Source code for torchvision.ops.stochastic_depth
import torch
import torch.fx
from torch import nn, Tensor
from ..utils import _log_api_usage_once
[docs]def stochastic_depth(input: Tensor, p: float, mode: str, training: bool = True) -> Tensor:
"""
Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
<https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
branches of residual architectures.
Args:
input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
being its batch i.e. a batch with ``N`` rows.
p (float): probability of the input to be zeroed.
mode (str): ``"batch"`` or ``"row"``.
``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
randomly selected rows from the batch.
training: apply stochastic depth if is ``True``. Default: ``True``
Returns:
Tensor[N, ...]: The randomly zeroed tensor.
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(stochastic_depth)
if p < 0.0 or p > 1.0:
raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
if mode not in ["batch", "row"]:
raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
if not training or p == 0.0:
return input
survival_rate = 1.0 - p
if mode == "row":
size = [input.shape[0]] + [1] * (input.ndim - 1)
else:
size = [1] * input.ndim
noise = torch.empty(size, dtype=input.dtype, device=input.device)
noise = noise.bernoulli_(survival_rate)
if survival_rate > 0.0:
noise.div_(survival_rate)
return input * noise
torch.fx.wrap("stochastic_depth")
[docs]class StochasticDepth(nn.Module):
"""
See :func:`stochastic_depth`.
"""
def __init__(self, p: float, mode: str) -> None:
super().__init__()
_log_api_usage_once(self)
self.p = p
self.mode = mode
[docs] def forward(self, input: Tensor) -> Tensor:
return stochastic_depth(input, self.p, self.mode, self.training)
def __repr__(self) -> str:
s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})"
return s