%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

Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Alignment Attention Fusion framework for Few-Shot Object Detection

Quick program made to generate alpha and delta tables for Hidden Markov Models

Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab

DziriBERT: a Pre-trained Language Model for the Algerian Dialect

Code and models for "Rethinking Deep Image Prior for Denoising" (ICCV 2021)

Pytorch implementation of forward and inverse Haar Wavelets 2D

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021)

ADB-IP-ROTATION - Use your mobile phone to gain a temporary IP address using ADB and data tethering

Voice Conversion Using Speech-to-Speech Neuro-Style Transfer

HGCN: Harmonic Gated Compensation Network For Speech Enhancement

Convert Table data to approximate values with GUI

Code for Subgraph Federated Learning with Missing Neighbor Generation (NeurIPS 2021)

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

IGCN : Image-to-graph convolutional network

ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022)

Implementation of the CVPR 2021 paper "Online Multiple Object Tracking with Cross-Task Synergy"

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction (3DV 2021)