# 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 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 MllamaImageProcessor if is_torchvision_available(): from transformers import MllamaImageProcessorFast if is_torch_available(): import torch class MllamaImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, num_images=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, max_image_tiles=4, ): size = size if size is not None else {"height": 224, "width": 224} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.max_image_tiles = max_image_tiles self.image_size = image_size self.num_images = num_images 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 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, "max_image_tiles": self.max_image_tiles, } 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, ): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. One can specify whether the images are of the same resolution or not. """ 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: # To avoid getting image width/height 0 if size_divisor is not None: # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor` 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: # PIL expects the channel dimension as last dimension 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] return images_list def expected_output_image_shape(self, images): expected_output_image_shape = ( max(len(images) for images in images), self.max_image_tiles, self.num_channels, self.size["height"], self.size["width"], ) return expected_output_image_shape @require_torch @require_vision class MllamaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = MllamaImageProcessor if is_vision_available() else None fast_image_processing_class = MllamaImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = MllamaImageProcessingTester(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, "max_image_tiles")) 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) expected_output_image_shape = ( max(len(images) for images in image_inputs), self.image_processor_tester.max_image_tiles, self.image_processor_tester.num_channels, self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ) # 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_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_channels_last(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # a white 1x1 pixel RGB image image_inputs = [[np.full(shape=(1, 1, 3), fill_value=1.0, dtype=float)]] encoded_images = image_processing( image_inputs, return_tensors="pt", input_data_format="channels_last" ).pixel_values expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) def test_ambiguous_channel_pil_image(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) image_inputs = [[Image.new("RGB", (1, 1))], [Image.new("RGB", (100, 1))]] 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), (2, *expected_output_image_shape)) def test_resize_impractical_aspect_ratio(self): for image_processing_class in self.image_processor_list: # Initialize image_processing image_processing = image_processing_class(**self.image_processor_dict) # Ensure that no error is raised even if the aspect ratio is impractical image_inputs = [[Image.new("RGB", (9999999, 1))]] 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), (1, *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_call_numpy_4_channels(self): self.skipTest("4 channels input is not supported yet") def test_image_correctly_tiled(self): def get_empty_tiles(pixel_values): # image has shape batch_size, max_num_images, max_image_tiles, num_channels, height, width # we want to get a binary mask of shape batch_size, max_num_images, max_image_tiles # of empty tiles, i.e. tiles that are completely zero return torch.all(pixel_values == 0, dim=(3, 4, 5)) for image_processing_class in self.image_processor_list: image_processor_dict = { **self.image_processor_dict, "size": {"height": 50, "width": 50}, "max_image_tiles": 4, } image_processor = image_processing_class(**image_processor_dict) # image fits 2x2 tiles grid (width x height) image = Image.new("RGB", (80, 95)) inputs = image_processor(image, return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist() self.assertEqual(empty_tiles, [False, False, False, False]) aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0] self.assertEqual(aspect_ratio_ids, 6) aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist() self.assertEqual(aspect_ratio_mask, [1, 1, 1, 1]) # image fits 3x1 grid (width x height) image = Image.new("RGB", (101, 50)) inputs = image_processor(image, return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist() self.assertEqual(empty_tiles, [False, False, False, True]) aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0] self.assertEqual(aspect_ratio_ids, 3) num_tiles = inputs.aspect_ratio_mask[0, 0].sum() self.assertEqual(num_tiles, 3) aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist() self.assertEqual(aspect_ratio_mask, [1, 1, 1, 0]) # image fits 1x1 grid (width x height) image = Image.new("RGB", (20, 39)) inputs = image_processor(image, return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist() self.assertEqual(empty_tiles, [False, True, True, True]) aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0] self.assertEqual(aspect_ratio_ids, 1) aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist() self.assertEqual(aspect_ratio_mask, [1, 0, 0, 0]) # image fits 2x1 grid (width x height) image = Image.new("RGB", (51, 20)) inputs = image_processor(image, return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist() self.assertEqual(empty_tiles, [False, False, True, True]) aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0] self.assertEqual(aspect_ratio_ids, 2) aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist() self.assertEqual(aspect_ratio_mask, [1, 1, 0, 0]) # image is greater than 2x2 tiles grid (width x height) image = Image.new("RGB", (150, 150)) inputs = image_processor(image, return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist() self.assertEqual(empty_tiles, [False, False, False, False]) aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0] self.assertEqual(aspect_ratio_ids, 6) # (2 - 1) * 4 + 2 = 6 aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist() self.assertEqual(aspect_ratio_mask, [1, 1, 1, 1]) # batch of images image1 = Image.new("RGB", (80, 95)) image2 = Image.new("RGB", (101, 50)) image3 = Image.new("RGB", (23, 49)) inputs = image_processor([[image1], [image2, image3]], return_tensors="pt") pixel_values = inputs.pixel_values empty_tiles = get_empty_tiles(pixel_values).tolist() expected_empty_tiles = [ # sample 1 with 1 image 2x2 grid [ [False, False, False, False], [True, True, True, True], # padding ], # sample 2 [ [False, False, False, True], # 3x1 [False, True, True, True], # 1x1 ], ] self.assertEqual(empty_tiles, expected_empty_tiles) aspect_ratio_ids = inputs.aspect_ratio_ids.tolist() expected_aspect_ratio_ids = [[6, 0], [3, 1]] self.assertEqual(aspect_ratio_ids, expected_aspect_ratio_ids) aspect_ratio_mask = inputs.aspect_ratio_mask.tolist() expected_aspect_ratio_mask = [ [ [1, 1, 1, 1], [1, 0, 0, 0], ], [ [1, 1, 1, 0], [1, 0, 0, 0], ], ] self.assertEqual(aspect_ratio_mask, expected_aspect_ratio_mask)