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transformers/tests/models/eomt/test_modeling_eomt.py
Yaswanth Gali b61023a1b7
🚨🚨🚨 [eomt] make EoMT compatible with pipeline (#39122) 
* Make EoMT compatible with pipeline

* Implicit patch offsets

* remove patch offsets from arg

* Modify tests

* Update example

* fix proc testcase

* Add few more args

* add pipeline test suite

* fix

* docstring fixes

* add pipeline test

* changes w.r.t review

* 🙈 MB

* should fix device mismatch

* debug

* Fixes device mismatch

* use decorator

* we can split mlp

* expected values update

---------

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
2025-07-02 12:25:26 +01:00

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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, _config_zero_init, 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=4,
):
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_pruning = False
test_head_masking = 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()
def test_initialization(self):
# Apart from the below params, all other parameters are initialized using kaiming uniform.
non_uniform_init_parms = [
"layernorm.bias",
"layernorm.weight",
"norm1.bias",
"norm1.weight",
"norm2.bias",
"norm2.weight",
"layer_scale1.lambda1",
"layer_scale2.lambda1",
"register_tokens",
"cls_token",
]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if any(x in name for x in non_uniform_init_parms):
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@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, torch_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)
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