deconv2d.py 文件源码

def Deconv2D(
    name, 
    input_dim, 
    output_dim, 
    filter_size, 
    inputs, 
    he_init=True,
    weightnorm=None,
    ):
    """
    inputs: tensor of shape (batch size, num channels, height, width)
    returns: tensor of shape (batch size, num channels, 2*height, 2*width)
    """
    def uniform(stdev, size):
        return np.random.uniform(
            low=-stdev * np.sqrt(3),
            high=stdev * np.sqrt(3),
            size=size
        ).astype(theano.config.floatX)

    filters_stdev = np.sqrt(1./(input_dim * filter_size**2))
    filters_stdev *= 2. # Because of the stride
    if he_init:
        filters_stdev *= np.sqrt(2.)

    filter_values = uniform(
        filters_stdev,
        (input_dim, output_dim, filter_size, filter_size)
    )

    filters = lib.param(
        name+'.Filters',
        filter_values
    )

    if weightnorm==None:
        weightnorm = _default_weightnorm
    if weightnorm:
        norm_values = np.sqrt(np.sum(np.square(filter_values), axis=(0,2,3)))
        norms = lib.param(
            name + '.g',
            norm_values
        )
        filters = filters * (norms / T.sqrt(T.sum(T.sqr(filters), axis=(0,2,3)))).dimshuffle('x',0,'x','x')

    biases = lib.param(
        name+'.Biases',
        np.zeros(output_dim, dtype=theano.config.floatX)
    )

    pad = (filter_size-1)/2
    result = _deconv2d(
        inputs, 
        filters, 
        subsample=(2,2),
        border_mode=(pad,pad),
    )
    result = result + biases[None, :, None, None]
    # result = lib.debug.print_stats(name, result)
    return result