Pytorch从入门到精通：二、dataset与datalodar
数据是深度学习的基础，一般来说，数据量越大，训练出来的模型也越强大。如果现在有了一些数据，该怎么把这些数据加到模型中呢？Pytorch中提供了dataset和dataloader，让我们一起来学习一下吧，dataset和dataloader博主将用几个例子来说明，感谢支持！
文章目录
- 一、dataset
- 二、查看dataset
- 三、os操作读取文件夹下的对象
- 四、Dataset
-
- Dataset实操一
- Dataset 实操二
- dataset实操三
- 五、 datalodar
-
- 自定义dataset并用datalodar加载
- 六、os的一些操作
一、dataset
提供一种方式去获取数据及其label
● 如何获取每一个数据及其label
● 告诉我们有多少数据
查看pytorch是否可用
print(torch.cuda.is_available()) # 查看当前cuda是否可用
True
二、查看dataset
from torch.utils.data import Dataset
help(Dataset) # 用帮助文档查看Dataset
Help on class Dataset in module torch.utils.data.dataset:
class Dataset(typing.Generic)
| Dataset(*args, **kwds)
|
| An abstract class representing a :class:Dataset.
|
| All datasets that represent a map from keys to data samples should subclass
| it. All subclasses should overwrite :meth:__getitem__, supporting fetching a
| data sample for a given key. Subclasses could also optionally overwrite
| :meth:__len__, which is expected to return the size of the dataset by many
| :class:~torch.utils.data.Samplerimplementations and the default options
| of :class:~torch.utils.data.DataLoader.
|
| … note::
| :class:~torch.utils.data.DataLoaderby default constructs a index
| sampler that yields integral indices. To make it work with a map-style
| dataset with non-integral indices/keys, a custom sampler must be provided.
|
| Method resolution order:
| Dataset
| typing.Generic
| builtins.object
|
| Methods defined here:
|
| add(self, other: ‘Dataset[T_co]’) -> ‘ConcatDataset[T_co]’
|
| getattr(self, attribute_name)
|
| getitem(self, index) -> +T_co
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| register_datapipe_as_function(function_name, cls_to_register, enable_df_api_tracing=False) from builtins.type
|
| register_function(function_name, function) from builtins.type
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| dict
| dictionary for instance variables (if defined)
|
| weakref
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| annotations = {‘functions’: typing.Dict[str, typing.Callable]}
|
| orig_bases = (typing.Generic[+T_co],)
|
| parameters = (+T_co,)
|
| functions = {‘concat’: functools.partial(|
| ----------------------------------------------------------------------
| Class methods inherited from typing.Generic:
|
| class_getitem(params) from builtins.type
|
| init_subclass(*args, **kwargs) from builtins.type
| This method is called when a class is subclassed.
|
| The default implementation does nothing. It may be
| overridden to extend subclasses.
|
| ----------------------------------------------------------------------
| Static methods inherited from typing.Generic:
|
| new(cls, *args, **kwds)
| Create and return a new object. See help(type) for accurate signature.
三、os操作读取文件夹下的对象
import os
dir_path = "hymenoptera_data\\hymenoptera_data\\train\\ants" # 文件夹目录
data_dir = os.listdir(dir_path) # 获取文件夹目录中的对象
data_dir
[‘0013035.jpg’,
‘1030023514_aad5c608f9.jpg’,
‘1095476100_3906d8afde.jpg’,
‘1099452230_d1949d3250.jpg’,
‘116570827_e9c126745d.jpg’,
‘1225872729_6f0856588f.jpg’,
‘1262877379_64fcada201.jpg’,
‘1269756697_0bce92cdab.jpg’,
‘1286984635_5119e80de1.jpg’,
‘132478121_2a430adea2.jpg’,
‘1360291657_dc248c5eea.jpg’,
‘1368913450_e146e2fb6d.jpg’,
‘1473187633_63ccaacea6.jpg’,
‘148715752_302c84f5a4.jpg’,
‘1489674356_09d48dde0a.jpg’,
‘149244013_c529578289.jpg’,
‘150801003_3390b73135.jpg’,
‘150801171_cd86f17ed8.jpg’,
‘154124431_65460430f2.jpg’,
‘162603798_40b51f1654.jpg’,
‘1660097129_384bf54490.jpg’,
‘167890289_dd5ba923f3.jpg’,
‘1693954099_46d4c20605.jpg’,
‘175998972.jpg’,
‘178538489_bec7649292.jpg’,
‘1804095607_0341701e1c.jpg’,
‘1808777855_2a895621d7.jpg’,
‘188552436_605cc9b36b.jpg’,
‘1917341202_d00a7f9af5.jpg’,
‘1924473702_daa9aacdbe.jpg’,
‘196057951_63bf063b92.jpg’,
‘196757565_326437f5fe.jpg’,
‘201558278_fe4caecc76.jpg’,
‘201790779_527f4c0168.jpg’,
‘2019439677_2db655d361.jpg’,
‘207947948_3ab29d7207.jpg’,
‘20935278_9190345f6b.jpg’,
‘224655713_3956f7d39a.jpg’,
‘2265824718_2c96f485da.jpg’,
‘2265825502_fff99cfd2d.jpg’,
‘226951206_d6bf946504.jpg’,
‘2278278459_6b99605e50.jpg’,
‘2288450226_a6e96e8fdf.jpg’,
‘2288481644_83ff7e4572.jpg’,
‘2292213964_ca51ce4bef.jpg’,
‘24335309_c5ea483bb8.jpg’,
‘245647475_9523dfd13e.jpg’,
‘255434217_1b2b3fe0a4.jpg’,
‘258217966_d9d90d18d3.jpg’,
‘275429470_b2d7d9290b.jpg’,
‘28847243_e79fe052cd.jpg’,
‘318052216_84dff3f98a.jpg’,
‘334167043_cbd1adaeb9.jpg’,
‘339670531_94b75ae47a.jpg’,
‘342438950_a3da61deab.jpg’,
‘36439863_0bec9f554f.jpg’,
‘374435068_7eee412ec4.jpg’,
‘382971067_0bfd33afe0.jpg’,
‘384191229_5779cf591b.jpg’,
‘386190770_672743c9a7.jpg’,
‘392382602_1b7bed32fa.jpg’,
‘403746349_71384f5b58.jpg’,
‘408393566_b5b694119b.jpg’,
‘424119020_6d57481dab.jpg’,
‘424873399_47658a91fb.jpg’,
‘450057712_771b3bfc91.jpg’,
‘45472593_bfd624f8dc.jpg’,
‘459694881_ac657d3187.jpg’,
‘460372577_f2f6a8c9fc.jpg’,
‘460874319_0a45ab4d05.jpg’,
‘466430434_4000737de9.jpg’,
‘470127037_513711fd21.jpg’,
‘474806473_ca6caab245.jpg’,
‘475961153_b8c13fd405.jpg’,
‘484293231_e53cfc0c89.jpg’,
‘49375974_e28ba6f17e.jpg’,
‘506249802_207cd979b4.jpg’,
‘506249836_717b73f540.jpg’,
‘512164029_c0a66b8498.jpg’,
‘512863248_43c8ce579b.jpg’,
‘518773929_734dbc5ff4.jpg’,
‘522163566_fec115ca66.jpg’,
‘522415432_2218f34bf8.jpg’,
‘531979952_bde12b3bc0.jpg’,
‘533848102_70a85ad6dd.jpg’,
‘535522953_308353a07c.jpg’,
‘540889389_48bb588b21.jpg’,
‘541630764_dbd285d63c.jpg’,
‘543417860_b14237f569.jpg’,
‘560966032_988f4d7bc4.jpg’,
‘5650366_e22b7e1065.jpg’,
‘6240329_72c01e663e.jpg’,
‘6240338_93729615ec.jpg’,
‘649026570_e58656104b.jpg’,
‘662541407_ff8db781e7.jpg’,
‘67270775_e9fdf77e9d.jpg’,
‘6743948_2b8c096dda.jpg’,
‘684133190_35b62c0c1d.jpg’,
‘69639610_95e0de17aa.jpg’,
‘707895295_009cf23188.jpg’,
‘7759525_1363d24e88.jpg’,
‘795000156_a9900a4a71.jpg’,
‘822537660_caf4ba5514.jpg’,
‘82852639_52b7f7f5e3.jpg’,
‘841049277_b28e58ad05.jpg’,
‘886401651_f878e888cd.jpg’,
‘892108839_f1aad4ca46.jpg’,
‘938946700_ca1c669085.jpg’,
‘957233405_25c1d1187b.jpg’,
‘9715481_b3cb4114ff.jpg’,
‘998118368_6ac1d91f81.jpg’,
‘ant photos.jpg’,
‘Ant_1.jpg’,
‘army-ants-red-picture.jpg’,
‘formica.jpeg’,
‘hormiga_co_por.jpg’,
‘imageNotFound.gif’,
‘kurokusa.jpg’,
‘MehdiabadiAnt2_600.jpg’,
‘Nepenthes_rafflesiana_ant.jpg’,
‘swiss-army-ant.jpg’,
‘termite-vs-ant.jpg’,
‘trap-jaw-ant-insect-bg.jpg’,
‘VietnameseAntMimicSpider.jpg’]
注意在windows下，路径使用双斜线\
四、Dataset
Dataset实操一
from torch.utils.data import Dataset
import os
from PIL import Image
class Mydata(Dataset):
def __init__(self,root_path,label_path):
self.root_path = root_path # hymenoptera_data/hymenoptera_data/train
self.label_path = label_path # /ants
self.path = os.path.join(self.root_path,self.label_path) # 从根目录开始的绝对路径
self.image_path = os.listdir(self.path) # 从根目录开始绝对路径文件夹下的对象 hymenoptera_data/hymenoptera_data/train/ants下的图片 type--> list
def __getitem__(self, idx):
image_name = self.image_path[idx] # 单一的图片名称
image_item_path = os.path.join(self.root_path,self.label_path,image_name)
img = Image.open(image_item_path)
label = self.label_path
return img,label
def __len__(self):
return len(self.image_path)
ants_root_path = "hymenoptera_data\\hymenoptera_data\\train"
ants_label_path = "ants"
Ants = Mydata(ants_root_path,ants_label_path)
Ants[0][0].show() # 第一个0是索引，拿到第一个图像和标签，第二个0是拿到第一个图像，并显示出来
D:\anaconda\envs\Gpu-Pytorch\lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
bee_label_path = "bees"
Bees = Mydata(bee_root_path,bee_label_path)
Bees[0][0].show()
# 创建训练集
train = Ants + Bees # 直接将数据集加起来
print("the length of Ants is ",Ants.__len__())
print("the length of Bees is ",Bees.__len__())
print("the length of train is ",train.__len__())
the length of Ants is 124
the length of Bees is 121
the length of train is 245
# 查看是否正确
train[123][0].show() # 应该为蚂蚁
train[124][0].show() # 应该为蜜蜂
Dataset 实操二
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch学习
@File ：task_3.py
@IDE ：PyCharm
@Author ：咋
@Date ：2023/6/29 14:29
"""
from torch.utils.data import Dataset
import os
from PIL import Image
class Mydata(Dataset):
def __init__(self,root_path,image_path,label_path):
self.root_path = root_path
self.image_path = image_path
self.label_path = label_path
self.A_image_path = os.path.join(self.root_path,self.image_path)
self.A_label_path = os.path.join(self.root_path,self.label_path)
self.img_item = os.listdir(self.A_image_path)
self.label_item = os.listdir(self.A_label_path)
def __getitem__(self, idx):
img_name = self.img_item[idx]
img_path = os.path.join(self.A_image_path, img_name)
label_list = [i.split(".")[0] for i in self.label_item if i.count(".") == 1]
# print(label_list)
if img_name.split(".")[0] in label_list:
img = Image.open(img_path)
label_path = os.path.join(self.A_label_path,img_name.split(".")[0])
label_path += ".txt"
file = open(label_path, 'r')
label = file.read()
file.close()
return img,label
else:
print("{0}没有对应的标签".format(img_name))
return 0
def __len__(self):
return len(self.img_item)
train_ants_root_path = "练手数据集\\train"
train_ants_image_path = "ants_image"
train_ants_label_path = "ants_label"
Ants = Mydata(train_ants_root_path,train_ants_image_path,train_ants_label_path)
for i in range(Ants.__len__()):
try:
print(Ants[i][1])
except TypeError:
print("跳过此张图片！")
# Ants[122][0].show()
# print(Ants[122][1])
ants
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formica.jpeg没有对应的标签
跳过此张图片！
ants
imageNotFound.gif没有对应的标签
跳过此张图片！
ants
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添加了异常捕获，解决了图片没有对应标签的问题！
dataset实操三
使用torchvision中的数据集创建dataset
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Pytorch_learn
@File ：dataset_3.py
@IDE ：PyCharm
@Author ：咋
@Date ：2023/7/2 14:58
"""
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from torchvision import transforms
dataset = torchvision.datasets.MNIST("./Mnist",train=True,download=True,transform=transforms.ToTensor())
dataloader = DataLoader(dataset,batch_size=64,shuffle=False,num_workers=0)
# 使用tensorboard将dataloader展示出来
'''方式一
# write = SummaryWriter("log_2")
# count = 0
# for data in dataloader:
# image,label = data
# # print(data[1])
# # print(image.shape)
# write.add_images("dataloader",image,count)
# count += 1
'''
# 方式二
write = SummaryWriter("log_3")
for i,data in enumerate(dataloader):
image,label = data
write.add_images("dataloader",image,i)
write.close()
enumerate会将可迭代对象中的内容和其索引一起返回：
例如对于一个seq，得到：
(0, seq[0]), (1, seq[1]), (2, seq[2])
五、 datalodar
为后面的网络提供不同的数据类型
自定义dataset并用datalodar加载
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from net import Net
import softmax
from torch.utils.data import Dataset
import os
from PIL import Image
import numpy as np
transform_tool = transforms.ToTensor() # 创建一个transform工具
# # image_tensor = transform_tool(image)
with open("mnist-label.txt", 'r') as f:
label_str = f.read().strip() # 打开文件读入缓存
class Mydata(Dataset):
def __init__(self,image_path):
self.image_path = image_path
# self.label_path = label_path # /ants
self.image = os.listdir(self.image_path) # 从根目录开始绝对路径文件夹下的对象 hymenoptera_data/hymenoptera_data/train/ants下的图片 type--> list
def __getitem__(self, idx):
image_name = self.image[idx] # 单一的图片名称
image_item_path = os.path.join(self.image_path,image_name)
img = Image.open(image_item_path)
# transform_tool = transforms.ToTensor() # 创建一个transform工具
img = transform_tool(img)
labels_list = [int(label) for label in label_str.split(',')] # 读取标签，不用每次都打开
labels = np.array(labels_list)
label = labels[idx]
return img,label
def __len__(self):
return len(self.image)
# trainset = Mydata("mnist-dataset")
# 设置训练参数
batch_size = 32
epochs = 5
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 数据集
# transform = transforms.Compose([transforms.ToTensor(),
# transforms.Normalize((0.5,), (0.5,))])
# trainset =
# trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)
trainset = Mydata("mnist-dataset")
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=False,num_workers=0)
print(len(trainloader))
# 输出提示信息
print("batch_size:", batch_size)
print("data_batches:", len(trainloader))
print("epochs:", epochs)
# 神经网络
net = Net().to(device)
# net.load_state_dict(torch.load('./model/model.pth'))
# 损失函数和优化器
# 负对数似然损失
criterion = nn.NLLLoss()
optimizer = optim.SGD(net.parameters(), lr=0.0005, momentum=0.9)
total_correct = 0
total_samples = 0
# 训练网络
```python
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(trainloader):
inputs, labels = data
inputs, labels = Variable(inputs).to(device), Variable(labels).to(device)
# 反向传播优化参数
optimizer.zero_grad()
outputs = net(inputs)
# outputs = int(net(inputs))
# print(outputs)
labels = labels.long()
# print(labels)
# print(type(labels))
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
# 计算每个batch的准确率
_, predicted = torch.max(outputs.data, 1)
total_samples += labels.size(0)
total_correct += (predicted == labels).sum().item()
if i % 5 == 0: # 每轮输出损失值
accuracy = 100.0 * total_correct / total_samples
print('[epoch: %d, batches: %d] loss: %.5f accuracy: %.2f%%' %
(epoch + 1, i + 1, running_loss / 2000, accuracy))
total_correct = 0
total_samples = 0
running_loss = 0.0
torch.save(net.state_dict(), 'model.pth') # 每轮保存模型参数
print('Finished Training')
打开文件可以在定义类之前打开，把文件信息读入缓存中，在__getitem__中读取各个标签，不用每次执行__getitem__都打开一次文件。
六、os的一些操作
windows使用两个\\表示路径
import os
dir_path = "/home/aistudio" # 文件夹目录
data_dir = os.listdir(dir_path) # 获取文件夹目录中的对象
label_path = "label"
all_path = os.path.join(dir_path,label_path)