PyTorch中torch.utils.data.Dataset的介紹與實(shí)戰(zhàn)
一、前言
訓(xùn)練模型一般都是先處理 數(shù)據(jù)的輸入問題 和 預(yù)處理問題 。Pytorch提供了幾個(gè)有用的工具：torch.utils.data.Dataset 類和 torch.utils.data.DataLoader 類 。
流程是先把原始數(shù)據(jù)轉(zhuǎn)變成 torch.utils.data.Dataset 類，隨后再把得到的 torch.utils.data.Dataset 類當(dāng)作一個(gè)參數(shù)傳遞給 torch.utils.data.DataLoader 類，得到一個(gè)數(shù)據(jù)加載器，這個(gè)數(shù)據(jù)加載器每次可以返回一個(gè) Batch 的數(shù)據(jù)供模型訓(xùn)練使用。
在 pytorch 中，提供了一種十分方便的數(shù)據(jù)讀取機(jī)制，即使用 torch.utils.data.Dataset 與 Dataloader 組合得到數(shù)據(jù)迭代器。在每次訓(xùn)練時(shí)，利用這個(gè)迭代器輸出每一個(gè) batch 數(shù)據(jù)，并能在輸出時(shí)對數(shù)據(jù)進(jìn)行相應(yīng)的預(yù)處理或數(shù)據(jù)增廣操作。
本文我們主要介紹對 torch.utils.data.Dataset 的理解，對 Dataloader 的介紹請參考我的另一篇文章：【PyTorch】torch.utils.data.DataLoader 簡單介紹與使用
在本文的最后將給出 torch.utils.data.Dataset 與 Dataloader 結(jié)合使用處理數(shù)據(jù)的實(shí)戰(zhàn)代碼。
二、torch.utils.data.Dataset 是什么
1. 干什么用的？
- pytorch 提供了一個(gè)數(shù)據(jù)讀取的方法，其由兩個(gè)類構(gòu)成：torch.utils.data.Dataset 和 DataLoader。
- 如果我們要自定義自己讀取數(shù)據(jù)的方法，就需要繼承類 torch.utils.data.Dataset ，并將其封裝到DataLoader 中。
- torch.utils.data.Dataset 是一個(gè) 類 Dataset 。通過重寫定義在該類上的方法，我們可以實(shí)現(xiàn)多種數(shù)據(jù)讀取及數(shù)據(jù)預(yù)處理方式。
2. 長什么樣子？
torch.utils.data.Dataset 的源碼：
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,
supporting integer indexing in range from 0 to len(self) exclusive.
"""
def __getitem__(self, index):
raise NotImplementedError
def __len__(self):
raise NotImplementedError
def __add__(self, other):
return ConcatDataset([self, other])
注釋翻譯：
表示一個(gè)數(shù)據(jù)集的抽象類。
所有其他數(shù)據(jù)集都應(yīng)該對其進(jìn)行子類化。 所有子類都應(yīng)該重寫提供數(shù)據(jù)集大小的 __len__ 和 __getitem__ ，支持從 0 到 len(self) 獨(dú)占的整數(shù)索引。
理解：
就是說，Dataset 是一個(gè) 數(shù)據(jù)集 抽象類，它是其他所有數(shù)據(jù)集類的父類（所有其他數(shù)據(jù)集類都應(yīng)該繼承它），繼承時(shí)需要重寫方法 __len__ 和 __getitem__ ， __len__ 是提供數(shù)據(jù)集大小的方法， __getitem__ 是可以通過索引號找到數(shù)據(jù)的方法。
三、通過繼承 torch.utils.data.Dataset 定義自己的數(shù)據(jù)集類
torch.utils.data.Dataset 是代表自定義數(shù)據(jù)集的抽象類，我們可以定義自己的數(shù)據(jù)類抽象這個(gè)類，只需要重寫__len__和__getitem__這兩個(gè)方法就可以。
要自定義自己的 Dataset 類，至少要重載兩個(gè)方法：__len__, __getitem__
- __len__返回的是數(shù)據(jù)集的大小
- __getitem__實(shí)現(xiàn)索引數(shù)據(jù)集中的某一個(gè)數(shù)據(jù)
下面將簡單實(shí)現(xiàn)一個(gè)返回 torch.Tensor 類型的數(shù)據(jù)集：
from torch.utils.data import Dataset
import torch
class TensorDataset(Dataset):
# TensorDataset繼承Dataset, 重載了__init__, __getitem__, __len__
# 實(shí)現(xiàn)將一組Tensor數(shù)據(jù)對封裝成Tensor數(shù)據(jù)集
# 能夠通過index得到數(shù)據(jù)集的數(shù)據(jù)，能夠通過len，得到數(shù)據(jù)集大小
def __init__(self, data_tensor, target_tensor):
self.data_tensor = data_tensor
self.target_tensor = target_tensor
def __getitem__(self, index):
return self.data_tensor[index], self.target_tensor[index]
def __len__(self):
return self.data_tensor.size(0) # size(0) 返回當(dāng)前張量維數(shù)的第一維
# 生成數(shù)據(jù)
data_tensor = torch.randn(4, 3) # 4 行 3 列，服從正態(tài)分布的張量
print(data_tensor)
target_tensor = torch.rand(4) # 4 個(gè)元素，服從均勻分布的張量
print(target_tensor)
# 將數(shù)據(jù)封裝成 Dataset （用 TensorDataset 類）
tensor_dataset = TensorDataset(data_tensor, target_tensor)
# 可使用索引調(diào)用數(shù)據(jù)
print('tensor_data[0]: ', tensor_dataset[0])
# 可返回?cái)?shù)據(jù)len
print('len os tensor_dataset: ', len(tensor_dataset))
輸出結(jié)果：
tensor([[ 0.8618, 0.4644, -0.5929],
[ 0.9566, -0.9067, 1.5781],
[ 0.3943, -0.7775, 2.0366],
[-1.2570, -0.3859, -0.3542]])
tensor([0.1363, 0.6545, 0.4345, 0.9928])
tensor_data[0]: (tensor([ 0.8618, 0.4644, -0.5929]), tensor(0.1363))
len os tensor_dataset: 4
四、為什么要定義自己的數(shù)據(jù)集類？
因?yàn)槲覀兛梢酝ㄟ^定義自己的數(shù)據(jù)集類并重寫該類上的方法 實(shí)現(xiàn)多種多樣的（自定義的）數(shù)據(jù)讀取方式。
比如，我們重寫 __init__ 實(shí)現(xiàn)用 pd.read_csv 讀取 csv 文件：
from torch.utils.data import Dataset
import pandas as pd # 這個(gè)包用來讀取CSV數(shù)據(jù)
# 繼承Dataset，定義自己的數(shù)據(jù)集類 mydataset
class mydataset(Dataset):
def __init__(self, csv_file): # self 參數(shù)必須，其他參數(shù)及其形式隨程序需要而不同，比如(self,*inputs)
self.csv_data = pd.read_csv(csv_file)
def __len__(self):
return len(self.csv_data)
def __getitem__(self, idx):
data = self.csv_data.values[idx]
return data
data = mydataset('spambase.csv')
print(data[3])
print(len(data))
輸出結(jié)果：
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 6.300e-01 0.000e+00 3.100e-01
6.300e-01 3.100e-01 6.300e-01 3.100e-01 3.100e-01 3.100e-01 0.000e+00
0.000e+00 3.100e-01 0.000e+00 0.000e+00 3.180e+00 0.000e+00 3.100e-01
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
1.370e-01 0.000e+00 1.370e-01 0.000e+00 0.000e+00 3.537e+00 4.000e+01
1.910e+02 1.000e+00]
4601
要點(diǎn)：
- 自己定義的 dataset 類需要繼承 Dataset。
- 需要實(shí)現(xiàn)必要的魔法方法:
在 __init__ 方法里面進(jìn)行 讀取數(shù)據(jù)文件 。
在 __getitem__ 方法里支持通過下標(biāo)訪問數(shù)據(jù)。
在 __len__ 方法里返回自定義數(shù)據(jù)集的大小，方便后期遍歷。
五、實(shí)戰(zhàn)：torch.utils.data.Dataset + Dataloader 實(shí)現(xiàn)數(shù)據(jù)集讀取和迭代
實(shí)例 1
數(shù)據(jù)集 spambase.csv 用的是 UCI 機(jī)器學(xué)習(xí)存儲(chǔ)庫里的垃圾郵件數(shù)據(jù)集，它一條數(shù)據(jù)有57個(gè)特征和1個(gè)標(biāo)簽。
import torch.utils.data as Data
import pandas as pd # 這個(gè)包用來讀取CSV數(shù)據(jù)
import torch
# 繼承Dataset，定義自己的數(shù)據(jù)集類 mydataset
class mydataset(Data.Dataset):
def __init__(self, csv_file): # self 參數(shù)必須，其他參數(shù)及其形式隨程序需要而不同，比如(self,*inputs)
data_csv = pd.DataFrame(pd.read_csv(csv_file)) # 讀數(shù)據(jù)
self.csv_data = data_csv.drop(axis=1, columns='58', inplace=False) # 刪除最后一列標(biāo)簽
def __len__(self):
return len(self.csv_data)
def __getitem__(self, idx):
data = self.csv_data.values[idx]
return data
data = mydataset('spambase.csv')
x = torch.tensor(data[:5]) # 前五個(gè)數(shù)據(jù)
y = torch.tensor([1, 1, 1, 1, 1]) # 標(biāo)簽
torch_dataset = Data.TensorDataset(x, y) # 對給定的 tensor 數(shù)據(jù)，將他們包裝成 dataset
loader = Data.DataLoader(
# 從數(shù)據(jù)庫中每次抽出batch size個(gè)樣本
dataset = torch_dataset, # torch TensorDataset format
batch_size = 2, # mini batch size
shuffle=True, # 要不要打亂數(shù)據(jù) (打亂比較好)
num_workers=2, # 多線程來讀數(shù)據(jù)
)
def show_batch():
for step, (batch_x, batch_y) in enumerate(loader):
print("steop:{}, batch_x:{}, batch_y:{}".format(step, batch_x, batch_y))
show_batch()
輸出結(jié)果：
steop:0, batch_x:tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3000e-01, 0.0000e+00,
3.1000e-01, 6.3000e-01, 3.1000e-01, 6.3000e-01, 3.1000e-01, 3.1000e-01,
3.1000e-01, 0.0000e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00,
3.1800e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.3500e-01, 0.0000e+00, 1.3500e-01, 0.0000e+00, 0.0000e+00,
3.5370e+00, 4.0000e+01, 1.9100e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3000e-01, 0.0000e+00,
3.1000e-01, 6.3000e-01, 3.1000e-01, 6.3000e-01, 3.1000e-01, 3.1000e-01,
3.1000e-01, 0.0000e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00,
3.1800e+00, 0.0000e+00, 3.1000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.3700e-01, 0.0000e+00, 1.3700e-01, 0.0000e+00, 0.0000e+00,
3.5370e+00, 4.0000e+01, 1.9100e+02]], dtype=torch.float64), batch_y:tensor([1, 1])
steop:1, batch_x:tensor([[2.1000e-01, 2.8000e-01, 5.0000e-01, 0.0000e+00, 1.4000e-01, 2.8000e-01,
2.1000e-01, 7.0000e-02, 0.0000e+00, 9.4000e-01, 2.1000e-01, 7.9000e-01,
6.5000e-01, 2.1000e-01, 1.4000e-01, 1.4000e-01, 7.0000e-02, 2.8000e-01,
3.4700e+00, 0.0000e+00, 1.5900e+00, 0.0000e+00, 4.3000e-01, 4.3000e-01,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
7.0000e-02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.3200e-01, 0.0000e+00, 3.7200e-01, 1.8000e-01, 4.8000e-02,
5.1140e+00, 1.0100e+02, 1.0280e+03],
[6.0000e-02, 0.0000e+00, 7.1000e-01, 0.0000e+00, 1.2300e+00, 1.9000e-01,
1.9000e-01, 1.2000e-01, 6.4000e-01, 2.5000e-01, 3.8000e-01, 4.5000e-01,
1.2000e-01, 0.0000e+00, 1.7500e+00, 6.0000e-02, 6.0000e-02, 1.0300e+00,
1.3600e+00, 3.2000e-01, 5.1000e-01, 0.0000e+00, 1.1600e+00, 6.0000e-02,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 6.0000e-02, 0.0000e+00, 0.0000e+00,
1.2000e-01, 0.0000e+00, 6.0000e-02, 6.0000e-02, 0.0000e+00, 0.0000e+00,
1.0000e-02, 1.4300e-01, 0.0000e+00, 2.7600e-01, 1.8400e-01, 1.0000e-02,
9.8210e+00, 4.8500e+02, 2.2590e+03]], dtype=torch.float64), batch_y:tensor([1, 1])
steop:2, batch_x:tensor([[ 0.0000, 0.6400, 0.6400, 0.0000, 0.3200, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.6400, 0.0000, 0.0000,
0.0000, 0.3200, 0.0000, 1.2900, 1.9300, 0.0000, 0.9600,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.7780, 0.0000, 0.0000, 3.7560, 61.0000,
278.0000]], dtype=torch.float64), batch_y:tensor([1])
一共 5 條數(shù)據(jù)，batch_size 設(shè)為 2 ，則數(shù)據(jù)被分為三組，每組的數(shù)據(jù)量為：2，2，1。
實(shí)例 2：進(jìn)階
import torch.utils.data as Data
import pandas as pd # 這個(gè)包用來讀取CSV數(shù)據(jù)
import numpy as np
# 繼承Dataset，定義自己的數(shù)據(jù)集類 mydataset
class mydataset(Data.Dataset):
def __init__(self, csv_file): # self 參數(shù)必須，其他參數(shù)及其形式隨程序需要而不同，比如(self,*inputs)
# 讀取數(shù)據(jù)
frame = pd.DataFrame(pd.read_csv('spambase.csv'))
spam = frame[frame['58'] == 1]
ham = frame[frame['58'] == 0]
SpamNew = spam.drop(axis=1, columns='58', inplace=False) # 刪除第58列,inplace=False不改變原數(shù)據(jù)，返回一個(gè)新dataframe
HamNew = ham.drop(axis=1, columns='58', inplace=False)
# 數(shù)據(jù)
self.csv_data = np.vstack([np.array(SpamNew), np.array(HamNew)]) # 將兩個(gè)N維數(shù)組進(jìn)行連接，形成X
# 標(biāo)簽
self.Label = np.array([1] * len(spam) + [0] * len(ham)) # 形成標(biāo)簽值列表y
def __len__(self):
return len(self.csv_data)
def __getitem__(self, idx):
data = self.csv_data[idx]
label = self.Label[idx]
return data, label
data = mydataset('spambase.csv')
print(len(data))
loader = Data.DataLoader(
# 從數(shù)據(jù)庫中每次抽出batch size個(gè)樣本
dataset = data, # torch TensorDataset format
batch_size = 460, # mini batch size
shuffle=True, # 要不要打亂數(shù)據(jù) (打亂比較好)
num_workers=2, # 多線程來讀數(shù)據(jù)
)
def show_batch():
for step, (batch_x, batch_y) in enumerate(loader):
print("steop:{}, batch_x:{}, batch_y:{}".format(step, batch_x, batch_y))
show_batch()
輸出結(jié)果：
4601
steop:0, batch_x:tensor([[0.0000e+00, 2.4600e+00, 0.0000e+00, ..., 2.1420e+00, 1.0000e+01,
7.5000e+01],
[0.0000e+00, 0.0000e+00, 1.6000e+00, ..., 2.0650e+00, 1.2000e+01,
9.5000e+01],
[0.0000e+00, 0.0000e+00, 3.6000e-01, ..., 3.7220e+00, 2.0000e+01,
2.6800e+02],
...,
[7.7000e-01, 3.8000e-01, 7.7000e-01, ..., 1.4619e+01, 5.2500e+02,
9.2100e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00, 1.0000e+00,
5.0000e+00],
[4.0000e-01, 1.8000e-01, 3.2000e-01, ..., 3.3050e+00, 1.8100e+02,
1.6130e+03]], dtype=torch.float64), batch_y:tensor([0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1,
0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,
0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,
1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,
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steop:1, batch_x:tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00, 1.0000e+00,
2.0000e+00],
[4.9000e-01, 0.0000e+00, 7.4000e-01, ..., 3.9750e+00, 4.7000e+01,
4.8500e+02],
[0.0000e+00, 0.0000e+00, 7.1000e-01, ..., 4.0220e+00, 9.7000e+01,
5.4300e+02],
...,
[0.0000e+00, 1.4000e-01, 1.4000e-01, ..., 5.3310e+00, 8.0000e+01,
1.0290e+03],
[0.0000e+00, 0.0000e+00, 3.6000e-01, ..., 3.1760e+00, 5.1000e+01,
2.7000e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.1660e+00, 2.0000e+00,
7.0000e+00]], dtype=torch.float64), batch_y:tensor([0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
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steop:2, batch_x:tensor([[0.0000e+00, 0.0000e+00, 1.4700e+00, ..., 3.0000e+00, 3.3000e+01,
1.7700e+02],
[2.6000e-01, 4.6000e-01, 9.9000e-01, ..., 1.3235e+01, 2.7200e+02,
1.5750e+03],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.0450e+00, 6.0000e+00,
4.5000e+01],
...,
[4.0000e-01, 0.0000e+00, 0.0000e+00, ..., 1.1940e+00, 5.0000e+00,
1.2900e+02],
[2.6000e-01, 0.0000e+00, 0.0000e+00, ..., 1.8370e+00, 1.1000e+01,
1.5800e+02],
[5.0000e-02, 0.0000e+00, 1.0000e-01, ..., 3.7150e+00, 1.0700e+02,
1.3860e+03]], dtype=torch.float64), batch_y:tensor([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
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steop:3, batch_x:tensor([[2.6000e-01, 0.0000e+00, 5.3000e-01, ..., 2.6460e+00, 7.7000e+01,
1.7200e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.4280e+00, 5.0000e+00,
1.7000e+01],
[3.4000e-01, 0.0000e+00, 1.7000e+00, ..., 6.6700e+02, 1.3330e+03,
1.3340e+03],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00, 1.0000e+00,
7.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.7010e+00, 2.0000e+01,
1.8100e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 4.0000e+00, 1.1000e+01,
3.6000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,
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steop:4, batch_x:tensor([[ 0.0000, 0.0000, 0.3100, ..., 5.7080, 138.0000, 274.0000],
[ 0.0000, 0.0000, 0.3400, ..., 2.2570, 17.0000, 158.0000],
[ 1.0400, 0.0000, 0.0000, ..., 1.0000, 1.0000, 17.0000],
...,
[ 0.0000, 0.0000, 0.0000, ..., 4.0000, 12.0000, 28.0000],
[ 0.3300, 0.0000, 0.0000, ..., 1.7880, 6.0000, 93.0000],
[ 0.0000, 14.2800, 0.0000, ..., 1.8000, 5.0000, 9.0000]],
dtype=torch.float64), batch_y:tensor([1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
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1, 1, 0, 0])
steop:5, batch_x:tensor([[7.0000e-01, 0.0000e+00, 1.0500e+00, ..., 1.1660e+00, 1.3000e+01,
1.8900e+02],
[0.0000e+00, 3.3600e+00, 1.9200e+00, ..., 6.1370e+00, 1.0700e+02,
1.7800e+02],
[5.4000e-01, 0.0000e+00, 1.0800e+00, ..., 5.4540e+00, 6.8000e+01,
1.8000e+02],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 3.8330e+00, 9.0000e+00,
2.3000e+01],
[6.0000e-02, 6.5000e-01, 7.1000e-01, ..., 4.7420e+00, 1.1700e+02,
1.3420e+03],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.6110e+00, 1.2000e+01,
4.7000e+01]], dtype=torch.float64), batch_y:tensor([1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
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steop:6, batch_x:tensor([[0.0000e+00, 1.4280e+01, 0.0000e+00, ..., 1.8000e+00, 5.0000e+00,
9.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.9280e+00, 1.5000e+01,
5.4000e+01],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0692e+01, 6.5000e+01,
1.3900e+02],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.5000e+00, 5.0000e+00,
2.4000e+01],
[7.6000e-01, 1.9000e-01, 3.8000e-01, ..., 3.7020e+00, 4.5000e+01,
1.0700e+03],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.0000e+00, 1.2000e+01,
8.8000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,
0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1,
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steop:7, batch_x:tensor([[0.0000e+00, 2.7000e-01, 0.0000e+00, ..., 5.8020e+00, 4.3000e+01,
4.1200e+02],
[0.0000e+00, 3.5000e-01, 7.0000e-01, ..., 3.6390e+00, 6.1000e+01,
3.1300e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.5920e+00, 7.0000e+00,
1.2900e+02],
...,
[8.0000e-02, 1.6000e-01, 8.0000e-02, ..., 2.7470e+00, 8.6000e+01,
1.9950e+03],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.6130e+00, 1.1000e+01,
7.1000e+01],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.9110e+00, 1.5000e+01,
6.5000e+01]], dtype=torch.float64), batch_y:tensor([0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0,
1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0,
0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1,
0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0])
steop:8, batch_x:tensor([[1.7000e-01, 0.0000e+00, 1.7000e-01, ..., 1.7960e+00, 1.2000e+01,
4.5800e+02],
[3.7000e-01, 0.0000e+00, 6.3000e-01, ..., 1.1810e+00, 4.0000e+00,
1.0400e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00, 1.0000e+00,
7.0000e+00],
...,
[2.3000e-01, 0.0000e+00, 4.7000e-01, ..., 2.4200e+00, 1.2000e+01,
3.3400e+02],
[0.0000e+00, 0.0000e+00, 1.2900e+00, ..., 1.3500e+00, 4.0000e+00,
2.7000e+01],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.3730e+00, 1.1000e+01,
1.6900e+02]], dtype=torch.float64), batch_y:tensor([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1,
0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1,
0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,
1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,
1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1,
0, 0, 0, 0])
steop:9, batch_x:tensor([[0.0000e+00, 6.3000e-01, 0.0000e+00, ..., 2.2150e+00, 2.2000e+01,
1.1300e+02],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00, 1.0000e+00,
5.0000e+00],
[0.0000e+00, 0.0000e+00, 2.0000e-01, ..., 1.1870e+00, 1.1000e+01,
1.1400e+02],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 2.3070e+00, 1.6000e+01,
3.0000e+01],
[5.1000e-01, 4.3000e-01, 2.9000e-01, ..., 6.5900e+00, 7.3900e+02,
2.3330e+03],
[6.8000e-01, 6.8000e-01, 6.8000e-01, ..., 2.4720e+00, 9.0000e+00,
8.9000e+01]], dtype=torch.float64), batch_y:tensor([0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,
0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,
0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1,
0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 1])
steop:10, batch_x:tensor([[0.0000e+00, 2.5000e-01, 7.5000e-01, 0.0000e+00, 1.0000e+00, 2.5000e-01,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 2.5000e-01, 2.5000e-01,
1.2500e+00, 0.0000e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00, 1.2500e+00,
2.5100e+00, 0.0000e+00, 1.7500e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00,
0.0000e+00, 0.0000e+00, 2.5000e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 4.2000e-02, 0.0000e+00, 0.0000e+00,
1.2040e+00, 7.0000e+00, 1.1800e+02]], dtype=torch.float64), batch_y:tensor([0])
一共 4601 條數(shù)據(jù)，按 batch_size = 460 來分：能劃分為 11 組，前 10 組的數(shù)據(jù)量為 460，最后一組的數(shù)據(jù)量為 1 。
參考鏈接
- torch.Tensor.size()方法的使用舉例
- Pytorch筆記05-自定義數(shù)據(jù)讀取方式orch.utils.data.Dataset與Dataloader
- pytorch 可訓(xùn)練數(shù)據(jù)集創(chuàng)建（torch.utils.data）
- Pytorch的第一步：(1) Dataset類的使用
- pytorch中的torch.utils.data.Dataset和torch.utils.data.DataLoader
總結(jié)
————————————————
版權(quán)聲明：本文為CSDN博主「想變厲害的大白菜」的原創(chuàng)文章，遵循CC 4.0 BY-SA版權(quán)協(xié)議，轉(zhuǎn)載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/weixin_44211968/article/details/123744513
到此這篇關(guān)于PyTorch中torch.utils.data.Dataset的介紹與實(shí)戰(zhàn)的文章就介紹到這了,更多相關(guān)PyTorch torch.utils.data.Dataset內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
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