# Copyright 2026 The HuggingFace Inc. team. All rights reserved. # # 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 import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from PIL import Image if is_torch_available(): import torch class SLANeXtImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=10, max_resolution=400, do_resize=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225], do_pad=True, ): size = size if size is not None else {"height": 512, "width": 512} 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_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_pad = do_pad def prepare_image_processor_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "do_pad": self.do_pad, } 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, ) def get_expected_value(self, image_inputs): image = image_inputs[0] if isinstance(image, Image.Image): width, height = image.size elif isinstance(image, np.ndarray): height, width = image.shape[0], image.shape[1] else: height, width = image.shape[1], image.shape[2] target_size = max(self.size["height"], self.size["width"]) scale = target_size / max(height, width) resize_height = round(height * scale) resize_width = round(width * scale) if self.do_pad: pad_height = max(target_size, resize_height) pad_width = max(target_size, resize_width) return pad_height, pad_width return resize_height, resize_width def expected_output_image_shape(self, images): height, width = self.get_expected_value(images) return self.num_channels, height, width @require_torch @require_vision class SLANeXtImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): def setUp(self): super().setUp() self.image_processor_tester = SLANeXtImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() # SLANeXt resizes images adaptively based on aspect ratio, leading to inconsistent output sizes across a batch. # Override to skip batched input tests. def test_call_pytorch(self): for image_processing_class in self.image_processing_classes.values(): # 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 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # SLANeXt resizes images adaptively based on aspect ratio, leading to inconsistent output sizes across a batch. # Override to skip batched input tests. def test_call_numpy(self): for image_processing_class in self.image_processing_classes.values(): # 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 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) # SLANeXt resizes images adaptively based on aspect ratio, leading to inconsistent output sizes across a batch. # Override to skip batched input tests. def test_call_pil(self): for image_processing_class in self.image_processing_classes.values(): # 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 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 = self.image_processor_tester.expected_output_image_shape([image_inputs[0]]) self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape)) @unittest.skip(reason="SLANeXtImageProcessorFast does not support 4 channel images yet") def test_call_numpy_4_channels(self): pass