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239 lines
10 KiB
Python
239 lines
10 KiB
Python
# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from typing import Union
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_vision_available():
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from PIL import Image
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from transformers import LlavaImageProcessor
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if is_torchvision_available():
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from torchvision.transforms import functional as F
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from transformers import LlavaImageProcessorFast
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class LlavaImageProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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image_size=18,
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min_resolution=30,
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max_resolution=400,
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do_pad=True,
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do_resize=True,
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size=None,
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do_center_crop=True,
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crop_size=None,
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do_normalize=True,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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do_convert_rgb=True,
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):
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super().__init__()
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size = size if size is not None else {"shortest_edge": 20}
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crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_pad = do_pad
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self.do_resize = do_resize
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self.size = size
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self.do_center_crop = do_center_crop
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self.crop_size = crop_size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_convert_rgb = do_convert_rgb
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def prepare_image_processor_dict(self):
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return {
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"do_pad": self.do_pad,
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"do_resize": self.do_resize,
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"size": self.size,
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_convert_rgb": self.do_convert_rgb,
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}
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
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def expected_output_image_shape(self, images):
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return self.num_channels, self.crop_size["height"], self.crop_size["width"]
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Llava
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class LlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = LlavaImageProcessor if is_vision_available() else None
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fast_image_processing_class = LlavaImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = LlavaImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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# Ignore copy
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def test_image_processor_properties(self):
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for image_processing_class in self.image_processor_list:
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image_processing = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "center_crop"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
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def test_image_processor_from_dict_with_kwargs(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"shortest_edge": 20})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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# Ignore copy
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def test_padding(self):
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"""
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LLaVA needs to pad images to square size before processing as per orig implementation.
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Checks that image processor pads images correctly given different background colors.
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"""
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# taken from original implementation: https://github.com/haotian-liu/LLaVA/blob/c121f0432da27facab705978f83c4ada465e46fd/llava/mm_utils.py#L152
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def pad_to_square_original(
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image: Image.Image, background_color: Union[int, tuple[int, int, int]] = 0
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) -> Image.Image:
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width, height = image.size
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if width == height:
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return image
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elif width > height:
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result = Image.new(image.mode, (width, width), background_color)
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result.paste(image, (0, (width - height) // 2))
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return result
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else:
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result = Image.new(image.mode, (height, height), background_color)
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result.paste(image, ((height - width) // 2, 0))
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return result
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for i, image_processing_class in enumerate(self.image_processor_list):
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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numpify = i == 0
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torchify = i == 1
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image_inputs = self.image_processor_tester.prepare_image_inputs(
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equal_resolution=False, numpify=numpify, torchify=torchify
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)
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# test with images in channel-last and channel-first format (only channel-first for torch)
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image)
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if i == 0:
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padded_image_original = pad_to_square_original(Image.fromarray(image))
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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padded_image = image_processor.pad_to_square(
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image.transpose(2, 0, 1), input_data_format="channels_first"
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)
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padded_image = padded_image.transpose(1, 2, 0)
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np.testing.assert_allclose(padded_image, padded_image_original)
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else:
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padded_image_original = pad_to_square_original(F.to_pil_image(image))
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padded_image = padded_image.permute(1, 2, 0)
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np.testing.assert_allclose(padded_image, padded_image_original)
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# test background color
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background_color = (122, 116, 104)
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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if i == 0:
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padded_image_original = pad_to_square_original(
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Image.fromarray(image), background_color=background_color
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)
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else:
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padded_image_original = pad_to_square_original(
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F.to_pil_image(image), background_color=background_color
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)
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padded_image = padded_image.permute(1, 2, 0)
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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background_color = 122
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for image in image_inputs:
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padded_image = image_processor.pad_to_square(image, background_color=background_color)
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if i == 0:
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padded_image_original = pad_to_square_original(
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Image.fromarray(image), background_color=background_color
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)
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else:
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padded_image_original = pad_to_square_original(
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F.to_pil_image(image), background_color=background_color
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)
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padded_image = padded_image.permute(1, 2, 0)
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padded_image_original = np.array(padded_image_original)
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np.testing.assert_allclose(padded_image, padded_image_original)
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# background color length should match channel length
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104))
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with self.assertRaises(ValueError):
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padded_image = image_processor.pad_to_square(image_inputs[0], background_color=(122, 104, 0, 0))
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@unittest.skip(reason="LLaVa does not support 4 channel images yet")
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# Ignore copy
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def test_call_numpy_4_channels(self):
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pass
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