Python caffe_pb2.BlobProto方法代码示例

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import BlobProto [as 别名]
def add_missing_biases(caffenet_weights):
    for layer in caffenet_weights.layer:
        if layer.type == 'Convolution' and len(layer.blobs) == 1:
            num_filters = layer.blobs[0].shape.dim[0]
            bias_blob = caffe_pb2.BlobProto()
            bias_blob.data.extend(np.zeros(num_filters))
            bias_blob.num, bias_blob.channels, bias_blob.height = 1, 1, 1
            bias_blob.width = num_filters
            layer.blobs.extend([bias_blob]) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import BlobProto [as 别名]
def array_to_blobproto(arr, diff=None):
    """Converts a 4-dimensional array to blob proto. If diff is given, also
    convert the diff. You need to make sure that arr and diff have the same
    shape, and this function does not do sanity check.
    """
    if arr.ndim != 4:
        raise ValueError('Incorrect array shape.')
    blob = caffe_pb2.BlobProto()
    blob.num, blob.channels, blob.height, blob.width = arr.shape
    blob.data.extend(arr.astype(float).flat)
    if diff is not None:
        blob.diff.extend(diff.astype(float).flat)
    return blob 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import BlobProto [as 别名]
def array_to_blobproto(arr, diff=None):
    """Converts a N-dimensional array to blob proto. If diff is given, also
    convert the diff. You need to make sure that arr and diff have the same
    shape, and this function does not do sanity check.
    """
    blob = caffe_pb2.BlobProto()
    blob.shape.dim.extend(arr.shape)
    blob.data.extend(arr.astype(float).flat)
    if diff is not None:
        blob.diff.extend(diff.astype(float).flat)
    return blob 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import BlobProto [as 别名]
def load_mean_bgr():
    """ bgr mean pixel value image, [0, 255]. [height, width, 3] """
    with open("data/ResNet_mean.binaryproto", mode='rb') as f:
        data = f.read()
    blob = caffe_pb2.BlobProto()
    blob.ParseFromString(data)

    mean_bgr = caffe.io.blobproto_to_array(blob)[0]
    assert mean_bgr.shape == (3, 224, 224)

    return mean_bgr.transpose((1, 2, 0)) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import BlobProto [as 别名]
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)

    dims = network.input_dim

    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) 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