Adds uniform processing kwargs to paligemma.

src/transformers/models/paligemma/processing_paligemma.py

@@ -17,19 +17,22 @@
 """
 
 import logging
+import sys
 from typing import List, Optional, Union
 
+
+if sys.version_info >= (3, 11):
+    from typing import Unpack
+else:
+    from typing_extensions import Unpack
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image
-from ...processing_utils import ProcessorMixin
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, TextKwargs
 from ...tokenization_utils_base import (
     AddedToken,
-    PaddingStrategy,
     PreTokenizedInput,
     TextInput,
-    TruncationStrategy,
 )
-from ...utils import TensorType
 
 
 logger = logging.getLogger(__name__)
@@ -73,6 +76,31 @@ def build_string_from_input(prompt, bos_token, image_seq_len, image_token):
     return f"{image_token * image_seq_len}{bos_token}{prompt}\n"
 
 
+class PaliGemmaTextKwargs(TextKwargs):
+    suffix: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]]
+
+
+class PaliGemmaImagesKwargs(ImagesKwargs):
+    do_convert_rgb: Optional[bool]
+    do_thumbnail: Optional[bool]
+    do_align_long_axis: Optional[bool]
+    do_rescale: Optional[bool]
+
+
+class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False):
+    text_kwargs: PaliGemmaTextKwargs
+    image_kwargs: PaliGemmaImagesKwargs
+    _defaults = {
+        "text_kwargs": {
+            "tokenize_newline_separately": True,
+            "padding": False,
+        },
+        "image_kwargs": {
+            "data_format": "channels_first",
+        },
+    }
+
+
 class PaliGemmaProcessor(ProcessorMixin):
     r"""
     Constructs a PaliGemma processor which wraps a PaliGemma image processor and a PaliGemma tokenizer into a single processor.
@@ -124,25 +152,8 @@ def __call__(
         self,
         text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
         images: ImageInput = None,
-        tokenize_newline_separately: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length=None,
-        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
-        do_resize: bool = None,
-        do_normalize: bool = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        data_format: Optional["ChannelDimension"] = "channels_first",  # noqa: F821
-        input_data_format: Optional[
-            Union[str, "ChannelDimension"]  # noqa: F821
-        ] = None,
-        resample: "PILImageResampling" = None,  # noqa: F821
-        do_convert_rgb: bool = None,
-        do_thumbnail: bool = None,
-        do_align_long_axis: bool = None,
-        do_rescale: bool = None,
-        suffix: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
+        video=None,
+        **kwargs: Unpack[PaliGemmaProcessorKwargs],
     ) -> BatchFeature:
         """
         Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
@@ -216,7 +227,12 @@ def __call__(
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
             - **labels** -- Labels compatible with training if `suffix` is not None
         """
-
+        output_kwargs = self._merge_kwargs(
+            PaliGemmaProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        suffix = output_kwargs["text_kwargs"]["suffix"]
         return_token_type_ids = True if suffix is not None else False
 
         if images is None:
@@ -253,27 +269,14 @@ def __call__(
 
         pixel_values = self.image_processor(
             images,
-            do_resize=do_resize,
-            do_normalize=do_normalize,
-            return_tensors=return_tensors,
-            image_mean=image_mean,
-            image_std=image_std,
-            input_data_format=input_data_format,
-            data_format=data_format,
-            resample=resample,
-            do_convert_rgb=do_convert_rgb,
+            **kwargs["image_kwargs"],
         )["pixel_values"]
-
-        if max_length is not None:
-            max_length += self.image_seq_length  # max_length has to account for the image tokens
-
+        if output_kwargs["text_kwargs"].get("max_length", None) is not None:
+            output_kwargs["text_kwargs"]["max_length"] += self.image_seq_length
         inputs = self.tokenizer(
             input_strings,
             text_pair=suffix,
-            return_tensors=return_tensors,
-            padding=padding,
-            max_length=max_length,
-            truncation=truncation,
+            **output_kwargs["text_kwargs"],
             return_token_type_ids=return_token_type_ids,
         )
 

tests/models/paligemma/test_processing_paligemma.py

@@ -0,0 +1,209 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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 shutil
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import (
+        AutoProcessor,
+        PaliGemmaProcessor,
+        is_vision_available,
+    )
+
+
+@require_vision
+class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = PaliGemmaProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224")
+        processor.save_pretrained(self.tmpdirname)
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_image_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def prepare_image_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+
+        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
+
+        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
+
+        return image_inputs
+
+    # Some kwargs tests are overriden from common tests to handle shortest_edge
+    # and size_divisor behaviour
+
+    @require_torch
+    @require_vision
+    def test_image_processor_defaults_preserved_by_image_kwargs(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component(
+            "image_processor",
+            crop_size={"shortest_edge": 234, "longest_edge": 234},
+        )
+        tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        inputs = processor(text=input_str, images=image_input)
+        self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
+
+    @require_torch
+    @require_vision
+    def test_structured_kwargs_nested_from_dict(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        # Define the kwargs for each modality
+        all_kwargs = {
+            "common_kwargs": {"return_tensors": "pt"},
+            "images_kwargs": {"crop_size": {"shortest_edge": 214}, "data_format": "channels_first"},
+            "text_kwargs": {"padding": "max_length", "max_length": 76, "tokenize_newline_separately": False},
+        }
+
+        inputs = processor(text=input_str, images=image_input, **all_kwargs)
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
+    @require_torch
+    @require_vision
+    def test_kwargs_overrides_default_image_processor_kwargs(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor", crop_size={"shortest_edge": 234})
+        tokenizer = self.get_component("tokenizer", max_length=117)
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+        inputs = processor(text=input_str, images=image_input, crop_size={"shortest_edge": 224})
+        self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
+
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs_batched(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = ["lower newer", "upper older longer string"]
+        image_input = self.prepare_image_inputs() * 2
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            crop_size={"shortest_edge": 214},
+            padding="longest",
+            max_length=76,
+        )
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+
+        self.assertEqual(len(inputs["input_ids"][0]), 6)
+
+    @require_torch
+    @require_vision
+    def test_unstructured_kwargs(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            crop_size={"shortest_edge": 214},
+            padding="max_length",
+            max_length=76,
+        )
+
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(len(inputs["input_ids"][0]), 76)
+
+    @require_torch
+    @require_vision
+    def test_structured_kwargs_nested(self):
+        if "image_processor" not in self.processor_class.attributes:
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[TEST_PAD]"
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = "lower newer"
+        image_input = self.prepare_image_inputs()
+
+        # Define the kwargs for each modality
+        all_kwargs = {
+            "common_kwargs": {"return_tensors": "pt"},
+            "images_kwargs": {"crop_size": {"shortest_edge": 214}, "data_format": "channels_first"},
+            "text_kwargs": {"padding": "max_length", "max_length": 76, "tokenize_newline_separately": False},
+        }
+
+        inputs = processor(text=input_str, images=image_input, **all_kwargs)
+        self.skip_processor_without_typed_kwargs(processor)
+
+        self.assertEqual(inputs["pixel_values"].shape[2], 214)
+        self.assertEqual(len(inputs["input_ids"][0]), 76)