def valid(x, y, batch_size, model):
x_batches = batch_list(x, batch_size)
y_batches = batch_list(y, batch_size)
total_loss, iter_num, val_acc = 0, 0, 0
model.eval()
with torch.no_grad():
for step, data in enumerate(zip(x_batches, y_batches)):
inputs, labels = np.array(data[0]), torch.tensor(data[1])
inputs = inputs.reshape(inputs.shape[0], 1, 100, 71)
inputs = torch.from_numpy(
inputs).float() #torch.Size([128, 1, 100, 71])
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, np.argmax(labels, -1))
total_loss += loss
iter_num += 1
v_acc = acc(torch.max(outputs, 1)[1], np.argmax(labels, -1))
val_acc += v_acc
return total_loss / iter_num, val_acc / iter_num
def train(model, iterator, optimizer, criterion, args, bert_tok):
total_loss = 0
iter_num = 0
train_acc = 0
global iteration
model.train()
if args.useKey == 'True':
keyword, refine_idx = keyword_loader(args, 'train', bert_tok)
for step, batch in enumerate(iterator):
optimizer.zero_grad()
enc_inputs = batch.que
copy_dec_inputs = copy.deepcopy(batch.ans)
copy_dec_target = copy.deepcopy(batch.ans)
dec_inputs = get_dec_inputs(copy_dec_inputs, gpt_pad_token,
gpt_eos_token)
target_ = get_target(copy_dec_target, gpt_pad_token)
target_ = target_.view(-1)
segment_ids, valid_len = get_segment_ids_vaild_len(
enc_inputs, pad_token_idx)
attention_mask = gen_attention_mask(enc_inputs, valid_len)
if args.useKey == 'True':
outputs = model(enc_inputs, dec_inputs, segment_ids,
attention_mask, keyword[step], refine_idx[step])
else:
outputs = model(enc_inputs, dec_inputs, segment_ids,
attention_mask, None, refine_idx[step])
loss = criterion(outputs, target_)
loss.backward()
optimizer.step()
total_loss += loss
iter_num += 1
with torch.no_grad():
tr_acc = acc(outputs, target_, gpt_pad_token)
train_acc += tr_acc
if step % 2 == 0:
total_train_loss.append(total_loss.data.cpu().numpy() / iter_num)
iteration_list.append(iteration)
iteration += 1
return total_loss.data.cpu().numpy() / iter_num, train_acc.data.cpu(
).numpy() / iter_num
def valid(model, iterator, optimizer, criterion, args, bert_tok):
total_loss = 0
iter_num = 0
test_acc = 0
model.eval()
if args.useKey == 'True':
keyword, refine_idx = keyword_loader(args, 'valid', bert_tok)
with torch.no_grad():
for step, batch in enumerate(iterator):
enc_inputs = batch.que
copy_dec_inputs = copy.deepcopy(batch.ans)
copy_dec_target = copy.deepcopy(batch.ans)
dec_inputs = get_dec_inputs(copy_dec_inputs, gpt_pad_token,
gpt_eos_token)
target_ = get_target(copy_dec_target, gpt_pad_token)
target_ = target_.view(-1)
segment_ids, valid_len = get_segment_ids_vaild_len(
enc_inputs, pad_token_idx)
attention_mask = gen_attention_mask(enc_inputs, valid_len)
if args.useKey == 'True':
outputs = model(enc_inputs, dec_inputs, segment_ids,
attention_mask, keyword[step],
refine_idx[step])
else:
outputs = model(enc_inputs, dec_inputs, segment_ids,
attention_mask, None, refine_idx[step])
loss = criterion(outputs, target_)
total_loss += loss
iter_num += 1
te_acc = acc(outputs, target_, gpt_pad_token)
test_time_visual(args, enc_inputs, outputs, target_,
bert_tokenizer, gpt_vocab)
test_acc += te_acc
return total_loss.data.cpu().numpy() / iter_num, test_acc.data.cpu(
).numpy() / iter_num