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

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]

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

任务2：分别把数据和标签都存在list里

• 不是我非让你存list里，因为dataloader到时候会在这里取数据
• 按照人家要求来，不要耍个性，让整list咱就给人家整

img_label = load_annotations('./flower_data/train.txt')

image_name = list(img_label.keys())
label = list(img_label.values())

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'

image_path = [os.path.join(train_dir,img) for img in image_name]
image_path

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, DataLoader
• 2.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_fileself.root_dir = root_dirself.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 = transformdef __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, labeldef 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

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=Btransforms.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])]),
}

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'])

val_dataset = FlowerDataset(root_dir=valid_dir, ann_file = './flower_data/val.txt', transform=data_transforms['valid'])

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=True)

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()))

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()))

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}

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 

# 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")

model_ft = models.resnet18()
model_ft

num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102))
input_size = 64
model_ft

# 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()

def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, filename='best.pth'):since = time.time()best_acc = 0model.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 = 0.0running_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() - sinceprint('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_accbest_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() - sinceprint('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 

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')