int8进行网络传输

目的:

  1. 我们项目需要用到instance segmentation, 所以 rcnn_mask输出层数据量特别大,同时因为图片尺寸有1024*1024*3这么大.
  2. 如果不压缩一下直接进行网络传输,数据量会很大,接口延迟很高.
  3. 为了部署后,请求推理服务时,数据输入层和输出层的传输量减少.
  4. 从float32 转变成 int8,传输量减少了4倍.

处理:

  • 输入层:

先转换成 int8,然后再转成float32

 def build(self, mode, config):         input_image = KL.Input(            shape=[None, None, config.IMAGE_SHAPE[2]], name="input_image",dtype=tf.int8)        input_image = KL.Input(tensor=K.cast(input_image,dtype= tf.float32))

  • 输出层:

       mrcnn_mask = build_fpn_mask_graph(detection_boxes, mrcnn_feature_maps,                                             input_image_meta,                                             config.MASK_POOL_SIZE,                                             config.NUM_CLASSES,

模型导出时加上去

        mrcnn_mask = KL.Lambda(lambda x: x*255)(mrcnn_mask)        mrcnn_mask = KL.Lambda(lambda x: tf.cast(x,dtype=tf.int8))(mrcnn_mask)
云函数服务 拿到`mrcnn_mask`输出层数据后,再转换成 `float32`- 模型导出:导出时注意 `output` 名称要对应.

def save_model():

config = QuesConfig()PRETRAINED_MODEL_PATH = "/xxxxx/weights"MODEL_NAME = 'xxxxx_0601.h5'export_dir = "./saved_model/1/"h5_to_saved_model(config, PRETRAINED_MODEL_PATH, MODEL_NAME, export_dir)

def h5_to_saved_model(config, model_dir, model_name, export_dir):

if tf.gfile.Exists(export_dir):    tf.gfile.DeleteRecursively(export_dir)config.display()model = modellib.MaskRCNN(mode="inference", config=config, model_dir=model_dir)model_path = os.path.join(model_dir, model_name)model.load_weights(model_path, by_name=True)with K.get_session() as session:    save_m(export_dir, session)

def save_m(export_dir, session):

if tf.gfile.Exists(export_dir):    tf.gfile.DeleteRecursively(export_dir)# legacy_init_op = tf.group(tf.tables_initializer())builder = tf.saved_model.builder.SavedModelBuilder(export_dir)signature_inputs = {    "input_image":        session.graph.get_tensor_by_name("input_image:0"),    "input_image_meta":        session.graph.get_tensor_by_name("input_image_meta:0"),    "input_anchors":        session.graph.get_tensor_by_name("input_anchors:0"),}signature_outputs = {    'mrcnn_detection':        session.graph.get_tensor_by_name('mrcnn_detection/Reshape_1:0'),    'mrcnn_mask':        session.graph.get_tensor_by_name('lambda_5/Cast:0')}sigs = {}sigs['serving_default'] = tf.saved_model.signature_def_utils.predict_signature_def(    inputs=signature_inputs,    outputs=signature_outputs)builder.add_meta_graph_and_variables(    session,    [tf.saved_model.tag_constants.SERVING],    signature_def_map=sigs)builder.save()