# Copyright 2025 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 import numpy as np from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import Gemma3ImageProcessor if is_torchvision_available(): from transformers import Gemma3ImageProcessorFast class Gemma3ImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=IMAGENET_STANDARD_MEAN, image_std=IMAGENET_STANDARD_STD, do_convert_rgb=True, do_pan_and_scan=True, pan_and_scan_min_crop_size=10, pan_and_scan_max_num_crops=2, pan_and_scan_min_ratio_to_activate=1.2, ): super().__init__() size = size if 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_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb self.do_pan_and_scan = do_pan_and_scan self.pan_and_scan_min_crop_size = pan_and_scan_min_crop_size self.pan_and_scan_max_num_crops = pan_and_scan_max_num_crops self.pan_and_scan_min_ratio_to_activate = pan_and_scan_min_ratio_to_activate def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_convert_rgb": self.do_convert_rgb, "do_pan_and_scan": self.do_pan_and_scan, "pan_and_scan_min_crop_size": self.pan_and_scan_min_crop_size, "pan_and_scan_max_num_crops": self.pan_and_scan_max_num_crops, "pan_and_scan_min_ratio_to_activate": self.pan_and_scan_min_ratio_to_activate, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.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 class Gemma3ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = Gemma3ImageProcessor if is_vision_available() else None fast_image_processing_class = Gemma3ImageProcessorFast if is_torchvision_available() else None # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Gemma3 def setUp(self): super().setUp() self.image_processor_tester = Gemma3ImageProcessingTester(self) @property # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) 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")) self.assertTrue(hasattr(image_processing, "do_pan_and_scan")) self.assertTrue(hasattr(image_processing, "pan_and_scan_min_crop_size")) self.assertTrue(hasattr(image_processing, "pan_and_scan_max_num_crops")) self.assertTrue(hasattr(image_processing, "pan_and_scan_min_ratio_to_activate")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=84) self.assertEqual(image_processor.size, {"height": 84, "width": 84}) def test_without_pan_and_scan(self): """ Disable do_pan_and_scan parameter. """ for image_processing_class in self.image_processor_list: # Initialize image_processing image_processor = image_processing_class.from_dict(self.image_processor_dict, do_pan_and_scan=False) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_pan_and_scan(self): """ Enables Pan and Scan path by choosing the correct input image resolution. If you are changing image processor attributes for PaS, please update this test. """ for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random numpy tensors """This function prepares a list of PIL images""" image_inputs = [np.random.randint(255, size=(3, 300, 600), dtype=np.uint8)] * 3 image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] # Test not batched input, 3 images because we have base image + 2 crops encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (3, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched, 9 images because we have base image + 2 crops per each item encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (9, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched unbalanced, 9 images because we have base image + 2 crops per each item encoded_images = image_processing( [[image_inputs[0], image_inputs[1]], [image_inputs[2]]], return_tensors="pt" ).pixel_values expected_output_image_shape = (9, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_pil(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_numpy(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_pytorch(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) @unittest.skip("Gemma3 doesn't work with 4 channels due to pan and scan method") def test_call_numpy_4_channels(self): pass @require_vision @require_torch def test_slow_fast_equivalence_batched_pas(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop: self.skipTest( reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors" ) crop_config = { "do_pan_and_scan": True, "pan_and_scan_max_num_crops": 448, "pan_and_scan_min_crop_size": 32, "pan_and_scan_min_ratio_to_activate": 0.3, } image_processor_dict = self.image_processor_dict image_processor_dict.update(crop_config) dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) image_processor_slow = self.image_processing_class(**image_processor_dict) image_processor_fast = self.fast_image_processing_class(**image_processor_dict) encoding_slow = image_processor_slow(dummy_images, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, return_tensors="pt") torch.testing.assert_close(encoding_slow.num_crops, encoding_fast.num_crops) self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)