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目录
🚗VGG介绍
🚓那VGG它到底厉害在哪里呢?
🚓那什么是感受野呢?
🚓计算公式
🚓问题一:
🚓问题二:
🚓网络图
🚗pytorch搭建VGG网络
🚓1. model.py
Ὡ5;神奇处理之处
🚓2. Train.py
3. preDict.py
🚗注意
VGG 在2014年由牛津大学著名研究组 VGG(Visual Geometry Group)提出c;
(论文地址:https://arxiv.org/abs/1409.1556)
获得该年 ImageNet 竞赛中 Localization Task(定位任务)第一名和 Classification Task(分类任务)第二名。(可以说是非常的厉害)
通过堆叠多个小卷积核来替代大尺度卷积核c;可以减少训练参数c;同时能保证相同的感受野
决定某一层输出结果中一个元素所对应的输入层的区域大小c;被称作感受野(receptive field)
(简单来说就是输出feature map上的一个单元 对应 输入层(上层)上的区域大小)
🚗例如上图:maxpool1 感受野为2 (意思是上一层1格 对应 下一层2格)
🚓计算公式
F ( i + 1) 为第 i +1 层感受野 Stride 为第 i 层的步距 Ksize 为 卷积核 或 池化核 尺寸
堆叠两个3×3的卷积核替代5x5的卷积核c;堆叠三个3×3的卷积核替代7x7的卷积核。
替代前后感受野是否相同呢?
根据公式
(第一层)Feature map: F(1) = 1
(第二层)Conv3x3(3):
(第三层)Conv3x3(2):
(5×5卷积核感受野)
(第四层)Conv3x3(1):
(7×7卷积核感受野)
2个3×3的卷积核和一个5x5的卷积核感受野相同
证明可以通过堆叠两个3×3的卷积核替代5x5的卷积核c;堆叠三个3×3的卷积核替代7x7的卷积核
堆叠3×3卷积核后训练参数是否真的减少了?
注:CNN参数个数 = 卷积核尺寸×卷积核深度 × 卷积核组数 = 卷积核尺寸 × 输入特征矩阵深度 × 输出特征矩阵深度 现假设 输入特征矩阵深度 = 输出特征矩阵深度 = C
使用7×7卷积核所需参数个数:
堆叠三个3×3的卷积核所需参数个数:
很明显27小于49
VGG网络有多个版本c;
我们一般采用VGG16 (16的意思是16层=12层卷积层+4层全连接层)
其网络结构如下如所示:
看图和计算我们可以知道c;经3×3卷积的特征矩阵的尺寸是不改变的:
out =(in −F+2p)/S+1=(in −3+2)/1+1= in
out = in 大小一样
VGG网络分为 卷积层提取特征 和 全连接层进行分类 这两个模块
import torch.nn as nn
import torch
class VGG(nn.ModulE):
def __init__(self, features, num_classes=1000, init_weights=falsE):
super(VGG, self).__init__()
self.features = features # 卷积层提取特征
self.classifier = nn.Sequential( # 全连接层进行分类
nn.Dropout(p=0.5),
nn.Linear(512*7*7, 2048),
nn.ReLU(true),
nn.Dropout(p=0.5),
nn.Linear(2048, 2048),
nn.ReLU(true),
nn.Linear(2048, num_classes)
)
if init_weights:
self._initialize_weights() #初始化权重
def forWARD(self, X):
# N x 3 x 224 x 224
x = self.features(X)
# N x 512 x 7 x 7
x = torch.flatten(x, start_dim=1)
# N x 512*7*7
x = self.classifier(X)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.KaiR_520_11845@ing_normal_(m.weight, mode='fan_out', nonlinearity='Relu')
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
# nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
# vgg网络模型配置列表c;数字表示卷积核个数c;'M'表示最大池化层
cfgs = {
'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], # 模型A
'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], # 模型B
'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], # 模型D
'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], # 模型E
}
# 卷积层提取特征
def make_features(cfg: list): # 传入的是具体某个模型的参数列表
layers = []
in_chAnnels = 3 # 输入的原始图像(rgb三通道)
for v in cfg:
# 如果是最大池化层c;就进行池化
if v == "M":
layers += [nn.MaxPool2d(kernel_size=2, Stride=2)]
# 不然就是卷积层
else:
conv2d = nn.Conv2d(in_chAnnels, v, kernel_size=3, padding=1)
layers += [conv2d, nn.ReLU(true)]
in_chAnnels = v
return nn.Sequential(*layers) # 单星号(*)将参数以元组(tuplE)的形式导入
def vgg(model_name="vgg16", **kwargs): # 双星号(**)将参数以字典的形式导入
try:
cfg = cfgs[model_name]
except:
print("Warning: model number {} not in cfgs Dict!".format(model_Name))
exit(-1)
model = VGG(make_features(cfg), **kwargs) #**kwargs是你传入的字典数据
return model
和pytorch——AlexNet——训练花分类数据集_heart_6662的博客-CSDN博客的一样(数据还是花的数据)
import os
import json
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm
from model import vgg
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))
Train_loader = torch.utils.data.DataLoader(Train_dataset,
batch_size=batch_size, shuffle=True,
num_workers=0)
validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
batch_size=batch_size, shuffle=false,
num_workers=0)
print("using {} images for Training, {} images for validation.".format(Train_num,
val_num))
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
model_name = "vgg16"
net = vgg(model_name=model_name, num_classes=5, init_weights=TruE)
net.to(devicE)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
epochs = 30
best_acc = 0.0
save_path = './{}Net.pth'.format(model_Name)
Train_steps = len(Train_loader)
for epoch in range(epochs):
# Train
net.Train()
running_loss = 0.0
Train_bar = tqdm(Train_loader)
for step, data in enumerate(Train_bar):
images, labels = data
optimizer.zero_grad()
outputs = net(images.to(devicE))
loss = loss_function(outputs, labels.to(devicE))
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
with torch.no_grad():
val_bar = tqdm(validate_loader)
for val_data in val_bar:
val_images, val_labels = val_data
outputs = net(val_images.to(devicE))
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()
pytorch——AlexNet——训练花分类数据集_heart_6662的博客-CSDN博客与之前一样
import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from model import vgg
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))])
# load image
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)
# read class_inDict
json_path = './class_inDices.json'
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
json_file = open(json_path, "r")
class_inDict = json.load(json_filE)
# create model
model = vgg(model_name="vgg16", num_classes=5).to(devicE)
# load model weights
weights_path = "./vgg16Net.pth"
assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
model.load_state_Dict(torch.load(weights_path, map_LOCATIOn=devicE))
model.eval()
with torch.no_grad():
# 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()
VGG网络模型深度较深c;需要使用算力强大GPU进行训练(而且要内存大一点的GPUc;我3050跑不动c;pytorch会报错GPU内存不足)
你也可以试试改小batch_size
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