def test_load_data_single_path(self):
test_args = test_utils.ModelParamsDict()
test_args.source_lang = "en"
test_args.target_lang = "fr"
test_args.log_verbose = False
src_dict, tgt_dict = test_utils.create_vocab_dictionaries()
src_text_file, tgt_text_file = test_utils.create_test_text_files()
src_bin_path = preprocess.binarize_text_file(
text_file=src_text_file,
dictionary=src_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
tgt_bin_path = preprocess.binarize_text_file(
text_file=tgt_text_file,
dictionary=tgt_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
task = tasks.PytorchTranslateTask(test_args, src_dict, tgt_dict)
split = "0"
task.load_dataset(split, src_bin_path, tgt_bin_path)
self.assertEqual(len(task.datasets[split]), 4)
self.assertIsInstance(task.datasets[split], LanguagePairDataset)
def test_load_data_multi_path(self):
test_args = test_utils.ModelParamsDict()
test_args.source_lang = "en"
test_args.target_lang = "fr"
test_args.log_verbose = False
src_dict, tgt_dict = test_utils.create_vocab_dictionaries()
num_paths = 4
src_bin_path, tgt_bin_path = {}, {}
for i in range(num_paths):
src_text_file, tgt_text_file = test_utils.create_test_text_files()
src_bin_path[i] = preprocess.binarize_text_file(
text_file=src_text_file,
dictionary=src_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
tgt_bin_path[i] = preprocess.binarize_text_file(
text_file=tgt_text_file,
dictionary=tgt_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
task = tasks.PytorchTranslateTask(test_args, src_dict, tgt_dict)
split = "1"
task.load_dataset(split, src_bin_path, tgt_bin_path)
self.assertEqual(len(task.datasets[split]), 16)
self.assertIsInstance(task.datasets[split], MultiCorpusSampledDataset)
def _prepare_data_multi_path(self, num_paths):
test_args = test_utils.ModelParamsDict()
test_args.source_lang = "en"
test_args.target_lang = "fr"
test_args.log_verbose = False
test_args.dataset_upsampling = None
test_args.dataset_relative_ratio = None
src_dict, tgt_dict = test_utils.create_vocab_dictionaries()
src_bin_path, tgt_bin_path = {}, {}
for i in range(num_paths):
src_text_file, tgt_text_file = test_utils.create_test_text_files()
src_bin_path[i] = preprocess.binarize_text_file(
text_file=src_text_file,
dictionary=src_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
tgt_bin_path[i] = preprocess.binarize_text_file(
text_file=tgt_text_file,
dictionary=tgt_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
return test_args, src_dict, tgt_dict, src_bin_path, tgt_bin_path
def test_load_data_noising(self):
test_args = test_utils.ModelParamsDict()
test_args.source_lang = "en"
test_args.target_lang = "fr"
test_args.log_verbose = False
src_dict, tgt_dict = test_utils.create_vocab_dictionaries()
num_paths = 4
src_bin_path, tgt_bin_path = {}, {}
for i in range(num_paths):
src_text_file, tgt_text_file = test_utils.create_test_text_files()
src_bin_path[i] = preprocess.binarize_text_file(
text_file=src_text_file,
dictionary=src_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
tgt_bin_path[i] = preprocess.binarize_text_file(
text_file=tgt_text_file,
dictionary=tgt_dict,
output_path=tempfile.NamedTemporaryFile().name,
append_eos=True,
reverse_order=False,
)
task = tasks.PytorchTranslateTask(test_args, src_dict, tgt_dict)
split = "1"
task.load_dataset(
split,
src_bin_path,
tgt_bin_path,
noiser={
0:
UnsupervisedMTNoising(
dictionary=src_dict,
max_word_shuffle_distance=3,
word_dropout_prob=0.2,
word_blanking_prob=0.2,
)
},
)
self.assertEqual(len(task.datasets[split]), 16)
self.assertIsInstance(task.datasets[split].datasets[0].src,
NoisingDataset)
