def create_and_check_context_encoder(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DPRContextEncoder(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 create_and_check_dpr_context_encoder(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = DPRContextEncoder(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])
