device- specifies which device will be used for infer (
gpu_0and so on).
model- path to params file specifying the numeric arrays used in the network.
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.
You also should specify all inputs for your model and provide their shapes, using specific parameter:
inputs. Each input description should has following info:
name- input layer name in network
type- 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
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
Nis batch size,
H- original image height,
W- original image width,
S- scale of original image (default 1).
ORIG_IMAGE_INFO- specific key for setting information about original image size before preprocessing.
IGNORE_INPUT- input which should be stayed empty during evaluation.
INPUT- network input for main data stream (e. g. images). If you have several data inputs, you should provide regular expression for identifier as
valuefor specifying which one data should be provided in specific input.
shape- shape of input layer described as comma-separated of all dimensions size except batch size. Optionally you can determine
layoutin case when your model was trained with non-standard data layout (For MXNet default layout is
FP16- signed shot,
U8- unsigned char,
U16- unsigned short int,
I8- signed char,
I16- short int,
I64- long int). You also can specify batch size for your model using
MXNet launcher config example: