Пример #1
0
 def create_and_check_model_as_decoder(
     self,
     config,
     input_ids,
     token_type_ids,
     input_mask,
     sequence_labels,
     token_labels,
     choice_labels,
     encoder_hidden_states,
     encoder_attention_mask,
 ):
     config.add_cross_attention = True
     model = YosoModel(config)
     model.to(torch_device)
     model.eval()
     result = model(
         input_ids,
         attention_mask=input_mask,
         token_type_ids=token_type_ids,
         encoder_hidden_states=encoder_hidden_states,
         encoder_attention_mask=encoder_attention_mask,
     )
     result = model(
         input_ids,
         attention_mask=input_mask,
         token_type_ids=token_type_ids,
         encoder_hidden_states=encoder_hidden_states,
     )
     result = model(input_ids,
                    attention_mask=input_mask,
                    token_type_ids=token_type_ids)
     self.parent.assertEqual(
         result.last_hidden_state.shape,
         (self.batch_size, self.seq_length, self.hidden_size))
Пример #2
0
    def test_inference_no_head(self):
        model = YosoModel.from_pretrained("uw-madison/yoso-4096")
        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])

        with torch.no_grad():
            output = model(input_ids)[0]

        expected_shape = torch.Size((1, 6, 768))
        self.assertEqual(output.shape, expected_shape)

        expected_slice = torch.tensor([[[-0.0611, 0.1242, 0.0840],
                                        [0.0280, -0.0048, 0.1125],
                                        [0.0106, 0.0226, 0.0751]]])

        self.assertTrue(
            torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
Пример #3
0
 def create_and_check_model(self, config, input_ids, token_type_ids,
                            input_mask, sequence_labels, token_labels,
                            choice_labels):
     model = YosoModel(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.last_hidden_state.shape,
         (self.batch_size, self.seq_length, self.hidden_size))
Пример #4
0
 def test_model_from_pretrained(self):
     for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         model = YosoModel.from_pretrained(model_name)
         self.assertIsNotNone(model)