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Add AMD test expectations to DETR model (#39539)

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* Add AMD test expectations to DETR model

* Fix baseline expectation

* Address review comments

* Make formatting a bit more consistent
This commit is contained in:
Ákos Hadnagy
2025-07-22 14:07:10 +02:00
committed by GitHub
parent 30567c28e8
commit ef99537f37
1 changed files with 103 additions and 35 deletions
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106
tests/models/detr/test_modeling_detr.py
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@@ -18,7 +18,7 @@ import math
import unittest import unittest
from transformers import DetrConfig, ResNetConfig, is_torch_available, is_vision_available from transformers import DetrConfig, ResNetConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device from transformers.testing_utils import Expectations, require_timm, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester from ...test_configuration_common import ConfigTester
@@ -585,13 +585,23 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
expected_shape = torch.Size((1, 100, 256)) expected_shape = torch.Size((1, 100, 256))
assert outputs.last_hidden_state.shape == expected_shape assert outputs.last_hidden_state.shape == expected_shape
expected_slice = torch.tensor( expected_slices = Expectations(
{
(None, None):
[ [
[0.0622, -0.5142, -0.4034], [0.0622, -0.5142, -0.4034],
[-0.7628, -0.4935, -1.7153], [-0.7628, -0.4935, -1.7153],
[-0.4751, -0.6386, -0.7818], [-0.4751, -0.6386, -0.7818],
] ],
).to(torch_device) ("rocm", (9, 5)):
[
[ 0.0616, -0.5146, -0.4032],
[-0.7629, -0.4934, -1.7153],
[-0.4768, -0.6403, -0.7826],
],
}
) # fmt: skip
expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
def test_inference_object_detection_head(self): def test_inference_object_detection_head(self):
@@ -609,13 +619,23 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
# verify outputs # verify outputs
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1))
self.assertEqual(outputs.logits.shape, expected_shape_logits) self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor( expected_slices = Expectations(
{
(None, None):
[ [
[-19.1211, -0.0881, -11.0188], [-19.1211, -0.0881, -11.0188],
[-17.3641, -1.8045, -14.0229], [-17.3641, -1.8045, -14.0229],
[-20.0415, -0.5833, -11.1005], [-20.0415, -0.5833, -11.1005],
] ],
).to(torch_device) ("rocm", (9, 5)):
[
[-19.1194, -0.0893, -11.0154],
[-17.3640, -1.8035, -14.0219],
[-20.0461, -0.5837, -11.1060],
],
}
) # fmt: skip
expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4)
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
@@ -657,27 +677,65 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
# verify outputs # verify outputs
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1)) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels + 1))
self.assertEqual(outputs.logits.shape, expected_shape_logits) self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_slice_logits = torch.tensor( expected_slices = Expectations(
{
(None, None):
[ [
[-18.1523, -1.7592, -13.5019], [-18.1523, -1.7592, -13.5019],
[-16.8866, -1.4139, -14.1025], [-16.8866, -1.4139, -14.1025],
[-17.5735, -2.5090, -11.8666], [-17.5735, -2.5090, -11.8666],
] ],
).to(torch_device) ("rocm", (9, 5)):
[
[-18.1565, -1.7568, -13.5029],
[-16.8888, -1.4138, -14.1028],
[-17.5709, -2.5080, -11.8654],
],
}
) # fmt: skip
expected_slice_logits = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=2e-4, atol=2e-4)
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
expected_slice_boxes = torch.tensor( expected_slices = Expectations(
[[0.5344, 0.1790, 0.9284], [0.4421, 0.0571, 0.0875], [0.6632, 0.6886, 0.1015]] {
).to(torch_device) (None, None):
[
[0.5344, 0.1790, 0.9284],
[0.4421, 0.0571, 0.0875],
[0.6632, 0.6886, 0.1015]
],
("rocm", (9, 5)):
[
[0.5344, 0.1789, 0.9285],
[0.4420, 0.0572, 0.0875],
[0.6630, 0.6887, 0.1017],
],
}
) # fmt: skip
expected_slice_boxes = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=2e-4, atol=2e-4) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=2e-4, atol=2e-4)
expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267)) expected_shape_masks = torch.Size((1, model.config.num_queries, 200, 267))
self.assertEqual(outputs.pred_masks.shape, expected_shape_masks) self.assertEqual(outputs.pred_masks.shape, expected_shape_masks)
expected_slice_masks = torch.tensor( expected_slices = Expectations(
[[-7.8408, -11.0104, -12.1279], [-12.0299, -16.6498, -17.9806], [-14.8995, -19.9940, -20.5646]] {
).to(torch_device) (None, None):
[
[-7.8408, -11.0104, -12.1279],
[-12.0299, -16.6498, -17.9806],
[-14.8995, -19.9940, -20.5646],
],
("rocm", (9, 5)):
[
[ -7.7558, -10.8789, -11.9798],
[-11.8882, -16.4330, -17.7452],
[-14.7317, -19.7384, -20.3005],
],
}
) # fmt: skip
expected_slice_masks = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=2e-3, atol=2e-3) torch.testing.assert_close(outputs.pred_masks[0, 0, :3, :3], expected_slice_masks, rtol=2e-3, atol=2e-3)
# verify postprocessing # verify postprocessing
@@ -731,11 +789,21 @@ class DetrModelIntegrationTests(unittest.TestCase):
expected_shape = torch.Size((1, 100, 256)) expected_shape = torch.Size((1, 100, 256))
assert outputs.last_hidden_state.shape == expected_shape assert outputs.last_hidden_state.shape == expected_shape
expected_slice = torch.tensor( expected_slices = Expectations(
{
(None, None):
[ [
[0.0622, -0.5142, -0.4034], [0.0622, -0.5142, -0.4034],
[-0.7628, -0.4935, -1.7153], [-0.7628, -0.4935, -1.7153],
[-0.4751, -0.6386, -0.7818], [-0.4751, -0.6386, -0.7818],
] ],
).to(torch_device) ("rocm", (9, 5)):
[
[ 0.0616, -0.5146, -0.4032],
[-0.7629, -0.4934, -1.7153],
[-0.4768, -0.6403, -0.7826]
],
}
) # fmt: skip
expected_slice = torch.tensor(expected_slices.get_expectation(), device=torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)