3.2 使用pytorch搭建AlexNet并训练花分类数据集

文章目录

class_indices.json
model.py
predict.py
train.py
创建自己的数据集

#详解

class_indices.json

{"0": "daisy","1": "dandelion","2": "roses","3": "sunflowers","4": "tulips"
}

model.py

import torch.nn as nn
import torchclass AlexNet(nn.Module):# 继承model类def __init__(self, num_classes=1000, init_weights=False):#初始化参数，定义参数与层结构super(AlexNet, self).__init__()self.features = nn.Sequential(#Sequential能把一系列的层结构打包成一个新的层结构，当前层结构被定义为提取特征的层结构nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2), #第一层 # input[3, 224, 224]  output[48, 55, 55]，他只用了一半的卷积核（padding=（1，2），计算后是小数，就又一样了）nn.ReLU(inplace=True),#inplace是pytorch通过一种操作增加计算量减少内存占用nn.MaxPool2d(kernel_size=3, stride=2),#卷积核大小是3，步距是2                  # output[48, 27, 27]nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]nn.ReLU(inplace=True),nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]nn.ReLU(inplace=True),nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6])self.classifier = nn.Sequential(#分类器nn.Dropout(p=0.5),#dropout的方法上全连接层随机失活（一般放在全裂阶层之间）p值随即失火的比例nn.Linear(128 * 6 * 6, 2048),#linear是全连接层nn.ReLU(inplace=True),#激活函数nn.Dropout(p=0.5),nn.Linear(2048, 2048),nn.ReLU(inplace=True),nn.Linear(2048, num_classes),#输出是数据集的类别个数)if init_weights:#初始化权重，定义在下面self._initialize_weights()def forward(self, x):x = self.features(x)x = torch.flatten(x, start_dim=1)#展平x = self.classifier(x)return xdef _initialize_weights(self):#其实不用，目前pytorch自动就是这个for m in self.modules():#会返回一个迭代器，遍历模型中所有的模块（遍历每一个层结构）if isinstance(m, nn.Conv2d):#是否是卷积nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')#是就去kaiming_normal初始化if m.bias is not None:#偏置不是0就置0nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):#如果是全连接层nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)

predict.py

import torch
from model import AlexNet
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import jsondata_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 = Image.open("../tulip.jpg")
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)# read class_indict
try:json_file = open('./class_indices.json', 'r')class_indict = json.load(json_file)
except Exception as e:print(e)exit(-1)# create model
model = AlexNet(num_classes=5)
# load model weights
model_weight_path = "./AlexNet.pth"
model.load_state_dict(torch.load(model_weight_path))
model.eval()
with torch.no_grad():# predict classoutput = torch.squeeze(model(img))#压缩掉batch的维度predict = torch.softmax(output, dim=0)predict_cla = torch.argmax(predict).numpy()
print(class_indict[str(predict_cla)], predict[predict_cla].item())
plt.show()

train.py

import torch
import torch.nn as nn
from torchvision import transforms, datasets, utils
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from model import AlexNet
import os
import json
import timedevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")#指定训练过程中使用的设备
print(device)data_transform = {#数据预处理函数"train": transforms.Compose([transforms.RandomResizedCrop(224),#随机裁剪到224*224像素大小transforms.RandomHorizontalFlip(),#水平随即反转transforms.ToTensor(),#转化成tensortransforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),"val": transforms.Compose([transforms.Resize((224, 224)),  # cannot 224, must (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#获取数据集的根目录（os.getcwd()：获取当前稳健所在目录；os.path.join合并到一起）
image_path = data_root + "/data_set/flower_data/"  # flower data set path
train_dataset = datasets.ImageFolder(root=image_path + "/train",#加载数据集，train下面每一类是一个文件夹transform=data_transform["train"])#transform是数据预处理（之前定义的），map
train_num = len(train_dataset)#数据集有多少张图片# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx####################！！！！！获取类的名称对应的索引
cla_dict = dict((val, key) for key, val in flower_list.items())#江键值反转
# write dict into json file
json_str = json.dumps(cla_dict, indent=4)#编码成json的格式
with open('class_indices.json', 'w') as json_file:json_file.write(json_str)batch_size = 32
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size, shuffle=True,num_workers=0)validate_dataset = datasets.ImageFolder(root=image_path + "/val",transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,batch_size=4, shuffle=True,num_workers=0)#查看数据集的代码
# test_data_iter = iter(validate_loader)
# test_image, test_label = test_data_iter.next()
#
# def imshow(img):
#     img = img / 2 + 0.5  # unnormalize
#     npimg = img.numpy()
#     plt.imshow(np.transpose(npimg, (1, 2, 0)))
#     plt.show()
#
# print(' '.join('%5s' % cla_dict[test_label[j].item()] for j in range(4)))
# imshow(utils.make_grid(test_image))net = AlexNet(num_classes=5, init_weights=True)#实例化，分类集有五类，初始化权重是truenet.to(device)#分设备
#损失函数与优化器
loss_function = nn.CrossEntropyLoss()
# pata = list(net.parameters())
optimizer = optim.Adam(net.parameters(), lr=0.0002)save_path = './AlexNet.pth'
best_acc = 0.0#用来保存最佳平均准确率，为了保存效果最好的一次模型
for epoch in range(10):#10轮# trainnet.train()#用net.train()与net.eavl() 因为用了dropout，希望只在训练时失活，所以用这个来管理dropoutrunning_loss = 0.0t1 = time.perf_counter()#统计训练一个epoch所使用的时间for step, data in enumerate(train_loader, start=0):#遍历数据集images, labels = data#将数据分成图像与对应的标签optimizer.zero_grad()#清空梯度信息outputs = net(images.to(device))#正向传播，将图像也指认到设备上loss = loss_function(outputs, labels.to(device))#计算损失loss.backward()#反向传播optimizer.step()#更新参数# print statisticsrunning_loss += loss.item()#将loss的值累加到runningloss中（loss。item才是loss的值）# print train process，打印训练进度rate = (step + 1) / len(train_loader)a = "*" * int(rate * 50)b = "." * int((1 - rate) * 50)print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")print()print(time.perf_counter()-t1)# validate验证net.eval()acc = 0.0  # accumulate accurate number / epochwith torch.no_grad():#禁止损失跟踪for val_data in validate_loader:val_images, val_labels = val_dataoutputs = net(val_images.to(device))predict_y = torch.max(outputs, dim=1)[1]acc += (predict_y == val_labels.to(device)).sum().item()#计算准确个数val_accurate = acc / val_numif val_accurate > best_acc:best_acc = val_accuratetorch.save(net.state_dict(), save_path)#保存权重print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %(epoch + 1, running_loss / step, val_accurate))print('Finished Training')

创建自己的数据集

偷懒的办法
在flowerdata下直接全删了改自己的

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3.2 使用pytorch搭建AlexNet并训练花分类数据集

文章目录

class_indices.json

model.py

predict.py

train.py

创建自己的数据集

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