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: EncoderFactory.Config = ImageEncoderFactory.Config(
type=ImageEncoderTypes.resnet152, params=ResNet152ImageEncoder.Config()
)
text_encoder: EncoderFactory.Config = TextEncoderFactory.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_mmft_from_build_model(self):
modalities_config = [
MMFTransformerModalityConfig(
type="image",
key="image",
embedding_dim=256,
position_dim=1,
segment_id=0,
encoder=ImageEncoderFactory.Config(
type=ImageEncoderTypes.resnet152,
params=ResNet152ImageEncoder.Config(pretrained=False),
),
),
MMFTransformerModalityConfig(
type="text",
key="text",
embedding_dim=756,
position_dim=512,
segment_id=1,
encoder=TextEncoderFactory.Config(
type=TextEncoderTypes.identity),
),
]
config = MMFTransformer.Config(modalities=modalities_config,
num_labels=2)
mmft = build_model(config)
self.assertIsNotNone(mmft)
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)
def test_preprocessing_with_resnet_encoder(self):
self._image_modality_config = MMFTransformerModalityConfig(
type="image",
key="image",
embedding_dim=2048,
position_dim=1,
segment_id=0,
encoder=ImageEncoderFactory.Config(
type=ImageEncoderTypes.resnet152,
params=ResNet152ImageEncoder.Config(pretrained=False),
),
)
modalities_config = [
self._image_modality_config, self._text_modality_config
]
config = MMFTransformer.Config(modalities=modalities_config,
num_labels=2)
mmft = build_model(config)
sample_list = SampleList()
sample_list.image = torch.rand(2, 3, 224, 224)
sample_list.text = torch.randint(0, 512, (2, 128))
transformer_input = mmft.preprocess_sample(sample_list)
input_ids = transformer_input["input_ids"]
self.assertEqual(input_ids["image"].dim(), 3)
self.assertEqual(list(input_ids["image"].size()), [2, 1, 2048])
self.assertEqual(input_ids["text"].dim(), 2)
self.assertEqual(list(input_ids["text"].size()), [2, 128])
position_ids = transformer_input["position_ids"]
test_utils.compare_tensors(position_ids["image"],
torch.tensor([[0], [0]]))
test_utils.compare_tensors(
position_ids["text"],
torch.arange(0, 128).unsqueeze(0).expand((2, 128)))
masks = transformer_input["masks"]
test_utils.compare_tensors(masks["image"], torch.tensor([[1], [1]]))
test_utils.compare_tensors(masks["text"], torch.ones((2, 128)).long())
segment_ids = transformer_input["segment_ids"]
test_utils.compare_tensors(segment_ids["image"],
torch.tensor([[0], [0]]))
test_utils.compare_tensors(segment_ids["text"],
torch.ones((2, 128)).long())
class Config(BaseTransformer.Config):
model: str = "mmft"
transformer_base: str = "bert-base-uncased"
heads: List[BaseTransformerHead.Config] = field(
default_factory=lambda: [MLP.Config()]
)
num_labels: int = MISSING
initializer_range: float = 0.02
initializer_mean: float = 0.0
token_noise_std: float = 0.01
token_noise_mean: float = 0.0
layer_norm_weight_fill: float = 1.0
random_initialize: bool = False
freeze_transformer: bool = False
freeze_image_encoder: bool = False
tie_weight_to_encoder: Optional[str] = None
finetune_lr_multiplier: float = 1
backend: BaseTransformerBackendConfig = MMFTransformerBackendConfig(
type="huggingface"
)
modalities: List[BaseTransformerModalityConfig] = field(
default_factory=lambda: [
MMFTransformerModalityConfig(
type="text",
key="text",
position_dim=512,
embedding_dim=768,
segment_id=0,
),
MMFTransformerModalityConfig(
type="image",
key="image",
embedding_dim=2048,
position_dim=1,
segment_id=1,
# NOTE: One can also specify encoder in factory mode as
# encoder=ImageEncoderFactory.Config(
# type="resnet152",
# params=ResNet152ImageEncoder.Config()
# )
encoder=ResNet152ImageEncoder.Config(),
),
]
)