def setUp(self):
layer_dim = 512
events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
self.model = DefaultTranslator(
src_reader=PlainTextReader(vocab=src_vocab),
trg_reader=PlainTextReader(vocab=trg_vocab),
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),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
def setUp(self):
events.clear()
ParamManager.init_param_col()
# Load a pre-trained model
load_experiment = LoadSerialized(filename=f"test/data/tiny_jaen.model",
overwrite=[
{
"path": "train",
"val": None
},
{
"path": "status",
"val": None
},
])
EXP_DIR = '.'
EXP = "decode"
uninitialized_experiment = YamlPreloader.preload_obj(load_experiment,
exp_dir=EXP_DIR,
exp_name=EXP)
loaded_experiment = initialize_if_needed(uninitialized_experiment)
ParamManager.populate()
# Pull out the parts we need from the experiment
self.model = loaded_experiment.model
src_vocab = self.model.src_reader.vocab
trg_vocab = self.model.trg_reader.vocab
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("test/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("test/data/head.en"))
def setUp(self):
events.clear()
self.hyp = ["the taro met the hanako".split()]
self.ref = ["taro met hanako".split()]
vocab = Vocab(i2w=["the", "taro", "met", "hanako"])
self.hyp_id = list(map(vocab.convert, self.hyp[0]))
self.ref_id = list(map(vocab.convert, self.ref[0]))
def setUp(self):
events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="test/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="test/data/head.en.vocab")
self.src_reader = PlainTextReader(vocab=src_vocab)
self.trg_reader = PlainTextReader(vocab=trg_vocab)
self.src_data = list(self.src_reader.read_sents("test/data/head.ja"))
self.trg_data = list(self.trg_reader.read_sents("test/data/head.en"))
def setUp(self):
events.clear()
xnmt.resolved_serialize_params = {}
yaml.add_representer(DummyArgClass, xnmt.init_representer)
yaml.add_representer(DummyArgClass2, xnmt.init_representer)
self.out_dir = os.path.join("test", "tmp")
utils.make_parent_dir(os.path.join(self.out_dir, "asdf"))
self.model_file = os.path.join(self.out_dir, "saved.mod")
param_collections.ParamManager.init_param_col()
param_collections.ParamManager.param_col.model_file = self.model_file
def setUp(self): events.clear()
def setUp(self):
events.clear()
self.input_reader = PlainTextReader(vocab=Vocab(vocab_file="examples/data/head.ja.vocab"))
list(self.input_reader.read_sents('examples/data/head.ja'))
ParamManager.init_param_col()
def setUp(self): events.clear() ParamManager.init_param_col()
