resnet.py 文件源码

def _shortcut(input, residual):
    """Adds a shortcut between input and residual block and merges them with "sum"
    """
    # Expand channels of shortcut to match residual.
    # Stride appropriately to match residual (width, height)
    # Should be int if network architecture is correctly configured.
    input_shape = K.int_shape(input)
    residual_shape = K.int_shape(residual)
    stride_width = int(round(input_shape[ROW_AXIS] / residual_shape[ROW_AXIS]))
    stride_height = int(round(input_shape[COL_AXIS] / residual_shape[COL_AXIS]))
    equal_channels = input_shape[CHANNEL_AXIS] == residual_shape[CHANNEL_AXIS]

    shortcut = input
    # 1 X 1 conv if shape is different. Else identity.
    if stride_width > 1 or stride_height > 1 or not equal_channels:
        shortcut = Convolution2D(nb_filter=residual_shape[CHANNEL_AXIS],
                                 nb_row=1, nb_col=1,
                                 subsample=(stride_width, stride_height),
                                 init="he_normal", border_mode="valid",
                                 W_regularizer=l2(0.0001))(input)

    return merge([shortcut, residual], mode="sum")