symbol_darknet19.py 文件源码

def conv_act_layer(from_layer, name, num_filter, kernel=(3, 3), pad=(1, 1), \
    stride=(1,1), act_type="relu", use_batchnorm=True):
    """
    wrapper for a small Convolution group

    Parameters:
    ----------
    from_layer : mx.symbol
        continue on which layer
    name : str
        base name of the new layers
    num_filter : int
        how many filters to use in Convolution layer
    kernel : tuple (int, int)
        kernel size (h, w)
    pad : tuple (int, int)
        padding size (h, w)
    stride : tuple (int, int)
        stride size (h, w)
    act_type : str
        activation type, can be relu...
    use_batchnorm : bool
        whether to use batch normalization

    Returns:
    ----------
    (conv, relu) mx.Symbols
    """
    conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \
        stride=stride, num_filter=num_filter, name="{}".format(name))
    if use_batchnorm:
        conv = mx.symbol.BatchNorm(data=conv, name="bn_{}".format(name))
    if act_type in ['elu', 'leaky', 'prelu', 'rrelu']:
        relu = mx.symbol.LeakyReLU(data=conv, act_type=act_type,
        name="{}_{}".format(act_type, name), slope=0.1)
    elif act_type in ['relu', 'sigmoid', 'softrelu', 'tanh']:
        relu = mx.symbol.Activation(data=conv, act_type=act_type, \
        name="{}_{}".format(act_type, name))
    else:
        assert isinstance(act_type, str)
        raise ValueError("Invalid activation type: " + str(act_type))
    return relu