def test_modality_key_preprocessing(self):
self._text_modality_config.key = "body"
second_text_modality_config = MMFTransformerModalityConfig(
type="text",
key="ocr",
embedding_dim=756,
position_dim=128,
segment_id=2,
encoder=TextEncoderFactory.Config(type=TextEncoderTypes.identity),
)
modalities_config = [
self._image_modality_config,
self._text_modality_config,
second_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, 256)
sample_list.body = torch.randint(0, 512, (2, 128))
sample_list.ocr = torch.randint(0, 512, (2, 128))
sample_list.lm_label_ids = torch.randint(-1, 30522, (2, 128))
lm_labels_sum = sample_list.lm_label_ids.sum().item() * 2
transformer_input = mmft.preprocess_sample(sample_list)
self._compare_processed_for_multimodality(transformer_input,
lm_labels_sum)
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_mmf_from_params_encoder_factory(self):
modalities_config = [
MMFTransformerModalityConfig(
type="image",
key="image",
embedding_dim=256,
position_dim=1,
segment_id=0,
encoder=ImageEncoderFactory.Config(
type=ImageEncoderTypes.identity),
),
MMFTransformerModalityConfig(
type="text",
key="text",
embedding_dim=756,
position_dim=512,
segment_id=0,
encoder=TextEncoderFactory.Config(
type=TextEncoderTypes.identity),
),
]
mmft = MMFTransformer.from_params(modalities=modalities_config,
num_labels=2)
mmft.build()
config = OmegaConf.structured(
MMFTransformer.Config(modalities=modalities_config, num_labels=2))
self.assertIsNotNone(mmft)
self.assertEqual(mmft.config, config)
def test_tie_mlm_head_weight_to_encoder(self):
self._text_modality_config = MMFTransformerModalityConfig(
type="text",
key="text",
embedding_dim=768,
position_dim=128,
segment_id=0,
encoder=TextEncoderFactory.Config(
type=TextEncoderTypes.transformer),
)
heads = [MLM.Config()]
modalities_config = [
self._image_modality_config, self._text_modality_config
]
config = MMFTransformer.Config(
heads=heads,
modalities=modalities_config,
num_labels=2,
tie_weight_to_encoder="text",
)
mmft = build_model(config)
test_utils.compare_tensors(
mmft.heads[0].cls.predictions.decoder.weight,
mmft.encoders["text"].embeddings.word_embeddings.weight,
)
def test_mmbt_directly_from_config(self):
config = OmegaConf.structured(
MMBT.Config(
modal_encoder=ImageEncoderFactory.Config(
type=ImageEncoderTypes.resnet152,
params=ResNet152ImageEncoder.Config(pretrained=False),
),
text_encoder=TextEncoderFactory.Config(
type=TextEncoderTypes.identity),
))
mmbt = MMBT(config)
self.assertIsNotNone(mmbt)
# Make sure that the config is created from MMBT.Config
self.assertEqual(mmbt.config, config)
def setUp(self):
test_utils.setup_proxy()
setup_imports()
self._image_modality_config = MMFTransformerModalityConfig(
type="image",
key="image",
embedding_dim=256,
position_dim=1,
segment_id=0,
encoder=ImageEncoderFactory.Config(
type=ImageEncoderTypes.identity),
)
self._text_modality_config = MMFTransformerModalityConfig(
type="text",
key="text",
embedding_dim=756,
position_dim=128,
segment_id=1,
encoder=TextEncoderFactory.Config(type=TextEncoderTypes.identity),
)
