alexnet.py

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# =============================================================================
''' This model is created following the structure from
https://github.com/soumith/convnet-benchmarks/blob/master/caffe/imagenet_winners/alexnet.prototxt
'''

from singa import layer
from singa import loss
from singa import metric
from singa import net as ffnet


def create_net(input_shape, use_cpu=False, use_ocl=False):
    if use_cpu:
        layer.engine = 'singacpp'
    if use_ocl:
        layer.engine = 'singacl'

    net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())

    # Conv 1
    net.add(layer.Conv2D("conv1", 64, 11, 4, pad=2,
                         input_sample_shape=input_shape))
    net.add(layer.Activation("conv1/relu"	))
    net.add(layer.MaxPooling2D("pool1/3x3_s2", 3, 2))

    # Conv 2
    net.add(layer.Conv2D("conv1/5x5_s1", 192, 5, 1, pad=2))
    net.add(layer.Activation("conv2/relu"))
    net.add(layer.MaxPooling2D("pool2/3x3_s2", 3, 2))

    # Conv 3
    net.add(layer.Conv2D("conv3/3x3_s1", 384, 3, 1, pad=1))
    net.add(layer.Activation("conv3/relu"))

    # Conv 4
    net.add(layer.Conv2D("conv4/3x3_s1", 256, 3, 1, pad=1))
    net.add(layer.Activation("conv4/relu"))

    # Conv 5
    net.add(layer.Conv2D("conv5/3x3_s1", 256, 3, 1, pad=1))
    net.add(layer.Activation("conv5/relu"))
    net.add(layer.MaxPooling2D("pool5/3x3_s2", 3, 2))

    # L2 Norm -> Inner product
    net.add(layer.Flatten("flat"))
    net.add(layer.Dense("fc6", 4096))
    net.add(layer.Activation("fc6/relu6"))

    net.add(layer.Dense("fc7", 4096))
    net.add(layer.Activation("fc7/relu7"))

    net.add(layer.Dense("fc8", 1000))

    for (val, spec) in zip(net.param_values(), net.param_specs()):
        filler = spec.filler
        if filler.type == 'gaussian':
            val.gaussian(filler.mean, filler.std)
        else:
            val.set_value(0)
        print spec.name, filler.type, val.l1()

    return net