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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import torch.nn as nn
class DropPath(nn.Module):
"""Stochastic drop paths per sample for residual blocks.
Based on:
https://github.com/rwightman/pytorch-image-models
"""
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True) -> None:
"""
Args:
drop_prob: drop path probability.
scale_by_keep: scaling by non-dropped probability.
"""
super().__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
if not (0 <= drop_prob <= 1):
raise ValueError("Drop path prob should be between 0 and 1.")
def drop_path(self, x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True):
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
def forward(self, x):
return self.drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
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