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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from annotator.uniformer.mmcv import build_from_cfg
from .registry import DROPOUT_LAYERS
def drop_path(x, drop_prob=0., training=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
We follow the implementation
https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
# handle tensors with different dimensions, not just 4D tensors.
shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(
shape, dtype=x.dtype, device=x.device)
output = x.div(keep_prob) * random_tensor.floor()
return output
@DROPOUT_LAYERS.register_module()
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of
residual blocks).
We follow the implementation
https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py # noqa: E501
Args:
drop_prob (float): Probability of the path to be zeroed. Default: 0.1
"""
def __init__(self, drop_prob=0.1):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
@DROPOUT_LAYERS.register_module()
class Dropout(nn.Dropout):
"""A wrapper for ``torch.nn.Dropout``, We rename the ``p`` of
``torch.nn.Dropout`` to ``drop_prob`` so as to be consistent with
``DropPath``
Args:
drop_prob (float): Probability of the elements to be
zeroed. Default: 0.5.
inplace (bool): Do the operation inplace or not. Default: False.
"""
def __init__(self, drop_prob=0.5, inplace=False):
super().__init__(p=drop_prob, inplace=inplace)
def build_dropout(cfg, default_args=None):
"""Builder for drop out layers."""
return build_from_cfg(cfg, DROPOUT_LAYERS, default_args)
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