def validation_new(encoder, decoder, val_dataloader, lang_en, m_type):
encoder.train(False)
decoder.train(False)
pred_corpus = []
true_corpus = []
running_loss = 0
running_total = 0
bl = BLEU_SCORE()
for data in val_dataloader:
encoder_i = data[0].cuda()
bs, sl = encoder_i.size()[:2]
en_h = encoder.initHidden(bs)
en_out, en_hid = encoder(encoder_i, en_h)
decoder_hidden = en_hid
decoder_input = torch.tensor([[SOS_token]] * bs).cuda()
d_out = []
for i in range(sl + 20):
if m_type == "attention":
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden, en_out)
else:
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden)
topv, topi = decoder_output.topk(1)
decoder_input = topi.squeeze().detach().view(-1, 1)
d_out.append(topi.item())
if topi.item() == EOS_token:
break
d_hid = decoder_hidden
true_corpus.append(data[-1])
pred_corpus.append(convert_id_list_2_sent(d_out, lang_en))
score = bl.corpus_bleu(pred_corpus, [true_corpus], lowercase=True)[0]
return score
def validation(encoder, decoder, dataloader, loss_fun, lang_en, max_len,m_type):
encoder.train(False)
decoder.train(False)
pred_corpus = []
true_corpus = []
running_loss = 0
running_total = 0
bl = BLEU_SCORE()
for data in dataloader:
encoder_i = data[0].cuda()
decoder_i = data[1].cuda()
bs,sl = encoder_i.size()[:2]
out, hidden = encode_decode(encoder,decoder,encoder_i,decoder_i,max_len,m_type, rand_num = 0)
# print("v",out.size(),decoder_i.size())
outo = out.view(-1,lang_en.n_words)
decoder_io = decoder_i.view(-1)
# print("v",out.size(),decoder_i.size())
loss = loss_fun(outo.float(), decoder_io.long())
running_loss += loss.item() * bs
running_total += bs
pred = torch.max(out,dim = 2)[1]
for t,p in zip(decoder_i,pred):
# print(t,p)
t,p = convert_idx_2_sent(t,lang_en), convert_idx_2_sent(p,lang_en)
true_corpus.append(t)
pred_corpus.append(p)
score = bl.corpus_bleu(pred_corpus,[true_corpus],lowercase=True)[0]
return running_loss/running_total, score
def validation_new(encoder, decoder, val_dataloader, lang_en,lang_vi,m_type, verbose = False, replace_unk = False):
encoder.eval()
decoder.eval()
pred_corpus = []
true_corpus = []
src_corpus = []
running_loss = 0
running_total = 0
bl = BLEU_SCORE()
attention_scores_for_all_val = []
for data in val_dataloader:
encoder_i = data[0].to(device)
src_len = data[2].to(device)
bs,sl = encoder_i.size()[:2]
en_out,en_hid,en_c = encoder(encoder_i,src_len)
max_src_len_batch = max(src_len).item()
prev_hiddens = en_hid
prev_cs = en_c
decoder_input = torch.tensor([[SOS_token]]*bs).to(device)
prev_output = torch.zeros((bs, en_out.size(-1))).to(device)
d_out = []
attention_scores = []
for i in range(sl*2):
out_vocab, prev_output,prev_hiddens, prev_cs, attention_score = decoder(decoder_input, prev_output, prev_hiddens, prev_cs, en_out, src_len)
topv, topi = out_vocab.topk(1)
d_out.append(topi.item())
decoder_input = topi.squeeze().detach().view(-1,1)
if m_type == 'attention':
attention_scores.append(attention_score.unsqueeze(-1))
if topi.item() == EOS_token:
break
if replace_unk:
true_sent = convert_id_list_2_sent(data[1][0],lang_en)
true_corpus.append(true_sent)
else:
true_corpus.append(data[-1])
src_sent = convert_id_list_2_sent(data[0][0],lang_vi)
src_corpus.append(src_sent)
pred_sent = convert_id_list_2_sent(d_out,lang_en)
pred_corpus.append(pred_sent)
if m_type == 'attention':
attention_scores = torch.cat(attention_scores, dim = -1)
attention_scores_for_all_val.append(attention_scores)
if verbose:
print("True Sentence:",data[-1])
print("Pred Sentence:", pred_sent)
print('-*'*50)
score = bl.corpus_bleu(pred_corpus,[true_corpus],lowercase=True)[0]
return score, attention_scores_for_all_val, pred_corpus, src_corpus
def validation(encoder, decoder, dataloader, loss_fun, lang_en):
encoder.train(False)
decoder.train(False)
pred_corpus = []
true_corpus = []
running_loss = 0
running_total = 0
bl = BLEU_SCORE()
for data in dataloader:
encoder_i = data[0].cuda()
decoder_i = data[1].cuda()
bs, sl = encoder_i.size()[:2]
out, hidden = encode_decode(encoder, decoder, encoder_i, decoder_i)
loss = loss_fun(out.float(), decoder_i.long())
running_loss += loss.item() * bs
running_total += bs
pred = torch.max(out, dim=1)[1]
for t, p in zip(data[1], pred):
t, p = convert_idx_2_sent(t,
lang_en), convert_idx_2_sent(p, lang_en)
true_corpus.append(t)
pred_corpus.append(p)
score = bl.corpus_bleu(pred_corpus, [true_corpus], lowercase=True)[0]
return running_loss / running_total, score
def validation_beam_search(encoder,
decoder,
val_dataloader,
lang_en,
beam_size,
verbose=False,
keep_unk=False,
return_attention=False):
encoder.eval()
decoder.eval()
encoder = encoder.to(device)
decoder = decoder.to(device)
pred_corpus = []
true_corpus = []
running_loss = 0
running_total = 0
bl = BLEU_SCORE()
attention_scores_for_all_val = []
j = 0
for data in val_dataloader:
encoder_i = data[0].to(device)
src_len = data[2].to(device)
bs, sl = encoder_i.size()[:2]
en_out, en_hid, en_c = encoder(encoder_i, src_len)
max_src_len_batch = max(src_len).item()
prev_hiddens = en_hid
prev_cs = en_c
decoder_input = torch.tensor([[SOS_token]] * bs).to(device)
prev_output = torch.zeros((bs, en_out.size(-1))).to(device)
list_decoder_input = [None] * beam_size
beam_stop_flags = [False] * beam_size
beam_score = torch.zeros((bs, beam_size)).to(device)
list_d_outs = [[] for _ in range(beam_size)]
select_beam_size = beam_size
attention_scores = [[] for _ in range(beam_size)]
for i in range(sl + 20):
if i == 0:
out_vocab, prev_output,prev_hiddens, prev_cs, attention_score = decoder(decoder_input,prev_output, \
prev_hiddens,prev_cs, en_out,\
src_len)
bss, vocab_size = out_vocab.size()
topv, topi = out_vocab.topk(beam_size)
list_prev_output = [prev_output] * beam_size
list_prev_hiddens = [prev_hiddens] * beam_size
list_prev_cs = [prev_cs] * beam_size
for b in range(beam_size):
beam_score[0][b] = topv[0][b].item()
list_decoder_input[b] = topi[0][b].squeeze().detach().view(
-1, 1)
list_d_outs[b].append(topi[0][b].item())
if decoder.att_layer is not None and return_attention:
attention_scores[b].append(
attention_score.unsqueeze(-1))
if topi[0][b].item() == EOS_token:
beam_stop_flags[b] = True
else:
beam_out_vocab = [None] * beam_size
temp_out = [None] * beam_size
temp_hid = [None] * beam_size
temp_c = [None] * beam_size
prev_d_outs = copy.deepcopy(list_d_outs)
for b in range(beam_size):
if not beam_stop_flags[b]:
beam_out_vocab[b], temp_out[b], temp_hid[b], temp_c[b], attention_score =\
decoder(list_decoder_input[b],list_prev_output[b],list_prev_hiddens[b],list_prev_cs[b],\
en_out,src_len)
beam_out_vocab[
b] = beam_out_vocab[b] + beam_score[0][b]
if beam_stop_flags[b]:
beam_out_vocab[b] = torch.zeros(bss, vocab_size).fill_(
float('-inf')).to(device)
beam_out_vocab = torch.cat(beam_out_vocab, dim=1)
topv, topi = beam_out_vocab.topk(beam_size)
id_for_hid = topi // vocab_size
topi_idx = topi % vocab_size
for b in range(beam_size):
if not beam_stop_flags[b]:
beam_score[0][b] = topv[0][b].item()
list_decoder_input[b] = topi_idx[0][b].squeeze(
).detach().view(-1, 1)
list_d_outs[b] = copy.deepcopy(
prev_d_outs[id_for_hid[0][b]])
list_d_outs[b].append(topi_idx[0][b].item())
if topi_idx[0][b].item() == EOS_token:
beam_stop_flags[b] = True
else:
list_prev_output[b] = temp_out[id_for_hid[0][b]]
list_prev_hiddens[b] = temp_hid[id_for_hid[0][b]]
list_prev_cs[b] = temp_c[id_for_hid[0][b]]
if all(beam_stop_flags):
break
id_max_score = np.argmax(beam_score)
d_out = list_d_outs[id_max_score]
if keep_unk:
true_sent = convert_id_list_2_sent(data[1][0], lang_en)
true_corpus.append(true_sent)
else:
true_corpus.append(data[-1])
pred_sent = convert_id_list_2_sent(d_out, lang_en)
pred_corpus.append(pred_sent)
if decoder.att_layer is not None and return_attention:
attention_scores = torch.cat(attention_scores, dim=-1)
attention_scores_for_all_val.append(attention_scores)
if verbose:
print("True Sentence:", data[-1])
print("Pred Sentence:", pred_sent)
print('-*' * 50)
score = bl.corpus_bleu(pred_corpus, [true_corpus], lowercase=True)[0]
if decoder.att_layer is not None and return_attention:
return score, true_corpus, pred_corpus, attention_scores_for_all_val
return score