example/image-classification/symbols/resnet-v1-fp16.py [128:185]:
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    fc1 = mx.symbol.FullyConnected(data=flat, weight=weight, bias=bias, num_hidden=num_classes, name='fc1')
    fc1 = mx.symbol.Cast(data=fc1, dtype=np.float32)
    return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')

def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, **kwargs):
    """
    Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py
    (Original author Wei Wu) by Antti-Pekka Hynninen
    Implementing the original resnet ILSVRC 2015 winning network from:
    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"
    """
    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)
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example/image-classification/symbols/resnet_fp16.py [136:191]:
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    fc1 = mx.symbol.FullyConnected(data=flat, weight=weight, bias=bias, num_hidden=num_classes, name='fc1')
    fc1 = mx.symbol.Cast(data=fc1, dtype=np.float32)
    return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')

def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, **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)
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