pytorch Dataset, DataLoader产生自定义的训练数据_pytorch 自定义 dataloader nii格式

pytorch Dataset, DataLoader产生自定义的训练数据

---

目录

pytorch Dataset, DataLoader产生自定义的训练数据

1. torch.utils.data.Dataset

2. torch.utils.data.DataLoader

3. 使用Dataset, DataLoader产生自定义训练数据

3.1 自定义Dataset

3.2 DataLoader产生批训练数据

3.3 附件：image_processing.py

3.4 完整的代码

---

1. torch.utils.data.Dataset

  datasets这是一个pytorch定义的dataset的源码集合。下面是一个自定义Datasets的基本框架，初始化放在__init__()中，其中__getitem__()和__len__()两个方法是必须重写的。__getitem__()返回训练数据，如图片和label，而__len__()返回数据长度。

class CustomDataset(data.Dataset):#需要继承data.Dataset
    def __init__(self):
        # TODO
        # 1. Initialize file path or list of file names.
        pass
    def __getitem__(self, index):
        # TODO
        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
        # 2. Preprocess the data (e.g. torchvision.Transform).
        # 3. Return a data pair (e.g. image and label).
        #这里需要注意的是，第一步：read one data，是一个data
        pass
    def __len__(self):
        # You should change 0 to the total size of your dataset.
        return 0

---

2. torch.utils.data.DataLoader

DataLoader(object)可用参数：

1. dataset(Dataset): 传入的数据集
2. batch_size(int, optional): 每个batch有多少个样本
3. shuffle(bool, optional): 在每个epoch开始的时候，对数据进行重新排序
4. sampler(Sampler, optional): 自定义从数据集中取样本的策略，如果指定这个参数，那么shuffle必须为False
5. batch_sampler(Sampler, optional): 与sampler类似，但是一次只返回一个batch的indices（索引），需要注意的是，一旦指定了这个参数，那么batch_size,shuffle,sampler,drop_last就不能再制定了（互斥——Mutually exclusive）
6. num_workers (int, optional): 这个参数决定了有几个进程来处理data loading。0意味着所有的数据都会被load进主进程。（默认为0）
7. collate_fn (callable, optional): 将一个list的sample组成一个mini-batch的函数
8. pin_memory (bool, optional)： 如果设置为True，那么data loader将会在返回它们之前，将tensors拷贝到CUDA中的固定内存（CUDA pinned memory）中.
9. drop_last (bool, optional):如果设置为True：这个是对最后的未完成的batch来说的，比如你的batch_size设置为64，而一个epoch只有100个样本，那么训练的时候后面的36个就被扔掉了。 如果为False（默认），那么会继续正常执行，只是最后的batch_size会小一点。
10. timeout(numeric, optional):如果是正数，表明等待从worker进程中收集一个batch等待的时间，若超出设定的时间还没有收集到，那就不收集这个内容了。这个numeric应总是大于等于0。默认为0
11. worker_init_fn (callable, optional): 每个worker初始化函数 If not None, this will be called on eachworker subprocess with the worker id (an int in [0, num_workers - 1]) as input, after seeding and before data loading. (default: None) 

---

3. 使用Dataset, DataLoader产生自定义训练数据

假设TXT文件保存了数据的图片和label,格式如下：第一列是图片的名字，第二列是label

0.jpg 0
1.jpg 1
2.jpg 2
3.jpg 3
4.jpg 4
5.jpg 5
6.jpg 6
7.jpg 7
8.jpg 8
9.jpg 9

也可以是多标签的数据，如：

0.jpg 0 10
1.jpg 1 11
2.jpg 2 12
3.jpg 3 13
4.jpg 4 14
5.jpg 5 15
6.jpg 6 16
7.jpg 7 17
8.jpg 8 18
9.jpg 9 19

图库十张原始图片放在./dataset/images目录下，然后我们就可以自定义一个Dataset解析这些数据并读取图片，再使用DataLoader类产生batch的训练数据

---

3.1 自定义Dataset

首先先自定义一个TorchDataset类，用于读取图片数据，产生标签：

注意初始化函数：

import torch
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import numpy as np
from utils import image_processing
import os
 
class TorchDataset(Dataset):
    def __init__(self, filename, image_dir, resize_height=256, resize_width=256, repeat=1):
        '''
        :param filename: 数据文件TXT：格式：imge_name.jpg label1_id labe2_id
        :param image_dir: 图片路径：image_dir+imge_name.jpg构成图片的完整路径
        :param resize_height 为None时，不进行缩放
        :param resize_width  为None时，不进行缩放，
                              PS：当参数resize_height或resize_width其中一个为None时，可实现等比例缩放
        :param repeat: 所有样本数据重复次数，默认循环一次，当repeat为None时，表示无限循环<sys.maxsize
        '''
        self.image_label_list = self.read_file(filename)
        self.image_dir = image_dir
        self.len = len(self.image_label_list)
        self.repeat = repeat
        self.resize_height = resize_height
        self.resize_width = resize_width
 
        # 相关预处理的初始化
        '''class torchvision.transforms.ToTensor'''
        # 把shape=(H,W,C)的像素值范围为[0, 255]的PIL.Image或者numpy.ndarray数据
        # 转换成shape=(C,H,W)的像素数据，并且被归一化到[0.0, 1.0]的torch.FloatTensor类型。
        self.toTensor = transforms.ToTensor()
 
        '''class torchvision.transforms.Normalize(mean, std)
        此转换类作用于torch. * Tensor,给定均值(R, G, B) 和标准差(R, G, B)，
        用公式channel = (channel - mean) / std进行规范化。
        '''
        # self.normalize=transforms.Normalize()
 
    def __getitem__(self, i):
        index = i % self.len
        # print("i={},index={}".format(i, index))
        image_name, label = self.image_label_list[index]
        image_path = os.path.join(self.image_dir, image_name)
        img = self.load_data(image_path, self.resize_height, self.resize_width, normalization=False)
        img = self.data_preproccess(img)
        label=np.array(label)
        return img, label
 
    def __len__(self):
        if self.repeat == None:
            data_len = 10000000
        else:
            data_len = len(self.image_label_list) * self.repeat
        return data_len
 
    def read_file(self, filename):
        image_label_list = []
        with open(filename, 'r') as f:
            lines = f.readlines()
            for line in lines:
                # rstrip：用来去除结尾字符、空白符(包括\n、\r、\t、' '，即：换行、回车、制表符、空格)
                content = line.rstrip().split(' ')
                name = content[0]
                labels = []
                for value in content[1:]:
                    labels.append(int(value))
                image_label_list.append((name, labels))
        return image_label_list
 
    def load_data(self, path, resize_height, resize_width, normalization):
        '''
        加载数据
        :param path:
        :param resize_height:
        :param resize_width:
        :param normalization: 是否归一化
        :return:
        '''
        image = image_processing.read_image(path, resize_height, resize_width, normalization)
        return image
 
    def data_preproccess(self, data):
        '''
        数据预处理
        :param data:
        :return:
        '''
        data = self.toTensor(data)
        return data

---

3.2 DataLoader产生批训练数据

if __name__=='__main__':
    train_filename="../dataset/train.txt"
    # test_filename="../dataset/test.txt"
    image_dir='../dataset/images'
 
 
    epoch_num=2   #总样本循环次数
    batch_size=7  #训练时的一组数据的大小
    train_data_nums=10
    max_iterate=int((train_data_nums+batch_size-1)/batch_size*epoch_num) #总迭代次数
 
    train_data = TorchDataset(filename=train_filename, image_dir=image_dir,repeat=1)
    # test_data = TorchDataset(filename=test_filename, image_dir=image_dir,repeat=1)
    train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=False)
    # test_loader = DataLoader(dataset=test_data, batch_size=batch_size,shuffle=False)
 
    # [1]使用epoch方法迭代，TorchDataset的参数repeat=1
    for epoch in range(epoch_num):
        for batch_image, batch_label in train_loader:
            image=batch_image[0,:]
            image=image.numpy()#image=np.array(image)
            image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
            image_processing.cv_show_image("image",image)
            print("batch_image.shape:{},batch_label:{}".format(batch_image.shape,batch_label))
            # batch_x, batch_y = Variable(batch_x), Variable(batch_y)

上面的迭代代码是通过两个for实现，其中参数epoch_num表示总样本循环次数，比如epoch_num=2，那就是所有样本循环迭代2次。但这会出现一个问题，当样本总数train_data_nums与batch_size不能整取时，最后一个batch会少于规定batch_size的大小，比如这里样本总数train_data_nums=10，batch_size=7，第一次迭代会产生7个样本，第二次迭代会因为样本不足，只能产生3个样本。

我们希望，每次迭代都会产生相同大小的batch数据，因此可以如下迭代：注意本人在构造TorchDataset类时，就已经考虑循环迭代的方法，因此，你现在只需修改repeat为None时，就表示无限循环了，调用方法如下：

   '''
    下面两种方式，TorchDataset设置repeat=None可以实现无限循环，退出循环由max_iterate设定
    '''
    train_data = TorchDataset(filename=train_filename, image_dir=image_dir,repeat=None)
    train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=False)
    # [2]第2种迭代方法
    for step, (batch_image, batch_label) in enumerate(train_loader):
        image=batch_image[0,:]
        image=image.numpy()#image=np.array(image)
        image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
        image_processing.cv_show_image("image",image)
        print("step:{},batch_image.shape:{},batch_label:{}".format(step,batch_image.shape,batch_label))
        # batch_x, batch_y = Variable(batch_x), Variable(batch_y)
        if step>=max_iterate:
            break
    # [3]第3种迭代方法
    # for step in range(max_iterate):
    #     batch_image, batch_label=train_loader.__iter__().__next__()
    #     image=batch_image[0,:]
    #     image=image.numpy()#image=np.array(image)
    #     image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
    #     image_processing.cv_show_image("image",image)
    #     print("batch_image.shape:{},batch_label:{}".format(batch_image.shape,batch_label))
    #     # batch_x, batch_y = Variable(batch_x), Variable(batch_y)

---

3.3 附件：image_processing.py

上面代码，用到image_processing，这是本人封装好的图像处理包，包含读取图片，画图等基本方法：

# -*-coding: utf-8 -*-
"""
    @Project: IntelligentManufacture
    @File   : image_processing.py
    @Author : panjq
    @E-mail : pan_jinquan@163.com
    @Date   : 2019-02-14 15:34:50
"""
 
import os
import glob
import cv2
import numpy as np
import matplotlib.pyplot as plt
 
def show_image(title, image):
    '''
    调用matplotlib显示RGB图片
    :param title: 图像标题
    :param image: 图像的数据
    :return:
    '''
    # plt.figure("show_image")
    # print(image.dtype)
    plt.imshow(image)
    plt.axis('on')  # 关掉坐标轴为 off
    plt.title(title)  # 图像题目
    plt.show()
 
def cv_show_image(title, image):
    '''
    调用OpenCV显示RGB图片
    :param title: 图像标题
    :param image: 输入RGB图像
    :return:
    '''
    channels=image.shape[-1]
    if channels==3:
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # 将BGR转为RGB
    cv2.imshow(title,image)
    cv2.waitKey(0)
 
def read_image(filename, resize_height=None, resize_width=None, normalization=False):
    '''
    读取图片数据,默认返回的是uint8,[0,255]
    :param filename:
    :param resize_height:
    :param resize_width:
    :param normalization:是否归一化到[0.,1.0]
    :return: 返回的RGB图片数据
    '''
 
    bgr_image = cv2.imread(filename)
    # bgr_image = cv2.imread(filename,cv2.IMREAD_IGNORE_ORIENTATION|cv2.IMREAD_COLOR)
    if bgr_image is None:
        print("Warning:不存在:{}", filename)
        return None
    if len(bgr_image.shape) == 2:  # 若是灰度图则转为三通道
        print("Warning:gray image", filename)
        bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)
 
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)  # 将BGR转为RGB
    # show_image(filename,rgb_image)
    # rgb_image=Image.open(filename)
    rgb_image = resize_image(rgb_image,resize_height,resize_width)
    rgb_image = np.asanyarray(rgb_image)
    if normalization:
        # 不能写成:rgb_image=rgb_image/255
        rgb_image = rgb_image / 255.0
    # show_image("src resize image",image)
    return rgb_image
 
def fast_read_image_roi(filename, orig_rect, ImreadModes=cv2.IMREAD_COLOR, normalization=False):
    '''
    快速读取图片的方法
    :param filename: 图片路径
    :param orig_rect:原始图片的感兴趣区域rect
    :param ImreadModes: IMREAD_UNCHANGED
                        IMREAD_GRAYSCALE
                        IMREAD_COLOR
                        IMREAD_ANYDEPTH
                        IMREAD_ANYCOLOR
                        IMREAD_LOAD_GDAL
                        IMREAD_REDUCED_GRAYSCALE_2
                        IMREAD_REDUCED_COLOR_2
                        IMREAD_REDUCED_GRAYSCALE_4
                        IMREAD_REDUCED_COLOR_4
                        IMREAD_REDUCED_GRAYSCALE_8
                        IMREAD_REDUCED_COLOR_8
                        IMREAD_IGNORE_ORIENTATION
    :param normalization: 是否归一化
    :return: 返回感兴趣区域ROI
    '''
    # 当采用IMREAD_REDUCED模式时，对应rect也需要缩放
    scale=1
    if ImreadModes == cv2.IMREAD_REDUCED_COLOR_2 or ImreadModes == cv2.IMREAD_REDUCED_COLOR_2:
        scale=1/2
    elif ImreadModes == cv2.IMREAD_REDUCED_GRAYSCALE_4 or ImreadModes == cv2.IMREAD_REDUCED_COLOR_4:
        scale=1/4
    elif ImreadModes == cv2.IMREAD_REDUCED_GRAYSCALE_8 or ImreadModes == cv2.IMREAD_REDUCED_COLOR_8:
        scale=1/8
    rect = np.array(orig_rect)*scale
    rect = rect.astype(int).tolist()
    bgr_image = cv2.imread(filename,flags=ImreadModes)
 
    if bgr_image is None:
        print("Warning:不存在:{}", filename)
        return None
    if len(bgr_image.shape) == 3:  #
        rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)  # 将BGR转为RGB
    else:
        rgb_image=bgr_image #若是灰度图
    rgb_image = np.asanyarray(rgb_image)
    if normalization:
        # 不能写成:rgb_image=rgb_image/255
        rgb_image = rgb_image / 255.0
    roi_image=get_rect_image(rgb_image , rect)
    # show_image_rect("src resize image",rgb_image,rect)
    # cv_show_image("reROI",roi_image)
    return roi_image
 
def resize_image(image,resize_height, resize_width):
    '''
    :param image:
    :param resize_height:
    :param resize_width:
    :return:
    '''
    image_shape=np.shape(image)
    height=image_shape[0]
    width=image_shape[1]
    if (resize_height is None) and (resize_width is None):#错误写法：resize_height and resize_width is None
        return image
    if resize_height is None:
        resize_height=int(height*resize_width/width)
    elif resize_width is None:
        resize_width=int(width*resize_height/height)
    image = cv2.resize(image, dsize=(resize_width, resize_height))
    return image
def scale_image(image,scale):
    '''
    :param image:
    :param scale: (scale_w,scale_h)
    :return:
    '''
    image = cv2.resize(image,dsize=None, fx=scale[0],fy=scale[1])
    return image
 
 
def get_rect_image(image,rect):
    '''
    :param image:
    :param rect: [x,y,w,h]
    :return:
    '''
    x, y, w, h=rect
    cut_img = image[y:(y+ h),x:(x+w)]
    return cut_img
def scale_rect(orig_rect,orig_shape,dest_shape):
    '''
    对图像进行缩放时，对应的rectangle也要进行缩放
    :param orig_rect: 原始图像的rect=[x,y,w,h]
    :param orig_shape: 原始图像的维度shape=[h,w]
    :param dest_shape: 缩放后图像的维度shape=[h,w]
    :return: 经过缩放后的rectangle
    '''
    new_x=int(orig_rect[0]*dest_shape[1]/orig_shape[1])
    new_y=int(orig_rect[1]*dest_shape[0]/orig_shape[0])
    new_w=int(orig_rect[2]*dest_shape[1]/orig_shape[1])
    new_h=int(orig_rect[3]*dest_shape[0]/orig_shape[0])
    dest_rect=[new_x,new_y,new_w,new_h]
    return dest_rect
 
def show_image_rect(win_name,image,rect):
    '''
    :param win_name:
    :param image:
    :param rect:
    :return:
    '''
    x, y, w, h=rect
    point1=(x,y)
    point2=(x+w,y+h)
    cv2.rectangle(image, point1, point2, (0, 0, 255), thickness=2)
    cv_show_image(win_name, image)
 
def rgb_to_gray(image):
    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    return image
 
def save_image(image_path, rgb_image,toUINT8=True):
    if toUINT8:
        rgb_image = np.asanyarray(rgb_image * 255, dtype=np.uint8)
    if len(rgb_image.shape) == 2:  # 若是灰度图则转为三通道
        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_GRAY2BGR)
    else:
        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imwrite(image_path, bgr_image)
 
def combime_save_image(orig_image, dest_image, out_dir,name,prefix):
    '''
    命名标准：out_dir/name_prefix.jpg
    :param orig_image:
    :param dest_image:
    :param image_path:
    :param out_dir:
    :param prefix:
    :return:
    '''
    dest_path = os.path.join(out_dir, name + "_"+prefix+".jpg")
    save_image(dest_path, dest_image)
 
    dest_image = np.hstack((orig_image, dest_image))
    save_image(os.path.join(out_dir, "{}_src_{}.jpg".format(name,prefix)), dest_image)

---

3.4 完整的代码

# -*-coding: utf-8 -*-
"""
    @Project: pytorch-learning-tutorials
    @File   : dataset.py
    @Author : panjq
    @E-mail : pan_jinquan@163.com
    @Date   : 2019-03-07 18:45:06
"""
import torch
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import numpy as np
from utils import image_processing
import os
 
class TorchDataset(Dataset):
    def __init__(self, filename, image_dir, resize_height=256, resize_width=256, repeat=1):
        '''
        :param filename: 数据文件TXT：格式：imge_name.jpg label1_id labe2_id
        :param image_dir: 图片路径：image_dir+imge_name.jpg构成图片的完整路径
        :param resize_height 为None时，不进行缩放
        :param resize_width  为None时，不进行缩放，
                              PS：当参数resize_height或resize_width其中一个为None时，可实现等比例缩放
        :param repeat: 所有样本数据重复次数，默认循环一次，当repeat为None时，表示无限循环<sys.maxsize
        '''
        self.image_label_list = self.read_file(filename)
        self.image_dir = image_dir
        self.len = len(self.image_label_list)
        self.repeat = repeat
        self.resize_height = resize_height
        self.resize_width = resize_width
 
        # 相关预处理的初始化
        '''class torchvision.transforms.ToTensor'''
        # 把shape=(H,W,C)的像素值范围为[0, 255]的PIL.Image或者numpy.ndarray数据
        # 转换成shape=(C,H,W)的像素数据，并且被归一化到[0.0, 1.0]的torch.FloatTensor类型。
        self.toTensor = transforms.ToTensor()
 
        '''class torchvision.transforms.Normalize(mean, std)
        此转换类作用于torch. * Tensor,给定均值(R, G, B) 和标准差(R, G, B)，
        用公式channel = (channel - mean) / std进行规范化。
        '''
        # self.normalize=transforms.Normalize()
 
    def __getitem__(self, i):
        index = i % self.len
        # print("i={},index={}".format(i, index))
        image_name, label = self.image_label_list[index]
        image_path = os.path.join(self.image_dir, image_name)
        img = self.load_data(image_path, self.resize_height, self.resize_width, normalization=False)
        img = self.data_preproccess(img)
        label=np.array(label)
        return img, label
 
    def __len__(self):
        if self.repeat == None:
            data_len = 10000000
        else:
            data_len = len(self.image_label_list) * self.repeat
        return data_len
 
    def read_file(self, filename):
        image_label_list = []
        with open(filename, 'r') as f:
            lines = f.readlines()
            for line in lines:
                # rstrip：用来去除结尾字符、空白符(包括\n、\r、\t、' '，即：换行、回车、制表符、空格)
                content = line.rstrip().split(' ')
                name = content[0]
                labels = []
                for value in content[1:]:
                    labels.append(int(value))
                image_label_list.append((name, labels))
        return image_label_list
 
    def load_data(self, path, resize_height, resize_width, normalization):
        '''
        加载数据
        :param path:
        :param resize_height:
        :param resize_width:
        :param normalization: 是否归一化
        :return:
        '''
        image = image_processing.read_image(path, resize_height, resize_width, normalization)
        return image
 
    def data_preproccess(self, data):
        '''
        数据预处理
        :param data:
        :return:
        '''
        data = self.toTensor(data)
        return data
 
if __name__=='__main__':
    train_filename="../dataset/train.txt"
    # test_filename="../dataset/test.txt"
    image_dir='../dataset/images'
 
 
    epoch_num=2   #总样本循环次数
    batch_size=7  #训练时的一组数据的大小
    train_data_nums=10
    max_iterate=int((train_data_nums+batch_size-1)/batch_size*epoch_num) #总迭代次数
 
    train_data = TorchDataset(filename=train_filename, image_dir=image_dir,repeat=1)
    # test_data = TorchDataset(filename=test_filename, image_dir=image_dir,repeat=1)
    train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=False)
    # test_loader = DataLoader(dataset=test_data, batch_size=batch_size,shuffle=False)
 
    # [1]使用epoch方法迭代，TorchDataset的参数repeat=1
    for epoch in range(epoch_num):
        for batch_image, batch_label in train_loader:
            image=batch_image[0,:]
            image=image.numpy()#image=np.array(image)
            image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
            image_processing.cv_show_image("image",image)
            print("batch_image.shape:{},batch_label:{}".format(batch_image.shape,batch_label))
            # batch_x, batch_y = Variable(batch_x), Variable(batch_y)
 
    '''
    下面两种方式，TorchDataset设置repeat=None可以实现无限循环，退出循环由max_iterate设定
    '''
    train_data = TorchDataset(filename=train_filename, image_dir=image_dir,repeat=None)
    train_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=False)
    # [2]第2种迭代方法
    for step, (batch_image, batch_label) in enumerate(train_loader):
        image=batch_image[0,:]
        image=image.numpy()#image=np.array(image)
        image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
        image_processing.cv_show_image("image",image)
        print("step:{},batch_image.shape:{},batch_label:{}".format(step,batch_image.shape,batch_label))
        # batch_x, batch_y = Variable(batch_x), Variable(batch_y)
        if step>=max_iterate:
            break
    # [3]第3种迭代方法
    # for step in range(max_iterate):
    #     batch_image, batch_label=train_loader.__iter__().__next__()
    #     image=batch_image[0,:]
    #     image=image.numpy()#image=np.array(image)
    #     image = image.transpose(1, 2, 0)  # 通道由[c,h,w]->[h,w,c]
    #     image_processing.cv_show_image("image",image)
    #     print("batch_image.shape:{},batch_label:{}".format(batch_image.shape,batch_label))
    #     # batch_x, batch_y = Variable(batch_x), Variable(batch_y)