# Copyright 2025 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. """Testing suite for the PyTorch EoMT Image Processor.""" import unittest import numpy as np import requests from datasets import load_dataset 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 EomtImageProcessor if is_torchvision_available(): from transformers import EomtImageProcessorFast from transformers.models.eomt.modeling_eomt import EomtForUniversalSegmentationOutput class EomtImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, size=None, do_resize=True, do_pad=True, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], num_labels=10, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.do_pad = do_pad self.size = size if size is not None else {"shortest_edge": 18, "longest_edge": 18} self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std # for the post_process_functions self.batch_size = 2 self.num_queries = 3 self.num_classes = 2 self.height = 18 self.width = 18 self.num_labels = num_labels 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_pad": self.do_pad, "num_labels": self.num_labels, } def prepare_fake_eomt_outputs(self, batch_size, patch_offsets=None): return EomtForUniversalSegmentationOutput( masks_queries_logits=torch.randn((batch_size, self.num_queries, self.height, self.width)), class_queries_logits=torch.randn((batch_size, self.num_queries, self.num_classes + 1)), patch_offsets=patch_offsets, ) 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 prepare_semantic_single_inputs(): ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") example = ds[0] return example["image"], example["map"] 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 EomtImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = EomtImageProcessor if is_vision_available() else None fast_image_processing_class = EomtImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = EomtImageProcessingTester(self) self.model_id = "tue-mps/coco_panoptic_eomt_large_640" @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, "resample")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"shortest_edge": 42}) 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 = (2, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) @unittest.skip(reason="Not supported") def test_call_numpy_4_channels(self): pass def test_call_pil(self): image_processing = self.image_processing_class(**self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test Non 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 = (2, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_pytorch(self): image_processing = self.image_processing_class(**self.image_processor_dict) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) 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) encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (2, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_slow_fast_equivalence(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") dummy_image, dummy_map = prepare_semantic_single_inputs() image_processor_slow = self.image_processing_class(**self.image_processor_dict) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict) image_encoding_slow = image_processor_slow(dummy_image, segmentation_maps=dummy_map, return_tensors="pt") image_encoding_fast = image_processor_fast(dummy_image, segmentation_maps=dummy_map, return_tensors="pt") self.assertTrue(torch.allclose(image_encoding_slow.pixel_values, image_encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(image_encoding_slow.pixel_values - image_encoding_fast.pixel_values)).item(), 1e-3 ) # Lets check whether 99.9% of mask_labels values match or not. match_ratio = (image_encoding_slow.mask_labels[0] == image_encoding_fast.mask_labels[0]).float().mean().item() self.assertGreaterEqual(match_ratio, 0.999, "Mask labels do not match between slow and fast image processor.") 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, dummy_maps = prepare_semantic_batch_inputs() 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, segmentation_maps=dummy_maps, return_tensors="pt") encoding_fast = image_processor_fast(dummy_images, segmentation_maps=dummy_maps, return_tensors="pt") self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1)) self.assertLessEqual( torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3 ) for idx in range(len(dummy_maps)): match_ratio = (encoding_slow.mask_labels[idx] == encoding_fast.mask_labels[idx]).float().mean().item() self.assertGreaterEqual( match_ratio, 0.999, "Mask labels do not match between slow and fast image processors." ) def test_post_process_semantic_segmentation(self): processor = self.image_processing_class(**self.image_processor_dict) # Set longest_edge to None to test for semantic segmentatiom. processor.size = {"shortest_edge": 18, "longest_edge": None} image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor(images=image, do_split_image=True, return_tensors="pt") patch_offsets = inputs["patch_offsets"] target_sizes = [image.size[::-1]] # For semantic segmentation, the BS of output is 2 coz, two patches are created for the image. outputs = self.image_processor_tester.prepare_fake_eomt_outputs(inputs["pixel_values"].shape[0], patch_offsets) segmentation = processor.post_process_semantic_segmentation(outputs, target_sizes) self.assertEqual(segmentation[0].shape, (image.height, image.width)) def test_post_process_panoptic_segmentation(self): processor = self.image_processing_class(**self.image_processor_dict) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) original_sizes = [image.size[::-1], image.size[::-1]] # lets test for batched input of 2 outputs = self.image_processor_tester.prepare_fake_eomt_outputs(2) segmentation = processor.post_process_panoptic_segmentation(outputs, original_sizes) self.assertTrue(len(segmentation) == 2) for el in segmentation: self.assertTrue("segmentation" in el) self.assertTrue("segments_info" in el) self.assertEqual(type(el["segments_info"]), list) self.assertEqual(el["segmentation"].shape, (image.height, image.width)) def test_post_process_instance_segmentation(self): processor = self.image_processing_class(**self.image_processor_dict) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) original_sizes = [image.size[::-1], image.size[::-1]] # lets test for batched input of 2 outputs = self.image_processor_tester.prepare_fake_eomt_outputs(2) segmentation = processor.post_process_instance_segmentation(outputs, original_sizes) self.assertTrue(len(segmentation) == 2) for el in segmentation: self.assertTrue("segmentation" in el) self.assertTrue("segments_info" in el) self.assertEqual(type(el["segments_info"]), list) self.assertEqual(el["segmentation"].shape, (image.height, image.width))