def eval_bs(test_set: Dataset, vocab: Vocab, model: Seq2Seq, params: Params):
test_gen = test_set.generator(1, vocab, None, True if params.pointer else False)
n_samples = int(params.test_sample_ratio * len(test_set.pairs))
if params.test_save_results and params.model_path_prefix:
result_file = tarfile.open(params.model_path_prefix + ".results.tgz", 'w:gz')
else:
result_file = None
model.eval()
r1, r2, rl, rsu4 = 0, 0, 0, 0
prog_bar = tqdm(range(1, n_samples + 1))
for i in prog_bar:
batch = next(test_gen)
scores, file_content = eval_bs_batch(batch, model, vocab, pack_seq=params.pack_seq,
beam_size=params.beam_size,
min_out_len=params.min_out_len,
max_out_len=params.max_out_len,
len_in_words=params.out_len_in_words,
details=result_file is not None)
if file_content:
file_content = file_content.encode('utf-8')
file_info = tarfile.TarInfo(name='%06d.txt' % i)
file_info.size = len(file_content)
result_file.addfile(file_info, fileobj=BytesIO(file_content))
if scores:
r1 += scores[0]['1_f']
r2 += scores[0]['2_f']
rl += scores[0]['l_f']
rsu4 += scores[0]['su4_f']
prog_bar.set_postfix(R1='%.4g' % (r1 / i * 100), R2='%.4g' % (r2 / i * 100),
RL='%.4g' % (rl / i * 100), RSU4='%.4g' % (rsu4 / i * 100))
arg_name = None
if arg_name is not None:
print("Warning: Argument %s lacks a value and is ignored." %
arg_name)
dataset = Dataset(p.data_path,
max_src_len=p.max_src_len,
max_tgt_len=p.max_tgt_len,
truncate_src=p.truncate_src,
truncate_tgt=p.truncate_tgt)
if m is None:
v = dataset.build_vocab(p.vocab_size, embed_file=p.embed_file)
m = Seq2Seq(v, p)
else:
v = dataset.build_vocab(p.vocab_size)
train_gen = dataset.generator(p.batch_size, v, v,
True if p.pointer else False)
if p.val_data_path:
val_dataset = Dataset(p.val_data_path,
max_src_len=p.max_src_len,
max_tgt_len=p.max_tgt_len,
truncate_src=p.truncate_src,
truncate_tgt=p.truncate_tgt)
val_gen = val_dataset.generator(p.val_batch_size, v, v,
True if p.pointer else False)
else:
val_gen = None
train(train_gen, v, m, p, val_gen, train_status)
