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# 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]
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