Attribute¶
- class torch.jit.Attribute(value, type)[source]¶
This method is a pass-through function that returns value, mostly used to indicate to the TorchScript compiler that the left-hand side expression is a class instance attribute with type of type. Note that torch.jit.Attribute should only be used in __init__ method of jit.ScriptModule subclasses.
Though TorchScript can infer correct type for most Python expressions, there are some cases where type inference can be wrong, including:
Empty containers like [] and {}, which TorchScript assumes to be container of Tensor
Optional types like Optional[T] but assigned a valid value of type T, TorchScript would assume it is type T rather than Optional[T]
In eager mode, it is simply a pass-through function that returns value without other implications.
Example:
import torch from typing import Dict class AttributeModule(torch.jit.ScriptModule): def __init__(self) -> None: super().__init__() self.foo = torch.jit.Attribute(0.1, float) # we should be able to use self.foo as a float here assert 0.0 < self.foo self.names_ages = torch.jit.Attribute({}, Dict[str, int]) self.names_ages["someone"] = 20 assert isinstance(self.names_ages["someone"], int) m = AttributeModule() # m will contain two attributes # 1. foo of type float # 2. names_ages of type Dict[str, int]
Note: it’s now preferred to instead use type annotations instead of torch.jit.Attribute:
import torch from typing import Dict class AttributeModule(torch.nn.Module): names: Dict[str, int] def __init__(self) -> None: super().__init__() self.names = {} m = AttributeModule()
- Parameters
value – An initial value to be assigned to attribute.
type – A Python type
- Returns
Returns value
- count(value, /)¶
Return number of occurrences of value.
- index(value, start=0, stop=9223372036854775807, /)¶
Return first index of value.
Raises ValueError if the value is not present.
- type¶
Alias for field number 1
- value¶
Alias for field number 0