def setUp(self):
self.tgt_dict, self.w1, self.w2, self.src_tokens, self.src_lengths, self.model = (
test_utils.sequence_generator_setup())
dummy_src_samples = self.src_tokens
self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
def setUp(self):
self.tgt_dict, self.w1, self.w2, src_tokens, src_lengths, self.model = (
test_utils.sequence_generator_setup()
)
self.sample = {
"net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths}
}
def setUp(self):
self.tgt_dict, self.w1, self.w2, src_tokens, src_lengths, self.model = (
test_utils.sequence_generator_setup()
)
self.encoder_input = {
'src_tokens': src_tokens, 'src_lengths': src_lengths,
}
def setUpClass(cls):
(
cls.tgt_dict,
cls.w1,
cls.w2,
src_tokens,
src_lengths,
cls.model,
) = test_utils.sequence_generator_setup()
return cls
def setUp(self):
(
self.tgt_dict,
self.w1,
self.w2,
self.src_tokens,
self.src_lengths,
self.model,
) = test_utils.sequence_generator_setup()
dummy_src_samples = self.src_tokens
self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
self.cuda = torch.cuda.is_available()
def setUp(self):
self.tgt_dict, self.w1, self.w2, self.src_tokens, self.src_lengths, self.model = (
test_utils.sequence_generator_setup())
backtranslation_args = argparse.Namespace()
"""
Same as defaults from fairseq/options.py
"""
backtranslation_args.backtranslation_unkpen = 0
backtranslation_args.backtranslation_sampling = False
backtranslation_args.backtranslation_max_len_a = 0
backtranslation_args.backtranslation_max_len_b = 200
backtranslation_args.backtranslation_beam = 2
self.backtranslation_args = backtranslation_args
dummy_src_samples = self.src_tokens
self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
