# 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 from datasets import load_dataset from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torchvision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available() and is_torchvision_available(): from transformers import Sam2ImageProcessorFast class Sam2ImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, mask_size=None, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 20, "width": 20} mask_size = mask_size if mask_size is not None else {"height": 12, "width": 12} 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.mask_size = mask_size self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std 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, "mask_size": self.mask_size, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.size["width"] 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, ) # Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs def prepare_semantic_single_inputs(): ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") example = ds[0] return example["image"], example["map"] # Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs def prepare_semantic_batch_inputs(): ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") return list(ds["image"][:2]), list(ds["map"][:2]) @require_torch @require_vision class SamImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): fast_image_processing_class = Sam2ImageProcessorFast if is_torchvision_available() else None test_slow_image_processor = False def setUp(self): super().setUp() self.image_processor_tester = Sam2ImageProcessingTester(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, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) 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, "mask_size")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processing_class = image_processing_class(**self.image_processor_dict) image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 20, "width": 20}) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) def test_call_segmentation_maps(self): for image_processing_class in self.image_processor_list: # Initialize image_processor image_processor = 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) maps = [] for image in image_inputs: self.assertIsInstance(image, torch.Tensor) maps.append(torch.zeros(image.shape[-2:]).long()) # Test not batched input encoding = image_processor(image_inputs[0], maps[0], return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 1, self.image_processor_tester.mask_size["height"], self.image_processor_tester.mask_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test batched encoding = image_processor(image_inputs, maps, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.mask_size["height"], self.image_processor_tester.mask_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test not batched input (PIL images) image, segmentation_map = prepare_semantic_single_inputs() encoding = image_processor(image, segmentation_map, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 1, self.image_processor_tester.mask_size["height"], self.image_processor_tester.mask_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test batched input (PIL images) images, segmentation_maps = prepare_semantic_batch_inputs() encoding = image_processor(images, segmentation_maps, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 2, self.image_processor_tester.num_channels, self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 2, self.image_processor_tester.mask_size["height"], self.image_processor_tester.mask_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255)