def create_and_check_xxx_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = XxxModel(config=config) model.eval() sequence_output, pooled_output = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model( input_ids, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids) result = { "sequence_output": sequence_output, "pooled_output": pooled_output, } self.parent.assertListEqual( list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = XxxModel(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)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def test_model_from_pretrained(self): cache_dir = "/tmp/transformers_test/" for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = XxxModel.from_pretrained(model_name, cache_dir=cache_dir) shutil.rmtree(cache_dir) self.assertIsNotNone(model)
def test_model_from_pretrained(self): for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = XxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model)