DropPath in TIMM seems like a Dropout?

user_7677894 0
Viewed 3094

The code below (taken from here) seems to implement only a simple Dropout, neither the DropPath nor DropConnect. Is that true?

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
conv-neural-network pytorch deep-learning computer-vision python
2 Answers

No, it is different from 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)

Let's compare the outputs (I removed the line break between channel dimension for readability):

# 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]]]])

As you can see, they are different. DropPath is dropping an entire sample from the batch, which effectively results in stochastic depth when used as in Eq. 2 of their paper. On the other hand, Dropout is dropping random values, as expected (from the docs):

During training, randomly zeroes some of the elements of the input tensor with probability p using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call.

Also note that both scale the output values based on the probability, i.e., the non-zeroed out elements are identical for the same p.

user_42282750

Usually, droppath is used together with residual connections. For example:

x = x + droppath(block(x))

If block(x) is dropped, it equals to say block() is skipped and the input x is output directly.

user_176195450
Related