def __init__(self, config):
super().__init__(config)
trainable_position_encoding_kwargs_decoder = dict(
num_channels=config.d_latents, index_dims=1)
self.num_labels = config.num_labels
self.perceiver = PerceiverModel(
config,
input_preprocessor=PerceiverTextPreprocessor(config),
decoder=PerceiverTokenClassificationDecoder(
config,
num_channels=config.d_latents,
trainable_position_encoding_kwargs=
trainable_position_encoding_kwargs_decoder,
),
)
# Initialize weights and apply final processing
self.post_init()
def test_model_from_pretrained(self):
for model_name in PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = PerceiverModel.from_pretrained(model_name)
self.assertIsNotNone(model)