conf.yaml

model:                                               # mandatory. lpot uses this model name and framework name to decide where to save tuning history and deploy yaml.
  name: inception_v1
  framework: tensorflow                              # mandatory. supported values are tensorflow, pytorch, or mxnet; allow new framework backend extension.

quantization:                                        # optional. tuning constraints on model-wise for advance user to reduce tuning space.
  calibration:
    sampling_size: 20                            # optional. default value is the size of whole dataset. used to set how many portions of calibration dataset is used. exclusive with iterations field.
    dataloader:
      batch_size: 1
      dataset:
        Imagenet:
          root: /path/to/imagenet/         # NOTE: modify to calibration dataset location if needed
      transform:
        ParseDecodeImagenet:
        BilinearImagenet: 
          height: 224
          width: 224
  model_wise:                                        # optional. tuning constraints on model-wise for advance user to reduc
    activation:
      algorithm: minmax
    weight:
      granularity: per_channel

evaluation:                                          # optional. required if user doesn't provide eval_func in lpot.Quantization.
  accuracy:                                          # optional. required if user doesn't provide eval_func in lpot.Quantization.
    metric:
      topk: 1                                        # built-in metrics are topk, map, f1, allow user to register new metric.
    dataloader:
      batch_size: 1 
      last_batch: discard 
      dataset:
        Imagenet:
          root: /path/to/imagenet/          # NOTE: modify to evaluation dataset location if needed
      transform:
        ParseDecodeImagenet:
        BilinearImagenet: 
          height: 224
          width: 224