## 一、简介

• 引入了 Inception 结构（融合不同尺度的特征信息）

• 使用1x1的卷积核进行降维以及映射处理 （虽然VGG网络中也有，但该论文介绍的更详细）

• 添加两个辅助分类器帮助训练

• 丢弃全连接层，使用平均池化层（大大减少模型参数，除去两个辅助分类器，网络大小只有vgg的1/20）

## 二、详解

inception原始结构

1×1卷积核的降维功能

### 2.辅助分类器（Auxiliary Classifier）

•An average pooling layer with 5×5 filter size and stride 3, resulting in an4×4×512 output for the
(4a), and 4×4×528 for the
(4d) stage.
（两个辅助分类器分别来自
inception4a和inception4d

•A 1×1 convolution with 128 filters for dimension reduction and rectified linearactivation.

•A fully connected layer with 1024 units and rectified linear activation.

•A dropout layer with 70% ratio of dropped outputs.

•A linear layer with softmax loss as the classifier (predicting thesame 1000 classes as the main classifier, but removed at inference time).

Inception V1的参数量=5607184，约为560w

• VGG网络搭建比较方便

## 三、网络搭建

### 1.model.py

``````import torch.nn as nn
import torch
import torch.nn.functional as F

def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
self.aux_logits = aux_logits

self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

self.conv2 = BasicConv2d(64, 64, kernel_size=1)
self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

if self.aux_logits:
self.aux1 = InceptionAux(512, num_classes)
self.aux2 = InceptionAux(528, num_classes)

self.dropout = nn.Dropout(0.4)
self.fc = nn.Linear(1024, num_classes)
if init_weights:
self._initialize_weights()

def forward(self, x):
# N x 3 x 224 x 224
x = self.conv1(x)
# N x 64 x 112 x 112
x = self.maxpool1(x)
# N x 64 x 56 x 56
x = self.conv2(x)
# N x 64 x 56 x 56
x = self.conv3(x)
# N x 192 x 56 x 56
x = self.maxpool2(x)

# N x 192 x 28 x 28
x = self.inception3a(x)
# N x 256 x 28 x 28
x = self.inception3b(x)
# N x 480 x 28 x 28
x = self.maxpool3(x)
# N x 480 x 14 x 14
x = self.inception4a(x)
# N x 512 x 14 x 14
if self.training and self.aux_logits:    # eval model lose this layer
aux1 = self.aux1(x)

x = self.inception4b(x)
# N x 512 x 14 x 14
x = self.inception4c(x)
# N x 512 x 14 x 14
x = self.inception4d(x)
# N x 528 x 14 x 14
if self.training and self.aux_logits:    # eval model lose this layer
aux2 = self.aux2(x)

x = self.inception4e(x)
# N x 832 x 14 x 14
x = self.maxpool4(x)
# N x 832 x 7 x 7
x = self.inception5a(x)
# N x 832 x 7 x 7
x = self.inception5b(x)
# N x 1024 x 7 x 7

x = self.avgpool(x)
# N x 1024 x 1 x 1
x = torch.flatten(x, 1)
# N x 1024
x = self.dropout(x)
x = self.fc(x)
# N x 1000 (num_classes)
if self.training and self.aux_logits:   # eval model lose this layer
return x, aux2, aux1
return x

def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)

class Inception(nn.Module):     #定义Inception结构
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
super(Inception, self).__init__()

self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)

self.branch2 = nn.Sequential(
BasicConv2d(in_channels, ch3x3red, kernel_size=1),
BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)   # 保证输出大小等于输入大小
)

self.branch3 = nn.Sequential(
BasicConv2d(in_channels, ch5x5red, kernel_size=1),
# 在官方的实现中，其实是3x3的kernel并不是5x5，这里我也懒得改了，具体可以参考下面的issue
# Please see https://github.com/pytorch/vision/issues/906 for details.
BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)   # 保证输出大小等于输入大小
)

self.branch4 = nn.Sequential(
BasicConv2d(in_channels, pool_proj, kernel_size=1)
)

def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)

outputs = [branch1, branch2, branch3, branch4]

class InceptionAux(nn.Module):      #定义辅助分类器结构
def __init__(self, in_channels, num_classes):
super(InceptionAux, self).__init__()
self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
self.conv = BasicConv2d(in_channels, 128, kernel_size=1)  # output[batch, 128, 4, 4]

self.fc1 = nn.Linear(2048, 1024)
self.fc2 = nn.Linear(1024, num_classes)

def forward(self, x):
# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
x = self.averagePool(x)
# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
x = self.conv(x)
# N x 128 x 4 x 4
x = torch.flatten(x, 1)
x = F.dropout(x, 0.5, training=self.training)
# N x 2048
x = F.relu(self.fc1(x), inplace=True)
x = F.dropout(x, 0.5, training=self.training)
# N x 1024
x = self.fc2(x)
# N x num_classes
return x

class BasicConv2d(nn.Module):       #包含卷积和激活函数的卷积模板
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
self.relu = nn.ReLU(inplace=True)

def forward(self, x):
x = self.conv(x)
x = self.relu(x)
return x``````

### 2.train.py

1. 实例化网络时的参数

``net = GoogLeNet(num_classes=5, aux_logits=True, init_weights=True)``

``````logits, aux_logits2, aux_logits1 = net(images.to(device))
loss0 = loss_function(logits, labels.to(device))
loss1 = loss_function(aux_logits1, labels.to(device))
loss2 = loss_function(aux_logits2, labels.to(device))
loss = loss0 + loss1 * 0.3 + loss2 * 0.3
loss.backward()
optimizer.step()``````
``````import os
import sys
import json

import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm

def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("using {} device.".format(device))

data_transform = {
"train": transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
"val": transforms.Compose([transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}

data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),
transform=data_transform["train"])
train_num = len(train_dataset)

# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)

batch_size = 32
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
print('Using {} dataloader workers every process'.format(nw))

batch_size=batch_size, shuffle=True,
num_workers=nw)

validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
batch_size=batch_size, shuffle=False,
num_workers=nw)

print("using {} images for training, {} images for validation.".format(train_num,
val_num))

# test_image, test_label = test_data_iter.next()

# 如果要使用官方的预训练权重，注意是将权重载入官方的模型，不是我们自己实现的模型
# 官方的模型中使用了bn层以及改了一些参数，不能混用
# import torchvision
# model_dict = net.state_dict()
# del_list = ["aux1.fc2.weight", "aux1.fc2.bias",
#             "aux2.fc2.weight", "aux2.fc2.bias",
#             "fc.weight", "fc.bias"]
# pretrain_dict = {k: v for k, v in pretrain_model.items() if k not in del_list}
# model_dict.update(pretrain_dict)
net.to(device)
loss_function = nn.CrossEntropyLoss()

epochs = 30
best_acc = 0.0
for epoch in range(epochs):
# train
net.train()
running_loss = 0.0
for step, data in enumerate(train_bar):
images, labels = data
logits, aux_logits2, aux_logits1 = net(images.to(device))
loss0 = loss_function(logits, labels.to(device))
loss1 = loss_function(aux_logits1, labels.to(device))
loss2 = loss_function(aux_logits2, labels.to(device))
loss = loss0 + loss1 * 0.3 + loss2 * 0.3
loss.backward()
optimizer.step()

# print statistics
running_loss += loss.item()

train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
epochs,
loss)

# validate
net.eval()
acc = 0.0  # accumulate accurate number / epoch
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(device))  # eval model only have last output layer
predict_y = torch.max(outputs, dim=1)[1]
acc += torch.eq(predict_y, val_labels.to(device)).sum().item()

val_accurate = acc / val_num
print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
(epoch + 1, running_loss / train_steps, val_accurate))

if val_accurate > best_acc:
best_acc = val_accurate
torch.save(net.state_dict(), save_path)

print('Finished Training')

if __name__ == '__main__':
main()
``````

### 3.predict.py

``````# create model

``missing_keys, unexpected_keys = model.load_state_dict(torch.load(model_weight_path), strict=False)``
``````import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

data_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

img_path = "../tulip.jpg"
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path)
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)

json_path = './class_indices.json'
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

with open(json_path, "r") as f:

# create model

assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
strict=False)

model.eval()
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()

print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
predict[predict_cla].numpy())
plt.title(print_res)
for i in range(len(predict)):
print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],
predict[i].numpy()))
plt.show()

if __name__ == '__main__':
main()``````

THE END