# 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 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) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # Check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @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): 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)