3.2 使用pytorch搭建AlexNet并训练花分类数据集

文章目录

class_indices.jsonmodel.pypredict.pytrain.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 torch class 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 x def _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0 nn.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 json 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 = 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 class output = 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 time device = 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tensor transforms.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true net.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 # train net.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dropout running_loss = 0.0 t1 = 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 statistics running_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 / epoch with torch.no_grad():#禁止损失跟踪 for val_data in validate_loader: val_images, val_labels = val_data outputs = 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_num if val_accurate > best_acc: best_acc = val_accurate torch.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