")
print(getWords(tgt_idx[0].tolist()))
print("Predicted Words:")
model.eval()
predicted_words = util.greedy_decode(model,
src_idx,
src_mask,
max_len=30,
start_symbol=2)
print(predicted_words)
print(getWords(predicted_words.squeeze().tolist()))
'''
#y = F.one_hot(qw_idx, num_classes=len(word_vectors))
#print(getWords(cw_idx.squeeze().tolist()))
#print(getWords(qw_idx.squeeze().tolist()))
#util.TeacherForce(model, word2Idx, Idx2Word, cw_idx, qw_idx, device)
#util.evaluateRandomly(model, word2Idx, Idx2Word, cw_idx, device)
hypotheses = util.beamSearch(model, word2Idx, Idx2Word, cw_idx, device)
loss = 0.
pred_dict[cw_idx] = []
for hyp in hypotheses:
loss = loss + hyp.score
pred_dict[cw_idx].append(hyp.value)
nll_meter.update(loss, batch_size)
#wait = input("Sab chill hai.. press to continue")
cw_list.append(cw_idx)
qw_list.append(qw_idx)
'''
# Log info
progress_bar.update(batch_size)
if args.split != 'test':
# No labels for the test set, so NLL would be invalid
progress_bar.set_postfix(NLL=nll_meter.avg)
def train_QaNet(args):
device, args.gpu_ids = util.get_available_devices()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
word_mat = util.torch_from_json(args.word_emb_file)
char_mat = util.torch_from_json(args.char_emb_file)
with open(args.dev_eval_file, 'r') as fh:
dev_eval_file = json_load(fh)
print("Building model...")
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_dataset = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_dataset = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
lr = args.lr
base_lr = 1
lr_warm_up_num = args.lr_warm_up_num
model = QaNet(word_mat, char_mat, args.connector_dim, args.glove_dim,
args.char_dim, args.drop_prob, args.dropout_char,
args.num_heads, args.c_len, args.q_len).to(device)
ema = util.EMA(model, args.ema_decay)
parameters = filter(lambda param: param.requires_grad, model.parameters())
optimizer = optim.Adam(lr=base_lr,
betas=(0.9, 0.999),
eps=1e-7,
weight_decay=5e-8,
params=parameters)
cr = lr / math.log2(lr_warm_up_num)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda ee: cr * math.log2(ee + 1)
if ee < lr_warm_up_num else lr)
best_f1 = 0
best_em = 0
patience = 0
unused = False
for iter in range(args.num_epochs):
train(model, optimizer, scheduler, train_dataset, dev_dataset,
dev_eval_file, iter, ema, device)
ema.assign(model)
metrics = test(model, dev_dataset, dev_eval_file,
(iter + 1) * len(train_dataset))
dev_f1 = metrics["f1"]
dev_em = metrics["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience += 1
if patience > args.early_stop:
break
else:
patience = 0
best_f1 = max(best_f1, dev_f1)
best_em = max(best_em, dev_em)
fn = os.path.join(args.save_dir, "model.pt")
torch.save(model, fn)
ema.resume(model)
def main(args, actions=None):
""""
actions is a tuple (action, number of actions to be taken)
action can be either: "substitute", "delete" or "add".
number of actions to be taken: the number of words to apply the "substitute", "delete" or "add" action.
"""
# check that actions parameters received
#print("actions: ",actions)
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
# Get model
log.info('Building model...')
if args.model == 'bidaf':
model = BiDAF(word_vectors=word_vectors, hidden_size=args.hidden_size)
elif args.model == 'bidafextra':
model = BiDAFExtra(word_vectors=word_vectors, args=args)
elif args.model == 'fusionnet':
model = FusionNet(word_vectors=word_vectors, args=args)
model = nn.DataParallel(model, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path}...')
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)[f'{args.split}_record_file']
dataset = SQuAD(record_file, args)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# print("*"*80)
#print(len(dataset.question_idxs))
#for question_idx in dataset.question_idxs:
# print(question_idx)
# print("*" * 80)
#print(self.question_idxs[question_idx])
#self.question_idxs[idx]
# print("data_loader: ",data_loader)
# Evaluate
log.info(f'Evaluating on {args.split} split...')
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions for TensorBoard
sub_dict = {} # Predictions for submission
eval_file = vars(args)[f'{args.split}_eval_file']
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
count_questions_type = defaultdict(lambda: 0)
audit_trail_from_question_type = defaultdict(lambda: [])
list_of_interrogative_pronouns = [
"what", "whose", "why", "which", "where", "when", "how", "who", "whom"
]
for index in range(1, len(gold_dict)):
# transform the question in lower case to simplify the analysis, thus losing the benefit of the capital letters
# possibly indicating the position of the interrogative pronoun in the sentence.
question_lower_case = gold_dict[str(index)]['question'].lower()
list_question_lower_case_with_punctuation = question_lower_case.translate(
{ord(i): " "
for i in "'"}).split()
#
question_lower_case = []
for item in list_question_lower_case_with_punctuation:
question_lower_case.append(
item.translate({ord(i): ""
for i in ",.<>!@£$%^&*()_-+=?"}))
# defining a variable for the first word
first_word_question_lower_case = question_lower_case[0]
# defining variable for the second word
second_word_question_lower_case = question_lower_case[1]
# defining variable for the first and second word
combined_first_and_second_words = first_word_question_lower_case + " " + second_word_question_lower_case
# Analyzing the sentence
if first_word_question_lower_case in list_of_interrogative_pronouns:
count_questions_type[first_word_question_lower_case] += 1
audit_trail_from_question_type[
first_word_question_lower_case].append(str(index))
# composed question starting by in
elif first_word_question_lower_case == "in":
if second_word_question_lower_case in list_of_interrogative_pronouns and second_word_question_lower_case != "whose":
count_questions_type[combined_first_and_second_words] += 1
audit_trail_from_question_type[
combined_first_and_second_words].append(str(index))
else:
pronoun = find_first_interrogative_pronoun(
list_of_interrogative_pronouns, question_lower_case)
count_questions_type[pronoun] += 1
audit_trail_from_question_type[pronoun].append(str(index))
# composed question starting by by
elif first_word_question_lower_case == "by":
if second_word_question_lower_case in list_of_interrogative_pronouns \
and second_word_question_lower_case != "whom" \
and second_word_question_lower_case != "which" \
and second_word_question_lower_case != "when" \
and second_word_question_lower_case != "how":
count_questions_type[combined_first_and_second_words] += 1
audit_trail_from_question_type[
combined_first_and_second_words].append(str(index))
else:
pronoun = find_first_interrogative_pronoun(
list_of_interrogative_pronouns, question_lower_case)
count_questions_type[pronoun] += 1
audit_trail_from_question_type[pronoun].append(str(index))
else:
pronoun = find_first_interrogative_pronoun(
list_of_interrogative_pronouns, question_lower_case)
count_questions_type[pronoun] += 1
audit_trail_from_question_type[pronoun].append(str(index))
reverse_dict_by_value = OrderedDict(
sorted(count_questions_type.items(), key=lambda x: x[1]))
total_questions = sum(count_questions_type.values())
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, cw_pos, cw_ner, cw_freq, cqw_extra, y1, y2, ids in data_loader:
# Setup for forward
# **********************************************************
#
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
#
# Where we make the modif:
if actions[0] == "substitute":
# substitute to random token in each question of the batch (substitution is made within the same sentence:
batch_size = cw_idxs.size()[0]
number_of_actions = actions[1]
for _ in range(number_of_actions):
length_index_batch = cw_idxs.size()[1]
for i in range(batch_size):
tensor_with_zero_value = ((
cw_idxs[i] == 0).nonzero()).squeeze()
try:
first_zero_value = torch.min(
tensor_with_zero_value)
except:
first_zero_value = length_index_batch
if first_zero_value > 2:
select_item_idx_1 = random.randint(
0, first_zero_value - 1)
select_item_idx_2 = random.randint(
0, first_zero_value - 1)
save_value_1 = copy.deepcopy(
cw_idxs[i, select_item_idx_1])
cw_idxs[i, select_item_idx_1] = cw_idxs[
i, select_item_idx_2]
cw_idxs[i, select_item_idx_2] = save_value_1
elif actions[0] == "delete":
# substitute to random token in each question of the batch (substitution is made within the same sentence:
batch_size = cw_idxs.size()[0]
number_of_actions = actions[1]
for _ in range(number_of_actions):
length_index_batch = cw_idxs.size()[1]
for i in range(batch_size):
tensor_with_zero_value = ((
cw_idxs[i] == 0).nonzero()).squeeze()
try:
first_zero_value = torch.min(
tensor_with_zero_value)
except:
first_zero_value = length_index_batch
if first_zero_value > 2:
print("debug:", i)
print("> size before amendment of cw_idxs",
cw_idxs.size())
print(cw_idxs[i])
select_item_idx_1 = random.randint(
0, first_zero_value - 1)
#print("section 1 :", cw_idxs[i, 0:select_item_idx_1)
#print("remove:", cw_idxs[i,0:select_item_idx_1)
#print("section 2 :", cw_idxs[i, 0:select_item_idx_1)
cw_idxs[i, :] = torch.cat(
(cw_idxs[i, 0:select_item_idx_1],
cw_idxs[i, select_item_idx_1 + 1:],
torch.tensor([0])), -1)
print("> size before amendment of cw_idxs",
cw_idxs.size())
print(cw_idxs[i])
elif actions[0] == "add":
batch_size = cw_idxs.size()[0]
number_of_actions = actions[1]
for _ in range(number_of_actions):
length_index_batch = cw_idxs.size()[1]
for i in range(batch_size):
tensor_with_zero_value = ((
cw_idxs[i] == 0).nonzero()).squeeze()
try:
first_zero_value = torch.min(
tensor_with_zero_value)
except:
first_zero_value = length_index_batch
if first_zero_value > 2:
select_item_idx_1 = random.randint(
0, first_zero_value - 1)
cw_idxs[i, select_item_idx_1] = random.randint(
1, 50000)
elif actions[0] == "add2":
# substitute to random token in each question of the batch (substitution is made within the same sentence:
batch_size = cw_idxs.size()[0]
number_of_actions = actions[1]
for _ in range(number_of_actions):
length_index_batch = cw_idxs.size()[1]
for i in range(batch_size):
tensor_with_zero_value = ((
cw_idxs[i] == 0).nonzero()).squeeze()
try:
first_zero_value = torch.min(
tensor_with_zero_value)
except:
first_zero_value = length_index_batch
if first_zero_value > 2:
select_item_idx_1 = random.randint(
0, first_zero_value - 1)
select_item_idx_2 = random.randint(
0, first_zero_value - 1)
cw_idxs[i, select_item_idx_1] = random.randint(
1, 50000)
cw_idxs[i, select_item_idx_2] = random.randint(
1, 50000)
else:
print("Incorrect command: exiting")
exit()
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
if args.model == 'bidaf':
log_p1, log_p2 = model(cw_idxs, qw_idxs)
else:
log_p1, log_p2 = model(cw_idxs, qw_idxs, cw_pos, cw_ner,
cw_freq, cqw_extra)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, args.max_ans_len,
args.use_squad_v2)
# Log info
progress_bar.update(batch_size)
if args.split != 'test':
# No labels for the test set, so NLL would be invalid
progress_bar.set_postfix(NLL=nll_meter.avg)
idx2pred, uuid2pred = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(),
ends.tolist(),
args.use_squad_v2)
pred_dict.update(idx2pred)
sub_dict.update(uuid2pred)
# Log results (except for test set, since it does not come with labels)
if args.split != 'test':
results = util.eval_dicts(gold_dict, pred_dict, args.use_squad_v2)
# Printing information for questions without interrogative pronouns
""""
print("len(gold_dict): ", len(gold_dict))
print("len(pred_dict): ", len(pred_dict))
print("Is gold_dict.keys() identical to pred_dict.keys(): ", gold_dict.keys()==pred_dict.keys())
if gold_dict.keys()!=pred_dict.keys():
for key in gold_dict.keys():
if key not in pred_dict.keys():
print("key ", key, " missing in pred_dict.keys(")
"""
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if args.use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
# Computing the F1 score for each type of question
print("for ", actions, ": ", results['F1'])
# create a list of the types of questions by extracting the keys from the dict audit_trail_from_question_type
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}' for k, v in results.items())
log.info(f'{args.split.title()} {results_str}')
# Log to TensorBoard
tbx = SummaryWriter(args.save_dir)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=eval_file,
step=0,
split=args.split,
num_visuals=args.num_visuals)
# Write submission file
sub_path = join(args.save_dir, args.split + '_' + args.sub_file)
log.info(f'Writing submission file to {sub_path}...')
with open(sub_path, 'w', newline='', encoding='utf-8') as csv_fh:
csv_writer = csv.writer(csv_fh, delimiter=',')
csv_writer.writerow(['Id', 'Predicted'])
for uuid in sorted(sub_dict):
csv_writer.writerow([uuid, sub_dict[uuid]])
return results['F1']
def main(args):
###############################################################################
# Build the model
###############################################################################
def init_weight(weight):
nn.init.uniform_(weight, -args.init_range, args.init_range)
def init_bias(bias):
nn.init.constant_(bias, 0.0)
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None:
init_weight(m.weight)
if hasattr(m, 'bias') and m.bias is not None:
init_bias(m.bias)
elif classname.find('AdaptiveEmbedding') != -1:
if hasattr(m, 'emb_projs'):
for i in range(len(m.emb_projs)):
if m.emb_projs[i] is not None:
nn.init.normal_(m.emb_projs[i], 0.0,
args.proj_init_std)
elif classname.find('Embedding') != -1:
if hasattr(m, 'weight'):
init_weight(m.weight)
elif classname.find('ProjectedAdaptiveLogSoftmax') != -1:
if hasattr(m, 'cluster_weight') and m.cluster_weight is not None:
init_weight(m.cluster_weight)
if hasattr(m, 'cluster_bias') and m.cluster_bias is not None:
init_bias(m.cluster_bias)
if hasattr(m, 'out_projs'):
for i in range(len(m.out_projs)):
if m.out_projs[i] is not None:
nn.init.normal_(m.out_projs[i], 0.0,
args.proj_init_std)
elif classname.find('LayerNorm') != -1:
if hasattr(m, 'weight'):
nn.init.normal_(m.weight, 1.0, args.init_std)
if hasattr(m, 'bias') and m.bias is not None:
init_bias(m.bias)
elif classname.find('TransformerLM') != -1:
if hasattr(m, 'r_emb'):
init_weight(m.r_emb)
if hasattr(m, 'r_w_bias'):
init_weight(m.r_w_bias)
if hasattr(m, 'r_r_bias'):
init_weight(m.r_r_bias)
if hasattr(m, 'r_bias'):
init_bias(m.r_bias)
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
# Args: word_vectors: word vector tensor of dimension [vocab_size * wemb_dim]
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
# Get Model
log.info('Building Model...')
model = QANet(
word_vectors,
char_vectors,
args.para_limit,
args.ques_limit,
args.f_model,
args.d_head,
same_length=False, #Check back on this
mem_len=args.mem_len,
clamp_len=-1,
num_head=args.num_head,
train_cemb=(not args.pretrained_char))
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(args.load_path))
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
#model.apply(weights_init)
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(params=parameters,
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=1e-8,
weight_decay=3e-7)
cr = 1.0 / math.log(args.lr_warm_up_num)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lambda ee: cr * math.log(ee + 1)
if ee < args.lr_warm_up_num else 1)
loss_f = torch.nn.CrossEntropyLoss()
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn,
drop_last=True)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
drop_last=True)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
mems = (tuple(), tuple(), tuple())
while epoch != args.num_epochs:
epoch += 1
log.info('Starting epoch {}...'.format(epoch))
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
log_p1, log_p2, mems = model(cw_idxs, cc_idxs, qw_idxs,
qc_idxs, *mems)
y1, y2 = y1.to(device), y2.to(device)
loss = torch.mean(loss_f(log_p1, y1) + loss_f(log_p2, y2))
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(k), v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get embeddings
log.info('Loading word embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
log.info('Loading char embeddings...')
char_vectors = util.torch_from_json(args.char_emb_file)
# Get model
log.info('Building model...')
# ---
if args.name == "baseline":
model = BiDAF(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
elif args.name == "slqa":
model = SLQA(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
# ---
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(args.load_path))
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
if args.opt == 'adam':
print("args.opt: ", args.opt)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
elif args.opt == 'adadelta':
print("args.opt: ", args.opt)
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info('Starting epoch {}...'.format(epoch))
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# For char_embedding
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
y1, y2 = y1.to(device), y2.to(device)
# Forward
# Original model forwad, without char_embedding
if args.name == "baseline":
log_p1, log_p2 = model(cw_idxs, qw_idxs)
loss_classify = 0
elif args.name == "slqa":
#========================================================================
log_p1, log_p2, pred_score = model(cw_idxs, qw_idxs,
cc_idxs, qc_idxs)
y2_mask = torch.zeros_like(y2) != y2
y2_mask = y2_mask.to(dtype=torch.float)
loss_BCE = nn.BCELoss()
loss_classify = loss_BCE(
pred_score,
y2_mask.to(dtype=torch.float).unsqueeze(1))
#========================================================================
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss = loss + 2 * loss_classify
loss_val = loss.item()
# Backward
# print("===================================BACKWARD===================================")
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
# print("===================================Log info===================================")
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
# print("===================================EVAL===================================")
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2, args)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(k), v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
def main(args):
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
# NEW : load the tag embeddings
pos_vectors = util.torch_from_json(args.pos_emb_file)
ner_vectors = util.torch_from_json(args.ner_emb_file)
# Choose model
log.info('Building model {}...'.format(args.name))
if 'baseline' in args.name:
model = BiDAF(word_vectors=word_vectors, hidden_size=args.hidden_size)
elif args.name == 'BiDAF_char':
model = BiDAF_char(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size)
# NEW
elif (args.name == 'BiDAF_tag') or (args.name == 'BiDAF_tag_unfrozen') or (
args.name == 'BiDAF_tag_loss') or (args.name
== 'BiDAF_tag_unfrozen_loss'):
model = BiDAF_tag(word_vectors=word_vectors,
char_vectors=char_vectors,
pos_vectors=pos_vectors,
ner_vectors=ner_vectors,
hidden_size=args.hidden_size)
elif (args.name == 'BiDAF_tag_ext') or (args.name
== 'BiDAF_tag_ext_unfrozen'):
model = BiDAF_tag_ext(word_vectors=word_vectors,
char_vectors=char_vectors,
pos_vectors=pos_vectors,
ner_vectors=ner_vectors,
hidden_size=args.hidden_size)
elif args.name == 'coattn':
model = CoattentionModel(hidden_dim=args.hidden_size,
embedding_matrix=word_vectors,
train_word_embeddings=False,
dropout=0.35,
pooling_size=16,
number_of_iters=4,
number_of_layers=2,
device=device)
else:
raise NameError('No model named ' + args.name)
model = nn.DataParallel(model, gpu_ids)
log.info('Loading checkpoint from {}...'.format(args.load_path))
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)['{}_record_file'.format(args.split)]
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Evaluate
log.info('Evaluating on {} split...'.format(args.split))
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions for TensorBoard
sub_dict = {} # Predictions for submission
eval_file = vars(args)['{}_eval_file'.format(args.split)]
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, cpos_idxs, cner_idxs, cw_ems, cw_tfs, qw_idxs, qc_idxs, qpos_idxs, qner_idxs, qw_ems, qw_tfs, y1, y2, ids in data_loader: # NEW
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
if 'baseline' in args.name:
log_p1, log_p2 = model(cw_idxs, qw_idxs)
elif args.name == 'BiDAF_char':
# Additional setup for forward
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs)
elif args.name == 'coattn':
max_c_len = cw_idxs.size(1)
max_q_len = qw_idxs.size(1)
c_len = []
q_len = []
for i in range(cw_idxs.size(0)):
if len((cw_idxs[i] == 0).nonzero()) != 0:
c_len_i = (cw_idxs[i] == 0).nonzero()[0].item()
else:
c_len_i = cw_idxs.size(1)
if len((qw_idxs[i] == 0).nonzero()) != 0:
q_len_i = (qw_idxs[i] == 0).nonzero()[0].item()
else:
q_len_i = qw_idxs.size(1)
c_len.append(int(c_len_i))
q_len.append(int(q_len_i))
c_len = torch.Tensor(c_len).int()
q_len = torch.Tensor(q_len).int()
num_examples = int(cw_idxs.size(0) / len(gpu_ids))
log_p1, log_p2 = model(max_c_len, max_q_len, cw_idxs, qw_idxs,
c_len, q_len, num_examples, True, False)
# NEW
elif (args.name
== 'BiDAF_tag') or (args.name == 'BiDAF_tag_unfrozen') or (
args.name
== 'BiDAF_tag_loss') or (args.name
== 'BiDAF_tag_unfrozen_loss'):
# Additional setup for forward
cc_idxs = cc_idxs.to(device)
cpos_idxs = cpos_idxs.to(device)
cner_idxs = cner_idxs.to(device)
qc_idxs = qc_idxs.to(device)
qpos_idxs = qpos_idxs.to(device)
qner_idxs = qner_idxs.to(device)
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs,
cpos_idxs, qpos_idxs, cner_idxs,
qner_idxs)
elif (args.name
== 'BiDAF_tag_ext') or (args.name
== 'BiDAF_tag_ext_unfrozen'):
# Additional setup for forward
cc_idxs = cc_idxs.to(device)
cpos_idxs = cpos_idxs.to(device)
cner_idxs = cner_idxs.to(device)
cw_ems = cw_ems.to(device)
cw_tfs = cw_tfs.to(device)
qc_idxs = qc_idxs.to(device)
qpos_idxs = qpos_idxs.to(device)
qner_idxs = qner_idxs.to(device)
qw_ems = qw_ems.to(device)
qw_tfs = qw_tfs.to(device)
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs,
cpos_idxs, qpos_idxs, cner_idxs,
qner_idxs, cw_ems, qw_ems, cw_tfs,
qw_tfs)
else:
raise NameError('No model named ' + args.name)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, args.max_ans_len,
args.use_squad_v2)
# Log info
progress_bar.update(batch_size)
if args.split != 'test':
# No labels for the test set, so NLL would be invalid
progress_bar.set_postfix(NLL=nll_meter.avg)
idx2pred, uuid2pred = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(),
ends.tolist(),
args.use_squad_v2)
pred_dict.update(idx2pred)
sub_dict.update(uuid2pred)
# Log results (except for test set, since it does not come with labels)
if args.split != 'test':
results = util.eval_dicts(gold_dict, pred_dict, args.use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if args.use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('{} {}'.format(args.split.title(), results_str))
# More detailed error analysis
results = util.analysis_dicts(gold_dict, pred_dict, args.use_squad_v2)
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('{} {}'.format(args.split.title(), results_str))
# Log to TensorBoard
tbx = SummaryWriter(args.save_dir)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=eval_file,
step=0,
split=args.split,
num_visuals=args.num_visuals)
# Write submission file
sub_path = join(args.save_dir, args.split + '_' + args.sub_file)
log.info('Writing submission file to {}...'.format(sub_path))
with open(sub_path, 'w', newline='', encoding='utf-8') as csv_fh:
csv_writer = csv.writer(csv_fh, delimiter=',')
csv_writer.writerow(['Id', 'Predicted'])
for uuid in sorted(sub_dict):
csv_writer.writerow([uuid, sub_dict[uuid]])
def main(args):
# Set up logging and devices
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info('Args: {}'.format(dumps(vars(args), indent=4, sort_keys=True)))
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seed
log.info('Using random seed {}...'.format(args.seed))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Get word embeddings
log.info('Loading word embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
### start our code:
# Get char-embeddings
log.info('Loading char-embeddings...')
char_vectors = util.torch_from_json(args.char_emb_file)
### end our code
# Get model
log.info('Building model...')
# model = BiDAF(word_vectors=word_vectors,
# hidden_size=args.hidden_size,
# char_vectors = char_vectors,
# drop_prob=args.drop_prob)
### start our code: (QANet)
model = QANet(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
kernel_size=7,
filters=128,
drop_prob=args.drop_prob)
### end our code
model = nn.DataParallel(model, args.gpu_ids)
if args.load_path:
log.info('Loading checkpoint from {}...'.format(args.load_path))
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
# https://pytorch.org/docs/stable/optim.html
# Original:
# optimizer = optim.Adadelta(model.parameters(), args.lr,
# weight_decay=args.l2_wd)
# # default: lr=0.5, rho=0.9, eps=1e-06, weight_decay=0
# scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# A function which computes a multiplicative factor given an integer parameter epoch,
# or a list of such functions, one for each group in optimizer.param_groups
### start our code:
optimizer = optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.8, 0.999),
eps=1e-7,
weight_decay=3e-7,
amsgrad=False)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Adabound
# optimizer = adabound.AdaBound(model.parameters(), lr=1e-3, final_lr=0.1)
# scheduler = sched.LambdaLR(optimizer, lambda s: 1.)
### end our code
# Get data loader
log.info('Building dataset...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
while epoch != args.num_epochs:
epoch += 1
log.info('Starting epoch {}...'.format(epoch))
with torch.enable_grad(), \
tqdm(total=len(train_loader.dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
# print("cc_idxs: ", cc_idxs)
# print("qc_idxs: ", qc_idxs)
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
### start our code:
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
### end our code
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
# Forward
# log_p1, log_p2 = model(cw_idxs, qw_idxs) # original
### start our code:
log_p1, log_p2 = model(cw_idxs, qw_idxs, cc_idxs, qc_idxs)
### end our code
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
### start our code:
# optimizer = LR_decay(optimizer, epoch, lr = 0.8) # initial learning rate 0.8
if step < 1000:
optimizer = LR_warmup(optimizer, step, lr=0.)
### end our code
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
steps_till_eval -= batch_size
if steps_till_eval <= 0:
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info('Evaluating at step {}...'.format(step))
ema.assign(model)
results, pred_dict = evaluate(model, dev_loader, device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2)
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
results_str = ', '.join('{}: {:05.2f}'.format(k, v)
for k, v in results.items())
log.info('Dev {}'.format(results_str))
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar('dev/{}'.format(k), v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
