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)