def create_and_check_lxmert_model(
self,
config,
input_ids,
visual_feats,
bounding_boxes,
token_type_ids,
input_mask,
obj_labels,
masked_lm_labels,
matched_label,
ans,
output_attentions,
):
model = TFLxmertModel(config=config)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=output_attentions,
)
result = model(
input_ids,
visual_feats,
bounding_boxes,
token_type_ids=token_type_ids,
attention_mask=input_mask,
output_attentions=not output_attentions,
)
result = model(input_ids,
visual_feats,
bounding_boxes,
return_dict=False)
result = model(input_ids,
visual_feats,
bounding_boxes,
return_dict=True)
self.parent.assertEqual(
result.language_output.shape,
(self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(
result.vision_output.shape,
(self.batch_size, self.num_visual_features, self.hidden_size))
self.parent.assertEqual(result.pooled_output.shape,
(self.batch_size, self.hidden_size))
def test_model_from_pretrained(self):
for model_name in ["unc-nlp/lxmert-base-uncased"]:
model = TFLxmertModel.from_pretrained(model_name)
self.assertIsNotNone(model)