Spaces:
Sleeping
Sleeping
| # coding=utf-8 | |
| # Copyright 2021 the HuggingFace Inc. team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import json | |
| import sys | |
| import tempfile | |
| import unittest | |
| from pathlib import Path | |
| from transformers import ( | |
| CONFIG_MAPPING, | |
| IMAGE_PROCESSOR_MAPPING, | |
| AutoConfig, | |
| AutoImageProcessor, | |
| CLIPConfig, | |
| CLIPImageProcessor, | |
| ) | |
| from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER | |
| sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) | |
| from test_module.custom_configuration import CustomConfig # noqa E402 | |
| from test_module.custom_image_processing import CustomImageProcessor # noqa E402 | |
| class AutoImageProcessorTest(unittest.TestCase): | |
| def test_image_processor_from_model_shortcut(self): | |
| config = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| self.assertIsInstance(config, CLIPImageProcessor) | |
| def test_image_processor_from_local_directory_from_key(self): | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" | |
| config_tmpfile = Path(tmpdirname) / "config.json" | |
| json.dump( | |
| {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}, | |
| open(processor_tmpfile, "w"), | |
| ) | |
| json.dump({"model_type": "clip"}, open(config_tmpfile, "w")) | |
| config = AutoImageProcessor.from_pretrained(tmpdirname) | |
| self.assertIsInstance(config, CLIPImageProcessor) | |
| def test_image_processor_from_local_directory_from_feature_extractor_key(self): | |
| # Ensure we can load the image processor from the feature extractor config | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" | |
| config_tmpfile = Path(tmpdirname) / "config.json" | |
| json.dump( | |
| {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}, | |
| open(processor_tmpfile, "w"), | |
| ) | |
| json.dump({"model_type": "clip"}, open(config_tmpfile, "w")) | |
| config = AutoImageProcessor.from_pretrained(tmpdirname) | |
| self.assertIsInstance(config, CLIPImageProcessor) | |
| def test_image_processor_from_local_directory_from_config(self): | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| model_config = CLIPConfig() | |
| # Create a dummy config file with image_proceesor_type | |
| processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" | |
| config_tmpfile = Path(tmpdirname) / "config.json" | |
| json.dump( | |
| {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}, | |
| open(processor_tmpfile, "w"), | |
| ) | |
| json.dump({"model_type": "clip"}, open(config_tmpfile, "w")) | |
| # remove image_processor_type to make sure config.json alone is enough to load image processor locally | |
| config_dict = AutoImageProcessor.from_pretrained(tmpdirname).to_dict() | |
| config_dict.pop("image_processor_type") | |
| config = CLIPImageProcessor(**config_dict) | |
| # save in new folder | |
| model_config.save_pretrained(tmpdirname) | |
| config.save_pretrained(tmpdirname) | |
| config = AutoImageProcessor.from_pretrained(tmpdirname) | |
| # make sure private variable is not incorrectly saved | |
| dict_as_saved = json.loads(config.to_json_string()) | |
| self.assertTrue("_processor_class" not in dict_as_saved) | |
| self.assertIsInstance(config, CLIPImageProcessor) | |
| def test_image_processor_from_local_file(self): | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" | |
| json.dump( | |
| {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}, | |
| open(processor_tmpfile, "w"), | |
| ) | |
| config = AutoImageProcessor.from_pretrained(processor_tmpfile) | |
| self.assertIsInstance(config, CLIPImageProcessor) | |
| def test_repo_not_found(self): | |
| with self.assertRaisesRegex( | |
| EnvironmentError, "clip-base is not a local folder and is not a valid model identifier" | |
| ): | |
| _ = AutoImageProcessor.from_pretrained("clip-base") | |
| def test_revision_not_found(self): | |
| with self.assertRaisesRegex( | |
| EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" | |
| ): | |
| _ = AutoImageProcessor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") | |
| def test_image_processor_not_found(self): | |
| with self.assertRaisesRegex( | |
| EnvironmentError, | |
| "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.", | |
| ): | |
| _ = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model") | |
| def test_from_pretrained_dynamic_image_processor(self): | |
| image_processor = AutoImageProcessor.from_pretrained( | |
| "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True | |
| ) | |
| self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor") | |
| # Test image processor can be reloaded. | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| image_processor.save_pretrained(tmp_dir) | |
| reloaded_image_processor = AutoImageProcessor.from_pretrained(tmp_dir, trust_remote_code=True) | |
| self.assertEqual(reloaded_image_processor.__class__.__name__, "NewImageProcessor") | |
| def test_new_image_processor_registration(self): | |
| try: | |
| AutoConfig.register("custom", CustomConfig) | |
| AutoImageProcessor.register(CustomConfig, CustomImageProcessor) | |
| # Trying to register something existing in the Transformers library will raise an error | |
| with self.assertRaises(ValueError): | |
| AutoImageProcessor.register(CLIPConfig, CLIPImageProcessor) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" | |
| config_tmpfile = Path(tmpdirname) / "config.json" | |
| json.dump( | |
| {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}, | |
| open(processor_tmpfile, "w"), | |
| ) | |
| json.dump({"model_type": "clip"}, open(config_tmpfile, "w")) | |
| image_processor = CustomImageProcessor.from_pretrained(tmpdirname) | |
| # Now that the config is registered, it can be used as any other config with the auto-API | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| image_processor.save_pretrained(tmp_dir) | |
| new_image_processor = AutoImageProcessor.from_pretrained(tmp_dir) | |
| self.assertIsInstance(new_image_processor, CustomImageProcessor) | |
| finally: | |
| if "custom" in CONFIG_MAPPING._extra_content: | |
| del CONFIG_MAPPING._extra_content["custom"] | |
| if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content: | |
| del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig] | |