TIMM 中的 DropPath 看起来像是 Dropout？

Question

下面的代码（取自此处）似乎只实现了一个简单的Dropout，既不是DropPath也不是DropConnect。真的吗？

def drop_path(x, drop_prob: float = 0., training: bool = False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output

Answer 1

不，它不同于Dropout：

import torch
from torch.nn.functional import dropout

torch.manual_seed(2021)

def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output

x = torch.rand(3, 2, 2, 2)

# DropPath
d1_out = drop_path(x, drop_prob=0.33, training=True)

# Dropout
d2_out = dropout(x, p=0.33, training=True)

让我们比较输出（为了便于阅读，我删除了通道维度之间的换行符）：

# DropPath
print(d1_out)
#  tensor([[[[0.1947, 0.7662],
#            [1.1083, 1.0685]],
#           [[0.8515, 0.2467],
#            [0.0661, 1.4370]]],
#
#          [[[0.0000, 0.0000],
#            [0.0000, 0.0000]],
#           [[0.0000, 0.0000],
#            [0.0000, 0.0000]]],
#
#          [[[0.7658, 0.4417],
#            [1.1692, 1.1052]],
#           [[1.2014, 0.4532],
#            [1.4840, 0.7499]]]])

# Dropout
print(d2_out)
#  tensor([[[[0.1947, 0.7662],
#            [1.1083, 1.0685]],
#           [[0.8515, 0.2467],
#            [0.0661, 1.4370]]],
#
#          [[[0.0000, 0.1480],
#            [1.2083, 0.0000]],
#           [[1.2272, 0.1853],
#            [0.0000, 0.5385]]],
#
#          [[[0.7658, 0.0000],
#            [1.1692, 1.1052]],
#           [[1.2014, 0.4532],
#            [0.0000, 0.7499]]]])

正如您所看到的，它们是不同的。DropPath正在从批次中删除整个样本，这在如等式中使用时有效地产生随机深度。2 他们的论文。另一方面，按照预期（来自文档Dropout）删除随机值：

在训练期间，使用伯努利分布中的样本以概率将输入张量的某些元素随机归零。p每个通道将在每次前转呼叫时独立清零。

另请注意，两者都根据概率缩放输出值，即，对于相同的 ，非清零元素是相同的p。