这次是PyTorch的自定义数据读取pipeline模板和相关trciks以及如何优化数据读取的pipeline等。
因为有torch也放人工智能模块了~
从PyTorch的数据对象类Dataset开始。Dataset在PyTorch中的模块位于utils.data下。
from torch.utils.data import Dataset
围绕Dataset对象分别从原始模板、torchvision的transforms模块、使用pandas来辅助读取、torch内置数据划分功能和DataLoader来展开阐述。
Dataset原始模板
PyTorch官方为我们提供了自定义数据读取的标准化代码代码模块，作为一个读取框架，我们这里称之为原始模板。其代码结构如下：
from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self, ...):
# stuff
def __getitem__(self, index):
# stuff
return (img, label)
def __len__(self):
# return examples size
return count
根据这个标准化的代码模板，我们只需要根据自己的数据读取任务，分别往__init__()、__getitem__()和__len__()三个方法里添加读取逻辑即可。作为PyTorch范式下的数据读取以及为了后续的data loader，三个方法缺一不可。其中：
-
__init__()函数用于初始化数据读取逻辑，比如读取包含标签和图片地址的csv文件、定义transform组合等。
-
__getitem__()函数用来返回数据和标签。目的上是为了能够被后续的dataloader所调用。
-
__len__()函数则用于返回样本数量。
现在我们往这个框架里填几行代码来形成一个简单的数字案例。创建一个从1到100的数字例子：
from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self):
self.samples = list(range(1, 101))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
if __name__ == '__main__':
dataset = CustomDataset()
print(len(dataset))
print(dataset[50])
print(dataset[1:100])
添加torchvision.transforms
然后我们来看如何从内存中读取数据以及如何在读取过程中嵌入torchvision中的transforms功能。torchvision是一个独立于torch的关于数据、模型和一些图像增强操作的辅助库。主要包括datasets默认数据集模块、models经典模型模块、transforms图像增强模块以及utils模块等。在使用torch读取数据的时候，一般会搭配上transforms模块对数据进行一些处理和增强工作。
添加了tranforms之后的读取模块可以改写为：
from torch.utils.data import Dataset
from torchvision import transforms as T
class CustomDataset(Dataset):
def __init__(self, ...):
# stuff
...
# compose the transforms methods
self.transform = T.Compose([T.CenterCrop(100),
T.ToTensor()])
def __getitem__(self, index):
# stuff
...
data = # Some data read from a file or image
# execute the transform
data = self.transform(data)
return (img, label)
def __len__(self):
# return examples size
return count
if __name__ == '__main__':
# Call the dataset
custom_dataset = CustomDataset(...)
可以看到，我们使用了Compose方法来把各种数据处理方法聚合到一起进行定义数据转换方法。通常作为初始化方法放在__init__()函数下。我们以猫狗图像数据为例进行说明。
定义数据读取方法如下：
class DogCat(Dataset):
def __init__(self, root, transforms=None, train=True, val=False):
"""
get images and execute transforms.
"""
self.val = val
imgs = [os.path.join(root, img) for img in os.listdir(root)]
# train: Cats_Dogs/trainset/cat.1.jpg
# val: Cats_Dogs/valset/cat.10004.jpg
imgs = sorted(imgs, key=lambda x: x.split('.')[-2])
self.imgs = imgs
if transforms is None:
# normalize
normalize = T.Normalize(mean = [0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225])
# trainset and valset have different data transform
# trainset need data augmentation but valset don't.
# valset
if self.val:
self.transforms = T.Compose([
T.Resize(224),
T.CenterCrop(224),
T.ToTensor(),
normalize
])
# trainset
else:
self.transforms = T.Compose([
T.Resize(256),
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ToTensor(),
normalize
])
def __getitem__(self, index):
"""
return data and label
"""
img_path = self.imgs[index]
label = 1 if 'dog' in img_path.split('/')[-1] else 0
data = Image.open(img_path)
data = self.transforms(data)
return data, label
def __len__(self):
"""
return images size.
"""
return len(self.imgs)
if __name__ == "__main__":
train_dataset = DogCat('./Cats_Dogs/trainset/', train=True)
print(len(train_dataset))
print(train_dataset[0])
因为这个数据集已经分好了训练集和验证集，所以在读取和transforms的时候需要进行区分。运行示例如下：
与pandas一起使用
很多时候数据的目录地址和标签都是通过csv文件给出的。如下所示：
此时在数据读取的pipeline中我们需要在__init__()方法中利用pandas把csv文件中包含的图片地址和标签融合进去。相应的数据读取pipeline模板可以改写为：
class CustomDatasetFromCSV(Dataset):
def __init__(self, csv_path):
"""
Args:
csv_path (string): path to csv file
transform: pytorch transforms for transforms and tensor conversion
"""
# Transforms
self.to_tensor = transforms.ToTensor()
# Read the csv file
self.data_info = pd.read_csv(csv_path, header=None)
# First column contains the image paths
self.image_arr = np.asarray(self.data_info.iloc[:, 0])
# Second column is the labels
self.label_arr = np.asarray(self.data_info.iloc[:, 1])
# Calculate len
self.data_len = len(self.data_info.index)
def __getitem__(self, index):
# Get image name from the pandas df
single_image_name = self.image_arr[index]
# Open image
img_as_img = Image.open(single_image_name)
# Transform image to tensor
img_as_tensor = self.to_tensor(img_as_img)
# Get label of the image based on the cropped pandas column
single_image_label = self.label_arr[index]
return (img_as_tensor, single_image_label)
def __len__(self):
return self.data_len
if __name__ == "__main__":
# Call dataset
dataset = CustomDatasetFromCSV('./labels.csv')
以mnist_label.csv文件为示例：
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torchvision import transforms as T
from PIL import Image
import os
import numpy as np
import pandas as pd
class CustomDatasetFromCSV(Dataset):
def __init__(self, csv_path):
"""
Args:
csv_path (string): path to csv file
transform: pytorch transforms for transforms and tensor conversion
"""
# Transforms
self.to_tensor = T.ToTensor()
# Read the csv file
self.data_info = pd.read_csv(csv_path, header=None)
# First column contains the image paths
self.image_arr = np.asarray(self.data_info.iloc[:, 0])
# Second column is the labels
self.label_arr = np.asarray(self.data_info.iloc[:, 1])
# Third column is for an operation indicator
self.operation_arr = np.asarray(self.data_info.iloc[:, 2])
# Calculate len
self.data_len = len(self.data_info.index)
def __getitem__(self, index):
# Get image name from the pandas df
single_image_name = self.image_arr[index]
# Open image
img_as_img = Image.open(single_image_name)
# Check if there is an operation
some_operation = self.operation_arr[index]
# If there is an operation
if some_operation:
# Do some operation on image
# ...
# ...
pass
# Transform image to tensor
img_as_tensor = self.to_tensor(img_as_img)
# Get label of the image based on the cropped pandas column
single_image_label = self.label_arr[index]
return (img_as_tensor, single_image_label)
def __len__(self):
return self.data_len
if __name__ == "__main__":
transform = T.Compose([T.ToTensor()])
dataset = CustomDatasetFromCSV('./mnist_labels.csv')
print(len(dataset))
print(dataset[5])
运行示例如下：
训练集验证集划分
一般来说，为了模型训练的稳定，我们需要对数据划分训练集和验证集。torch的Dataset对象也提供了random_split函数作为数据划分工具，且划分结果可直接供后续的DataLoader使用。
以kaggle的花朵数据为例： whaosoft aiot http://143ai.com
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms as T
from torch.utils.data import random_split
transform = T.Compose([
T.Resize((224, 224)),
T.RandomHorizontalFlip(),
T.ToTensor()
])
dataset = ImageFolder('./flowers_photos', transform=transform)
print(dataset.class_to_idx)
trainset, valset = random_split(dataset,
[int(len(dataset)*0.7), len(dataset)-int(len(dataset)*0.7)])
trainloader = DataLoader(dataset=trainset, batch_size=32, shuffle=True, num_workers=1)
for i, (img, label) in enumerate(trainloader):
img, label = img.numpy(), label.numpy()
print(img, label)
valloader = DataLoader(dataset=valset, batch_size=32, shuffle=True, num_workers=1)
for i, (img, label) in enumerate(trainloader):
img, label = img.numpy(), label.numpy()
print(img.shape, label)
这里使用了ImageFolder模块，可以直接读取各标签对应的文件夹，部分运行示例如下：
使用DataLoader
dataset方法写好之后，我们还需要使用DataLoader将其逐个喂给模型。上一节的数据划分我们已经用到了DataLoader函数。从本质上来讲，DataLoader只是调用了__getitem__()方法并按批次返回数据和标签。使用方法如下：
from torch.utils.data import DataLoader
from torchvision import transforms as T
if __name__ == "__main__":
# Define transforms
transformations = T.Compose([T.ToTensor()])
# Define custom dataset
dataset = CustomDatasetFromCSV('./labels.csv')
# Define data loader
data_loader = DataLoader(dataset=dataset, batch_size=10, shuffle=True)
for images, labels in data_loader:
# Feed the data to the model
以上就是PyTorch读取数据的Pipeline主要方法和流程。基于Dataset对象的基本框架不变，具体细节可自定义化调整。