def create_and_check_question_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DPRQuestionEncoder(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
def test_init_changed_config(self):
config = self.model_tester.prepare_config_and_inputs()[0]
model = DPRQuestionEncoder(config=config)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = DPRQuestionEncoder.from_pretrained(tmp_dirname,
projection_dim=512)
self.assertIsNotNone(model)
def create_and_check_dpr_question_encoder(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = DPRQuestionEncoder(config=config)
model.to(torch_device)
model.eval()
embeddings = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)[0]
embeddings = model(input_ids, token_type_ids=token_type_ids)[0]
embeddings = model(input_ids)[0]
result = {
"embeddings": embeddings,
}
self.parent.assertListEqual(
list(result["embeddings"].size()),
[self.batch_size, self.projection_dim or self.hidden_size])