apply_stochastic_depth#

composer.functional.apply_stochastic_depth(model, target_layer_name, stochastic_method='block', drop_rate=0.2, drop_distribution='linear')[source]#

Applies Stochastic Depth (Huang et al, 2016) 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.

Note

Stochastic Depth only works on instances of torchvision.models.resnet.ResNet for now.

Parameters

  • model (Module) โ€“ model containing modules to be replaced with stochastic versions.

  • target_layer_name (str) โ€“ 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 torchvision.models.resnet.Bottleneck is supported.

  • stochastic_method (str, optional) โ€“ The version of stochastic depth to use. "block" randomly drops blocks during training. "sample" randomly drops samples within a block during training. Default: "block".

  • drop_rate (float, optional) โ€“ The base probability of dropping a layer or sample. Must be between 0.0 and 1.0. Default: 0.2`.

  • drop_distribution (str, optional) โ€“ 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. Default: "linear".

Example

import composer.functional as cf
from torchvision import models
model = models.resnet50()
cf.apply_stochastic_depth(
    model,
    target_layer_name='ResNetBottleneck'
)