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e16f6ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | # Implementation modified from torchvision:
# https://github.com/pytorch/vision/blob/main/torchvision/ops/stochastic_depth.py
#
# License:
# BSD 3-Clause License
#
# Copyright (c) Soumith Chintala 2016,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
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# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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import torch
import torch.fx
from torch import nn, Tensor
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 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")
class StochasticDepth(nn.Module):
"""
See :func:`stochastic_depth`.
"""
def __init__(self, p: float, mode: str) -> None:
super().__init__()
self.p = p
self.mode = mode
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
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