pytorch_nndct.IterativePruningRunner - 3.5 English

This API has the following methods:

• __init__(model, inputs)

  model

  A torch.nn.Module object to prune.

  inputs

  A single or a list of torch. Tensor used as inputs for model inference. It does not need to be real data. It can be a randomly generated tensor of the same shape and data type as real data.

• ana(eval_fn, args=(), gpus=None, excludes=None, forced=False)

  eval_fn

  Callable object that takes a torch.nn.Module object as its first argument and returns the evaluation score.

  args

  A tuple of arguments that are passed to eval_fn.

  gpus

  A tuple or list of GPU indices to be used. If not set, the default GPU is used.

  excludes

  A list of node names or torch modules to be excluded from pruning.

  forced

  If False, skip model analysis and use cached result.

• prune(removal_ratio=None, threshold=None, spec_path=None, excludes=None, mode='sparse')

  removal_ratio

  The expected percentage of MACs reduction.

  threshold

  Relative proportion of model performance loss that can be tolerated.

  spec_path

  Pre-defined pruning specification.

  excludes

  A list of node names or torch modules to be excluded from pruning.

  mode

  One of ['sparse', 'slim']. Always use 'sparse' in an iterative loop. A slim model is used for quantization-aware training.