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Update expected values (after switching to A10) - part 3 (#39179)

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---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-07-02 22:48:30 +02:00
committed by GitHub
parent 9326fc332d
commit 37a239ca50
12 changed files with 181 additions and 68 deletions
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14
tests/models/dpt/test_modeling_dpt.py
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@@ -18,7 +18,7 @@ import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
@@ -342,11 +342,15 @@ class DPTModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 384, 384))
self.assertEqual(predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]
).to(torch_device)
expectations = Expectations(
{
(None, None): [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]],
("cuda", 8): [[6.3215, 6.3635, 6.4155], [6.3863, 6.3622, 6.4174], [6.3530, 6.3184, 6.3583]],
}
)
expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
def test_inference_semantic_segmentation(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")

54
tests/models/dpt/test_modeling_dpt_auto_backbone.py
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@@ -17,7 +17,7 @@ import unittest
from transformers import Dinov2Config, DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.testing_utils import Expectations, require_torch, require_vision, slow, torch_device
from transformers.utils.import_utils import get_torch_major_and_minor_version
from ...test_configuration_common import ConfigTester
@@ -267,11 +267,15 @@ class DPTModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 576, 736))
self.assertEqual(predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[6.0336, 7.1502, 7.4130], [6.8977, 7.2383, 7.2268], [7.9180, 8.0525, 8.0134]]
).to(torch_device)
expectations = Expectations(
{
(None, None): [[6.0336, 7.1502, 7.4130], [6.8977, 7.2383, 7.2268], [7.9180, 8.0525, 8.0134]],
("cuda", 8): [[6.0350, 7.1518, 7.4144], [6.8992, 7.2396, 7.2280], [7.9194, 8.0538, 8.0145]],
}
)
expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
def test_inference_depth_estimation_beit(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384")
@@ -289,11 +293,23 @@ class DPTModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 384, 384))
self.assertEqual(predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[2669.7061, 2663.7144, 2674.9399], [2633.9326, 2650.9092, 2665.4270], [2621.8271, 2632.0129, 2637.2290]]
).to(torch_device)
expectations = Expectations(
{
(None, None): [
[2669.7061, 2663.7144, 2674.9399],
[2633.9326, 2650.9092, 2665.4270],
[2621.8271, 2632.0129, 2637.2290],
],
("cuda", 8): [
[2669.4292, 2663.4121, 2674.6233],
[2633.7400, 2650.7026, 2665.2085],
[2621.6572, 2631.8452, 2637.0525],
],
}
)
expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
def test_inference_depth_estimation_swinv2(self):
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
@@ -311,8 +327,20 @@ class DPTModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 256, 256))
self.assertEqual(predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[1032.7719, 1025.1886, 1030.2661], [1023.7619, 1021.0075, 1024.9121], [1022.5667, 1018.8522, 1021.4145]]
).to(torch_device)
expectations = Expectations(
{
(None, None): [
[1032.7719, 1025.1886, 1030.2661],
[1023.7619, 1021.0075, 1024.9121],
[1022.5667, 1018.8522, 1021.4145],
],
("cuda", 8): [
[1032.7170, 1025.0629, 1030.1941],
[1023.7309, 1020.9786, 1024.8594],
[1022.5233, 1018.8235, 1021.3312],
],
}
)
expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
torch.testing.assert_close(outputs.predicted_depth[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)

3
tests/models/dpt/test_modeling_dpt_hybrid.py
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@@ -194,6 +194,9 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
def test_config(self):
self.config_tester.run_common_tests()
def test_batching_equivalence(self, atol=2e-5, rtol=2e-5):
super().test_batching_equivalence(atol=atol, rtol=rtol)
@unittest.skip(reason="DPT does not use inputs_embeds")
def test_inputs_embeds(self):
pass