composer.algorithms.functional.apply_stochastic_depth

composer.algorithms.functional.apply_stochastic_depth(model: torch.nn.modules.module.Module, stochastic_method: str, target_layer_name: str, drop_rate: float = 0.2, drop_distribution: str = 'linear', use_same_gpu_seed: bool = True) → None[source]

Applies Stochastic Depth (Huang et al.) to the specified model.

The algorithm replaces the specified target layer with a stochastic version of the layer. The stochastic layer will randomly drop either samples or the layer itself depending on the stochastic method specified. The block-wise version follows the original paper. The sample-wise version follows the implementation used for EfficientNet in the Tensorflow/TPU repo.

Parameters

  • model – model containing modules to be replaced with stochastic versions

  • stochastic_method – The version of stochastic depth to use. "block" randomly drops blocks during training. "sample" randomly drops samples within a block during training.

  • target_layer_name – Block to replace with a stochastic block equivalent. The name must be registered in STOCHASTIC_LAYER_MAPPING dictionary with the target layer class and the stochastic layer class. Currently, only 'ResNetBottleneck' is supported.

  • drop_rate – The base probability of dropping a layer or sample. Must be between 0.0 and 1.0.

  • drop_distribution – How drop_rate is distributed across layers. Value must be one of "uniform" or "linear". "uniform" assigns the same drop_rate across all layers. "linear" linearly increases the drop rate across layer depth starting with 0 drop rate and ending with drop_rate.

  • use_same_gpu_seed – Set to True to have the same layers dropped across GPUs when using multi-GPU training. Set to False to have each GPU drop a different set of layers. Only used with "block" stochastic method.