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[GoogleNet] 논문 리뷰 및 구현 (코드 설명 포함)

by 이 정규 2023. 2. 8.
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GoogleNet

Description

  • GoogleNet의 특징:
  1. GoogleNet Architecture:

    • GoogLeNet은 네트워크의 depth와 width를 늘리면서도 내부적으로 Inception Module을 활용해 computational efficiency를 확보함
    • 이전에 나온 VGGNet이 깊은 네트워크로 AlexNet보다 높은 성능을 얻었지만, 파라미터 측면에서 효율적이지 못하다는점을 보완하기 위해 만듬
  2. Things to discuss about issues:
    • 네트워크의 성능을 올리는 가장 직접적인 방법은 depth, width같은 size를 증대 할 수 있다
    • 모델의 층이 깊어질수록 성능은 향상 됨
    • 하지만 계산해야 할 연산량이 늘어나 overfitting할 가능성이 증가
    • RAM을 너무나 많이 사용해 시간이 오래걸리는 비효율성 발생
  3. Solution:
    • 적은 연산량을 가지고 효율적으로 모델의 특징을 추출하는 방법 고안
    • Sparse Connection - fully connected 를 sparsely connected architecture로 변경
    • Inception Module - 3개의 inception blocks (3a,3b), (4a~4e), (5a,5b), 총 9개의 inception modules
    • Auxiliary Classifier - gradient vanishing 문제 해결 위해 2개의 auxiliary classifier 추가,
  4. Global Average Pooling(GAP):

    • patch size = 7x7
    • stride = 1
    • global average pooling은 전 층에서 산출된 특성맵들을 각각 평균냄
    • 이후 이어서 1차원 벡터를 만듬
    • why? 1차원 벡터로 만들어야만 최종적으로 이미지 분류를 위한 softmax 층을 연결함
    • 가장 큰 이유는 finetuning의 용이성을 위함
  5. Max Pooling:
    • patch size = 3x3
    • stride = 2
    • number of max pooling = 4
  1. Inception Module:
    • 1x1 Convolution: kernel_size=1, stride= 1, padding=0
    • 3x3 Convolution: kernel_size=3, stride= 1, padding=1
    • 5x5 Convolution: kernel_size=5, stride= 1, pading=2
    • Max-Pooling: kernel_size=3, stride=1, padding=1
  2. Auxiliary Classifier:
    • 1x1 convolution output channel = 128 적용
    • dropout rate = 0.7 적용
    • uxiliary Classifier의 input dimensioin = aux1(512), aux2(528)
  1. Fully Connected Layer(FC Layer):
    • 1개의 1024 channel
    • Softmax
  2. Augmentation:
    • Input image shape = 224x224x3
    • resize = 227x227
    • Mean subtraction of RGB per channel
  1. Hyperparameter
    • Optimizer = SGD -> Adam으로 변경
    • Momentum = 0.9
    • Batch size = 64 -> 128 변경
    • learning rate = learning rate scheduler 사용 -> 0.0001으로 변경
    • Epoch = not mentioned -> 20 적용
    • Dropout = 0.4(FC layer)
  2. Dataset
    • 논문 : ImageNet Large Scale Visual Recognition Challenge(ILSVRC)-2014
    • 구현 : CIFAR-10
  3. System Environment:
    • Google Colab Pro
      ​

Reference

Load Modules

# Utils
import numpy as np
from tqdm import tqdm 
import matplotlib.pyplot as plt

# Torch
import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional
import torch.nn.functional as F
from torchsummary import summary
from torchvision import transforms
import torchvision

# TensorBoard
from torch.utils.tensorboard import SummaryWriter
! pip install tensorboardX
from tensorboardX import SummaryWriter

Set Hyperparameters

batch_size = 128
num_epochs = 20
learning_rate = 0.0001

Load Data - CIFAR10

train_set = torchvision.datasets.CIFAR10(root='./cifar10', train=True, download=True, transform=transforms.ToTensor())
test_set = torchvision.datasets.CIFAR10(root='./cifar10', train=False, download=True, transform=transforms.ToTensor())

Mean subtraction of RGB per channel

train_meanRGB = [np.mean(x.numpy(), axis=(1,2)) for x, _ in train_set]
train_stdRGB = [np.std(x.numpy(), axis=(1,2)) for x, _ in train_set]

train_meanR = np.mean([m[0] for m in train_meanRGB])
train_meanG = np.mean([m[1] for m in train_meanRGB])
train_meanB = np.mean([m[2] for m in train_meanRGB])
train_stdR = np.mean([s[0] for s in train_stdRGB])
train_stdG = np.mean([s[1] for s in train_stdRGB])
train_stdB = np.mean([s[2] for s in train_stdRGB])

print(train_meanR, train_meanG, train_meanB)
print(train_stdR, train_stdG, train_stdB)

Define the image transformation for data

train_transformer = transforms.Compose([transforms.Resize((227,227)),
                                        transforms.ToTensor(),
                                        transforms.Normalize(mean=[train_meanR, train_meanG, train_meanB], std=[train_stdR, train_stdG, train_stdB])])

train_set.transform = train_transformer
test_set.transform = train_transformer

Define DataLoader

trainloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=True, num_workers=2)

Created GoogleNet Model

  • Convolutional Block
class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super(ConvBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
        self.batchnorm = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.batchnorm(x)
        x = self.relu(x)
        
        return x
  • Inception Modules
class Inception(nn.Module):
    def __init__(self, in_channels, n1, n3_reduce, n3, n5_reduce, n5, pool_proj):
        super().__init__()
        self.branch1 = ConvBlock(in_channels, n1, kernel_size=1, stride=1, padding=0)

        self.branch2 = nn.Sequential(
          ConvBlock(in_channels, n3_reduce, kernel_size=1, stride=1, padding=0),
          ConvBlock(n3_reduce, n3, kernel_size=3, stride=1, padding=1)
        )
        
        self.branch3 = nn.Sequential(
          ConvBlock(in_channels, n5_reduce, kernel_size=1, stride=1, padding=0),
          ConvBlock(n5_reduce, n5, kernel_size=5, stride=1, padding=2)
        )

        self.branch4 = nn.Sequential(
          nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
          ConvBlock(in_channels, pool_proj, kernel_size=1, stride=1, padding=0)
        )
        
    def forward(self, x):
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        x4 = self.branch4(x)

        return torch.cat([x1, x2, x3, x4], dim=1)
  • Auxiliary classifier
class InceptionAux(nn.Module):
    def __init__(self, in_channels, num_classes):
        super().__init__()
        self.avg_conv = nn.Sequential(
          nn.AvgPool2d(kernel_size=5, stride=3),
          ConvBlock(in_channels, 128, kernel_size=1, stride=1, padding=0)
        )

        self.fc = nn.Sequential(
          nn.Linear(2048, 1024),
          nn.ReLU(),
          nn.Dropout(p=0.7),
          nn.Linear(1024, num_classes)
        )

    def forward(self, x):
        x = self.avg_conv(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        
        return x
class GoogLeNet(nn.Module):
    def __init__(self, aux_logits=True, num_classes=10):
        super().__init__()

        self.aux_logits = aux_logits

        self.conv1 = ConvBlock(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3)
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)
        self.conv2 = ConvBlock(in_channels=64, out_channels=64, kernel_size=1, stride=1, padding=0)
        self.conv3 = ConvBlock(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1)
        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
        self.a3 = Inception(192, 64, 96, 128, 16, 32, 32)
        self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True)
        self.a4 = Inception(480, 192, 96, 208, 16, 48, 64)
        self.b4 = Inception(512, 160, 112, 224, 24, 64, 64)
        self.c4 = Inception(512, 128, 128, 256, 24, 64, 64)
        self.d4 = Inception(512, 112, 144, 288, 32, 64, 64)
        self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)
        self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
        self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)
        self.avgpool = nn.AvgPool2d(kernel_size=8, stride=1)
        self.dropout = nn.Dropout(p=0.4)
        self.linear = nn.Linear(1024, num_classes)

        if self.aux_logits:
            self.aux1 = InceptionAux(512, num_classes)
            self.aux2 = InceptionAux(528, num_classes)
        else:
            self.aux1 = None
            self.aux2 = None
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.maxpool2(x)
        x = self.a3(x)
        x = self.b3(x)
        x = self.maxpool3(x)
        x = self.a4(x)
        
        aux1 = None
        if self.aux_logits and self.training:
            aux1 = self.aux1(x)

        x = self.b4(x)
        x = self.c4(x)
        x = self.d4(x)
        
        aux2 = None
        if self.aux_logits and self.training:
            aux2 = self.aux2(x)

        x = self.e4(x)
        x = self.maxpool4(x)
        x = self.a5(x)
        x = self.b5(x)
        x = self.avgpool(x)
        x = x.view(x.shape[0], -1)
        x = self.linear(x)
        x = self.dropout(x)

        if self.aux_logits and self.training:
            return [x, aux1, aux2]
        else:
            return x

Set Device and Model

use_cuda = torch.cuda.is_available()
print("use_cuda : ", use_cuda)

FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
device = torch.device("cuda:0" if use_cuda else "cpu")

net = GoogLeNet().to(device)

X = torch.randn(size=(3,227,227)).type(FloatTensor)
print(summary(net, (3,227,227)))

Loss and Optimizer

use_cuda = torch.cuda.is_available()
print("use_cuda : ", use_cuda)
device = torch.device("cuda:0" if use_cuda else "cpu")
model = GoogLeNet().to(device)
criterion = F.cross_entropy
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

Training Loop

writer = SummaryWriter("./googlenet/tensorboard") 

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data,target) in enumerate(train_loader):
        target = target.type(torch.LongTensor)
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        if model.aux_logits:
          loss0 = criterion(output[0], target)
          loss1 = criterion(output[1], target)
          loss2 = criterion(output[2], target)
          loss = loss0 + (0.3 * loss1) + (0.3 * loss2)
        else:
          loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 30 == 0:
            print(f"{batch_idx*len(data)}/{len(train_loader.dataset)}")

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target, reduction='mean').item()
            writer.add_scalar("Test Loss", test_loss, epoch)
            pred = output.argmax(1)
            correct += float((pred == target).sum())
            writer.add_scalar("Test Accuracy", correct, epoch)
            
        test_loss /= len(test_loader.dataset)
        correct /= len(test_loader.dataset)
        return test_loss, correct
        writer.close()

Per-Epoch Activity

for epoch in tqdm(range(1, num_epochs + 1)):
    train(model, device, trainloader, optimizer, epoch)
    test_loss, test_accuracy = test(model, device, testloader)
    writer.add_scalar("Test Loss", test_loss, epoch)
    writer.add_scalar("Test Accuracy", test_accuracy, epoch)
    print(f"Processing Result = Epoch : {epoch}   Loss : {test_loss}   Accuracy : {test_accuracy}")
    writer.close()

Result

print(f" Result of ResNet = Epoch : {epoch}   Loss : {test_loss}   Accuracy : {test_accuracy}")

Visualization

%load_ext tensorboard
%tensorboard --logdir=./alexnet/tensorboard --port=6006

Visualize result of Accuracy and Loss by Tensorboard

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