%load_ext memory_profiler

Implementing Stochastic Depth/Drop Path In PyTorch

DropPath is available on glasses my computer vision library!

Introduction

Today we are going to implement Stochastic Depth also known as Drop Path in PyTorch! Stochastic Depth introduced by Gao Huang et al is technique to "deactivate" some layers during training.

Let's take a look at a normal ResNet Block that uses residual connections (like almost all models now).If you are not familiar with ResNet, I have an article showing how to implement it.

Basically, the block's output is added to its input: output = block(input) + input. This is called a residual connection

Here we see four ResnNet like blocks, one after the other.

Stochastic Depth/Drop Path will deactivate some of the block's weight

The idea is to reduce the number of layers/block used during training, saving time and make the network generalize better.

Practically, this means setting to zero the output of the block before adding.

Implementation

Let's start by importing our best friend, torch.

import torch
from torch import nn
from torch import Tensor

We can define a 4D tensor (batch x channels x height x width), in our case let's just send 4 images with one pixel each :)

x = torch.ones((4, 1, 1, 1))

We need a tensor of shape batch x 1 x 1 x 1 that will be used to set some of the elements in the batch to zero, using a given prob. Bernoulli to the rescue!

keep_prob: float = .5
mask: Tensor = x.new_empty(x.shape[0], 1, 1, 1).bernoulli_(keep_prob)
    
mask

tensor([[[[0.]]],


        [[[1.]]],


        [[[1.]]],


        [[[1.]]]])

Btw, this is equivelant to

mask: Tensor = (torch.rand(x.shape[0], 1, 1, 1) > keep_prob).float()
mask

tensor([[[[1.]]],


        [[[1.]]],


        [[[1.]]],


        [[[1.]]]])

Before we multiply x by the mask we need to divide x by keep_prob to rescale down the inputs activation during training, see cs231n. So

x_scaled : Tensor = x / keep_prob
x_scaled

tensor([[[[2.]]],


        [[[2.]]],


        [[[2.]]],


        [[[2.]]]])

Finally

output: Tensor = x_scaled * mask
output

tensor([[[[2.]]],


        [[[2.]]],


        [[[2.]]],


        [[[2.]]]])

We can put together in a function

def drop_path(x: Tensor, keep_prob: float = 1.0) -> Tensor:
    mask: Tensor = x.new_empty(x.shape[0], 1, 1, 1).bernoulli_(keep_prob)
    x_scaled: Tensor = x / keep_prob
    return x_scaled * mask

drop_path(x, keep_prob=0.5)

tensor([[[[0.]]],


        [[[0.]]],


        [[[2.]]],


        [[[0.]]]])

We can also do the operation in place

def drop_path(x: Tensor, keep_prob: float = 1.0) -> Tensor:
    mask: Tensor = x.new_empty(x.shape[0], 1, 1, 1).bernoulli_(keep_prob)
    x.div_(keep_prob)
    x.mul_(mask)
    return x


drop_path(x, keep_prob=0.5)

tensor([[[[2.]]],


        [[[2.]]],


        [[[0.]]],


        [[[0.]]]])

However, we may want to use x somewhere else, and dividing x or mask by keep_prob is the same thing. Let's arrive at the final implementation

def drop_path(x: Tensor, keep_prob: float = 1.0, inplace: bool = False) -> Tensor:
    mask: Tensor = x.new_empty(x.shape[0], 1, 1, 1).bernoulli_(keep_prob)
    mask.div_(keep_prob)
    if inplace:
        x.mul_(mask)
    else:
        x = x * mask
    return x

x = torch.ones((4, 1, 1, 1))
drop_path(x, keep_prob=0.8)

tensor([[[[1.2500]]],


        [[[1.2500]]],


        [[[1.2500]]],


        [[[1.2500]]]])

drop_path only works for 2d data, we need to automatically calculate the number of dimensions from the input size to make it work for any data time

def drop_path(x: Tensor, keep_prob: float = 1.0, inplace: bool = False) -> Tensor:
    mask_shape: Tuple[int] = (x.shape[0],) + (1,) * (x.ndim - 1) 
    # remember tuples have the * operator -> (1,) * 3 = (1,1,1)
    mask: Tensor = x.new_empty(mask_shape).bernoulli_(keep_prob)
    mask.div_(keep_prob)
    if inplace:
        x.mul_(mask)
    else:
        x = x * mask
    return x

x = torch.ones((4, 1))
drop_path(x, keep_prob=0.8)

tensor([[0.],
        [0.],
        [0.],
        [0.]])

Let's create a nice DropPath nn.Module

class DropPath(nn.Module):
    def __init__(self, p: float = 0.5, inplace: bool = False):
        super().__init__()
        self.p = p
        self.inplace = inplace

    def forward(self, x: Tensor) -> Tensor:
        if self.training and self.p > 0:
            x = drop_path(x, self.p, self.inplace)
        return x

    def __repr__(self):
        return f"{self.__class__.__name__}(p={self.p})"

    
DropPath()(torch.ones((4, 1)))

tensor([[2.],
        [0.],
        [0.],
        [0.]])

Usage with Residual Connections

We have our DropPath, cool but how do we use it? We need a classic ResNet block, let's implement our good old friend BottleNeckBlock

from torch import nn


class ConvBnAct(nn.Sequential):
    def __init__(self, in_features: int, out_features: int, kernel_size=1):
        super().__init__(
            nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=kernel_size // 2),
            nn.BatchNorm2d(out_features),
            nn.ReLU()
        )
         

class BottleNeck(nn.Module):
    def __init__(self, in_features: int, out_features: int, reduction: int = 4):
        super().__init__()
        self.block = nn.Sequential(
            # wide -> narrow
            ConvBnAct(in_features, out_features // reduction, kernel_size=1),
            # narrow -> narrow
            ConvBnAct( out_features // reduction, out_features // reduction, kernel_size=3),
            # wide -> narrow
            ConvBnAct( out_features // reduction, out_features, kernel_size=1),
        )
        # I am lazy, no shortcut etc
        
    def forward(self, x: Tensor) -> Tensor:
        res = x
        x = self.block(x)
        return x + res
    
    
BottleNeck(64, 64)(torch.ones((1,64, 28, 28))).shape

torch.Size([1, 64, 28, 28])

To deactivate the block the operation x + res must be equal to res, so our DropPath has to be applied after the block.

class BottleNeck(nn.Module):
    def __init__(self, in_features: int, out_features: int, reduction: int = 4):
        super().__init__()
        self.block = nn.Sequential(
            # wide -> narrow
            ConvBnAct(in_features, out_features // reduction, kernel_size=1),
            # narrow -> narrow
            ConvBnAct( out_features // reduction, out_features // reduction, kernel_size=3),
            # wide -> narrow
            ConvBnAct( out_features // reduction, out_features, kernel_size=1),
        )
        # I am lazy, no shortcut etc
        self.drop_path = DropPath()
        
    def forward(self, x: Tensor) -> Tensor:
        res = x
        x = self.block(x)
        x = self.drop_path(x)
        return x + res
    
BottleNeck(64, 64)(torch.ones((1,64, 28, 28)))

tensor([[[[1.0009, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0134, 1.0034, 1.0034,  ..., 1.0034, 1.0034, 1.0000],
          [1.0134, 1.0034, 1.0034,  ..., 1.0034, 1.0034, 1.0000],
          ...,
          [1.0134, 1.0034, 1.0034,  ..., 1.0034, 1.0034, 1.0000],
          [1.0134, 1.0034, 1.0034,  ..., 1.0034, 1.0034, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000]],

         [[1.0005, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0421],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0421],
          ...,
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0421],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0421],
          [1.0000, 1.0011, 1.0011,  ..., 1.0011, 1.0011, 1.0247]],

         [[1.0203, 1.0123, 1.0123,  ..., 1.0123, 1.0123, 1.0299],
          [1.0000, 1.0005, 1.0005,  ..., 1.0005, 1.0005, 1.0548],
          [1.0000, 1.0005, 1.0005,  ..., 1.0005, 1.0005, 1.0548],
          ...,
          [1.0000, 1.0005, 1.0005,  ..., 1.0005, 1.0005, 1.0548],
          [1.0000, 1.0005, 1.0005,  ..., 1.0005, 1.0005, 1.0548],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000]],

         ...,

         [[1.0011, 1.0180, 1.0180,  ..., 1.0180, 1.0180, 1.0465],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0245],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0245],
          ...,
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0245],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0245],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000]],

         [[1.0130, 1.0170, 1.0170,  ..., 1.0170, 1.0170, 1.0213],
          [1.0052, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0065],
          [1.0052, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0065],
          ...,
          [1.0052, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0065],
          [1.0052, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0065],
          [1.0012, 1.0139, 1.0139,  ..., 1.0139, 1.0139, 1.0065]],

         [[1.0103, 1.0181, 1.0181,  ..., 1.0181, 1.0181, 1.0539],
          [1.0001, 1.0016, 1.0016,  ..., 1.0016, 1.0016, 1.0231],
          [1.0001, 1.0016, 1.0016,  ..., 1.0016, 1.0016, 1.0231],
          ...,
          [1.0001, 1.0016, 1.0016,  ..., 1.0016, 1.0016, 1.0231],
          [1.0001, 1.0016, 1.0016,  ..., 1.0016, 1.0016, 1.0231],
          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000]]]],
       grad_fn=<AddBackward0>)

Tada 🎉 ! Now, randomly, our .block will be completely skipped!

Implementing DropPath/StochasticDepth in PyTorch

Related tags

Overview

Implementing Stochastic Depth/Drop Path In PyTorch

Introduction

Implementation

Usage with Residual Connections

Owner

Francesco Saverio Zuppichini

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

A general 3D Object Detection codebase in PyTorch.

Code corresponding to The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents

Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

CVPRW 2021: How to calibrate your event camera

Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

Neural Ensemble Search for Performant and Calibrated Predictions

Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

Moer Grounded Image Captioning by Distilling Image-Text Matching Model

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Editing a classifier by rewriting its prediction rules

MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

GLIP: Grounded Language-Image Pre-training

Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

Label Studio is a multi-type data labeling and annotation tool with standardized output format

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Convert Apple NeuralHash model for CSAM Detection to ONNX.

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

Density-aware Single Image De-raining using a Multi-stream Dense Network (CVPR 2018)