def test_composite(self): event_trigger.set_train(True) composite_loss = loss_calculators.CompositeLoss([loss_calculators.MLELoss(), loss_calculators.PolicyMLELoss()]) composite_loss.calc_loss(self.model, self.src[0], self.trg[0]) event_trigger.set_train(False) self.model.generate(batchers.mark_as_batch([self.src_data[0]]), GreedySearch())
def test_train_mle_only(self): self.model.policy_network = None event_trigger.set_train(True) mle_loss = loss_calculators.MLELoss() mle_loss.calc_loss(self.model, self.src[0], self.trg[0]) event_trigger.set_train(False) self.model.generate(batchers.mark_as_batch([self.src_data[0]]), GreedySearch())
def test_train_nll(self):
event_trigger.set_train(True)
mle_loss = loss_calculators.MLELoss()
mle_loss.calc_loss(self.model, self.src[0], self.trg[0])
pol_loss = loss_calculators.PolicyMLELoss()
pol_loss._perform_calc_loss(self.model, self.src[0], self.trg[0])
event_trigger.set_train(False)
self.model.generate(batchers.mark_as_batch([self.src_data[0]]), GreedySearch())
def setUp(self):
# Seeding
numpy.random.seed(2)
random.seed(2)
layer_dim = 32
xnmt.events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
self.src_reader = CompoundReader(readers=[
PlainTextReader(vocab=src_vocab),
SimultActionTextReader()
], vocab=src_vocab)
self.trg_reader = PlainTextReader(vocab=Vocab(vocab_file="examples/data/head.en.vocab"))
self.layer_dim = layer_dim
self.src_data = list(self.src_reader.read_sents(["examples/data/head.ja", "examples/data/simult/head.jaen.actions"]))
self.trg_data = list(self.trg_reader.read_sents("examples/data/head.en"))
self.input_vocab_size = len(self.src_reader.vocab.i2w)
self.output_vocab_size = len(self.trg_reader.vocab.i2w)
self.loss_calculator = loss_calculators.MLELoss()
self.model = SimultaneousTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=self.input_vocab_size),
encoder=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim,
decoder_input_dim=layer_dim, yaml_path="decoder"),
transform=AuxNonLinear(input_dim=layer_dim, output_dim=layer_dim,
aux_input_dim=layer_dim),
scorer=Softmax(vocab_size=self.output_vocab_size, input_dim=layer_dim),
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=self.output_vocab_size),
bridge=NoBridge(dec_dim=layer_dim, dec_layers=1)),
policy_network = network.PolicyNetwork(transforms.MLP(2*self.layer_dim, self.layer_dim, 2)),
policy_train_oracle=True,
policy_test_oracle=True
)
event_trigger.set_train(True)
my_batcher = batchers.TrgBatcher(batch_size=3)
self.src, self.trg = my_batcher.pack(self.src_data, self.trg_data)
dy.renew_cg(immediate_compute=True, check_validity=True)
def compute_losses_one(self, generator: 'models.GeneratorModel', src: sent.Sentence,
ref: sent.Sentence) -> losses.FactoredLossExpr:
loss_expr = loss_calculators.MLELoss().calc_loss(generator, src, ref)
return loss_expr
def test_simult_beam(self): event_trigger.set_train(False) mle_loss = loss_calculators.MLELoss() mle_loss.calc_loss(self.model, self.src[0], self.trg[0]) self.model.generate(batchers.mark_as_batch([self.src_data[0]]), BeamSearch(beam_size=2))
