misc

Utilities for reading and for writing neural network parameters

count_parameters(net)

Get the number of parameters the network.

Parameters:

Name	Type	Description	Default
net	Module	The torch module whose parameters will be counted.	required

Returns:

Type	Description
int	The number of parameters, as an integer.

Source code in evotorch/neuroevolution/net/misc.py

def count_parameters(net: nn.Module) -> int:
    """
    Get the number of parameters the network.

    Args:
        net: The torch module whose parameters will be counted.
    Returns:
        The number of parameters, as an integer.
    """

    count = 0

    for p in net.parameters():
        count += p.numel()

    return count

fill_parameters(net, vector)

Fill the parameters of a torch module (net) from a vector.

No gradient information is kept.

The vector's length must be exactly the same with the number of parameters of the PyTorch module.

Parameters:

Name	Type	Description	Default
net	Module	The torch module whose parameter values will be filled.	required
vector	Tensor	A 1-D torch tensor which stores the parameter values.	required

Source code in evotorch/neuroevolution/net/misc.py

@torch.no_grad()
def fill_parameters(net: nn.Module, vector: torch.Tensor):
    """Fill the parameters of a torch module (net) from a vector.

    No gradient information is kept.

    The vector's length must be exactly the same with the number
    of parameters of the PyTorch module.

    Args:
        net: The torch module whose parameter values will be filled.
        vector: A 1-D torch tensor which stores the parameter values.
    """
    address = 0
    for p in net.parameters():
        d = p.data.view(-1)
        n = len(d)
        d[:] = torch.as_tensor(vector[address : address + n], device=d.device)
        address += n

    if address != len(vector):
        raise IndexError("The parameter vector is larger than expected")

parameter_vector(net, *, device=None)

Get all the parameters of a torch module (net) into a vector

No gradient information is kept.

Parameters:

Name	Type	Description	Default
net	Module	The torch module whose parameters will be extracted.	required
device	Union[str, torch.device]	The device in which the parameter vector will be constructed. If the network has parameter across multiple devices, you can specify this argument so that concatenation of all the parameters will be successful.	None

Returns:

Type	Description
Tensor	The parameters of the module in a 1-D tensor.

Source code in evotorch/neuroevolution/net/misc.py

@torch.no_grad()
def parameter_vector(net: nn.Module, *, device: Optional[Device] = None) -> torch.Tensor:
    """Get all the parameters of a torch module (net) into a vector

    No gradient information is kept.

    Args:
        net: The torch module whose parameters will be extracted.
        device: The device in which the parameter vector will be constructed.
            If the network has parameter across multiple devices,
            you can specify this argument so that concatenation of all the
            parameters will be successful.
    Returns:
        The parameters of the module in a 1-D tensor.
    """
    dev_kwarg = {} if device is None else {"device": device}

    all_vectors = []
    for p in net.parameters():
        all_vectors.append(torch.as_tensor(p.data.view(-1), **dev_kwarg))

    return torch.cat(all_vectors)