Update image processor parameters if creating with kwargs (#20866)

* Update parameters if creating with kwargs

* Shallow copy to prevent mutating input

* Pass all args in constructor dict - warnings in init

* Fix typo

src/transformers/image_processing_utils.py

@@ -316,8 +316,17 @@ class ImageProcessingMixin(PushToHubMixin):
             [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
             parameters.
         """
+        image_processor_dict = image_processor_dict.copy()
         return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
 
+        # The `size` parameter is a dict and was previously an int or tuple in feature extractors.
+        # We set `size` here directly to the `image_processor_dict` so that it is converted to the appropriate
+        # dict within the image processor and isn't overwritten if `size` is passed in as a kwarg.
+        if "size" in kwargs and "size" in image_processor_dict:
+            image_processor_dict["size"] = kwargs.pop("size")
+        if "crop_size" in kwargs and "crop_size" in image_processor_dict:
+            image_processor_dict["crop_size"] = kwargs.pop("crop_size")
+
         image_processor = cls(**image_processor_dict)
 
         # Update image_processor with kwargs if needed

src/transformers/models/beit/image_processing_beit.py

@@ -15,7 +15,7 @@
 """Image processor class for Beit."""
 
 import warnings
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -131,6 +131,17 @@ class BeitImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
         self.do_reduce_labels = do_reduce_labels
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure `reduce_labels` is updated if image processor
+        is created using from_dict and kwargs e.g. `BeitImageProcessor.from_pretrained(checkpoint, reduce_labels=True)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "reduce_labels" in kwargs:
+            image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     def resize(
         self,
         image: np.ndarray,

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -815,6 +815,21 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
+    @classmethod
+    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->ConditionalDetr
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `ConditionalDetrImageProcessor.from_pretrained(checkpoint, size=600,
+        max_size=800)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "max_size" in kwargs:
+            image_processor_dict["max_size"] = kwargs.pop("max_size")
+        if "pad_and_return_pixel_mask" in kwargs:
+            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->ConditionalDetr
     def prepare_annotation(
         self,

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -813,6 +813,21 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
+    @classmethod
+    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->DeformableDetr
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `DeformableDetrImageProcessor.from_pretrained(checkpoint, size=600,
+        max_size=800)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "max_size" in kwargs:
+            image_processor_dict["max_size"] = kwargs.pop("max_size")
+        if "pad_and_return_pixel_mask" in kwargs:
+            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->DeformableDetr
     def prepare_annotation(
         self,

src/transformers/models/detr/image_processing_detr.py

@@ -797,6 +797,20 @@ class DetrImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `DetrImageProcessor.from_pretrained(checkpoint, size=600,
+        max_size=800)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "max_size" in kwargs:
+            image_processor_dict["max_size"] = kwargs.pop("max_size")
+        if "pad_and_return_pixel_mask" in kwargs:
+            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     def prepare_annotation(
         self,
         image: np.ndarray,

src/transformers/models/flava/image_processing_flava.py

@@ -17,7 +17,7 @@
 import math
 import random
 from functools import lru_cache
-from typing import Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -293,6 +293,19 @@ class FlavaImageProcessor(BaseImageProcessor):
         self.codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else FLAVA_CODEBOOK_MEAN
         self.codebook_image_std = codebook_image_std if codebook_image_std is not None else FLAVA_CODEBOOK_STD
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `FlavaImageProcessor.from_pretrained(checkpoint, codebook_size=600)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "codebook_size" in kwargs:
+            image_processor_dict["codebook_size"] = kwargs.pop("codebook_size")
+        if "codebook_crop_size" in kwargs:
+            image_processor_dict["codebook_crop_size"] = kwargs.pop("codebook_crop_size")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     @lru_cache()
     def masking_generator(
         self,

src/transformers/models/maskformer/image_processing_maskformer.py

@@ -400,7 +400,7 @@ class MaskFormerImageProcessor(BaseImageProcessor):
         if "size_divisibility" in kwargs:
             warnings.warn(
                 "The `size_divisibility` argument is deprecated and will be removed in v4.27. Please use "
-                "`size_divisibility` instead.",
+                "`size_divisor` instead.",
                 FutureWarning,
             )
             size_divisor = kwargs.pop("size_divisibility")
@@ -432,6 +432,19 @@ class MaskFormerImageProcessor(BaseImageProcessor):
         self.ignore_index = ignore_index
         self.reduce_labels = reduce_labels
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `MaskFormerImageProcessor.from_pretrained(checkpoint, max_size=800)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "max_size" in kwargs:
+            image_processor_dict["max_size"] = kwargs.pop("max_size")
+        if "size_divisibility" in kwargs:
+            image_processor_dict["size_divisibility"] = kwargs.pop("size_divisibility")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     @property
     def size_divisibility(self):
         warnings.warn(

src/transformers/models/segformer/image_processing_segformer.py

@@ -15,7 +15,7 @@
 """Image processor class for Segformer."""
 
 import warnings
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -119,6 +119,18 @@ class SegformerImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_reduce_labels = do_reduce_labels
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure `reduce_labels` is updated if image processor
+        is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint,
+        reduce_labels=True)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "reduce_labels" in kwargs:
+            image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     def resize(
         self,
         image: np.ndarray,

src/transformers/models/vilt/image_processing_vilt.py

@@ -185,6 +185,18 @@ class ViltImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
         self.do_pad = do_pad
 
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure `reduce_labels` is updated if image processor
+        is created using from_dict and kwargs e.g. `ViltImageProcessor.from_pretrained(checkpoint,
+        pad_and_return_pixel_mask=False)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "pad_and_return_pixel_mask" in kwargs:
+            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     def resize(
         self,
         image: np.ndarray,

src/transformers/models/yolos/image_processing_yolos.py

@@ -725,6 +725,21 @@ class YolosImageProcessor(BaseImageProcessor):
         self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
         self.do_pad = do_pad
 
+    @classmethod
+    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->Yolos
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `YolosImageProcessor.from_pretrained(checkpoint, size=600,
+        max_size=800)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "max_size" in kwargs:
+            image_processor_dict["max_size"] = kwargs.pop("max_size")
+        if "pad_and_return_pixel_mask" in kwargs:
+            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
+        return super().from_dict(image_processor_dict, **kwargs)
+
     # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation
     def prepare_annotation(
         self,

tests/models/beit/test_feature_extraction_beit.py

@@ -125,6 +125,19 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertTrue(hasattr(feature_extractor, "image_mean"))
         self.assertTrue(hasattr(feature_extractor, "image_std"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"height": 20, "width": 20})
+        self.assertEqual(feature_extractor.crop_size, {"height": 18, "width": 18})
+        self.assertEqual(feature_extractor.do_reduce_labels, False)
+
+        feature_extractor = self.feature_extraction_class.from_dict(
+            self.feat_extract_dict, size=42, crop_size=84, reduce_labels=True
+        )
+        self.assertEqual(feature_extractor.size, {"height": 42, "width": 42})
+        self.assertEqual(feature_extractor.crop_size, {"height": 84, "width": 84})
+        self.assertEqual(feature_extractor.do_reduce_labels, True)
+
     def test_batch_feature(self):
         pass
 

tests/models/chinese_clip/test_feature_extraction_chinese_clip.py

@@ -135,6 +135,15 @@ class ChineseCLIPFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittes
         self.assertTrue(hasattr(feature_extractor, "image_std"))
         self.assertTrue(hasattr(feature_extractor, "do_convert_rgb"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"height": 224, "width": 224})
+        self.assertEqual(feature_extractor.crop_size, {"height": 18, "width": 18})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42, crop_size=84)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42})
+        self.assertEqual(feature_extractor.crop_size, {"height": 84, "width": 84})
+
     def test_batch_feature(self):
         pass
 

tests/models/clip/test_feature_extraction_clip.py

@@ -135,6 +135,15 @@ class CLIPFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertTrue(hasattr(feature_extractor, "image_std"))
         self.assertTrue(hasattr(feature_extractor, "do_convert_rgb"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 20})
+        self.assertEqual(feature_extractor.crop_size, {"height": 18, "width": 18})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42, crop_size=84)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42})
+        self.assertEqual(feature_extractor.crop_size, {"height": 84, "width": 84})
+
     def test_batch_feature(self):
         pass
 

tests/models/conditional_detr/test_feature_extraction_conditional_detr.py

@@ -133,6 +133,17 @@ class ConditionalDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, uni
         self.assertTrue(hasattr(feature_extractor, "do_resize"))
         self.assertTrue(hasattr(feature_extractor, "size"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 18, "longest_edge": 1333})
+        self.assertEqual(feature_extractor.do_pad, True)
+
+        feature_extractor = self.feature_extraction_class.from_dict(
+            self.feat_extract_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
+        )
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42, "longest_edge": 84})
+        self.assertEqual(feature_extractor.do_pad, False)
+
     def test_batch_feature(self):
         pass
 

tests/models/convnext/test_feature_extraction_convnext.py

@@ -96,6 +96,13 @@ class ConvNextFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.T
         self.assertTrue(hasattr(feature_extractor, "image_mean"))
         self.assertTrue(hasattr(feature_extractor, "image_std"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 20})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42})
+
     def test_batch_feature(self):
         pass
 

tests/models/deformable_detr/test_feature_extraction_deformable_detr.py

@@ -135,6 +135,17 @@ class DeformableDetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unit
         self.assertTrue(hasattr(feature_extractor, "do_pad"))
         self.assertTrue(hasattr(feature_extractor, "size"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 18, "longest_edge": 1333})
+        self.assertEqual(feature_extractor.do_pad, True)
+
+        feature_extractor = self.feature_extraction_class.from_dict(
+            self.feat_extract_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
+        )
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42, "longest_edge": 84})
+        self.assertEqual(feature_extractor.do_pad, False)
+
     def test_batch_feature(self):
         pass
 

tests/models/deit/test_feature_extraction_deit.py

@@ -103,6 +103,15 @@ class DeiTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertTrue(hasattr(feature_extractor, "image_mean"))
         self.assertTrue(hasattr(feature_extractor, "image_std"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"height": 20, "width": 20})
+        self.assertEqual(feature_extractor.crop_size, {"height": 18, "width": 18})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42, crop_size=84)
+        self.assertEqual(feature_extractor.size, {"height": 42, "width": 42})
+        self.assertEqual(feature_extractor.crop_size, {"height": 84, "width": 84})
+
     def test_batch_feature(self):
         pass
 

tests/models/detr/test_feature_extraction_detr.py

@@ -136,6 +136,17 @@ class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
         self.assertTrue(hasattr(feature_extractor, "size"))
         self.assertTrue(hasattr(feature_extractor, "do_pad"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 18, "longest_edge": 1333})
+        self.assertEqual(feature_extractor.do_pad, True)
+
+        feature_extractor = self.feature_extraction_class.from_dict(
+            self.feat_extract_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
+        )
+        self.assertEqual(feature_extractor.size, {"shortest_edge": 42, "longest_edge": 84})
+        self.assertEqual(feature_extractor.do_pad, False)
+
     def test_batch_feature(self):
         pass
 

tests/models/donut/test_feature_extraction_donut.py

@@ -103,6 +103,17 @@ class DonutFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.Test
         self.assertTrue(hasattr(feature_extractor, "image_mean"))
         self.assertTrue(hasattr(feature_extractor, "image_std"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"height": 18, "width": 20})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42)
+        self.assertEqual(feature_extractor.size, {"height": 42, "width": 42})
+
+        # Previous config had dimensions in (width, height) order
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=(42, 84))
+        self.assertEqual(feature_extractor.size, {"height": 84, "width": 42})
+
     def test_batch_feature(self):
         pass
 

tests/models/dpt/test_feature_extraction_dpt.py

@@ -92,6 +92,13 @@ class DPTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCa
         self.assertTrue(hasattr(feature_extractor, "do_resize"))
         self.assertTrue(hasattr(feature_extractor, "size"))
 
+    def test_feat_extract_from_dict_with_kwargs(self):
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict)
+        self.assertEqual(feature_extractor.size, {"height": 18, "width": 18})
+
+        feature_extractor = self.feature_extraction_class.from_dict(self.feat_extract_dict, size=42)
+        self.assertEqual(feature_extractor.size, {"height": 42, "width": 42})
+
     def test_call_pil(self):
         # Initialize feature_extractor
         feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)