pytorch实现猫狗分类+数据集

2024-07-07 12:28:13| 来源: 网络整理| 查看: 265

PyTorch实现猫狗分类代码

实现环境：python3.7，pytorch，jupyter notebook 数据集： Kaggle网站猫狗分类数据集，训练集包括12500张猫和12500张狗，测试集为12500张猫狗无标签图片，我们需要把训练集的猫狗照片各取出2500张作为验证集。开始部分代码：导入一些必要的包，os包集成了一些对文件路径及目录进行操作的类，time包是一些和时间相关的方法

import torch import torchvision from torch.autograd import Variable from torchvision import datasets, transforms import os import matplotlib.pyplot as plt %matplotlib inline import time

数据的载入：读取数据并载入

data_dir = 'DogsVSCats' data_transform = {x:transforms.Compose([transforms.Scale([64, 64]), transforms.ToTensor()]) for x in ['train', 'valid']} image_datasets = {x:datasets.ImageFolder(root = os.path.join(data_dir,x), transform = data_transform[x]) for x in ['train', 'valid']} dataloader = {x:torch.utils.data.DataLoader(dataset = image_datasets[x], batch_size = 16, shuffle = True) for x in ['train', 'valid']}

数据预览：

# 数据预览 X_example, Y_example = next(iter(dataloader['train'])) print(u'X_example个数{}'.format(len(X_example))) print(u'Y_example个数{}'.format(len(Y_example))) index_classes = image_datasets['train'].class_to_idx print(index_classes) example_classes = image_datasets['train'].classes print(example_classes) img = torchvision.utils.make_grid(X_example) img = img.numpy().transpose([1,2,0]) print([example_classes[i] for i in Y_example]) plt.imshow(img) plt.show()

结果： X_example个数16 Y_example个数16 {‘Cat’: 0, ‘Dog’: 1} [‘Cat’, ‘Dog’] [‘Cat’, ‘Cat’, ‘Dog’, ‘Dog’, ‘Dog’, ‘Cat’, ‘Cat’, ‘Dog’, ‘Dog’, ‘Dog’, ‘Cat’, ‘Cat’, ‘Dog’, ‘Cat’, ‘Dog’, ‘Cat’] 在这里插入图片描述构建VGGnet

# 模型搭建简化了的VGGnet class Models(torch.nn.Module): def __init__(self): super(Models,self).__init__() self.Conv = torch.nn.Sequential( torch.nn.Conv2d(3, 64, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(64, 64, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size = 2, stride = 2), torch.nn.Conv2d(64, 128, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(128, 128, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size = 2, stride = 2), torch.nn.Conv2d(128, 256, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(256, 256, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(256, 256, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size = 2, stride = 2), torch.nn.Conv2d(256, 512, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(512, 512, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.Conv2d(512, 512, kernel_size = 3, stride = 1, padding = 1), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size = 2, stride = 2)) self.Classes = torch.nn.Sequential( torch.nn.Linear(4*4*512, 1024), torch.nn.ReLU(), torch.nn.Dropout(p = 0.5), torch.nn.Linear(1024, 1024), torch.nn.ReLU(), torch.nn.Dropout(p = 0.5), torch.nn.Linear(1024, 2)) def forward(self, inputs): x = self.Conv(inputs) x = x.view(-1, 4*4*512) x = self.Classes(x) return x model = Models() print(model)

模型训练

loss_f = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(),lr = 0.00001) Use_gpu = torch.cuda.is_available() if Use_gpu: model = model.cuda() epoch_n = 10 time_open = time.time() for epoch in range(epoch_n): print('epoch{}/{}'.format(epoch,epoch_n-1)) print('-'*10) for phase in ['train','valid']: if phase == 'train': print('training...') model.train(True) else: print('validing...') model.train(False) running_loss = 0.0 running_corrects = 0.0 for batch,data in enumerate(dataloader[phase],1): X,Y = data X,Y = Variable(X).cuda(),Variable(Y).cuda() y_pred = model(X) _,pred = torch.max(y_pred.data,1) optimizer.zero_grad() loss = loss_f(y_pred,Y) if phase == 'train': loss.backward() optimizer.step() running_loss += loss.data.item() running_corrects += torch.sum(pred == y.data) if batch%500 == 0 and phase =='train': print('batch{},trainLoss;{:.4f},trainAcc:{:.4f}'.format(batch,running_loss/bath,100*running_corrects/(16*batch))) epoch_loss = running_loss *16/len(image_datasets[phase]) epoch_acc = 100*running_corrects/len(image_datasets[phase]) print('{} Loss:{:.4f} Acc:{:.4f}%'.format(phase,epoch_loss,epoch_acc)) time_end = time.time()-time_open print(time_end)

补充：验证集划分程序，并将划分后的猫狗类别区分（略辣鸡）

import os import random import shutil from tqdm import tqdm def move(src_path,cat_path,dog_path): All_files = os.listdir(src_path) #读取原路径下的所有文件，即图片 length = len(All_files) cat,dog,i = 0,0,0 while i

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pytorch实现猫狗分类+数据集

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