init

2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
--- a/transformers/tests/models/eomt/test_image_processing_eomt.py
+++ b/transformers/tests/models/eomt/test_image_processing_eomt.py
@@ -0,0 +1,310 @@
+# 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
+from datasets import load_dataset
+
+from transformers.image_utils import load_image
+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
+from ...test_processing_common import url_to_local_path
+
+
+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 = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
+
+        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 = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
+
+        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 = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
+
+        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))