Python theano.tensor.signal.pool 模块，pool_3d() 实例源码

def pool(self, input, window, mode, stride, pad, autopad):
            if mode == 'max':
                mode = 'max'
            elif mode == 'sum':
                mode = 'sum'
            elif mode == 'avg':
                mode = 'average_exc_pad'
            elif mode == 'avgpad':
                mode = 'average_inc_pad'
            else:
                mode = 'sum'
            if input.ndim == 4:
                return P.pool_2d(input=input, ws=window, ignore_border=not autopad, stride=stride, pad=pad, mode=mode)
            elif input.ndim == 5:
                return P.pool_3d(input=input, ws=window, ignore_border=not autopad, stride=stride, pad=pad, mode=mode)
            else:
                basic.defaultreturn()

def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format:', data_format)

    if padding == 'same':
        w_pad = pool_size[0] - 2 if pool_size[0] % 2 == 1 else pool_size[0] - 1
        h_pad = pool_size[1] - 2 if pool_size[1] % 2 == 1 else pool_size[1] - 1
        d_pad = pool_size[2] - 2 if pool_size[2] % 2 == 1 else pool_size[2] - 1
        padding = (w_pad, h_pad, d_pad)
    elif padding == 'valid':
        padding = (0, 0, 0)
    else:
        raise ValueError('Invalid padding:', padding)
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format:', data_format)

    if data_format == 'channels_last':
        x = x.dimshuffle((0, 4, 1, 2, 3))

    if pool_mode == 'max':
        pool_out = pool.pool_3d(x, ws=pool_size, stride=strides,
                                ignore_border=True,
                                pad=padding,
                                mode='max')
    elif pool_mode == 'avg':
        pool_out = pool.pool_3d(x, ws=pool_size, stride=strides,
                                ignore_border=True,
                                pad=padding,
                                mode='average_exc_pad')
    else:
        raise ValueError('Invalid pooling mode:', pool_mode)

    if padding == 'same':
        expected_width = (x.shape[2] + strides[0] - 1) // strides[0]
        expected_height = (x.shape[3] + strides[1] - 1) // strides[1]
        expected_depth = (x.shape[4] + strides[2] - 1) // strides[2]

        pool_out = pool_out[:, :,
                            : expected_width,
                            : expected_height,
                            : expected_depth]

    if data_format == 'channels_last':
        pool_out = pool_out.dimshuffle((0, 2, 3, 4, 1))
    return pool_out