utils_classifiers.py 文件源码

def get_caffenet(netname):

    if netname=='googlenet':

        # caffemodel paths
        model_path = './Caffe_Models/googlenet/'
        net_fn   = model_path + 'deploy.prototxt'
        param_fn = model_path + 'bvlc_googlenet.caffemodel'

        # get the mean (googlenet doesn't do this per feature, but per channel, see train_val.prototxt)
        mean = np.float32([104.0, 117.0, 123.0]) 

        # define the neural network classifier
        net = caffe.Classifier(net_fn, param_fn, caffe.TEST, channel_swap = (2,1,0), mean = mean)

    elif netname=='alexnet':

        # caffemodel paths
        model_path = './Caffe_Models/bvlc_alexnet/'
        net_fn   = model_path + 'deploy.prototxt'
        param_fn = model_path + 'bvlc_alexnet.caffemodel'

        # get the mean
        mean = np.load('./Caffe_Models/ilsvrc_2012_mean.npy')
        # crop mean
        image_dims = (227,227) # see deploy.prototxt file
        excess_h = mean.shape[1] - image_dims[0]
        excess_w = mean.shape[2] - image_dims[1]
        mean = mean[:, excess_h:(excess_h+image_dims[0]), excess_w:(excess_w+image_dims[1])]

        # define the neural network classifier
        net = caffe.Classifier(net_fn, param_fn, caffe.TEST, channel_swap = (2,1,0), mean = mean)

    elif netname == 'vgg':

        # caffemodel paths
        model_path = './Caffe_Models/vgg network/'
        net_fn   = model_path + 'VGG_ILSVRC_16_layers_deploy.prototxt'
        param_fn = model_path + 'VGG_ILSVRC_16_layers.caffemodel'

        mean = np.float32([103.939, 116.779, 123.68])    

        # define the neural network classifier    
        net = caffe.Classifier(net_fn, param_fn, caffe.TEST, channel_swap = (2,1,0), mean = mean)

    else:

        print 'Provided netname unknown. Returning None.'
        net = None

    return net