classifier.py 文件源码

def get_transformer(deploy_file, mean_file=None):
    """
    Returns an instance of caffe.io.Transformer

    Arguments:
    deploy_file -- path to a .prototxt file

    Keyword arguments:
    mean_file -- path to a .binaryproto file (optional)
    """
    network = caffe_pb2.NetParameter()
    with open(deploy_file) as infile:
        text_format.Merge(infile.read(), network)

    if network.input_shape:
        dims = network.input_shape[0].dim
    else:
        dims = network.input_dim[:4]

    t = caffe.io.Transformer(
            inputs = {'data': dims}
            )
    t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)

    # color images
    if dims[1] == 3:
        # channel swap
        t.set_channel_swap('data', (2,1,0))

    if mean_file:
        # set mean pixel
        with open(mean_file,'rb') as infile:
            blob = caffe_pb2.BlobProto()
            blob.MergeFromString(infile.read())
            if blob.HasField('shape'):
                blob_dims = blob.shape
                assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
            elif blob.HasField('num') and blob.HasField('channels') and \
                    blob.HasField('height') and blob.HasField('width'):
                blob_dims = (blob.num, blob.channels, blob.height, blob.width)
            else:
                raise ValueError('blob does not provide shape or 4d dimensions')
            pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
            t.set_mean('data', pixel)

    return t