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import pytest
import torch
from torch_geometric.utils import from_nested_tensor, to_nested_tensor
def test_to_nested_tensor():
x = torch.randn(5, 4, 3)
out = to_nested_tensor(x, batch=torch.tensor([0, 0, 1, 1, 1]))
out = out.to_padded_tensor(padding=0)
assert out.size() == (2, 3, 4, 3)
assert torch.allclose(out[0, :2], x[0:2])
assert torch.allclose(out[1, :3], x[2:5])
out = to_nested_tensor(x, ptr=torch.tensor([0, 2, 5]))
out = out.to_padded_tensor(padding=0)
assert out.size() == (2, 3, 4, 3)
assert torch.allclose(out[0, :2], x[0:2])
assert torch.allclose(out[1, :3], x[2:5])
out = to_nested_tensor(x)
out = out.to_padded_tensor(padding=0)
assert out.size() == (1, 5, 4, 3)
assert torch.allclose(out[0], x)
def test_from_nested_tensor():
x = torch.randn(5, 4, 3)
nested = to_nested_tensor(x, batch=torch.tensor([0, 0, 1, 1, 1]))
out, batch = from_nested_tensor(nested, return_batch=True)
assert torch.equal(x, out)
assert batch.tolist() == [0, 0, 1, 1, 1]
nested = torch.nested.nested_tensor([torch.randn(4, 3), torch.randn(5, 4)])
with pytest.raises(ValueError, match="have the same size in dimension 1"):
from_nested_tensor(nested)
# Test zero-copy:
nested = to_nested_tensor(x, batch=torch.tensor([0, 0, 1, 1, 1]))
out = from_nested_tensor(nested)
out += 1 # Increment in-place (which should increment `nested` as well).
assert torch.equal(nested.to_padded_tensor(padding=0)[0, :2], out[0:2])
assert torch.equal(nested.to_padded_tensor(padding=0)[1, :3], out[2:5])
def test_to_and_from_nested_tensor_autograd():
x = torch.randn(5, 4, 3, requires_grad=True)
grad = torch.randn_like(x)
out = to_nested_tensor(x, batch=torch.tensor([0, 0, 1, 1, 1]))
out = from_nested_tensor(out)
out.backward(grad)
assert torch.equal(x.grad, grad)
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