pytorch 数据加载性能对比分析

传统方式需要10s，dat方式需要0.6s

import os
import time
import torch
import random
from common.coco_dataset import COCODataset
def 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 += 1
if __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

show_landmarks(**sample)

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脚本之家。如有错误或未考虑完全的地方，望不吝赐教。

来源：https://blog.csdn.net/jacke121/article/details/85236561