VGGNet 논문 리뷰

Very Deep Convolutional Networks for Large-Scale Image Recognition (ICLR 2015)

VGGNet Architecture

VGGNet Architecture는 매우 단순해서 VGG16 그림 한장으로 설명하겠음.

VGG16 Architecture

VGG19는 단순히 CONV3, CONV4, CONV5에 Convolution + Relu 연산을 한 번씩 더 한다.

 

VGG 코드

import math

import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo

__all__ = [
    "VGG",
    "vgg11",
    "vgg11_bn",
    "vgg13",
    "vgg13_bn",
    "vgg16",
    "vgg16_bn",
    "vgg19_bn",
    "vgg19",
]


model_urls = {
    "vgg11": "https://download.pytorch.org/models/vgg11-bbd30ac9.pth",
    "vgg13": "https://download.pytorch.org/models/vgg13-c768596a.pth",
    "vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth",
    "vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
}


class VGG(nn.Module):

    def __init__(self, features, num_classes=1000):
        super(VGG, self).__init__()
        self.features = features
        # self.classifier = nn.Sequential(
        #     nn.Linear(512 * 7 * 7, 4096),
        #     nn.ReLU(inplace=True),
        #     nn.Dropout(),
        #     nn.Linear(4096, 4096),
        #     nn.ReLU(inplace=True),
        #     nn.Dropout(),
        #     nn.Linear(4096, num_classes),
        # )
        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        # x = x.view(x.size(0), -1)
        # x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                n = m.weight.size(1)
                m.weight.data.normal_(0, 0.01)
                m.bias.data.zero_()


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 1
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"],
    "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"],
}


def vgg11(pretrained=False, model_root=None, **kwargs):
    """VGG 11-layer model (configuration "A")"""
    model = VGG(make_layers(cfg["A"]), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["vgg11"], model_root))
    return model


def vgg11_bn(**kwargs):
    """VGG 11-layer model (configuration "A") with batch normalization"""
    kwargs.pop("model_root", None)
    return VGG(make_layers(cfg["A"], batch_norm=True), **kwargs)


def vgg13(pretrained=False, model_root=None, **kwargs):
    """VGG 13-layer model (configuration "B")"""
    model = VGG(make_layers(cfg["B"]), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["vgg13"], model_root))
    return model


def vgg13_bn(**kwargs):
    """VGG 13-layer model (configuration "B") with batch normalization"""
    kwargs.pop("model_root", None)
    return VGG(make_layers(cfg["B"], batch_norm=True), **kwargs)


def vgg16(pretrained=False, model_root=None, **kwargs):
    """VGG 16-layer model (configuration "D")"""
    model = VGG(make_layers(cfg["D"]), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["vgg16"], model_root))
    return model


def vgg16_bn(**kwargs):
    """VGG 16-layer model (configuration "D") with batch normalization"""
    kwargs.pop("model_root", None)
    return VGG(make_layers(cfg["D"], batch_norm=True), **kwargs)


def vgg19(pretrained=False, model_root=None, **kwargs):
    """VGG 19-layer model (configuration "E")"""
    model = VGG(make_layers(cfg["E"]), **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["vgg19"], model_root))
    return model


def vgg19_bn(**kwargs):
    """VGG 19-layer model (configuration 'E') with batch normalization"""
    kwargs.pop("model_root", None)
    return VGG(make_layers(cfg["E"], batch_norm=True), **kwargs)

 

vgg16(pretrained=True)를 사용하면 pytorch에서 ImageNet 데이터를 사용하여 사전학습한 모델 weight를 가지고 올 수 있다.