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beb9b5b022
* process flattened images in fast image proc * process flattened images in low proc and add tests * remove print * add unbalanced batch test pas image proc * fix integration tests
294 lines
14 KiB
Python
294 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2025 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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import numpy as np
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from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, 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_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import Gemma3ImageProcessor
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if is_torchvision_available():
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from transformers import Gemma3ImageProcessorFast
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class Gemma3ImageProcessingTester:
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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_resize=True,
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size=None,
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do_normalize=True,
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image_mean=IMAGENET_STANDARD_MEAN,
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image_std=IMAGENET_STANDARD_STD,
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do_convert_rgb=True,
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do_pan_and_scan=True,
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pan_and_scan_min_crop_size=10,
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pan_and_scan_max_num_crops=2,
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pan_and_scan_min_ratio_to_activate=1.2,
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):
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super().__init__()
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size = size if 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_resize = do_resize
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self.size = 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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self.do_pan_and_scan = do_pan_and_scan
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self.pan_and_scan_min_crop_size = pan_and_scan_min_crop_size
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self.pan_and_scan_max_num_crops = pan_and_scan_max_num_crops
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self.pan_and_scan_min_ratio_to_activate = pan_and_scan_min_ratio_to_activate
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.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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"do_pan_and_scan": self.do_pan_and_scan,
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"pan_and_scan_min_crop_size": self.pan_and_scan_min_crop_size,
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"pan_and_scan_max_num_crops": self.pan_and_scan_max_num_crops,
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"pan_and_scan_min_ratio_to_activate": self.pan_and_scan_min_ratio_to_activate,
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}
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def expected_output_image_shape(self, images):
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return self.num_channels, self.size["height"], self.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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class Gemma3ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = Gemma3ImageProcessor if is_vision_available() else None
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fast_image_processing_class = Gemma3ImageProcessorFast if is_torchvision_available() else None
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Gemma3
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def setUp(self):
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super().setUp()
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self.image_processor_tester = Gemma3ImageProcessingTester(self)
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@property
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
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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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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_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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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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self.assertTrue(hasattr(image_processing, "do_pan_and_scan"))
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self.assertTrue(hasattr(image_processing, "pan_and_scan_min_crop_size"))
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self.assertTrue(hasattr(image_processing, "pan_and_scan_max_num_crops"))
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self.assertTrue(hasattr(image_processing, "pan_and_scan_min_ratio_to_activate"))
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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, {"height": 18, "width": 18})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size=84)
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self.assertEqual(image_processor.size, {"height": 84, "width": 84})
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def test_without_pan_and_scan(self):
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"""
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Disable do_pan_and_scan parameter.
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"""
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processor = image_processing_class.from_dict(self.image_processor_dict, do_pan_and_scan=False)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_pan_and_scan(self):
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"""
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Enables Pan and Scan path by choosing the correct input image resolution. If you are changing
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image processor attributes for PaS, please update this test.
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"""
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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"""This function prepares a list of PIL images"""
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image_inputs = [np.random.randint(255, size=(3, 300, 600), dtype=np.uint8)] * 3
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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# Test not batched input, 3 images because we have base image + 2 crops
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (3, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched, 9 images because we have base image + 2 crops per each item
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (9, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched unbalanced, 9 images because we have base image + 2 crops per each item
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encoded_images = image_processing(
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[[image_inputs[0], image_inputs[1]], [image_inputs[2]]], return_tensors="pt"
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).pixel_values
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expected_output_image_shape = (9, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_pil(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 18, 18)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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@unittest.skip("Gemma3 doesn't work with 4 channels due to pan and scan method")
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def test_call_numpy_4_channels(self):
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pass
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@require_vision
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@require_torch
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def test_slow_fast_equivalence_batched_pas(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
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self.skipTest(
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reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
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)
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crop_config = {
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"do_pan_and_scan": True,
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"pan_and_scan_max_num_crops": 448,
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"pan_and_scan_min_crop_size": 32,
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"pan_and_scan_min_ratio_to_activate": 0.3,
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}
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image_processor_dict = self.image_processor_dict
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image_processor_dict.update(crop_config)
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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image_processor_slow = self.image_processing_class(**image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**image_processor_dict)
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encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
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torch.testing.assert_close(encoding_slow.num_crops, encoding_fast.num_crops)
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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