class TestFreeDecodingLoss(unittest.TestCase): def setUp(self): layer_dim = 512 xnmt.events.clear() ParamManager.init_param_col() self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), encoder=BiLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim), attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim), trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), decoder=MlpSoftmaxDecoder(input_dim=layer_dim, lstm_dim=layer_dim, mlp_hidden_dim=layer_dim, trg_embed_dim=layer_dim, vocab_size=100, bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)), ) self.model.set_train(False) self.model.initialize_generator(beam=1) self.src_data = list(self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list(self.model.trg_reader.read_sents("examples/data/head.en")) def test_single(self): dy.renew_cg() self.model.initialize_generator(beam=1) outputs = self.model.generate_output(self.src_data[0], 0, forced_trg_ids=self.trg_data[0]) dy.renew_cg() train_loss = self.model.calc_loss(src=self.src_data[0], trg=outputs[0].actions, loss_calculator=LossCalculator()).value() self.assertAlmostEqual(-outputs[0].score, train_loss, places=4)
class TestGreedyVsBeam(unittest.TestCase): """ Test if greedy search produces same output as beam search with beam 1. """ def setUp(self): layer_dim = 512 xnmt.events.clear() ParamManager.init_param_col() self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), encoder=BiLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim), attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim), trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), decoder=MlpSoftmaxDecoder(input_dim=layer_dim, trg_embed_dim=layer_dim, rnn_layer=UniLSTMSeqTransducer( input_dim=layer_dim, hidden_dim=layer_dim, decoder_input_dim=layer_dim, yaml_path="model.decoder.rnn_layer"), mlp_layer=MLP( input_dim=layer_dim, hidden_dim=layer_dim, decoder_rnn_dim=layer_dim, vocab_size=100, yaml_path="model.decoder.rnn_layer"), bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)), ) self.model.set_train(False) self.src_data = list( self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list( self.model.trg_reader.read_sents("examples/data/head.en")) def test_greedy_vs_beam(self): dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output(self.src_data[0], 0, BeamSearch(beam_size=1), forced_trg_ids=self.trg_data[0]) output_score1 = outputs[0].score dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output(self.src_data[0], 0, GreedySearch(), forced_trg_ids=self.trg_data[0]) output_score2 = outputs[0].score self.assertAlmostEqual(output_score1, output_score2)
class TestForcedDecodingOutputs(unittest.TestCase): def assertItemsEqual(self, l1, l2): self.assertEqual(len(l1), len(l2)) for i in range(len(l1)): self.assertEqual(l1[i], l2[i]) def setUp(self): layer_dim = 512 xnmt.events.clear() ParamManager.init_param_col() self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), encoder=BiLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim), attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim), trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100), decoder=MlpSoftmaxDecoder(input_dim=layer_dim, trg_embed_dim=layer_dim, rnn_layer=UniLSTMSeqTransducer( input_dim=layer_dim, hidden_dim=layer_dim, decoder_input_dim=layer_dim, yaml_path="model.decoder.rnn_layer"), mlp_layer=MLP( input_dim=layer_dim, hidden_dim=layer_dim, decoder_rnn_dim=layer_dim, vocab_size=100, yaml_path="model.decoder.rnn_layer"), bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)), ) self.model.set_train(False) self.model.initialize_generator() self.src_data = list( self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list( self.model.trg_reader.read_sents("examples/data/head.en")) self.search = GreedySearch() def assert_forced_decoding(self, sent_id): dy.renew_cg() outputs = self.model.generate_output( self.src_data[sent_id], sent_id, self.search, forced_trg_ids=self.trg_data[sent_id]) self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions) def test_forced_decoding(self): for i in range(1): self.assert_forced_decoding(sent_id=i)
class TestGreedyVsBeam(unittest.TestCase): """ Test if greedy search produces same output as beam search with beam 1. """ def setUp(self): xnmt.events.clear() self.model_context = ModelContext() self.model_context.dynet_param_collection = PersistentParamCollection( "some_file", 1) self.model = DefaultTranslator( src_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), encoder=BiLSTMSeqTransducer(self.model_context), attender=MlpAttender(self.model_context), trg_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100, bridge=CopyBridge(self.model_context, dec_layers=1)), ) self.model.initialize_training_strategy(TrainingStrategy()) self.model.set_train(False) self.training_corpus = BilingualTrainingCorpus( train_src="examples/data/head.ja", train_trg="examples/data/head.en", dev_src="examples/data/head.ja", dev_trg="examples/data/head.en") self.corpus_parser = BilingualCorpusParser( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), training_corpus=self.training_corpus) def test_greedy_vs_beam(self): dy.renew_cg() self.model.initialize_generator(beam=1) outputs = self.model.generate_output( self.training_corpus.train_src_data[0], 0, forced_trg_ids=self.training_corpus.train_trg_data[0]) output_score1 = outputs[0].score dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output( self.training_corpus.train_src_data[0], 0, forced_trg_ids=self.training_corpus.train_trg_data[0]) output_score2 = outputs[0].score self.assertAlmostEqual(output_score1, output_score2)
class TestFreeDecodingLoss(unittest.TestCase): def setUp(self): xnmt.events.clear() self.model_context = ModelContext() self.model_context.dynet_param_collection = PersistentParamCollection( "some_file", 1) self.model = DefaultTranslator( src_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), encoder=BiLSTMSeqTransducer(self.model_context), attender=MlpAttender(self.model_context), trg_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100, bridge=CopyBridge(self.model_context, dec_layers=1)), ) self.model.initialize_training_strategy(TrainingStrategy()) self.model.set_train(False) self.model.initialize_generator() self.training_corpus = BilingualTrainingCorpus( train_src="examples/data/head.ja", train_trg="examples/data/head.en", dev_src="examples/data/head.ja", dev_trg="examples/data/head.en") self.corpus_parser = BilingualCorpusParser( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), training_corpus=self.training_corpus) def test_single(self): dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output( self.training_corpus.train_src_data[0], 0, forced_trg_ids=self.training_corpus.train_trg_data[0]) output_score = outputs[0].score dy.renew_cg() train_loss = self.model.calc_loss( src=self.training_corpus.train_src_data[0], trg=outputs[0].actions).value() self.assertAlmostEqual(-output_score, train_loss, places=5)
class TestForcedDecodingOutputs(unittest.TestCase): def assertItemsEqual(self, l1, l2): self.assertEqual(len(l1), len(l2)) for i in range(len(l1)): self.assertEqual(l1[i], l2[i]) def setUp(self): xnmt.events.clear() self.model_context = ModelContext() self.model_context.dynet_param_collection = PersistentParamCollection( "some_file", 1) self.model = DefaultTranslator( src_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), encoder=BiLSTMSeqTransducer(self.model_context), attender=MlpAttender(self.model_context), trg_embedder=SimpleWordEmbedder(self.model_context, vocab_size=100), decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100), ) self.model.initialize_training_strategy(TrainingStrategy()) self.model.set_train(False) self.model.initialize_generator() self.training_corpus = BilingualTrainingCorpus( train_src="examples/data/head.ja", train_trg="examples/data/head.en", dev_src="examples/data/head.ja", dev_trg="examples/data/head.en") self.corpus_parser = BilingualCorpusParser( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), training_corpus=self.training_corpus) def assert_forced_decoding(self, sent_id): dy.renew_cg() outputs = self.model.generate_output( self.training_corpus.train_src_data[sent_id], sent_id, forced_trg_ids=self.training_corpus.train_trg_data[sent_id]) self.assertItemsEqual(self.training_corpus.train_trg_data[sent_id], outputs[0].actions) def test_forced_decoding(self): for i in range(1): self.assert_forced_decoding(sent_id=i)
class TestGreedyVsBeam(unittest.TestCase): """ Test if greedy search produces same output as beam search with beam 1. """ def setUp(self): xnmt.events.clear() self.exp_global = ExpGlobal( dynet_param_collection=PersistentParamCollection("some_file", 1)) self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), encoder=BiLSTMSeqTransducer(exp_global=self.exp_global), attender=MlpAttender(exp_global=self.exp_global), trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), decoder=MlpSoftmaxDecoder(exp_global=self.exp_global, vocab_size=100, bridge=CopyBridge( exp_global=self.exp_global, dec_layers=1)), ) self.model.set_train(False) self.src_data = list( self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list( self.model.trg_reader.read_sents("examples/data/head.en")) def test_greedy_vs_beam(self): dy.renew_cg() self.model.initialize_generator(beam=1) outputs = self.model.generate_output(self.src_data[0], 0, forced_trg_ids=self.trg_data[0]) output_score1 = outputs[0].score dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output(self.src_data[0], 0, forced_trg_ids=self.trg_data[0]) output_score2 = outputs[0].score self.assertAlmostEqual(output_score1, output_score2)
class TestForcedDecodingOutputs(unittest.TestCase): def assertItemsEqual(self, l1, l2): self.assertEqual(len(l1), len(l2)) for i in range(len(l1)): self.assertEqual(l1[i], l2[i]) def setUp(self): xnmt.events.clear() self.exp_global = ExpGlobal( dynet_param_collection=PersistentParamCollection("some_file", 1)) self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), encoder=BiLSTMSeqTransducer(exp_global=self.exp_global), attender=MlpAttender(exp_global=self.exp_global), trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), decoder=MlpSoftmaxDecoder(exp_global=self.exp_global, vocab_size=100, bridge=CopyBridge( exp_global=self.exp_global, dec_layers=1)), ) self.model.set_train(False) self.model.initialize_generator(beam=1) self.src_data = list( self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list( self.model.trg_reader.read_sents("examples/data/head.en")) def assert_forced_decoding(self, sent_id): dy.renew_cg() outputs = self.model.generate_output( self.src_data[sent_id], sent_id, forced_trg_ids=self.trg_data[sent_id]) self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions) def test_forced_decoding(self): for i in range(1): self.assert_forced_decoding(sent_id=i)
class TestFreeDecodingLoss(unittest.TestCase): def setUp(self): xnmt.events.clear() self.exp_global = ExpGlobal( dynet_param_collection=PersistentParamCollection("some_file", 1)) self.model = DefaultTranslator( src_reader=PlainTextReader(), trg_reader=PlainTextReader(), src_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), encoder=BiLSTMSeqTransducer(exp_global=self.exp_global), attender=MlpAttender(exp_global=self.exp_global), trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global, vocab_size=100), decoder=MlpSoftmaxDecoder(exp_global=self.exp_global, vocab_size=100, bridge=CopyBridge( exp_global=self.exp_global, dec_layers=1)), ) self.model.set_train(False) self.model.initialize_generator() self.src_data = list( self.model.src_reader.read_sents("examples/data/head.ja")) self.trg_data = list( self.model.trg_reader.read_sents("examples/data/head.en")) def test_single(self): dy.renew_cg() self.model.initialize_generator() outputs = self.model.generate_output(self.src_data[0], 0, forced_trg_ids=self.trg_data[0]) output_score = outputs[0].score dy.renew_cg() train_loss = self.model.calc_loss( src=self.src_data[0], trg=outputs[0].actions, loss_calculator=LossCalculator()).value() self.assertAlmostEqual(-output_score, train_loss, places=5)