For enabling TensorFlow launcher you need to add framework: tf in launchers section of your configuration file and provide following parameters:
model - path to frozen graph file with TF model for your topology or checkpoint meta.adapter - approach how raw output will be converted to representation of dataset problem, some adapters can be specific to framework. You can find detailed instruction how to use adapters here.output_names - list of node names which will be used as model output (Optional, if not provided will be used from graph)device - specifies which device will be used for infer (cpu or gpu).In case when you model has several inputs you should provide list of input layers in launcher config section using key inputs. Each input description should has following info:
name - input layer name in networktype - type of input values, it has impact on filling policy. Available options:CONST_INPUT - input will be filled using constant provided in config. It also requires to provide value.IMAGE_INFO - specific key for setting information about input shape to layer (used in Faster RCNN based topologies). You do not need provide value, because it will be calculated in runtime. Format value is Nx[H, W, S], where N is batch size, H - original image height, W - original image width, S - scale of original image (default 1).INPUT - network input for main data stream (e. g. images). If you have several data inputs, you should provide regular expression for identifier as value for specifying which one data should be provided in specific input. Optionally you can determine shape of input and layout in case when your model was trained with non-standard data layout (For TensorFlow default layout is NHWC) and precision (Supported precisions: FP32 - float, FP16 - signed shot, U8 - unsigned char, U16 - unsigned short int, I8 - signed char, I16 - short int, I32 - int, I64 - long int).TensorFlow launcher config example: