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tests/models/eomt/test_modeling_eomt.py

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+# 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 model."""
+
+import unittest
+
+import requests
+
+from transformers import AutoImageProcessor, EomtConfig, EomtForUniversalSegmentation, pipeline
+from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class EomtForUniversalSegmentationTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        is_training=True,
+        image_size=40,
+        patch_size=2,
+        num_queries=5,
+        num_register_tokens=19,
+        num_labels=4,
+        hidden_size=8,
+        num_attention_heads=2,
+        num_hidden_layers=2,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.is_training = is_training
+        self.num_queries = num_queries
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_labels = num_labels
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.num_register_tokens = num_register_tokens
+
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1 + self.num_register_tokens
+
+    def get_config(self):
+        config = {
+            "image_size": self.image_size,
+            "patch_size": self.patch_size,
+            "num_labels": self.num_labels,
+            "hidden_size": self.hidden_size,
+            "num_attention_heads": self.num_attention_heads,
+            "num_hidden_layers": self.num_hidden_layers,
+            "num_register_tokens": self.num_register_tokens,
+            "num_queries": self.num_queries,
+            "num_blocks": 1,
+        }
+        return EomtConfig(**config)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, 3, self.image_size, self.image_size]).to(torch_device)
+
+        mask_labels = (
+            torch.rand([self.batch_size, self.num_labels, self.image_size, self.image_size], device=torch_device) > 0.5
+        ).float()
+        class_labels = (torch.rand((self.batch_size, self.num_labels), device=torch_device) > 0.5).long()
+
+        config = self.get_config()
+        return config, pixel_values, mask_labels, class_labels
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, mask_labels, class_labels = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+    def prepare_config_and_inputs_for_training(self):
+        config, pixel_values, mask_labels, class_labels = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values, "mask_labels": mask_labels, "class_labels": class_labels}
+        return config, inputs_dict
+
+
+@require_torch
+class EomtForUniversalSegmentationTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (EomtForUniversalSegmentation,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-segmentation": EomtForUniversalSegmentation} if is_torch_available() else {}
+    is_encoder_decoder = False
+
+    test_missing_keys = False
+    test_torch_exportable = False
+
+    def setUp(self):
+        self.model_tester = EomtForUniversalSegmentationTester(self)
+        self.config_tester = ConfigTester(self, config_class=EomtConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model_with_labels(self):
+        size = (self.model_tester.image_size,) * 2
+        inputs = {
+            "pixel_values": torch.randn((2, 3, *size), device=torch_device),
+            "mask_labels": torch.randn((2, 10, *size), device=torch_device),
+            "class_labels": torch.zeros(2, 10, device=torch_device).long(),
+        }
+        config = self.model_tester.get_config()
+
+        model = EomtForUniversalSegmentation(config).to(torch_device)
+        outputs = model(**inputs)
+        self.assertTrue(outputs.loss is not None)
+
+    @unittest.skip(reason="EoMT does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="EoMT does not have a get_input_embeddings method")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="EoMT is not a generative model")
+    def test_generate_without_input_ids(self):
+        pass
+
+    @unittest.skip(reason="EoMT does not use token embeddings")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    def test_training(self):
+        # We override this test because EoMT requires `mask_labels` and `class_labels` for training,
+        # which are not standard labels that `_prepare_for_class` can generate. We can't include
+        # these labels in `prepare_config_and_inputs_for_common` because that would break determinism
+        # tests (the Hungarian matching in the loss computation is non-deterministic).
+        if not self.model_tester.is_training:
+            self.skipTest(reason="ModelTester is not configured to run training tests")
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_training()
+            config.return_dict = True
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+
+@require_torch
+class EomtForUniversalSegmentationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_id = "tue-mps/coco_panoptic_eomt_large_640"
+
+    @slow
+    def test_inference(self):
+        model = EomtForUniversalSegmentation.from_pretrained(self.model_id, device_map="auto")
+        processor = AutoImageProcessor.from_pretrained(self.model_id)
+
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+        with torch.inference_mode():
+            outputs = model(**inputs)
+
+        self.assertTrue(outputs.class_queries_logits.shape == (1, 200, 134))
+        self.assertTrue(outputs.masks_queries_logits.shape == (1, 200, 160, 160))
+
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor([
+            [ 13.2540,   8.9279,   8.6631,  12.3760,  10.1429],
+            [ -3.4815, -36.4630, -45.5604, -46.8404, -37.5099],
+            [ -6.8689, -44.4206, -62.7591, -59.2928, -47.7035],
+            [ -2.9380, -42.0659, -57.4382, -55.1537, -43.5142],
+            [ -8.4387, -38.5275, -53.1383, -47.0064, -38.9667],
+        ]).to(model.device)
+        # fmt: on
+
+        output_slice = outputs.masks_queries_logits[0, 0, :5, :5]
+        torch.testing.assert_close(output_slice, EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
+
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor([
+            [-0.6977, -6.4907, -4.1178, -6.5554, -6.6529],
+            [-0.3650, -6.6560, -4.0143, -6.5776, -6.5879],
+            [-0.8820, -6.7175, -3.5334, -6.8569, -6.2415],
+            [ 0.4502, -5.3911, -3.0232, -5.9411, -6.3243],
+            [ 0.3157, -5.6321, -2.6716, -5.5740, -5.5607],
+        ]).to(model.device)
+        # fmt: on
+
+        output_slice = outputs.class_queries_logits[0, :5, :5]
+        torch.testing.assert_close(output_slice, EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
+
+    @require_torch_accelerator
+    @require_torch_fp16
+    @slow
+    def test_inference_fp16(self):
+        model = EomtForUniversalSegmentation.from_pretrained(self.model_id, dtype=torch.float16, device_map="auto")
+        processor = AutoImageProcessor.from_pretrained(self.model_id)
+
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+        with torch.inference_mode():
+            outputs = model(**inputs)
+
+        self.assertTrue(outputs.class_queries_logits.shape == (1, 200, 134))
+        self.assertTrue(outputs.masks_queries_logits.shape == (1, 200, 160, 160))
+
+    @slow
+    def test_semantic_segmentation_inference(self):
+        model_id = "tue-mps/ade20k_semantic_eomt_large_512"
+        model = EomtForUniversalSegmentation.from_pretrained(model_id, device_map="auto")
+        processor = AutoImageProcessor.from_pretrained(model_id)
+
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+        with torch.inference_mode():
+            outputs = model(**inputs)
+
+        self.assertTrue(outputs.class_queries_logits.shape == (2, 100, 151))
+        self.assertTrue(outputs.masks_queries_logits.shape == (2, 100, 128, 128))
+
+        preds = processor.post_process_semantic_segmentation(outputs, target_sizes=[(image.size[1], image.size[0])])[0]
+
+        self.assertTrue(preds.shape == (image.size[1], image.size[0]))
+
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor([
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39],
+            [39, 39, 39, 39, 39, 39, 39, 39, 39, 39]
+        ], device=model.device)
+        # fmt: on
+
+        output_slice = preds[:10, :10]
+        torch.testing.assert_close(output_slice, EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
+
+    @slow
+    def test_panoptic_segmentation_inference(self):
+        model = EomtForUniversalSegmentation.from_pretrained(self.model_id, device_map="auto")
+        processor = AutoImageProcessor.from_pretrained(self.model_id)
+
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+        with torch.inference_mode():
+            outputs = model(**inputs)
+
+        self.assertTrue(outputs.class_queries_logits.shape == (1, 200, 134))
+        self.assertTrue(outputs.masks_queries_logits.shape == (1, 200, 160, 160))
+
+        preds = processor.post_process_panoptic_segmentation(outputs, target_sizes=[(image.size[1], image.size[0])])[0]
+        segmentation, segments_info = preds["segmentation"], preds["segments_info"]
+
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor([
+            [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+            [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+            [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+            [-1, -1, -1, -1, -1,  2,  2,  2,  2,  2],
+            [-1, -1, -1,  2,  2,  2,  2,  2,  2,  2],
+            [ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2],
+            [ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2],
+            [ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2],
+            [ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2],
+            [ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2]
+        ], device=model.device)
+
+        EXPECTED_SEGMENTS_INFO = [
+            {"id": 0, "label_id": 15, "score": 0.99935},
+            {"id": 1, "label_id": 15, "score": 0.998688},
+            {"id": 2, "label_id": 57, "score": 0.954325},
+            {"id": 3, "label_id": 65, "score": 0.997285},
+            {"id": 4, "label_id": 65, "score": 0.99711}
+        ]
+        # fmt: on
+
+        output_slice = segmentation[:10, :10]
+        torch.testing.assert_close(output_slice, EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
+        for actual, expected in zip(segments_info, EXPECTED_SEGMENTS_INFO):
+            self.assertEqual(actual["id"], expected["id"])
+            self.assertEqual(actual["label_id"], expected["label_id"])
+            self.assertAlmostEqual(actual["score"], expected["score"], delta=1e-3)
+
+    @slow
+    def test_instance_segmentation_inference(self):
+        model_id = "tue-mps/coco_instance_eomt_large_640"
+        model = EomtForUniversalSegmentation.from_pretrained(model_id, device_map="auto")
+        processor = AutoImageProcessor.from_pretrained(model_id)
+
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        inputs = processor(images=image, return_tensors="pt").to(model.device)
+
+        with torch.inference_mode():
+            outputs = model(**inputs)
+
+        self.assertTrue(outputs.class_queries_logits.shape == (1, 200, 81))
+        self.assertTrue(outputs.masks_queries_logits.shape == (1, 200, 160, 160))
+
+        preds = processor.post_process_instance_segmentation(outputs, target_sizes=[(image.size[1], image.size[0])])[0]
+        segmentation, segments_info = preds["segmentation"], preds["segments_info"]
+
+        # fmt: off
+        EXPECTED_SLICE = torch.tensor([
+            [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
+            [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
+            [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
+            [-1., -1., -1.,  0.,  0.,  1.,  1.,  1.,  1.,  1.],
+            [ 0.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
+            [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
+            [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
+            [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
+            [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],
+            [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]
+        ], device=model.device)
+
+        EXPECTED_SEGMENTS_INFO = [
+            {'id': 0, 'label_id': 57, 'score': 0.871247},
+            {'id': 1, 'label_id': 57, 'score': 0.821225},
+            {'id': 2, 'label_id': 15, 'score': 0.976252},
+            {'id': 3, 'label_id': 65, 'score': 0.972960},
+            {'id': 4, 'label_id': 65, 'score': 0.981109},
+            {'id': 5, 'label_id': 15, 'score': 0.972689}
+        ]
+        # fmt: on
+
+        output_slice = segmentation[:10, :10]
+        torch.testing.assert_close(output_slice, EXPECTED_SLICE, rtol=1e-2, atol=1e-2)
+        for actual, expected in zip(segments_info, EXPECTED_SEGMENTS_INFO):
+            self.assertEqual(actual["id"], expected["id"])
+            self.assertEqual(actual["label_id"], expected["label_id"])
+            self.assertAlmostEqual(actual["score"], expected["score"], delta=1e-3)
+
+    @slow
+    def test_segmentation_pipeline(self):
+        image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
+
+        pipe = pipeline(model=self.model_id, subtask="panoptic", device=torch_device)
+        output = pipe(image)
+
+        EXPECTED_OUTPUT_LABELS = ["cat", "cat", "couch", "remote", "remote"]
+
+        output_labels = [segment["label"] for segment in output]
+        self.assertEqual(output_labels, EXPECTED_OUTPUT_LABELS)