Update expected values (after switching to A10) - part 2 (#39165)
* fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * empty * [skip ci] * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
@@ -21,6 +21,7 @@ from tests.test_modeling_common import floats_tensor
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from transformers import AutoModelForImageClassification, Mask2FormerConfig, is_torch_available, is_vision_available
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from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4
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from transformers.testing_utils import (
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Expectations,
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require_timm,
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require_torch,
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require_torch_accelerator,
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@@ -403,7 +404,7 @@ class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
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)
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TOLERANCE = 1e-4
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TOLERANCE = 2e-4
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# We will verify our results on an image of cute cats
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@@ -438,31 +439,52 @@ class Mask2FormerModelIntegrationTest(unittest.TestCase):
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outputs = model(**inputs)
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expected_slice_hidden_state = torch.tensor(
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[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]
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[
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[-0.2790, -1.0717, -1.1668],
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[-0.5128, -0.3128, -0.4987],
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[-0.5832, 0.1971, -0.0197],
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]
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).to(torch_device)
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self.assertTrue(
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torch.allclose(
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outputs.encoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
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)
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torch.testing.assert_close(
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outputs.encoder_last_hidden_state[0, 0, :3, :3],
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expected_slice_hidden_state,
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atol=TOLERANCE,
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rtol=TOLERANCE,
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)
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expected_slice_hidden_state = torch.tensor(
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[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]
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).to(torch_device)
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self.assertTrue(
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torch.allclose(
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outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
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)
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expectations = Expectations(
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{
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(None, None): [
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[0.8973, 1.1847, 1.1776],
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[1.1934, 1.5040, 1.5128],
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[1.1153, 1.4486, 1.4951],
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],
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("cuda", 8): [
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[0.8974, 1.1848, 1.1777],
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[1.1933, 1.5041, 1.5128],
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[1.1154, 1.4487, 1.4950],
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],
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}
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)
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expected_slice_hidden_state = torch.tensor(expectations.get_expectation()).to(torch_device)
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torch.testing.assert_close(outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE,rtol=TOLERANCE) # fmt: skip
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expected_slice_hidden_state = torch.tensor(
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[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]
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).to(torch_device)
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self.assertTrue(
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torch.allclose(
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outputs.transformer_decoder_last_hidden_state[0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
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)
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expectations = Expectations(
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{
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(None, None): [
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[2.1152, 1.7000, -0.8603],
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[1.5808, 1.8004, -0.9353],
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[1.6043, 1.7495, -0.5999],
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],
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("cuda", 8): [
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[2.1153, 1.7004, -0.8604],
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[1.5807, 1.8007, -0.9354],
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[1.6040, 1.7498, -0.6001],
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],
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}
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)
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expected_slice_hidden_state = torch.tensor(expectations.get_expectation()).to(torch_device)
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torch.testing.assert_close(outputs.transformer_decoder_last_hidden_state[0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE, rtol=TOLERANCE) # fmt: skip
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def test_inference_universal_segmentation_head(self):
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model = Mask2FormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval()
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@@ -482,23 +504,40 @@ class Mask2FormerModelIntegrationTest(unittest.TestCase):
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self.assertEqual(
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masks_queries_logits.shape, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)
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)
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expected_slice = [
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[-8.7839, -9.0056, -8.8121],
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[-7.4104, -7.0313, -6.5401],
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[-6.6105, -6.3427, -6.4675],
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]
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expected_slice = torch.tensor(expected_slice).to(torch_device)
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expectations = Expectations(
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{
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(None, None): [
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[-8.7839, -9.0056, -8.8121],
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[-7.4104, -7.0313, -6.5401],
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[-6.6105, -6.3427, -6.4675],
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],
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("cuda", 8): [
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[-8.7809, -9.0041, -8.8087],
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[-7.4075, -7.0307, -6.5385],
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[-6.6088, -6.3417, -6.4627],
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],
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}
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)
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expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
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torch.testing.assert_close(masks_queries_logits[0, 0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)
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# class_queries_logits
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class_queries_logits = outputs.class_queries_logits
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self.assertEqual(class_queries_logits.shape, (1, model.config.num_queries, model.config.num_labels + 1))
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expected_slice = torch.tensor(
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[
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[1.8324, -8.0835, -4.1922],
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[0.8450, -9.0050, -3.6053],
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[0.3045, -7.7293, -3.0275],
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]
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).to(torch_device)
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expectations = Expectations(
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{
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(None, None): [
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[1.8324, -8.0835, -4.1922],
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[0.8450, -9.0050, -3.6053],
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[0.3045, -7.7293, -3.0275],
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],
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("cuda", 8): [
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[1.8326, -8.0834, -4.1916],
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[0.8446, -9.0048, -3.6048],
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[0.3042, -7.7296, -3.0277],
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],
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}
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)
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expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device)
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torch.testing.assert_close(
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outputs.class_queries_logits[0, :3, :3], expected_slice, rtol=TOLERANCE, atol=TOLERANCE
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)
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