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.


import torch
from typing import Dict

class AttributeModule(torch.jit.ScriptModule):
    def __init__(self):
        super().__init__() = torch.jit.Attribute(0.1, float)

        # we should be able to use as a float here
        assert 0.0 <

        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):
        self.names = {}

m = AttributeModule()
  • value – An initial value to be assigned to attribute.

  • type – A Python type


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.


Alias for field number 1


Alias for field number 0


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