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