def _eval_epoch(sess, epoch, mode):
if mode == 'eval':
eval_data = dev_data
elif mode == 'test':
eval_data = test_data
else:
raise ValueError('`mode` should be either "eval" or "test".')
references, hypotheses = [], []
bsize = config_data.test_batch_size
for i in range(0, len(eval_data), bsize):
#print("eval {}/{}".format(i, len(eval_data)))
sources, targets = zip(*eval_data[i:i + bsize])
x_block = data_utils.source_pad_concat_convert(sources)
feed_dict = {
encoder_input: x_block,
tx.global_mode(): tf.estimator.ModeKeys.EVAL,
}
fetches = {
'inferred_ids': inferred_ids,
}
fetches_ = sess.run(fetches, feed_dict=feed_dict)
hypotheses.extend(h.tolist() for h in fetches_['inferred_ids'])
references.extend(r.tolist() for r in targets)
hypotheses = utils.list_strip_eos(hypotheses, eos_token_id)
references = utils.list_strip_eos(references, eos_token_id)
if mode == 'eval':
# Writes results to files to evaluate BLEU
# For 'eval' mode, the BLEU is based on token ids (rather than
# text tokens) and serves only as a surrogate metric to monitor
# the training process
fname = os.path.join(FLAGS.model_dir, 'tmp.eval')
hypotheses = tx.utils.str_join(hypotheses)
references = tx.utils.str_join(references)
hyp_fn, ref_fn = tx.utils.write_paired_text(hypotheses,
references,
fname,
mode='s')
eval_bleu = bleu_wrapper(ref_fn, hyp_fn, case_sensitive=True)
eval_bleu = 100. * eval_bleu
logger.info('epoch: %d, eval_bleu %.4f', epoch, eval_bleu)
print('epoch: %d, eval_bleu %.4f' % (epoch, eval_bleu))
if eval_bleu > best_results['score']:
logger.info('epoch: %d, best bleu: %.4f', epoch, eval_bleu)
best_results['score'] = eval_bleu
best_results['epoch'] = epoch
model_path = os.path.join(FLAGS.model_dir, 'best-model.ckpt')
logger.info('saving model to %s', model_path)
print('saving model to %s' % model_path)
saver.save(sess, model_path)
elif mode == 'test':
# For 'test' mode, together with the cmds in README.md, BLEU
# is evaluated based on text tokens, which is the standard metric.
fname = os.path.join(FLAGS.model_dir, 'test.output')
hwords, rwords = [], []
for hyp, ref in zip(hypotheses, references):
hwords.append([id2w[y] for y in hyp])
rwords.append([id2w[y] for y in ref])
hwords = tx.utils.str_join(hwords)
rwords = tx.utils.str_join(rwords)
hyp_fn, ref_fn = tx.utils.write_paired_text(hwords,
rwords,
fname,
mode='s')
logger.info('Test output writtn to file: %s', hyp_fn)
print('Test output writtn to file: %s' % hyp_fn)
def _eval_epoch(epoch, mode):
torch.cuda.empty_cache()
if mode == 'eval':
eval_data = dev_data
elif mode == 'test':
eval_data = test_data
else:
raise ValueError("`mode` should be either \"eval\" or \"test\".")
references, hypotheses = [], []
bsize = config_data.test_batch_size
for i in tqdm(range(0, len(eval_data), bsize)):
sources, targets = zip(*eval_data[i:i + bsize])
with torch.no_grad():
x_block = data_utils.source_pad_concat_convert(
sources, device=device)
predictions = model(
encoder_input=x_block,
is_train_mode=False,
beam_width=beam_width)
if beam_width == 1:
decoded_ids = predictions[0].sample_id
else:
decoded_ids = predictions["sample_id"][:, :, 0]
hypotheses.extend(h.tolist() for h in decoded_ids)
references.extend(r.tolist() for r in targets)
hypotheses = utils.list_strip_eos(hypotheses, eos_token_id)
references = utils.list_strip_eos(references, eos_token_id)
if mode == 'eval':
# Writes results to files to evaluate BLEU
# For 'eval' mode, the BLEU is based on token ids (rather than
# text tokens) and serves only as a surrogate metric to monitor
# the training process
# TODO: Use texar.evals.bleu
fname = os.path.join(args.model_dir, 'tmp.eval')
hwords, rwords = [], []
for hyp, ref in zip(hypotheses, references):
hwords.append([str(y) for y in hyp])
rwords.append([str(y) for y in ref])
hwords = tx.utils.str_join(hwords)
rwords = tx.utils.str_join(rwords)
hyp_fn, ref_fn = tx.utils.write_paired_text(
hwords, rwords, fname, mode='s',
src_fname_suffix='hyp', tgt_fname_suffix='ref')
eval_bleu = bleu_wrapper(ref_fn, hyp_fn, case_sensitive=True)
eval_bleu = 100. * eval_bleu
logger.info("epoch: %d, eval_bleu %.4f", epoch, eval_bleu)
print(f"epoch: {epoch:d}, eval_bleu {eval_bleu:.4f}")
if eval_bleu > best_results['score']:
logger.info("epoch: %d, best bleu: %.4f", epoch, eval_bleu)
best_results['score'] = eval_bleu
best_results['epoch'] = epoch
model_path = os.path.join(args.model_dir, args.model_fn)
logger.info("Saving model to %s", model_path)
print(f"Saving model to {model_path}")
states = {
'model': model.state_dict(),
'optimizer': optim.state_dict(),
'scheduler': scheduler.state_dict(),
}
torch.save(states, model_path)
elif mode == 'test':
# For 'test' mode, together with the cmds in README.md, BLEU
# is evaluated based on text tokens, which is the standard metric.
fname = os.path.join(args.model_dir, 'test.output')
hwords, rwords = [], []
for hyp, ref in zip(hypotheses, references):
hwords.append([id2w[y] for y in hyp])
rwords.append([id2w[y] for y in ref])
hwords = tx.utils.str_join(hwords)
rwords = tx.utils.str_join(rwords)
hyp_fn, ref_fn = tx.utils.write_paired_text(
hwords, rwords, fname, mode='s',
src_fname_suffix='hyp', tgt_fname_suffix='ref')
logger.info("Test output written to file: %s", hyp_fn)
print(f"Test output written to file: {hyp_fn}")
