def example_config(cls):
return cls.Config(
labels=[DocLabelConfig(), WordLabelConfig()],
model=BaggingDocEnsemble_Deprecated.Config(
models=[DocModel_Deprecated.Config()]
),
)
def test_freeze_all_embedding(self):
model = create_model(
DocModel_Deprecated.Config(),
FeatureConfig(freeze=True),
metadata=mock_metadata(),
)
for param in model.embedding.parameters():
self.assertFalse(param.requires_grad)
class Config(Task_Deprecated.Config):
model: DocModel_Deprecated.Config = DocModel_Deprecated.Config()
trainer: Trainer.Config = Trainer.Config()
features: DocClassification.ModelInputConfig = (
DocClassification.ModelInputConfig())
labels: DocClassification.TargetConfig = DocClassification.TargetConfig(
)
data_handler: DocClassificationDataHandler.Config = (
DocClassificationDataHandler.Config())
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
exporter: Optional[DenseFeatureExporter.Config] = None
def _create_dummy_model(self):
return create_model(
DocModel_Deprecated.Config(
representation=BiLSTMDocAttention.Config(
save_path=self.representation_path),
decoder=MLPDecoder.Config(save_path=self.decoder_path),
),
FeatureConfig(
word_feat=WordEmbedding.Config(
embed_dim=300, save_path=self.word_embedding_path),
save_path=self.embedding_path,
),
self._create_dummy_meta_data(),
)
def test_freeze_word_embedding(self):
model = create_model(
DocModel_Deprecated.Config(),
FeatureConfig(
word_feat=WordFeatConfig(freeze=True, mlp_layer_dims=[4]),
dict_feat=DictFeatConfig(),
),
metadata=mock_metadata(),
)
# word embedding
for param in model.embedding[0].word_embedding.parameters():
self.assertFalse(param.requires_grad)
for param in model.embedding[0].mlp.parameters():
self.assertTrue(param.requires_grad)
# dict feat embedding
for param in model.embedding[1].parameters():
self.assertTrue(param.requires_grad)
def test_load_save(self):
text_field_meta = FieldMeta()
text_field_meta.vocab = VocabStub()
text_field_meta.vocab_size = 4
text_field_meta.unk_token_idx = 1
text_field_meta.pad_token_idx = 0
text_field_meta.pretrained_embeds_weight = None
label_meta = FieldMeta()
label_meta.vocab = VocabStub()
label_meta.vocab_size = 3
metadata = CommonMetadata()
metadata.features = {DatasetFieldName.TEXT_FIELD: text_field_meta}
metadata.target = label_meta
saved_model = create_model(
DocModel.Config(
representation=BiLSTMDocAttention.Config(
save_path=self.representation_path),
decoder=MLPDecoder.Config(save_path=self.decoder_path),
),
FeatureConfig(save_path=self.embedding_path),
metadata,
)
saved_model.save_modules()
loaded_model = create_model(
DocModel.Config(
representation=BiLSTMDocAttention.Config(
load_path=self.representation_path),
decoder=MLPDecoder.Config(load_path=self.decoder_path),
),
FeatureConfig(load_path=self.embedding_path),
metadata,
)
random_model = create_model(
DocModel.Config(representation=BiLSTMDocAttention.Config(),
decoder=MLPDecoder.Config()),
FeatureConfig(),
metadata,
)
# Loaded and saved modules should be equal. Neither should be equal to
# a randomly initialised model.
for p1, p2, p3 in itertools.zip_longest(
saved_model.embedding.parameters(),
loaded_model.embedding.parameters(),
random_model.embedding.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))
for p1, p2, p3 in itertools.zip_longest(
saved_model.representation.parameters(),
loaded_model.representation.parameters(),
random_model.representation.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))
for p1, p2, p3 in itertools.zip_longest(
saved_model.decoder.parameters(),
loaded_model.decoder.parameters(),
random_model.decoder.parameters(),
):
self.assertTrue(p1.equal(p2))
self.assertFalse(p3.equal(p1))
self.assertFalse(p3.equal(p2))