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import inspect
from typing import Any, Dict, Final, List, Optional, Set, Tuple, Union
import torch
from torch import Tensor
from torch_geometric.inspector import Inspector, Parameter, Signature
from torch_geometric.nn import GATConv, SAGEConv
from torch_geometric.typing import OptPairTensor
def test_eval_type() -> None:
inspector = Inspector(SAGEConv)
assert inspector.eval_type('Tensor') == Tensor
assert inspector.eval_type('List[Tensor]') == List[Tensor]
assert inspector.eval_type('Tuple[Tensor, int]') == Tuple[Tensor, int]
assert inspector.eval_type('Tuple[int, ...]') == Tuple[int, ...]
def test_type_repr() -> None:
inspector = Inspector(SAGEConv)
assert inspector.type_repr(Any) == 'typing.Any'
assert inspector.type_repr(Final) == 'typing.Final'
assert inspector.type_repr(OptPairTensor) == (
'Tuple[Tensor, Optional[Tensor]]')
assert inspector.type_repr(
Final[Optional[Tensor]]) == ('typing.Final[Optional[Tensor]]')
assert inspector.type_repr(Union[None, Tensor]) == 'Optional[Tensor]'
assert inspector.type_repr(Optional[Tensor]) == 'Optional[Tensor]'
assert inspector.type_repr(Set[Tensor]) == 'typing.Set[Tensor]'
assert inspector.type_repr(List) == 'List'
assert inspector.type_repr(Tuple) == 'Tuple'
assert inspector.type_repr(Set) == 'typing.Set'
assert inspector.type_repr(Dict) == 'typing.Dict'
assert inspector.type_repr(Dict[str, Tuple[Tensor, Tensor]]) == ( #
'typing.Dict[str, Tuple[Tensor, Tensor]]')
assert inspector.type_repr(Tuple[int, ...]) == 'Tuple[int, ...]'
assert inspector.type_repr(Union[int, str, None]) == ( #
'Union[int, str, None]')
def test_inspector_sage_conv() -> None:
inspector = Inspector(SAGEConv)
assert str(inspector) == 'Inspector(SAGEConv)'
assert inspector.implements('message')
assert inspector.implements('message_and_aggregate')
out = inspector.inspect_signature(SAGEConv.message)
assert isinstance(out, Signature)
assert out.param_dict == {
'x_j': Parameter('x_j', Tensor, 'Tensor', inspect._empty)
}
assert out.return_type == Tensor
assert inspector.get_flat_params(['message', 'message']) == [
Parameter('x_j', Tensor, 'Tensor', inspect._empty),
]
assert inspector.get_flat_param_names(['message']) == ['x_j']
kwargs = {'x_j': torch.randn(5), 'x_i': torch.randn(5)}
data = inspector.collect_param_data('message', kwargs)
assert len(data) == 1
assert torch.allclose(data['x_j'], kwargs['x_j'])
assert inspector.get_params_from_method_call(SAGEConv.propagate) == {
'x': Parameter('x', OptPairTensor, 'OptPairTensor', inspect._empty),
}
def test_inspector_gat_conv() -> None:
inspector = Inspector(GATConv)
assert str(inspector) == 'Inspector(GATConv)'
assert inspector.implements('message')
assert not inspector.implements('message_and_aggregate')
out = inspector.inspect_signature(GATConv.message)
assert isinstance(out, Signature)
assert out.param_dict == {
'x_j': Parameter('x_j', Tensor, 'Tensor', inspect._empty),
'alpha': Parameter('alpha', Tensor, 'Tensor', inspect._empty),
}
assert out.return_type == Tensor
assert inspector.get_flat_params(['message', 'message']) == [
Parameter('x_j', Tensor, 'Tensor', inspect._empty),
Parameter('alpha', Tensor, 'Tensor', inspect._empty),
]
assert inspector.get_flat_param_names(['message']) == ['x_j', 'alpha']
kwargs = {'x_j': torch.randn(5), 'alpha': torch.randn(5)}
data = inspector.collect_param_data('message', kwargs)
assert len(data) == 2
assert torch.allclose(data['x_j'], kwargs['x_j'])
assert torch.allclose(data['alpha'], kwargs['alpha'])
assert inspector.get_params_from_method_call(SAGEConv.propagate) == {
'x': Parameter('x', OptPairTensor, 'OptPairTensor', inspect._empty),
'alpha': Parameter('alpha', Tensor, 'Tensor', inspect._empty),
}
def test_get_params_from_method_call() -> None:
class FromMethodCall1:
propagate_type = {'x': Tensor}
inspector = Inspector(FromMethodCall1)
assert inspector.get_params_from_method_call('propagate') == {
'x': Parameter('x', Tensor, 'Tensor', inspect._empty),
}
class FromMethodCall2:
# propagate_type: (x: Tensor)
pass
inspector = Inspector(FromMethodCall2)
assert inspector.get_params_from_method_call('propagate') == {
'x': Parameter('x', Tensor, 'Tensor', inspect._empty),
}
class FromMethodCall3:
def forward(self) -> None:
self.propagate( # type: ignore
torch.randn(5, 5),
x=None,
size=None,
)
inspector = Inspector(FromMethodCall3)
exclude = [0, 'size']
assert inspector.get_params_from_method_call('propagate', exclude) == {
'x': Parameter('x', Tensor, 'Tensor', inspect._empty),
}
class FromMethodCall4:
pass
inspector = Inspector(FromMethodCall4)
assert inspector.get_params_from_method_call('propagate') == {}
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