Refactor benchmark configurations and the framework

[fengyuentau]

Benchmark configuration files should be refactored as follows to improve robustness and readability:

Benchmark:
    name: str
    type: str 
    data:
      path: str # necessary
      files: List[str] # necessary and must be array of filenames
      sizes: List[Tuple[int, int]] # optional, but must be array of sizes (w, h); Omit to run withuout resizing
    metric:
      warmup: int
      repeat: int
      reduction: str # available reduction methods for now: median, gmean
    model:
      name: str # necessary and must match with the one in wrapper, such as 'YuNet' in face_detection_yunet/yunet.py
      backend: int
      target: int
      other_parameters: ...

Notes:

• type should be case-insensitive and is used to initialize dataloader and metric. If current dataloaders or metrics do not support the selected type, BaseImageLoader and BaseMetric will be initialized.
• data types are based on either _BaseImageLoader, or _BaseVideoLoader.
• metric: warmup and repeat do not work for video-stream input data.
• model is moved into the benchmark dictionary.
  • backend and target is moved into the model dictionary.

To do:

• Make the initialization of dataloaders and metrics fall back to base dataloader and metric when type is not supported.
• Set BaseImageLoader as base dataloder.
• Set BaseMetric as base metric.
• Instantiate model inside Benchmark class.
• Remove setBackend and setTarget from wrapper class.
• Add parameter backend and target in the constructor of wrapper class.
• Add a argument parser to override Benchmark[model][backend] and Benchmark[model][target].
• Add an example configuration file like the one above to show how to correctly construct a yaml for benchmarking.