<filename>openmixup/models/utils/layers/drop.py
# Copyright (c) OpenMMLab. All rights reserved.
# Reference: https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/bricks/drop.py
import torch
import torch.nn as nn
# 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)

<filename>openmixup/models/utils/layers/drop.py
# Copyright (c) OpenMMLab. All rights reserved.
# Reference: https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/bricks/drop.py
import torch
import torch.nn as nn
# 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)

# Copyright (c) OpenMMLab. All rights reserved. # Reference: https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/bricks/drop.py # from .registry import DROPOUT_LAYERS 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 # handle tensors with different dimensions, not just 4D tensors. # @DROPOUT_LAYERS.register_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