# coding=utf-8 # 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.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 if is_vision_available(): from PIL import Image from transformers import Idefics2ImageProcessor if is_torchvision_available(): from transformers import Idefics2ImageProcessorFast if is_torch_available(): import torch class Idefics2ImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, num_images=1, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_convert_rgb=True, do_pad=True, do_image_splitting=True, ): size = size if size is not None else {"shortest_edge": 378, "longest_edge": 980} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.num_images = num_images 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_rescale = do_rescale self.rescale_factor = rescale_factor self.do_convert_rgb = do_convert_rgb self.do_pad = do_pad self.do_image_splitting = do_image_splitting def prepare_image_processor_dict(self): return { "do_convert_rgb": self.do_convert_rgb, "do_resize": self.do_resize, "size": self.size, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_pad": self.do_pad, "do_image_splitting": self.do_image_splitting, } def get_expected_values(self, image_inputs, batched=False): if not batched: shortest_edge = self.size["shortest_edge"] longest_edge = self.size["longest_edge"] image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size elif isinstance(image, np.ndarray): h, w = image.shape[0], image.shape[1] else: h, w = image.shape[1], image.shape[2] aspect_ratio = w / h if w > h and w >= longest_edge: w = longest_edge h = int(w / aspect_ratio) elif h > w and h >= longest_edge: h = longest_edge w = int(h * aspect_ratio) w = max(w, shortest_edge) h = max(h, shortest_edge) expected_height = h expected_width = w else: expected_values = [] for images in image_inputs: for image in images: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) effective_nb_images = self.num_images * 5 if self.do_image_splitting else 1 return effective_nb_images, self.num_channels, height, width def prepare_image_inputs( self, batch_size=None, min_resolution=None, max_resolution=None, num_channels=None, num_images=None, size_divisor=None, equal_resolution=False, numpify=False, torchify=False, ): assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" batch_size = batch_size if batch_size is not None else self.batch_size min_resolution = min_resolution if min_resolution is not None else self.min_resolution max_resolution = max_resolution if max_resolution is not None else self.max_resolution num_channels = num_channels if num_channels is not None else self.num_channels num_images = num_images if num_images is not None else self.num_images images_list = [] for i in range(batch_size): images = [] for j in range(num_images): if equal_resolution: width = height = max_resolution else: if size_divisor is not None: min_resolution = max(size_divisor, min_resolution) width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2) images.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8)) images_list.append(images) if not numpify and not torchify: images_list = [[Image.fromarray(np.moveaxis(image, 0, -1)) for image in images] for images in images_list] if torchify: images_list = [[torch.from_numpy(image) for image in images] for images in images_list] if numpify: images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list] return images_list @require_torch @require_vision class Idefics2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = Idefics2ImageProcessor if is_vision_available() else None fast_image_processing_class = Idefics2ImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = Idefics2ImageProcessingTester(self) @property 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_convert_rgb")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) 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_pad")) self.assertTrue(hasattr(image_processing, "do_image_splitting")) 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=False, numpify=True) for sample_images in image_inputs: for image in sample_images: 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape) ) def test_call_numpy_4_channels(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processor_dict = self.image_processor_dict image_processor_dict["image_mean"] = [0.5, 0.5, 0.5, 0.5] image_processor_dict["image_std"] = [0.5, 0.5, 0.5, 0.5] image_processing = image_processing_class(**image_processor_dict) # create random numpy tensors self.image_processor_tester.num_channels = 4 image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True) for sample_images in image_inputs: for image in sample_images: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing( image_inputs[0], input_data_format="channels_last", return_tensors="pt" ).pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing( image_inputs, input_data_format="channels_last", return_tensors="pt" ).pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *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=False) for images in image_inputs: for image in images: 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *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=False, torchify=True) for images in image_inputs: for image in images: 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # Test batched expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual( tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape), ) def test_image_splitting(self): for image_processing_class in self.image_processor_list: image_processor_dict = self.image_processor_dict.copy() image_processor_dict["do_image_splitting"] = True image_processing = image_processing_class(**image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs( equal_resolution=True, torchify=True, num_images=1 ) result = image_processing(image_inputs[0], return_tensors="pt") self.assertEqual(result.pixel_values.shape[1], 5) image_processor_dict["do_image_splitting"] = False image_processing = image_processing_class(**image_processor_dict) result = image_processing(image_inputs[0], return_tensors="pt") if len(result.pixel_values.shape) == 5: self.assertEqual(result.pixel_values.shape[1], 1) else: self.assertEqual(result.pixel_values.shape[1], self.image_processor_tester.num_channels) def test_pixel_attention_mask(self): for image_processing_class in self.image_processor_list: image_processor_dict = self.image_processor_dict.copy() image_processor_dict["do_pad"] = True image_processing = image_processing_class(**image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) result = image_processing(image_inputs, return_tensors="pt") self.assertIn("pixel_attention_mask", result) self.assertEqual(result.pixel_attention_mask.shape[-2:], result.pixel_values.shape[-2:]) image_processor_dict["do_pad"] = False image_processor_dict["do_image_splitting"] = False image_processing = image_processing_class(**image_processor_dict) equal_size_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) result = image_processing(equal_size_inputs, return_tensors="pt") self.assertNotIn("pixel_attention_mask", result) def test_convert_rgb(self): for image_processing_class in self.image_processor_list: rgba_image = Image.new("RGBA", (100, 100), (255, 0, 0, 128)) # Test with do_convert_rgb=True - this should work for all processors image_processor_dict = self.image_processor_dict.copy() image_processor_dict["do_convert_rgb"] = True image_processing = image_processing_class(**image_processor_dict) result = image_processing([rgba_image], return_tensors="pt") self.assertIsNotNone(result.pixel_values) rgb_image = rgba_image.convert("RGB") image_processor_dict["do_convert_rgb"] = False image_processing = image_processing_class(**image_processor_dict) # Use the RGB image instead of RGBA when do_convert_rgb=False result = image_processing([rgb_image], return_tensors="pt") self.assertIsNotNone(result.pixel_values) # Additional test: verifying proper handling of regular RGB images rgb_image = Image.new("RGB", (100, 100), (255, 0, 0)) result = image_processing([rgb_image], return_tensors="pt") self.assertIsNotNone(result.pixel_values) def test_slow_fast_equivalence_batched(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" ) dummy_images = self.image_processor_tester.prepare_image_inputs( equal_resolution=False, num_images=5, torchify=True ) # pop some images to have non homogenous batches: indices_to_pop = [i if np.random.random() < 0.5 else None for i in range(len(dummy_images))] for i in indices_to_pop: if i is not None: dummy_images[i].pop() image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) encoding_slow = image_processor_slow(dummy_images, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) self._assert_slow_fast_tensors_equivalence( encoding_slow.pixel_attention_mask.float(), encoding_fast.pixel_attention_mask.float() )