def get_symbol()

in image_classification/symbols/resnet.py [0:0]

• 48 lines of code
• 15 McCabe index (conditional complexity)

def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, dtype='float32', **kwargs):
    """
    Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
    Original author Wei Wu
    """
    image_shape = [int(l) for l in image_shape.split(',')]
    (nchannel, height, width) = image_shape
    if height <= 28:
        num_stages = 3
        if (num_layers-2) % 9 == 0 and num_layers >= 164:
            per_unit = [(num_layers-2)//9]
            filter_list = [16, 64, 128, 256]
            bottle_neck = True
        elif (num_layers-2) % 6 == 0 and num_layers < 164:
            per_unit = [(num_layers-2)//6]
            filter_list = [16, 16, 32, 64]
            bottle_neck = False
        else:
            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
        units = per_unit * num_stages
    else:
        if num_layers >= 50:
            filter_list = [64, 256, 512, 1024, 2048]
            bottle_neck = True
        else:
            filter_list = [64, 64, 128, 256, 512]
            bottle_neck = False
        num_stages = 4
        if num_layers == 18:
            units = [2, 2, 2, 2]
        elif num_layers == 34:
            units = [3, 4, 6, 3]
        elif num_layers == 50:
            units = [3, 4, 6, 3]
        elif num_layers == 101:
            units = [3, 4, 23, 3]
        elif num_layers == 152:
            units = [3, 8, 36, 3]
        elif num_layers == 200:
            units = [3, 24, 36, 3]
        elif num_layers == 269:
            units = [3, 30, 48, 8]
        else:
            raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))

    return resnet(units       = units,
                  num_stages  = num_stages,
                  filter_list = filter_list,
                  num_classes = num_classes,
                  image_shape = image_shape,
                  bottle_neck = bottle_neck,
                  workspace   = conv_workspace,
                  dtype       = dtype)