class Config(BaseModel.Config):
model: str = "mmbt"
# classification or pretraining
training_head_type: str = "pretraining"
bert_model_name: str = "bert-base-uncased"
direct_features_input: bool = False
freeze_text: bool = False
freeze_modal: bool = False
freeze_complete_base: bool = False
finetune_lr_multiplier: float = 1
# Dimension of the embedding finally returned by the modal encoder
modal_hidden_size: int = 2048
text_hidden_size: int = 768
num_labels: int = 2
# This actually is Union[ImageEncoderConfig, ImageFeatureEncoderConfig]
modal_encoder: Encoder.Config = ImageEncoder.Config(
type=ImageEncoderTypes.resnet152, params=ResNet152ImageEncoder.Config()
)
text_encoder: Encoder.Config = TextEncoder.Config(
type=TextEncoderTypes.transformer,
params=TransformerEncoder.Config(bert_model_name=II("bert_model_name")),
)
use_modal_start_token: bool = True
use_modal_end_token: bool = True
fused_feature_only: bool = False
output_dim: int = 768
def test_mmbt_from_params(self):
# default init
mmbt = MMBT.from_params(
modal_encoder=ImageEncoder.Config(
type=ImageEncoderTypes.resnet152,
params=ResNet152ImageEncoder.Config(pretrained=False),
),
text_encoder=TextEncoder.Config(type=TextEncoderTypes.identity),
)
config = OmegaConf.structured(
MMBT.Config(
modal_encoder=ImageEncoder.Config(
type=ImageEncoderTypes.resnet152,
params=ResNet152ImageEncoder.Config(pretrained=False),
),
text_encoder=TextEncoder.Config(
type=TextEncoderTypes.identity),
))
self.assertIsNotNone(mmbt)
# Make sure that the config is created from MMBT.Config
self.assertEqual(mmbt.config, config)