3047dc9b500266d8197139fad5ef3a8a4a459985,test/torchaudio_unittest/functional/batch_consistency_test.py,TestFunctional,assert_batch_consistency,#TestFunctional#Any#Any#,16

Before Change

        // run then batch the result
        torch.random.manual_seed(seed)
        expected = functional(tensor.clone(), *args, **kwargs)
        expected = expected.repeat([batch_size] + [1] * expected.dim())

        // batch the input and run
        torch.random.manual_seed(seed)
        pattern = [batch_size] + [1] * tensor.dim()

After Change

        torch.random.manual_seed(seed)
        items_input = batch.clone()
        items_result = torch.stack([
            functional(items_input[i], *args, **kwargs) for i in range(n)
        ])

        // Batch the input and run
        torch.random.manual_seed(seed)

Instances