Module TeachMyAgent.teachers.utils.torch

Functions

def get_gradient(params)

Function used to get the value of the gradient of a set of torch parameters.

Args

parameters : list

list of parameters to be considered.

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def get_gradient(params):
    """
    Function used to get the value of the gradient of a set of
    torch parameters.

    Args:
        parameters (list): list of parameters to be considered.

    """
    views = []
    for p in params:
        if p.grad is None:
            view = p.new(p.numel()).zero_()
        else:
            view = p.grad.view(-1)
        views.append(view)
    return torch.cat(views, 0)

def get_weights(parameters)

Function used to get the value of a set of torch parameters as a single vector of values.

Args

parameters : list

list of parameters to be considered.

Returns

A numpy vector consisting of all the values of the vectors.

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def get_weights(parameters):
    """
    Function used to get the value of a set of torch parameters as
    a single vector of values.

    Args:
        parameters (list): list of parameters to be considered.

    Returns:
        A numpy vector consisting of all the values of the vectors.

    """
    weights = list()

    for p in parameters:
        w = p.data.detach().cpu().numpy()
        weights.append(w.flatten())

    weights = np.concatenate(weights, 0)

    return weights

def set_weights(parameters, weights, use_cuda)

Function used to set the value of a set of torch parameters given a vector of values.

Args

parameters : list

list of parameters to be considered;

weights : numpy.ndarray

array of the new values for the parameters;

use_cuda : bool

whether the parameters are cuda tensors or not;

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def set_weights(parameters, weights, use_cuda):
    """
    Function used to set the value of a set of torch parameters given a
    vector of values.

    Args:
        parameters (list): list of parameters to be considered;
        weights (numpy.ndarray): array of the new values for
            the parameters;
        use_cuda (bool): whether the parameters are cuda tensors or not;

    """
    idx = 0
    for p in parameters:
        shape = p.data.shape

        c = 1
        for s in shape:
            c *= s

        w = np.reshape(weights[idx:idx + c], shape)

        if not use_cuda:
            w_tensor = torch.from_numpy(w).type(p.data.dtype)
        else:
            w_tensor = torch.from_numpy(w).type(p.data.dtype).cuda()

        p.data = w_tensor
        idx += c

    assert idx == weights.size

def to_float_tensor(x, use_cuda=False)

Function used to convert a numpy array to a float torch tensor.

Args

x : np.ndarray

numpy array to be converted as torch tensor;

use_cuda : bool

whether to build a cuda tensors or not.

Returns

A float tensor build from the values contained in the input array.

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def to_float_tensor(x, use_cuda=False):
    """
    Function used to convert a numpy array to a float torch tensor.

    Args:
        x (np.ndarray): numpy array to be converted as torch tensor;
        use_cuda (bool): whether to build a cuda tensors or not.

    Returns:
        A float tensor build from the values contained in the input array.

    """
    x = torch.tensor(x, dtype=torch.float)
    return x.cuda() if use_cuda else x

def zero_grad(parameters)

Function used to set to zero the value of the gradient of a set of torch parameters.

Args

parameters : list

list of parameters to be considered.

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def zero_grad(parameters):
    """
    Function used to set to zero the value of the gradient of a set
    of torch parameters.

    Args:
        parameters (list): list of parameters to be considered.

    """

    for p in parameters:
        if p.grad is not None:
           p.grad.detach_()
           p.grad.zero_()