mirror of
https://github.com/huggingface/transformers.git
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ba6d72226d
* Firstly: Better detection of when we're a custom class * Trigger tests * Let's break everything * make fixup * fix mistaken line doubling * Let's try to get rid of it from config classes at least * Let's try to get rid of it from config classes at least * Fixup image processor * no more circular import * Let's go back to setting `_auto_class` again * Let's go back to setting `_auto_class` again * stash commit * Revert the irrelevant changes until we figure out AutoConfig * Change tests since we're breaking expectations * make fixup * do the same for all custom classes * Cleanup for feature extractor tests * Cleanup tokenization tests too * typo * Fix tokenizer tests * make fixup * fix image processor test * make fixup * Remove warning from register_for_auto_class * Stop adding model info to auto map entirely * Remove todo * Remove the other todo * Let's start slapping _auto_class on models why not * Let's start slapping _auto_class on models why not * Make sure the tests know what's up * Make sure the tests know what's up * Completely remove add_model_info_to_* * Start adding _auto_class to models * Start adding _auto_class to models * Add a flaky decorator * Add a flaky decorator and import * stash commit * More message cleanup * make fixup * fix indent * Fix trust_remote_code prompts * make fixup * correct indentation * Reincorporate changes into dynamic_module_utils * Update call to trust_remote_code * make fixup * Fix video processors too * Fix video processors too * Remove is_flaky additions * make fixup
268 lines
12 KiB
Python
268 lines
12 KiB
Python
# Copyright 2021 the HuggingFace Inc. team.
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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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import json
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import os
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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IMAGE_PROCESSOR_MAPPING,
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AutoConfig,
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AutoImageProcessor,
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CLIPConfig,
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CLIPImageProcessor,
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ViTImageProcessor,
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ViTImageProcessorFast,
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)
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from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_torchvision, require_vision
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sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
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from test_module.custom_configuration import CustomConfig # noqa E402
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from test_module.custom_image_processing import CustomImageProcessor # noqa E402
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class AutoImageProcessorTest(unittest.TestCase):
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def setUp(self):
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transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
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def test_image_processor_from_model_shortcut(self):
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config = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_image_processor_from_local_directory_from_key(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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config_tmpfile = Path(tmpdirname) / "config.json"
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json.dump(
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{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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json.dump({"model_type": "clip"}, open(config_tmpfile, "w"))
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config = AutoImageProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_image_processor_from_local_directory_from_feature_extractor_key(self):
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# Ensure we can load the image processor from the feature extractor config
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with tempfile.TemporaryDirectory() as tmpdirname:
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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config_tmpfile = Path(tmpdirname) / "config.json"
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json.dump(
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{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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json.dump({"model_type": "clip"}, open(config_tmpfile, "w"))
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config = AutoImageProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_image_processor_from_new_filename(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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config_tmpfile = Path(tmpdirname) / "config.json"
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json.dump(
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{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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json.dump({"model_type": "clip"}, open(config_tmpfile, "w"))
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config = AutoImageProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_image_processor_from_local_directory_from_config(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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model_config = CLIPConfig()
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# Create a dummy config file with image_proceesor_type
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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config_tmpfile = Path(tmpdirname) / "config.json"
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json.dump(
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{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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json.dump({"model_type": "clip"}, open(config_tmpfile, "w"))
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# remove image_processor_type to make sure config.json alone is enough to load image processor locally
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config_dict = AutoImageProcessor.from_pretrained(tmpdirname).to_dict()
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config_dict.pop("image_processor_type")
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config = CLIPImageProcessor(**config_dict)
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# save in new folder
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model_config.save_pretrained(tmpdirname)
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config.save_pretrained(tmpdirname)
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config = AutoImageProcessor.from_pretrained(tmpdirname)
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# make sure private variable is not incorrectly saved
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dict_as_saved = json.loads(config.to_json_string())
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self.assertTrue("_processor_class" not in dict_as_saved)
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_image_processor_from_local_file(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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json.dump(
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{"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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config = AutoImageProcessor.from_pretrained(processor_tmpfile)
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self.assertIsInstance(config, CLIPImageProcessor)
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def test_repo_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, "clip-base is not a local folder and is not a valid model identifier"
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):
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_ = AutoImageProcessor.from_pretrained("clip-base")
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def test_revision_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
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):
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_ = AutoImageProcessor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
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def test_image_processor_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError,
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"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.",
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):
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_ = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model")
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@require_vision
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@require_torchvision
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def test_use_fast_selection(self):
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checkpoint = "hf-internal-testing/tiny-random-vit"
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# TODO: @yoni, change in v4.48 (when use_fast set to True by default)
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# Slow image processor is selected by default
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image_processor = AutoImageProcessor.from_pretrained(checkpoint)
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self.assertIsInstance(image_processor, ViTImageProcessor)
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# Fast image processor is selected when use_fast=True
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image_processor = AutoImageProcessor.from_pretrained(checkpoint, use_fast=True)
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self.assertIsInstance(image_processor, ViTImageProcessorFast)
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# Slow image processor is selected when use_fast=False
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image_processor = AutoImageProcessor.from_pretrained(checkpoint, use_fast=False)
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self.assertIsInstance(image_processor, ViTImageProcessor)
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def test_from_pretrained_dynamic_image_processor(self):
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# If remote code is not set, we will time out when asking whether to load the model.
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with self.assertRaises(ValueError):
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image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
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# If remote code is disabled, we can't load this config.
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with self.assertRaises(ValueError):
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image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
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)
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image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
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)
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self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
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# Test the dynamic module is loaded only once.
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reloaded_image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
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)
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self.assertIs(image_processor.__class__, reloaded_image_processor.__class__)
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# Test image processor can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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image_processor.save_pretrained(tmp_dir)
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reloaded_image_processor = AutoImageProcessor.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_processor.py"))) # Assert we saved custom code
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self.assertEqual(
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reloaded_image_processor.auto_map["AutoImageProcessor"], "image_processor.NewImageProcessor"
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)
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self.assertEqual(reloaded_image_processor.__class__.__name__, "NewImageProcessor")
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# Test the dynamic module is reloaded if we force it.
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reloaded_image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True, force_download=True
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)
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self.assertIsNot(image_processor.__class__, reloaded_image_processor.__class__)
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def test_new_image_processor_registration(self):
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try:
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AutoConfig.register("custom", CustomConfig)
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AutoImageProcessor.register(CustomConfig, CustomImageProcessor)
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# Trying to register something existing in the Transformers library will raise an error
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with self.assertRaises(ValueError):
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AutoImageProcessor.register(CLIPConfig, CLIPImageProcessor)
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with tempfile.TemporaryDirectory() as tmpdirname:
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processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
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config_tmpfile = Path(tmpdirname) / "config.json"
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json.dump(
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{"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"},
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open(processor_tmpfile, "w"),
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)
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json.dump({"model_type": "clip"}, open(config_tmpfile, "w"))
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image_processor = CustomImageProcessor.from_pretrained(tmpdirname)
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# Now that the config is registered, it can be used as any other config with the auto-API
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with tempfile.TemporaryDirectory() as tmp_dir:
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image_processor.save_pretrained(tmp_dir)
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new_image_processor = AutoImageProcessor.from_pretrained(tmp_dir)
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self.assertIsInstance(new_image_processor, CustomImageProcessor)
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
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del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
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def test_from_pretrained_dynamic_image_processor_conflict(self):
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class NewImageProcessor(CLIPImageProcessor):
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is_local = True
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try:
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AutoConfig.register("custom", CustomConfig)
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AutoImageProcessor.register(CustomConfig, NewImageProcessor)
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# If remote code is not set, the default is to use local
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image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
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self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
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self.assertTrue(image_processor.is_local)
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# If remote code is disabled, we load the local one.
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image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
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)
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self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
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self.assertTrue(image_processor.is_local)
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# If remote is enabled, we load from the Hub
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image_processor = AutoImageProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
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)
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self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
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self.assertTrue(not hasattr(image_processor, "is_local"))
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
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del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
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