torch.utils.data.DataLoader中的collate_fn的使用

参考链接: class torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, multiprocessing_context=None)
补充链接: 自定义torch.utils.data中的Dataset,并使用DataLoader自动加载

词语释义:

功能:
	以自定义的方式将加载的多项数据组织成一个mini-batch,比如加载成numpy多维数组的方式。

在collate_fn中将数据集中的多项数据组织成批，比如将多个numpy小数组组合成一个大的numpy数组:

Python 3.7.4 (tags/v3.7.4:e09359112e, Jul  8 2019, 20:34:20) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>> import numpy as np
>>> a = np.array([0,1,2])
>>> b = np.array([3,4,5])
>>> c = np.array([6,7,8])
>>> ls = [a,b,c]
>>> a
array([0, 1, 2])
>>> b
array([3, 4, 5])
>>> c
array([6, 7, 8])
>>> ls
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8])]
>>> result = np.array(ls)
>>> result
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> np.array(ls)
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> 
>>> 

使用示例1:

def ssd_dataset_collate(batch):
    # print('ssd_dataset_collate函数被执行...')
    images = []
    bboxes = []
    for img, box in batch:
        images.append(img)
        bboxes.append(box)
    images = np.array(images)
    bboxes = np.array(bboxes)
    return images, bboxes


gen = DataLoader(train_dataset, \
                        batch_size=Batch_size, \
                        num_workers=8, \
                        pin_memory=True,\
                        drop_last=True, \
                        collate_fn=ssd_dataset_collate)
                        # collate_fn=None)

使用示例2:

# DataLoader中collate_fn使用
def deeplab_dataset_collate(batch):
    images = []
    pngs = []
    seg_labels = []
    for img, png, labels in batch:
        images.append(img)
        pngs.append(png)
        seg_labels.append(labels)
    images = np.array(images)
    pngs = np.array(pngs)
    seg_labels = np.array(seg_labels)
    return images, pngs, seg_labels



train_dataset = DeeplabDataset(train_lines, inputs_size, NUM_CLASSES, True)
val_dataset = DeeplabDataset(val_lines, inputs_size, NUM_CLASSES, False)
gen = DataLoader(train_dataset, batch_size=Batch_size, num_workers=2, pin_memory=True,
                        drop_last=True, collate_fn=deeplab_dataset_collate)
gen_val = DataLoader(val_dataset, batch_size=Batch_size, num_workers=2,pin_memory=True, 
                        drop_last=True, collate_fn=deeplab_dataset_collate)


train_dataset = DeeplabDataset(train_lines, inputs_size, NUM_CLASSES, True)
val_dataset = DeeplabDataset(val_lines, inputs_size, NUM_CLASSES, False)
gen = DataLoader(train_dataset, batch_size=Batch_size, num_workers=4, pin_memory=True,
                        drop_last=True, collate_fn=deeplab_dataset_collate)
gen_val = DataLoader(val_dataset, batch_size=Batch_size, num_workers=4,pin_memory=True, 
                        drop_last=True, collate_fn=deeplab_dataset_collate)

实验代码:

# 定义一个标准的用户定制Dataset模板
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader

class Dataset4cxq(Dataset):
    def __init__(self, datum,length):
        self.datum = datum
        self.length = length

    def __len__(self):
        return self.length
        
    def __getitem__(self, index):
        if type(index) != type(2) and type(index) != (slice):
           raise  TypeError('索引类型错误,程序退出...')
        
        # index 是单个数
        if type(index) == type(2):
            if index >= self.length or index < -1 * self.length:
                # print("索引越界,程序退出...")
                raise IndexError("索引越界,程序退出...")
            elif index < 0:
                index = index + self.length 
            sample4cxq, label4cxq = self.datum[index]
            return sample4cxq, label4cxq 
        
        # index 是切片
        elif type(index) == slice:
            sample4cxq = list(item[0] for item in self.datum[index])
            label4cxq = list(item[1] for item in self.datum[index])
            return sample4cxq, label4cxq

if __name__ == "__main__":
    
    cities = tuple([
        ("0浙江省0","0杭州市0"),
        ("1广东省1","1广州市1"),
        ("2福建省2","2福州市2"),
        ("3江苏省3","3南京市3"),
        ("4河北省4","4石家庄市4"),
        ("5河南省5","5郑州市5"),
        ("6山东省6","6济南市6"),
        ("7山西省7","7太原市7")
    ])
    print('\n')
    # print('pirnt tuple variable cities...')
    # print(cities)
    print("creating dataset".center(50,'-'))
    my_dataset = Dataset4cxq(cities, len(cities))
    print("数据集的长度:",len(my_dataset))

    print('\n'+ "iteration in DataLoader".center(50,'-'))
    
    
    dataloader4cxq = torch.utils.data.DataLoader(
        dataset=my_dataset, 
        batch_size=2,
        shuffle=True  #  True   False
    )
    for cnt, data in enumerate(dataloader4cxq, 20200910):
        # pass
        sample4cxq, label4cxq = data
        print('sample4cxq的类型: ',type(sample4cxq),'\tlabel4cxq的类型: ',type(label4cxq))
        print('迭代次数:', cnt, '  sample4cxq:', sample4cxq, '  label4cxq:', label4cxq)

    
    
    print('\n'+ "using collate_fn".center(50,'-'))
    def collate_fn4cxq(batch):
        samples = []
        labels = []
        for sample4cxq, label4cxq in batch:
            samples.append(sample4cxq)
            labels.append(label4cxq)
        samples = np.array(samples)
        labels = np.array(labels)
        return samples, labels
    
    dataloader4cxq = torch.utils.data.DataLoader(
        dataset=my_dataset, 
        batch_size=3,
        # batch_size=2,
        drop_last=True,
        # drop_last=False,
        shuffle=True,  #  True   False
        # shuffle=False,  #  True   False
        collate_fn=collate_fn4cxq,
        # collate_fn=None,
    )
    print('使用collate_fn参数...\n')
    for cnt, data in enumerate(dataloader4cxq, 0):
        # pass
        sample4cxq, label4cxq = data
        print('sample4cxq的类型: ',type(sample4cxq),'\tlabel4cxq的类型: ',type(label4cxq))
        print('迭代次数:', cnt, '  sample4cxq:', sample4cxq, '  label4cxq:', label4cxq)

控制台下输出:

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加载个人及系统配置文件用了 945 毫秒。
(base) PS C:\Users\chenxuqi\Desktop\News4cxq>  & 'D:\Anaconda3\python.exe' 'c:\Users\chenxuqi\.vscode\extensions\ms-python.python-2021.1.502429796\pythonFiles\lib\python\debugpy\launcher' '55416' '--' 'c:\Users\chenxuqi\Desktop\News4cxq\新建文本文档.py'


数据集的长度: 8

-------------iteration in DataLoader--------------
sample4cxq的类型:        label4cxq的类型:  
迭代次数: 20200910   sample4cxq: ('2福建省2', '1广东省1')   label4cxq: ('2福州市2', '1广州市1')
sample4cxq的类型:        label4cxq的类型:  
迭代次数: 20200911   sample4cxq: ('3江苏省3', '5河南省5')   label4cxq: ('3南京市3', '5郑州市5')
sample4cxq的类型:        label4cxq的类型:  
迭代次数: 20200912   sample4cxq: ('0浙江省0', '6山东省6')   label4cxq: ('0杭州市0', '6济南市6')
sample4cxq的类型:        label4cxq的类型:  
迭代次数: 20200913   sample4cxq: ('7山西省7', '4河北省4')   label4cxq: ('7太原市7', '4石家庄市4')

-----------------using collate_fn-----------------
使用collate_fn参数...

sample4cxq的类型:        label4cxq的类型:  
迭代次数: 0   sample4cxq: ['2福建省2' '0浙江省0' '6山东省6']   label4cxq: ['2福州市2' '0杭州市0' '6济南市6']
sample4cxq的类型:        label4cxq的类型:  
迭代次数: 1   sample4cxq: ['7山西省7' '1广东省1' '3江苏省3']   label4cxq: ['7太原市7' '1广州市1' '3南京市3']
(base) PS C:\Users\chenxuqi\Desktop\News4cxq> conda activate base
(base) PS C:\Users\chenxuqi\Desktop\News4cxq> 

补充说明:

torch.utils.data.DataLoader类的构造函数中,
batch_size参数用于指定一个批batch的长度，即一个batch包含多上数据项.
drop_last参数用于指定是否将最后一个长度不足batch_size的数据项丢弃.
shuffle参数用于是否需要顺序随机地加载的数据集.
collate_fn参数用于是否需要以自定义的方式组织一个batch,
例子中将一个mini-batch的数据组织成numpy.ndarray的类型.
默认情况下collate_fn=None时,数据以元组的方式返回.

以上参数的不同设置效果可以在代码第80到90行中自行设置后,
运行程序,并观察不同输出效果.