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)
char_vectors = util.torch_from_json(args.char_emb_file)
# Get model
log.info('Building model...')
nbr_model = 0
if (args.load_path_baseline):
model_baseline = Baseline(word_vectors=word_vectors, hidden_size=100)
model_baseline = nn.DataParallel(model_baseline, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_baseline}...')
model_baseline = util.load_model(model_baseline,
args.load_path_baseline,
gpu_ids,
return_step=False)
model_baseline = model_baseline.to(device)
model_baseline.eval()
nll_meter_baseline = util.AverageMeter()
nbr_model += 1
save_prob_baseline_start = []
save_prob_baseline_end = []
if (args.load_path_bidaf):
model_bidaf = BiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size)
model_bidaf = nn.DataParallel(model_bidaf, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_bidaf}...')
model_bidaf = util.load_model(model_bidaf,
args.load_path_bidaf,
gpu_ids,
return_step=False)
model_bidaf = model_bidaf.to(device)
model_bidaf.eval()
nll_meter_bidaf = util.AverageMeter()
nbr_model += 1
save_prob_bidaf_start = []
save_prob_bidaf_end = []
if (args.load_path_bidaf_fusion):
model_bidaf_fu = BiDAF_fus(word_vectors=word_vectors,
char_vectors=char_vectors,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size)
model_bidaf_fu = nn.DataParallel(model_bidaf_fu, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_bidaf_fusion}...')
model_bidaf_fu = util.load_model(model_bidaf_fu,
args.load_path_bidaf_fusion,
gpu_ids,
return_step=False)
model_bidaf_fu = model_bidaf_fu.to(device)
model_bidaf_fu.eval()
nll_meter_bidaf_fu = util.AverageMeter()
nbr_model += 1
save_prob_bidaf_fu_start = []
save_prob_bidaf_fu_end = []
if (args.load_path_qanet):
model_qanet = QANet(word_vectors=word_vectors,
char_vectors=char_vectors,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size,
n_heads=args.n_heads,
n_conv_emb_enc=args.n_conv_emb,
n_conv_mod_enc=args.n_conv_mod,
n_emb_enc_blocks=args.n_emb_blocks,
n_mod_enc_blocks=args.n_mod_blocks,
divisor_dim_kqv=args.divisor_dim_kqv)
model_qanet = nn.DataParallel(model_qanet, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_qanet}...')
model_qanet = util.load_model(model_qanet,
args.load_path_qanet,
gpu_ids,
return_step=False)
model_qanet = model_qanet.to(device)
model_qanet.eval()
nll_meter_qanet = util.AverageMeter()
nbr_model += 1
save_prob_qanet_start = []
save_prob_qanet_end = []
if (args.load_path_qanet_old):
model_qanet_old = QANet_old(word_vectors=word_vectors,
char_vectors=char_vectors,
device=device,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size,
n_heads=args.n_heads,
n_conv_emb_enc=args.n_conv_emb,
n_conv_mod_enc=args.n_conv_mod,
n_emb_enc_blocks=args.n_emb_blocks,
n_mod_enc_blocks=args.n_mod_blocks)
model_qanet_old = nn.DataParallel(model_qanet_old, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_qanet_old}...')
model_qanet_old = util.load_model(model_qanet_old,
args.load_path_qanet_old,
gpu_ids,
return_step=False)
model_qanet_old = model_qanet_old.to(device)
model_qanet_old.eval()
nll_meter_qanet_old = util.AverageMeter()
nbr_model += 1
save_prob_qanet_old_start = []
save_prob_qanet_old_end = []
if (args.load_path_qanet_inde):
model_qanet_inde = QANet_independant_encoder(
word_vectors=word_vectors,
char_vectors=char_vectors,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size,
n_heads=args.n_heads,
n_conv_emb_enc=args.n_conv_emb,
n_conv_mod_enc=args.n_conv_mod,
n_emb_enc_blocks=args.n_emb_blocks,
n_mod_enc_blocks=args.n_mod_blocks,
divisor_dim_kqv=args.divisor_dim_kqv)
model_qanet_inde = nn.DataParallel(model_qanet_inde, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_qanet_inde}...')
model_qanet_inde = util.load_model(model_qanet_inde,
args.load_path_qanet_inde,
gpu_ids,
return_step=False)
model_qanet_inde = model_qanet_inde.to(device)
model_qanet_inde.eval()
nll_meter_qanet_inde = util.AverageMeter()
nbr_model += 1
save_prob_qanet_inde_start = []
save_prob_qanet_inde_end = []
if (args.load_path_qanet_s_e):
model_qanet_s_e = QANet_S_E(word_vectors=word_vectors,
char_vectors=char_vectors,
char_emb_dim=args.char_emb_dim,
hidden_size=args.hidden_size,
n_heads=args.n_heads,
n_conv_emb_enc=args.n_conv_emb,
n_conv_mod_enc=args.n_conv_mod,
n_emb_enc_blocks=args.n_emb_blocks,
n_mod_enc_blocks=args.n_mod_blocks,
divisor_dim_kqv=args.divisor_dim_kqv)
model_qanet_s_e = nn.DataParallel(model_qanet_s_e, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path_qanet_s_e}...')
model_qanet_s_e = util.load_model(model_qanet_s_e,
args.load_path_qanet_s_e,
gpu_ids,
return_step=False)
model_qanet_s_e = model_qanet_s_e.to(device)
model_qanet_s_e.eval()
nll_meter_qanet_s_e = util.AverageMeter()
nbr_model += 1
save_prob_qanet_s_e_start = []
save_prob_qanet_s_e_end = []
# 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...')
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)
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)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
y1, y2 = y1.to(device), y2.to(device)
l_p1, l_p2 = [], []
# Forward
if (args.load_path_baseline):
log_p1_baseline, log_p2_baseline = model_baseline(
cw_idxs, cc_idxs)
loss_baseline = F.nll_loss(log_p1_baseline, y1) + F.nll_loss(
log_p2_baseline, y2)
nll_meter_baseline.update(loss_baseline.item(), batch_size)
l_p1 += [log_p1_baseline.exp()]
l_p2 += [log_p2_baseline.exp()]
if (args.save_probabilities):
save_prob_baseline_start += [
log_p1_baseline.exp().detach().cpu().numpy()
]
save_prob_baseline_end += [
log_p2_baseline.exp().detach().cpu().numpy()
]
if (args.load_path_qanet):
log_p1_qanet, log_p2_qanet = model_qanet(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_qanet = F.nll_loss(log_p1_qanet, y1) + F.nll_loss(
log_p2_qanet, y2)
nll_meter_qanet.update(loss_qanet.item(), batch_size)
# Get F1 and EM scores
l_p1 += [log_p1_qanet.exp()]
l_p2 += [log_p2_qanet.exp()]
if (args.save_probabilities):
save_prob_qanet_start += [
log_p1_qanet.exp().detach().cpu().numpy()
]
save_prob_qanet_end += [
log_p2_qanet.exp().detach().cpu().numpy()
]
if (args.load_path_qanet_old):
log_p1_qanet_old, log_p2_qanet_old = model_qanet_old(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_qanet_old = F.nll_loss(log_p1_qanet_old, y1) + F.nll_loss(
log_p2_qanet_old, y2)
nll_meter_qanet_old.update(loss_qanet_old.item(), batch_size)
# Get F1 and EM scores
l_p1 += [log_p1_qanet_old.exp()]
l_p2 += [log_p2_qanet_old.exp()]
if (args.save_probabilities):
save_prob_qanet_old_start += [
log_p1_qanet_old.exp().detach().cpu().numpy()
]
save_prob_qanet_old_end += [
log_p2_qanet_old.exp().detach().cpu().numpy()
]
if (args.load_path_qanet_inde):
log_p1_qanet_inde, log_p2_qanet_inde = model_qanet_inde(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_qanet_inde = F.nll_loss(
log_p1_qanet_inde, y1) + F.nll_loss(log_p2_qanet_inde, y2)
nll_meter_qanet_inde.update(loss_qanet_inde.item(), batch_size)
# Get F1 and EM scores
l_p1 += [log_p1_qanet_inde.exp()]
l_p2 += [log_p2_qanet_inde.exp()]
if (args.save_probabilities):
save_prob_qanet_inde_start += [
log_p1_qanet_inde.exp().detach().cpu().numpy()
]
save_prob_qanet_inde_end += [
log_p2_qanet_inde.exp().detach().cpu().numpy()
]
if (args.load_path_qanet_s_e):
log_p1_qanet_s_e, log_p2_qanet_s_e = model_qanet_s_e(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_qanet_s_e = F.nll_loss(log_p1_qanet_s_e, y1) + F.nll_loss(
log_p2_qanet_s_e, y2)
nll_meter_qanet_s_e.update(loss_qanet_s_e.item(), batch_size)
# Get F1 and EM scores
l_p1 += [log_p1_qanet_s_e.exp()]
l_p2 += [log_p2_qanet_s_e.exp()]
if (args.save_probabilities):
save_prob_qanet_s_e_start += [
log_p1_qanet_s_e.exp().detach().cpu().numpy()
]
save_prob_qanet_s_e_end += [
log_p2_qanet_s_e.exp().detach().cpu().numpy()
]
if (args.load_path_bidaf):
log_p1_bidaf, log_p2_bidaf = model_bidaf(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_bidaf = F.nll_loss(log_p1_bidaf, y1) + F.nll_loss(
log_p2_bidaf, y2)
nll_meter_bidaf.update(loss_bidaf.item(), batch_size)
l_p1 += [log_p1_bidaf.exp()]
l_p2 += [log_p2_bidaf.exp()]
if (args.save_probabilities):
save_prob_bidaf_start += [
log_p1_bidaf.exp().detach().cpu().numpy()
]
save_prob_bidaf_end += [
log_p2_bidaf.exp().detach().cpu().numpy()
]
if (args.load_path_bidaf_fusion):
log_p1_bidaf_fu, log_p2_bidaf_fu = model_bidaf_fu(
cw_idxs, cc_idxs, qw_idxs, qc_idxs)
loss_bidaf_fu = F.nll_loss(log_p1_bidaf_fu, y1) + F.nll_loss(
log_p2_bidaf_fu, y2)
nll_meter_bidaf_fu.update(loss_bidaf_fu.item(), batch_size)
l_p1 += [log_p1_bidaf_fu.exp()]
l_p2 += [log_p2_bidaf_fu.exp()]
if (args.save_probabilities):
save_prob_bidaf_fu_start += [
log_p1_bidaf_fu.exp().detach().cpu().numpy()
]
save_prob_bidaf_fu_end += [
log_p2_bidaf_fu.exp().detach().cpu().numpy()
]
p1, p2 = l_p1[0], l_p2[0]
for i in range(1, nbr_model):
p1 += l_p1[i]
p2 += l_p2[i]
p1 /= nbr_model
p2 /= nbr_model
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
if (args.load_path_qanet):
progress_bar.set_postfix(NLL=nll_meter_qanet.avg)
elif (args.load_path_bidaf):
progress_bar.set_postfix(NLL=nll_meter_bidaf.avg)
elif (args.load_path_bidaf_fusion):
progress_bar.set_postfix(NLL=nll_meter_bidaf_fu.avg)
elif (args.load_path_qanet_old):
progress_bar.set_postfix(NLL=nll_meter_qanet_old.avg)
elif (args.load_path_qanet_inde):
progress_bar.set_postfix(NLL=nll_meter_qanet_inde.avg)
elif (args.load_path_qanet_s_e):
progress_bar.set_postfix(NLL=nll_meter_qanet_s_e.avg)
else:
progress_bar.set_postfix(NLL=nll_meter_baseline.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)
if (args.save_probabilities):
if (args.load_path_baseline):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_baseline_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_baseline_end, fp)
if (args.load_path_bidaf):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_bidaf_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_bidaf_end, fp)
if (args.load_path_bidaf_fusion):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_bidaf_fu_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_bidaf_fu_end, fp)
if (args.load_path_qanet):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_end, fp)
if (args.load_path_qanet_old):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_old_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_old_end, fp)
if (args.load_path_qanet_inde):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_inde_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_inde_end, fp)
if (args.load_path_qanet_s_e):
with open(args.save_dir + "/probs_start", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_s_e_start, fp)
with open(args.save_dir + "/probs_end", "wb") as fp: #Pickling
pickle.dump(save_prob_qanet_s_e_end, fp)
# 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)
if (args.load_path_qanet):
meter_avg = nll_meter_qanet.avg
elif (args.load_path_bidaf):
meter_avg = nll_meter_bidaf.avg
elif (args.load_path_bidaf_fusion):
meter_avg = nll_meter_bidaf_fu.avg
elif (args.load_path_qanet_inde):
meter_avg = nll_meter_qanet_inde.avg
elif (args.load_path_qanet_s_e):
meter_avg = nll_meter_qanet_s_e.avg
elif (args.load_path_qanet_old):
meter_avg = nll_meter_qanet_old.avg
else:
meter_avg = nll_meter_baseline.avg
results_list = [('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(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]])
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)
# Get model
log.info('Building model...')
model = QANet(word_vectors,
char_vectors,
args.test_para_limit,
args.test_ques_limit,
args.f_model,
num_head=args.num_head,
train_cemb=(not args.pretrained_char))
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))
loss_f = torch.nn.CrossEntropyLoss()
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)
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
batch_size = cw_idxs.size(0)
# Forward
log_p1, log_p2 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
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()
# 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 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,
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 = 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)
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)
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 = model(cw_idxs, cc_idxs, qw_idxs, qc_idxs)
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(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 = BiDAF(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
"""
'''
model = charBiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
emb_size=char_vectors.size(1),
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
'''
model = QANet(word_vectors=word_vectors,
char_vectors=char_vectors,
emb_size=char_vectors.size(1),
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
print(
"YOU ARE ENTERING THE QA NET MODEL TRAINING_____________________________________________________________________"
)
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()
#print("Model status - ", cuda_or_cpu(model))
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...')
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
#torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL = 300
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=0,
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)
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()
#print(torch.cuda.memory_allocated(device=None))
scheduler.step(step // batch_size)
ema(model, step // batch_size)
#torch.cuda.empty_cache()
# 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}...')
# print('Memory 1: ', torch.cuda.memory_allocated())
ema.assign(model)
# print('Memory 2: ', torch.cuda.memory_allocated())
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('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)
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)