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fix
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@ -317,9 +317,9 @@ class MobileViTV2ModelIntegrationTest(unittest.TestCase):
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expected_shape = torch.Size((1, 1000))
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self.assertEqual(outputs.logits.shape, expected_shape)
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expected_slice = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(torch_device)
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expected_slice = torch.tensor([-1.6341, -0.0665, -0.5158]).to(torch_device)
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torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
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torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)
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@slow
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def test_inference_semantic_segmentation(self):
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@ -342,14 +342,14 @@ class MobileViTV2ModelIntegrationTest(unittest.TestCase):
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expected_slice = torch.tensor(
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[
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[[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
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[[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
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[[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
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[[7.0866, 7.1509, 6.8188], [6.6935, 6.8757, 6.8927], [6.2988, 7.0365, 6.9631]],
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[[-3.7113, -3.6686, -3.6643], [-3.5801, -3.3516, -3.4739], [-3.3432, -3.3966, -3.2832]],
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[[-2.9359, -2.8037, -2.7387], [-3.0595, -2.4798, -2.0222], [-2.6901, -1.9306, -1.7659]],
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],
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device=torch_device,
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)
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torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
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torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=2e-4, atol=2e-4)
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@slow
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def test_post_processing_semantic_segmentation(self):
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