Add LlavaImageProcessor (#33191)

* First draft * Add equivalence test * Update docstrings * Add tests * Use numpy * Fix tests * Improve variable names * Improve docstring * Add link * Remove script * Add copied from * Address comment * Add note in docs * Add docstring, data format * Improve test * Add test * update * Update src/transformers/models/llava/image_processing_llava.py Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com> * Update src/transformers/models/llava/image_processing_llava.py Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com> * loop once only --------- Co-authored-by: raushan <raushan@huggingface.co> Co-authored-by: Raushan Turganbay <raushan.turganbay@alumni.nu.edu.kz> Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
huggingface · Jan 21, 2025 · 78f5ee0 · 78f5ee0
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diff --git a/docs/source/en/model_doc/llava.md b/docs/source/en/model_doc/llava.md
@@ -162,6 +162,16 @@ For multiple turns conversation:
 "USER: <image>\n<prompt1> ASSISTANT: <answer1></s>USER: <prompt2> ASSISTANT: <answer2></s>USER: <prompt3> ASSISTANT:"
 ```
 
+## Note regarding reproducing original implementation
+
+In order to match the logits of the [original implementation](https://github.com/haotian-liu/LLaVA/tree/main), one needs to additionally specify `do_pad=True` when instantiating `LLavaImageProcessor`:
+
+```python
+from transformers import LLavaImageProcessor
+
+image_processor = LLavaImageProcessor.from_pretrained("https://huggingface.co/llava-hf/llava-1.5-7b-hf", do_pad=True)
+```
+
 ### Using Flash Attention 2
 
 Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the [Flash Attention 2 section of performance docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one).
@@ -180,6 +190,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] LlavaConfig
 
+## LlavaImageProcessor
+
+[[autodoc]] LlavaImageProcessor
+    - preprocess
+
 ## LlavaProcessor
 
 [[autodoc]] LlavaProcessor

diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
@@ -1243,6 +1243,7 @@
     _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"])
     _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"])
     _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"])
+    _import_structure["models.llava"].append("LlavaImageProcessor")
     _import_structure["models.llava_next"].append("LlavaNextImageProcessor")
     _import_structure["models.llava_next_video"].append("LlavaNextVideoImageProcessor")
     _import_structure["models.llava_onevision"].extend(
@@ -6334,6 +6335,7 @@
             LayoutLMv3ImageProcessor,
         )
         from .models.levit import LevitFeatureExtractor, LevitImageProcessor
+        from .models.llava import LlavaImageProcessor
         from .models.llava_next import LlavaNextImageProcessor
         from .models.llava_next_video import LlavaNextVideoImageProcessor
         from .models.llava_onevision import LlavaOnevisionImageProcessor, LlavaOnevisionVideoProcessor

diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py
@@ -101,7 +101,7 @@
             ("layoutlmv2", ("LayoutLMv2ImageProcessor",)),
             ("layoutlmv3", ("LayoutLMv3ImageProcessor",)),
             ("levit", ("LevitImageProcessor",)),
-            ("llava", ("CLIPImageProcessor",)),
+            ("llava", ("LlavaImageProcessor",)),
             ("llava_next", ("LlavaNextImageProcessor",)),
             ("llava_next_video", ("LlavaNextVideoImageProcessor",)),
             ("llava_onevision", ("LlavaOnevisionImageProcessor",)),

diff --git a/src/transformers/models/llava/image_processing_llava.py b/src/transformers/models/llava/image_processing_llava.py
diff --git a/src/transformers/models/llava/processing_llava.py b/src/transformers/models/llava/processing_llava.py
@@ -39,13 +39,13 @@ class LlavaProcessorKwargs(ProcessingKwargs, total=False):
 
 class LlavaProcessor(ProcessorMixin):
     r"""
-    Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
+    Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor.
 
-    [`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`LlavaProcessor`] offers all the functionalities of [`LlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
     [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
 
     Args:
-        image_processor ([`CLIPImageProcessor`], *optional*):
+        image_processor ([`LlavaImageProcessor`], *optional*):
             The image processor is a required input.
         tokenizer ([`LlamaTokenizerFast`], *optional*):
             The tokenizer is a required input.

diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py
@@ -380,6 +380,13 @@ def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
+class LlavaImageProcessor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
 class LlavaNextImageProcessor(metaclass=DummyObject):
     _backends = ["vision"]
 

diff --git a/tests/models/llava/test_image_processing_llava.py b/tests/models/llava/test_image_processing_llava.py
@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# 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 unittest
+from typing import Tuple, Union
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import LlavaImageProcessor
+
+
+class LlavaImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_pad=True,
+        do_resize=True,
+        size=None,
+        do_center_crop=True,
+        crop_size=None,
+        do_normalize=True,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+        do_convert_rgb=True,
+    ):
+        size = size if size is not None else {"shortest_edge": 20}
+        crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_pad = do_pad
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_pad": self.do_pad,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Llava
+class LlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = LlavaImageProcessor if is_vision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = LlavaImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    # Ignore copy
+    def test_image_processor_properties(self):
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        self.assertTrue(hasattr(image_processing, "do_pad"))
+        self.assertTrue(hasattr(image_processing, "do_resize"))
+        self.assertTrue(hasattr(image_processing, "size"))
+        self.assertTrue(hasattr(image_processing, "do_center_crop"))
+        self.assertTrue(hasattr(image_processing, "center_crop"))
+        self.assertTrue(hasattr(image_processing, "do_normalize"))
+        self.assertTrue(hasattr(image_processing, "image_mean"))
+        self.assertTrue(hasattr(image_processing, "image_std"))
+        self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        self.assertEqual(image_processor.size, {"shortest_edge": 20})
+        self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
+
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+        self.assertEqual(image_processor.size, {"shortest_edge": 42})
+        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+
+    # Ignore copy
+    def test_padding(self):
+        """
+        LLaVA needs to pad images to square size before processing as per orig implementation.
+        Checks that image processor pads images correctly given different background colors.
+        """
+
+        # taken from original implementation: https://github.com/haotian-liu/LLaVA/blob/c121f0432da27facab705978f83c4ada465e46fd/llava/mm_utils.py#L152
+        def pad_to_square_original(
+            image: Image.Image, background_color: Union[int, Tuple[int, int, int]] = 0
+        ) -> Image.Image:
+            width, height = image.size
+            if width == height:
+                return image
+            elif width > height:
+                result = Image.new(image.mode, (width, width), background_color)
+                result.paste(image, (0, (width - height) // 2))
+                return result
+            else:
+                result = Image.new(image.mode, (height, height), background_color)
+                result.paste(image, ((height - width) // 2, 0))
+                return result
+
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+
+        # test with images in channel-last and channel-first format
+        for image in image_inputs:
+            padded_image = image_processor.pad_to_square(image)
+            padded_image_original = pad_to_square_original(Image.fromarray(image))
+            padded_image_original = np.array(padded_image_original)
+
+            np.testing.assert_allclose(padded_image, padded_image_original)
+
+            padded_image = image_processor.pad_to_square(image.transpose(2, 0, 1), input_data_format="channels_first")
+            padded_image = padded_image.transpose(1, 2, 0)
+
+            np.testing.assert_allclose(padded_image, padded_image_original)
+
+        # test background color
+        background_color = (122, 116, 104)
+        for image in image_inputs:
+            padded_image = image_processor.pad_to_square(image, background_color=background_color)
+            padded_image_original = pad_to_square_original(Image.fromarray(image), background_color=background_color)
+            padded_image_original = np.array(padded_image_original)
+
+            np.testing.assert_allclose(padded_image, padded_image_original)
+
+        background_color = 122
+        for image in image_inputs:
+            padded_image = image_processor.pad_to_square(image, background_color=background_color)
+            padded_image_original = pad_to_square_original(Image.fromarray(image), background_color=background_color)
+            padded_image_original = np.array(padded_image_original)
+
+            np.testing.assert_allclose(padded_image, padded_image_original)
+
+        # background color length should match channel length
+        with self.assertRaises(ValueError):
+            padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104))
+
+        with self.assertRaises(ValueError):
+            padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104, 0, 0))
+
+    @unittest.skip(reason="LLaVa does not support 4 channel images yet")
+    # Ignore copy
+    def test_call_numpy_4_channels(self):
+        pass