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transformers/tests/models/eomt/__init__.py Normal file
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transformers/tests/models/eomt/test_image_processing_eomt.py Normal file
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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 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))

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transformers/tests/models/eomt/test_modeling_eomt.py Normal file
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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=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_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, 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)