Dataloader数据集的制作
数据集Dataloader制作
如何自定义数据集： 1.数据和标签的目录结构先搞定(得知道到哪读数据)2.写好读取数据和标签路径的函数(根据自己数据集情况来写)3.完成单个数据与标签读取函数(给dataloader举一个例子) 咱们以花朵数据集为例： 原来数据集都是以文件夹为类别ID，现在咱们换一个套路，用txt文件指定数据路径与标签(实际情况基本都这样)这回咱们的任务就是在txt文件中获取图像路径与标签，然后把他们交给dataloader核心代码非常简单，按照对应格式传递需要的数据和标签就可以啦
import os import matplotlib.pyplot as plt %matplotlib inline import numpy as np import torch from torch import nn import torch.optim as optim import torchvision #pip install torchvision from torchvision import transforms, models, datasets #https://pytorch.org/docs/stable/torchvision/index.html import imageio import time import warnings import random import sys import copy import json from PIL import Image 12345678910111213141516171819
先来分细节整明白咱一会要干啥！
任务1：读取txt文件中的路径和标签 第一个小任务，从标注文件中读取数据和标签至于你准备存成什么格式，都可以的，一会能取出来东西就行def load_annotations(ann_file): data_infos = [] with open(ann_file) as f: samples = [x.strip().split(' ')for x in f.readlines] 1234'
def load_annotations(ann_file): data_infos = {} with open(ann_file) as f: samples = [x.strip().split(' ') for x in f.readlines()] for filename, gt_label in samples: data_infos[filename] = np.array(gt_label, dtype=np.int64) return data_infos 1234567'
img_label = load_annotations('./flower_data/train.txt') 1
image_name = list(img_label.keys()) label = list(img_label.values()) 12 任务3：图像数据路径得完整 因为一会咱得用这个路径去读数据，所以路径得加上前缀以后大家任务不同，数据不同，怎么加你看着来就行，反正得能读到图像
data_dir = './flower_data/' train_dir = data_dir + '/train_filelist' valid_dir = data_dir + '/val_filelist' 123'
image_path = [os.path.join(train_dir,img) for img in image_name] image_path 12 任务4：把上面那几个事得写在一起 1.注意要使用from torch.utils.data import Dataset, DataLoader2.类名定义class FlowerDataset(Dataset)，其中FlowerDataset可以改成自己的名字3.def init(self, root_dir, ann_file, transform=None):咱们要根据自己任务重写4.def getitem(self, idx):根据自己任务，返回图像数据和标签数据
from torch.utils.data import Dataset, DataLoader class FlowerDataset(Dataset): def __init__(self, root_dir, ann_file, transform=None): self.ann_file = ann_file self.root_dir = root_dir self.img_label = self.load_annotations() self.img = [os.path.join(self.root_dir,img) for img in list(self.img_label.keys())] self.label = [label for label in list(self.img_label.values())] self.transform = transform def __len__(self): return len(self.img) def __getitem__(self, idx): image = Image.open(self.img[idx]) label = self.label[idx] if self.transform: image = self.transform(image) label = torch.from_numpy(np.array(label)) return image, label def load_annotations(self): data_infos = {} with open(self.ann_file) as f: samples = [x.strip().split(' ') for x in f.readlines()] for filename, gt_label in samples: data_infos[filename] = np.array(gt_label, dtype=np.int64) return data_infos 123456789101112131415161718192021222324252627 任务5：数据预处理(transform) 1.预处理的事都在上面的__getitem__中完成，需要对图像和标签咋咋地的，要整啥事，都在上面整2.返回的数据和标签就是建模时模型的输入和损失函数中标签的输入，一定整明白自己模型要啥3.预处理这个事是你定的，不同的数据需要的方法也不一样，下面给出的是比较通用的方法
data_transforms = { 'train': transforms.Compose([ transforms.Resize(64), transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选 transforms.CenterCrop(64),#从中心开始裁剪 transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率 transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转 transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相 transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=B transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差 ]), 'valid': transforms.Compose([ transforms.Resize(64), transforms.CenterCrop(64), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } 123456789101112131415161718192021 任务6：根据写好的class FlowerDataset(Dataset):来实例化咱们的dataloader 1.构建数据集：分别创建训练和验证用的数据集（如果需要测试集也一样的方法）2.用Torch给的DataLoader方法来实例化(batch啥的自己定，根据你的显存来选合适的)3.打印看看数据里面是不是有东西了
train_dataset = FlowerDataset(root_dir=train_dir, ann_file = './flower_data/train.txt', transform=data_transforms['train']) 1
val_dataset = FlowerDataset(root_dir=valid_dir, ann_file = './flower_data/val.txt', transform=data_transforms['valid']) 1
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=64, shuffle=True) 12 任务7：用之前先试试，整个数据和标签对应下，看看对不对 1.别着急往模型里传，对不对都不知道呢2.用这个方法：iter(train_loader).next()来试试，得到的数据和标签是啥3.看不出来就把图画出来，标签打印出来，确保自己整的数据集没啥问题
image, label = iter(train_loader).next() sample = image[0].squeeze() sample = sample.permute((1, 2, 0)).numpy() sample *= [0.229, 0.224, 0.225] sample += [0.485, 0.456, 0.406] plt.imshow(sample) plt.show() print('Label is: {}'.format(label[0].numpy())) 12345678
image, label = iter(val_loader).next() sample = image[0].squeeze() sample = sample.permute((1, 2, 0)).numpy() sample *= [0.229, 0.224, 0.225] sample += [0.485, 0.456, 0.406] plt.imshow(sample) plt.show() print('Label is: {}'.format(label[0].numpy())) 12345678 任务8：咋用就是你来定了，把模型啥的整好往里面传吧 下面这些事之前都唠过了，按照自己习惯的方法整就得了
dataloaders = {'train':train_loader,'valid':val_loader} 1
model_name = 'resnet' #可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception'] #是否用人家训练好的特征来做 feature_extract = True 123'
# 是否用GPU训练 train_on_gpu = torch.cuda.is_available() if not train_on_gpu: print('CUDA is not available. Training on CPU ...') else: print('CUDA is available! Training on GPU ...') device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 123456789
model_ft = models.resnet18() model_ft 12
num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102)) input_size = 64 model_ft 1234
# 优化器设置 optimizer_ft = optim.Adam(model_ft.parameters(), lr=1e-3) scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)#学习率每7个epoch衰减成原来的1/10 criterion = nn.CrossEntropyLoss() 1234
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, filename='best.pth'): since = time.time() best_acc = 0 model.to(device) val_acc_history = [] train_acc_history = [] train_losses = [] valid_losses = [] LRs = [optimizer.param_groups[0]['lr']] best_model_wts = copy.deepcopy(model.state_dict()) for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # 训练和验证 for phase in ['train', 'valid']: if phase == 'train': model.train() # 训练 else: model.eval() # 验证 running_loss = 0.0 running_corrects = 0 # 把数据都取个遍 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # 清零 optimizer.zero_grad() # 只有训练的时候计算和更新梯度 with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) loss = criterion(outputs, labels) _, preds = torch.max(outputs, 1) #print(loss) # 训练阶段更新权重 if phase == 'train': loss.backward() optimizer.step() # 计算损失 running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / len(dataloaders[phase].dataset) epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset) time_elapsed = time.time() - since print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc)) # 得到最好那次的模型 if phase == 'valid' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) state = { 'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重 'best_acc': best_acc, 'optimizer' : optimizer.state_dict(),#优化器的状态信息 } torch.save(state, filename) if phase == 'valid': val_acc_history.append(epoch_acc) valid_losses.append(epoch_loss) scheduler.step(epoch_loss)#学习率衰减 if phase == 'train': train_acc_history.append(epoch_acc) train_losses.append(epoch_loss) print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr'])) LRs.append(optimizer.param_groups[0]['lr']) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # 训练完后用最好的一次当做模型最终的结果,等着一会测试 model.load_state_dict(best_model_wts) return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788'
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20, filename='best.pth') 1
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