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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
2. torch.utils.data.DataLoader
3. 使用Dataset, DataLoader产生自定义训练数据
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 02. torch.utils.data.DataLoader
DataLoader(object)可用参数：
- dataset(Dataset): 传入的数据集
- batch_size(int, optional): 每个batch有多少个样本
- shuffle(bool, optional): 在每个epoch开始的时候，对数据进行重新排序
- 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
- 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）
- 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）
- collate_fn (callable, optional): 将一个list的sample组成一个mini-batch的函数
- 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.
- 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。
- 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
- 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)
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