mirror of
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107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import, division, print_function
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import logging
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import unittest
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from transformers import is_torch_available
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from .utils import SMALL_MODEL_IDENTIFIER, require_torch, slow
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if is_torch_available():
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from transformers import (
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AutoConfig,
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BertConfig,
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AutoModel,
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BertModel,
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AutoModelWithLMHead,
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BertForMaskedLM,
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AutoModelForSequenceClassification,
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BertForSequenceClassification,
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AutoModelForQuestionAnswering,
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BertForQuestionAnswering,
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)
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from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class AutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModel.from_pretrained(model_name)
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model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertModel)
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for value in loading_info.values():
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self.assertEqual(len(value), 0)
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@slow
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def test_lmhead_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelWithLMHead.from_pretrained(model_name)
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model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForMaskedLM)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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model, loading_info = AutoModelForSequenceClassification.from_pretrained(
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model_name, output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForQuestionAnswering)
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def test_from_pretrained_identifier(self):
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logging.basicConfig(level=logging.INFO)
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model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(model, BertForMaskedLM)
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if __name__ == "__main__":
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unittest.main()
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