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tests/models/auto/test_image_processing_auto.py

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+# 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 os
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+import transformers
+from transformers import (
+    CONFIG_MAPPING,
+    IMAGE_PROCESSOR_MAPPING,
+    AutoConfig,
+    AutoImageProcessor,
+    CLIPConfig,
+    CLIPImageProcessor,
+    ViTImageProcessor,
+    ViTImageProcessorPil,
+)
+from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_torchvision, require_vision
+
+
+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 setUp(self):
+        transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
+
+    @require_torchvision
+    def test_image_processor_from_model_shortcut(self):
+        config = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
+        self.assertIsInstance(config, CLIPImageProcessor)
+
+    @require_torchvision
+    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)
+
+    @require_torchvision
+    def test_image_processor_from_local_directory_from_feature_extractor_key(self):
+        # Ensure we can load the image processor from the feature extractor config
+        # Though we don't have any `CLIPFeatureExtractor` class, we can't be sure that
+        # there are no models in the hub serialized with `processor_type=CLIPFeatureExtractor`
+        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)
+
+    @require_torchvision
+    def test_image_processor_from_new_filename(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)
+            # Now loading fast image processor by default
+            self.assertIsInstance(config, CLIPImageProcessor)
+
+    @require_torchvision
+    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_processor_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)
+
+    @require_torchvision
+    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,
+            "Can't load image processor for 'hf-internal-testing/config-no-model'.",
+        ):
+            _ = AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model")
+
+    @require_vision
+    @require_torchvision
+    def test_use_fast_selection(self):
+        checkpoint = "hf-internal-testing/tiny-random-vit"
+
+        # Fast image processor is selected by default
+        image_processor = AutoImageProcessor.from_pretrained(checkpoint)
+        self.assertIsInstance(image_processor, ViTImageProcessor)
+
+        # Fast image processor is selected when use_fast=True
+        image_processor = AutoImageProcessor.from_pretrained(checkpoint, use_fast=True)
+        self.assertIsInstance(image_processor, ViTImageProcessor)
+
+        # Slow image processor is selected when use_fast=False
+        image_processor = AutoImageProcessor.from_pretrained(checkpoint, use_fast=False)
+        self.assertIsInstance(image_processor, ViTImageProcessorPil)
+
+    def test_from_pretrained_dynamic_image_processor(self):
+        # If remote code is not set, we will time out when asking whether to load the model.
+        with self.assertRaises(ValueError):
+            image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
+        # If remote code is disabled, we can't load this config.
+        with self.assertRaises(ValueError):
+            image_processor = AutoImageProcessor.from_pretrained(
+                "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
+            )
+
+        image_processor = AutoImageProcessor.from_pretrained(
+            "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
+        )
+        self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
+
+        # Test the dynamic module is loaded only once.
+        reloaded_image_processor = AutoImageProcessor.from_pretrained(
+            "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
+        )
+        self.assertIs(image_processor.__class__, reloaded_image_processor.__class__)
+
+        # 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.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_processor.py")))  # Assert we saved custom code
+            self.assertEqual(
+                reloaded_image_processor.auto_map["AutoImageProcessor"], "image_processor.NewImageProcessor"
+            )
+        self.assertEqual(reloaded_image_processor.__class__.__name__, "NewImageProcessor")
+
+        # Test the dynamic module is reloaded if we force it.
+        reloaded_image_processor = AutoImageProcessor.from_pretrained(
+            "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True, force_download=True
+        )
+        self.assertIsNot(image_processor.__class__, reloaded_image_processor.__class__)
+
+    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]
+
+    def test_from_pretrained_dynamic_image_processor_conflict(self):
+        class NewImageProcessor(CLIPImageProcessor):
+            is_local = True
+
+        try:
+            AutoConfig.register("custom", CustomConfig)
+            AutoImageProcessor.register(CustomConfig, NewImageProcessor)
+            # If remote code is not set, the default is to use local
+            image_processor = AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor")
+            self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
+            self.assertTrue(image_processor.is_local)
+
+            # If remote code is disabled, we load the local one.
+            image_processor = AutoImageProcessor.from_pretrained(
+                "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=False
+            )
+            self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
+            self.assertTrue(image_processor.is_local)
+
+            # If remote code is enabled but the user explicitly registered the local one, we load the local one.
+            image_processor = AutoImageProcessor.from_pretrained(
+                "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
+            )
+            self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
+            self.assertTrue(image_processor.is_local)
+
+            # If remote code is enabled but local code originated from transformers, we load the remote one.
+            NewImageProcessor.__module__ = "transformers.models.custom.configuration_custom"
+            image_processor = AutoImageProcessor.from_pretrained(
+                "hf-internal-testing/test_dynamic_image_processor", trust_remote_code=True
+            )
+            self.assertEqual(image_processor.__class__.__name__, "NewImageProcessor")
+            self.assertTrue(not hasattr(image_processor, "is_local"))
+
+        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]
+
+    @require_vision
+    def test_backend_kwarg_pil(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
+            json.dump({"image_processor_type": "ViTImageProcessor"}, open(processor_tmpfile, "w"))
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname, backend="pil")
+            self.assertIsInstance(image_processor, ViTImageProcessorPil)
+
+    @require_torchvision
+    def test_backend_kwarg_torchvision(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
+            json.dump({"image_processor_type": "ViTImageProcessor"}, open(processor_tmpfile, "w"))
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname, backend="torchvision")
+            self.assertIsInstance(image_processor, ViTImageProcessor)
+
+    @require_torchvision
+    def test_default_to_pil_backend_for_lanczos_processors(self):
+        # Even when torchvision is available, processors that rely on Lanczos interpolation
+        # (listed in DEFAULT_TO_PIL_BACKEND_IMAGE_PROCESSORS) must default to the PIL backend
+        # when backend='auto'.
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
+            json.dump({"image_processor_type": "FlavaImageProcessor"}, open(processor_tmpfile, "w"))
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname)
+            self.assertEqual(type(image_processor).__name__, "FlavaImageProcessorPil")
+
+    @require_torchvision
+    def test_explicit_backend_overrides_lanczos_default(self):
+        # An explicit backend="torchvision" must bypass the DEFAULT_TO_PIL_BACKEND_IMAGE_PROCESSORS
+        # override; only the auto-resolved backend is affected by the list.
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
+            json.dump({"image_processor_type": "FlavaImageProcessor"}, open(processor_tmpfile, "w"))
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname, backend="torchvision")
+            self.assertEqual(type(image_processor).__name__, "FlavaImageProcessor")
+
+    @require_torchvision
+    def test_legacy_fast_class_name_in_config(self):
+        # Checkpoints saved before the rename used names like "ViTImageProcessorFast".
+        # The *Fast suffix must be stripped and the correct backend variant returned.
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json"
+            json.dump({"image_processor_type": "ViTImageProcessorFast"}, open(processor_tmpfile, "w"))
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname, backend="torchvision")
+            self.assertIsInstance(image_processor, ViTImageProcessor)
+
+            image_processor = AutoImageProcessor.from_pretrained(tmpdirname, backend="pil")
+            self.assertIsInstance(image_processor, ViTImageProcessorPil)
+
+    @require_vision
+    def test_register_with_image_processor_classes_dict(self):
+        # New image_processor_classes={} dict API for register().
+        try:
+            AutoImageProcessor.register(CustomConfig, image_processor_classes={"pil": CustomImageProcessor})
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                json.dump(
+                    {"image_processor_type": "CustomImageProcessor"},
+                    open(Path(tmp_dir) / "preprocessor_config.json", "w"),
+                )
+                image_processor = AutoImageProcessor.from_pretrained(tmp_dir, backend="pil")
+                self.assertIsInstance(image_processor, CustomImageProcessor)
+        finally:
+            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
+                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
+
+    @require_vision
+    def test_register_legacy_slow_fast_params(self):
+        # slow_image_processor_class= and fast_image_processor_class= are deprecated but
+        # must still work; they map to "pil" and "torchvision" backends respectively.
+        try:
+            AutoImageProcessor.register(CustomConfig, slow_image_processor_class=CustomImageProcessor)
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                json.dump(
+                    {"image_processor_type": "CustomImageProcessor"},
+                    open(Path(tmp_dir) / "preprocessor_config.json", "w"),
+                )
+                image_processor = AutoImageProcessor.from_pretrained(tmp_dir, backend="pil")
+                self.assertIsInstance(image_processor, CustomImageProcessor)
+        finally:
+            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
+                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]