这篇教程pytorch 数据加载性能对比分析写得很实用，希望能帮到您。
传统方式需要10s，dat方式需要0.6s import osimport timeimport torchimport randomfrom common.coco_dataset import COCODatasetdef gen_data(batch_size,data_path,target_path): os.makedirs(target_path,exist_ok=True) dataloader = torch.utils.data.DataLoader(COCODataset(data_path, (352, 352), is_training=False, is_scene=True), batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=False, drop_last=True) # DataLoader start = time.time() for step, samples in enumerate(dataloader): images, labels, image_paths = samples["image"], samples["label"], samples["img_path"] print("time", images.size(0), time.time() - start) start = time.time() # torch.save(samples,target_path+ '/' + str(step) + '.dat') print(step)def cat_100(target_path,batch_size=100): paths = os.listdir(target_path) li = [i for i in range(len(paths))] random.shuffle(li) images = [] labels = [] image_paths = [] start = time.time() for i in range(len(paths)): samples = torch.load(target_path + str(li[i]) + ".dat") image, label, image_path = samples["image"], samples["label"], samples["img_path"] images.append(image.cuda()) labels.append(label.cuda()) image_paths.append(image_path) if i % batch_size == batch_size - 1: images = torch.cat((images), 0) print("time", images.size(0), time.time() - start) images = [] labels = [] image_paths = [] start = time.time() i += 1if __name__ == '__main__': os.environ["CUDA_VISIBLE_DEVICES"] = '3' batch_size=320 # target_path='d:/test_1000/' target_path='d:/img_2/' data_path = r'D:/dataset/origin_all_datas/_2train' gen_data(batch_size,data_path,target_path) # get_data(target_path,batch_size) # cat_100(target_path,batch_size)这个读取数据也比较快：320 batch_size 450ms def cat_100(target_path,batch_size=100): paths = os.listdir(target_path) li = [i for i in range(len(paths))] random.shuffle(li) images = [] labels = [] image_paths = [] start = time.time() for i in range(len(paths)): samples = torch.load(target_path + str(li[i]) + ".dat") image, label, image_path = samples["image"], samples["label"], samples["img_path"] images.append(image)#.cuda()) labels.append(label)#.cuda()) image_paths.append(image_path) if i % batch_size < batch_size - 1: i += 1 continue i += 1 images = torch.cat(([image.cuda() for image in images]), 0) print("time", images.size(0), time.time() - start) images = [] labels = [] image_paths = [] start = time.time()补充：pytorch数据加载和处理问题解决方案 最近跟着pytorch中文文档学习遇到一些小问题，已经解决，在此对这些错误进行记录： 在读取数据集时报错： AttributeError: 'Series' object has no attribute 'as_matrix'
在显示图片是时报错： ValueError: Masked arrays must be 1-D
显示单张图片时figure一闪而过 在显示多张散点图的时候报错： TypeError: show_landmarks() got an unexpected keyword argument 'image'
解决方案主要问题在这一行： 最终目的是将Series转为Matrix，即调用np.mat即可完成。 修改前 landmarks =landmarks_frame.iloc[n, 1:].as_matrix() 修改后 landmarks =np.mat(landmarks_frame.iloc[n, 1:]) 打散点的x和y坐标应该均为向量或列表，故将landmarks后使用tolist()方法即可 修改前 plt.scatter(landmarks[:,0],landmarks[:,1],s=10,marker='.',c='r') 修改后 plt.scatter(landmarks[:,0].tolist(),landmarks[:,1].tolist(),s=10,marker='.',c='r') 前面使用plt.ion()打开交互模式，则后面在plt.show()之前一定要加上plt.ioff()。这里直接加到函数里面，避免每次plt.show()之前都用plt.ioff() 修改前 def show_landmarks(imgs,landmarks): '''显示带有地标的图片''' plt.imshow(imgs) plt.scatter(landmarks[:,0].tolist(),landmarks[:,1].tolist(),s=10,marker='.',c='r')#打上红色散点 plt.pause(1)#绘图窗口延时 修改后 def show_landmarks(imgs,landmarks): '''显示带有地标的图片''' plt.imshow(imgs) plt.scatter(landmarks[:,0].tolist(),landmarks[:,1].tolist(),s=10,marker='.',c='r')#打上红色散点 plt.pause(1)#绘图窗口延时 plt.ioff() 网上说对于字典类型的sample可通过 **sample的方式获取每个键下的值，但是会报错，于是把输入写的详细一点，就成功了。 修改前 修改后 show_landmarks(sample['image'],sample['landmarks']) 以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。如有错误或未考虑完全的地方，望不吝赐教。
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