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 @@ import numpy as np
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from tests.test_modeling_common import floats_tensor
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from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
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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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@@ -478,7 +479,7 @@ class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
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self.assertEqual(model.model.pixel_level_module.encoder.out_indices, [1, 2, 3])
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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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@@ -513,31 +514,43 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
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outputs = model(**inputs)
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expected_slice_hidden_state = torch.tensor(
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[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]
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[
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[-0.0482, 0.9228, 0.4951],
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[-0.2547, 0.8017, 0.8527],
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[-0.0069, 0.3385, -0.0089],
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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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)
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torch.allclose(outputs.encoder_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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[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]
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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): [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]],
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("cuda", 8): [
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[-0.8422, -0.8435, -0.9717],
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[-1.0145, -0.5564, -0.4195],
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[-1.0040, -0.4486, -0.1962],
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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.allclose(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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[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]
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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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[0.2852, -0.0159, 0.9735],
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[0.6254, 0.1858, 0.8529],
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[-0.0680, -0.4116, 1.8413],
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],
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("cuda", 8): [
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[0.2853, -0.0162, 0.9736],
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[0.6256, 0.1856, 0.8530],
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[-0.0679, -0.4118, 1.8416],
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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.allclose(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_instance_segmentation_head(self):
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model = (
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@@ -562,25 +575,42 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
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masks_queries_logits.shape,
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(1, model.config.decoder_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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[-1.3737124, -1.7724937, -1.9364233],
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[-1.5977281, -1.9867939, -2.1523695],
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[-1.5795398, -1.9269832, -2.093942],
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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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[-1.3737124, -1.7724937, -1.9364233],
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[-1.5977281, -1.9867939, -2.1523695],
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[-1.5795398, -1.9269832, -2.093942],
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],
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("cuda", 8): [
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[-1.3737, -1.7727, -1.9367],
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[-1.5979, -1.9871, -2.1527],
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[-1.5797, -1.9271, -2.0941],
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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(
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class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)
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)
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expected_slice = torch.tensor(
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[
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[1.6512e00, -5.2572e00, -3.3519e00],
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[3.6169e-02, -5.9025e00, -2.9313e00],
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[1.0766e-04, -7.7630e00, -5.1263e00],
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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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[1.6512e00, -5.2572e00, -3.3519e00],
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[3.6169e-02, -5.9025e00, -2.9313e00],
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[1.0766e-04, -7.7630e00, -5.1263e00],
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],
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("cuda", 8): [
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[1.6507e00, -5.2568e00, -3.3520e00],
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[3.5767e-02, -5.9023e00, -2.9313e00],
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[-6.2712e-04, -7.7627e00, -5.1268e00],
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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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@@ -608,17 +638,34 @@ class MaskFormerModelIntegrationTest(unittest.TestCase):
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masks_queries_logits.shape,
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(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4),
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)
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expected_slice = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
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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): [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]],
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("cuda", 8): [[-0.9000, -2.6283, -4.5964], [-3.4123, -5.7789, -8.7919], [-4.9132, -7.6444, -10.7557]],
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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(
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class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)
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)
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expected_slice = torch.tensor(
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[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]
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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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[4.7188, -3.2585, -2.8857],
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[6.6871, -2.9181, -1.2487],
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[7.2449, -2.2764, -2.1874],
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],
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("cuda", 8): [
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[4.7177, -3.2586, -2.8853],
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[6.6845, -2.9186, -1.2491],
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[7.2443, -2.2760, -2.1858],
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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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