Pytorch入门-数据载入和预处理实例

最近在Pytorch官方网站学习入门课程,第一步当然就是数据的载入和预处理啦,下面对本章学习做一下总结,本文中只提炼必要的关键性步骤。
以下内容均源自:DATA LOADING AND PROCESSING TUTORIAL

为确保顺利运行,环境中需要有以下两个包:

  • skimage:python自带的图像处理库
  • pandas:可以快速遍历csv文件

准备工作

首先,用import操作导入需要的包:

from __future__ import print_function,division
import os
import torch
import pandas as pd
from skimage import io,transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,utils

然后,在这里下载人脸数据集,其中包括:

  • face_landmarks.csv(人脸标记点坐标)
  • create_landmark_dataset.py
  • 示例图片

其中,csv文件中每一行的第一项为img_name,之后有68个标记点坐标。

image_name,part_0_x,part_0_y,part_1_x,part_1_y,part_2_x, ... ,part_67_x,part_67_y

下载完成后,在本地导入csv文件,这里使用绝对路径

landmarks_frame = pd.read_csv("C:\\Users\\85233\\Desktop\\faces\\face_landmarks.csv")

定义一个函数,来显示一张图片和上面的标记点:

def show_landmarks(image, landmarks):
    plt.imshow(image)
    plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')
    plt.pause(0.001)  # pause a bit so that plots are updated

定义数据集类

torch.utils.data.Dataset 是表示一个数据集的抽象类,我们接下来写的数据集类必须继承它,并重写以下两个方法:

  • __ len __ : 返回数据集的长度
  • __ get_item __: 读取 dataset[i]

整个数据集为一个字典:{‘image’: image, ‘landmarks’: landmarks}

class FaceLandmarksDataset(Dataset):
    """Face Landmarks dataset."""

    def __init__(self, csv_file, root_dir, transform=None):
        """
        Args:
            csv_file (string): Path to the csv file with annotations.
            root_dir (string): Directory with all the images.
            transform (callable, optional): Optional transform to be applied
                on a sample.
        """
        self.landmarks_frame = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.landmarks_frame)

    def __getitem__(self, idx):
        if torch.is_tensor(idx):
            idx = idx.tolist()

        img_name = os.path.join(self.root_dir,
                                self.landmarks_frame.iloc[idx, 0])  #得到图片路径
        image = io.imread(img_name)
        landmarks = self.landmarks_frame.iloc[idx, 1:]
        landmarks = np.array([landmarks])
        landmarks = landmarks.astype('float').reshape(-1, 2)
        sample = {'image': image, 'landmarks': landmarks}

        if self.transform:
            sample = self.transform(sample)

        return sample

接下来,实例化数据集类,并打印前4张图片(非必需)

dataset = FaceLandmarksDataset(csv_file = "C:\\Users\\85233\\Desktop\\faces\\face_landmarks.csv",
                               root_dir = "C:\\Users\\85233\\Desktop\\faces\\")
fig = plt.figure()

for i in range(len(face_dataset)):
    sample = face_dataset[i]

    print(i, sample['image'].shape, sample['landmarks'].shape)

    ax = plt.subplot(1, 4, i + 1)
    plt.tight_layout()
    ax.set_title('Sample #{}'.format(i))
    ax.axis('off')
    show_landmarks(**sample)

    if i == 3:
        plt.show()
        break

运行代码后,得到如下结果:
Pytorch入门-数据载入和预处理实例_第1张图片

图像转换(Transform)

在工程中,由于图像尺寸不符等原因,经常需要对图片进行转换,再输入网络。下面实现三个变换:

  • Rescale:重新设定图片尺寸
  • RandomCrop:随机截取一部分图片
  • ToTensor:把numpy表示的图像数组转换为tensor向量(转换坐标轴即可)

我们需要把Transform定义为可调用的类(callable class) 而不是简单的函数,这样做的好处是Transform的参数就不需要在每次调用的时候都传递一次。为此,我们需要重写__ call 方法和 init __方法(如果需要的话)。我们可以这样使用一种Transform:

tsfm = Transform(params)
transformed_sample = tsfm(sample)

以下是三种转换的实现:

class Rescale(object):
    """Rescale the image in a sample to a given size.

    Args:
        output_size (tuple or int): Desired output size. If tuple, output is
            matched to output_size. If int, smaller of image edges is matched
            to output_size keeping aspect ratio the same.
    """

    def __init__(self, output_size):
        assert isinstance(output_size, (int, tuple))
        self.output_size = output_size

    def __call__(self, sample):
        image, landmarks = sample['image'], sample['landmarks']

        h, w = image.shape[:2]
        if isinstance(self.output_size, int):
            if h > w:
                new_h, new_w = self.output_size * h / w, self.output_size
            else:
                new_h, new_w = self.output_size, self.output_size * w / h
        else:
            new_h, new_w = self.output_size

        new_h, new_w = int(new_h), int(new_w)

        img = transform.resize(image, (new_h, new_w))

        # h and w are swapped for landmarks because for images,
        # x and y axes are axis 1 and 0 respectively
        landmarks = landmarks * [new_w / w, new_h / h]

        return {'image': img, 'landmarks': landmarks}


class RandomCrop(object):
    """Crop randomly the image in a sample.

    Args:
        output_size (tuple or int): Desired output size. If int, square crop
            is made.
    """

    def __init__(self, output_size):
        assert isinstance(output_size, (int, tuple))
        if isinstance(output_size, int):
            self.output_size = (output_size, output_size)
        else:
            assert len(output_size) == 2
            self.output_size = output_size

    def __call__(self, sample):
        image, landmarks = sample['image'], sample['landmarks']

        h, w = image.shape[:2]
        new_h, new_w = self.output_size

        top = np.random.randint(0, h - new_h)
        left = np.random.randint(0, w - new_w)

        image = image[top: top + new_h,
                      left: left + new_w]

        landmarks = landmarks - [left, top]

        return {'image': image, 'landmarks': landmarks}


class ToTensor(object):
    """Convert ndarrays in sample to Tensors."""

    def __call__(self, sample):
        image, landmarks = sample['image'], sample['landmarks']

        # swap color axis because
        # numpy image: H x W x C
        # torch image: C X H X W
        image = image.transpose((2, 0, 1))
        return {'image': torch.from_numpy(image),
                'landmarks': torch.from_numpy(landmarks)}

组合转换

torchvision.transforms.Compose函数可以很方便地帮助我们组合转换,例如:

scale = Rescale(256)
crop = RandomCrop(128)
composed = transforms.Compose([Rescale(256),
                               RandomCrop(224)])

# Apply each of the above transforms on sample.
fig = plt.figure()
sample = face_dataset[65]
for i, tsfrm in enumerate([scale, crop, composed]):
    transformed_sample = tsfrm(sample)

    ax = plt.subplot(1, 3, i + 1)
    plt.tight_layout()
    ax.set_title(type(tsfrm).__name__)
    show_landmarks(**transformed_sample)

plt.show()

在数据集中迭代

首先,实例化数据集类:

transformed_dataset = FaceLandmarksDataset(csv_file="C:\\Users\\85233\\Desktop\\faces\\face_landmarks.csv",
                                          root_dir="C:\\Users\\85233\\Desktop\\faces\\",
                                           transform=transforms.Compose([
                                               Rescale(256),
                                               RandomCrop(224),
                                               ToTensor()
                                           ]))

之后,我们可以使用 torch.utils.data.DataLoader 加载数据,它提供以下功能:

  • 给数据分片
  • 随机打乱数据
  • 使用GPU并行加载数据
dataloader = DataLoader(transformed_dataset, batch_size=4,
                        shuffle=True, num_workers=4)

实现一个函数来展示一个batch:

def show_landmarks_batch(sample_batched):
    """Show image with landmarks for a batch of samples."""
    images_batch, landmarks_batch = \
            sample_batched['image'], sample_batched['landmarks']
    batch_size = len(images_batch)
    im_size = images_batch.size(2)
    grid_border_size = 2

    grid = utils.make_grid(images_batch)
    plt.imshow(grid.numpy().transpose((1, 2, 0)))

    for i in range(batch_size):
        plt.scatter(landmarks_batch[i, :, 0].numpy() + i * im_size + (i + 1) * grid_border_size,
                    landmarks_batch[i, :, 1].numpy() + grid_border_size,
                    s=10, marker='.', c='r')

        plt.title('Batch from dataloader')

for i_batch, sample_batched in enumerate(dataloader):
    print(i_batch, sample_batched['image'].size(),
          sample_batched['landmarks'].size())

    # observe 4th batch and stop.
    if i_batch == 3:
        plt.figure()
        show_landmarks_batch(sample_batched)
        plt.axis('off')
        plt.ioff()
        plt.show()
        break

运行代码后,得到以下结果:
Pytorch入门-数据载入和预处理实例_第2张图片

0 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2])
1 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2])
2 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2])
3 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2])

Afterword: torchvision

在实际工程中,我们并不需要自己定义图像的转换,可以在torchvision中直接调用。

import torch
from torchvision import transforms, datasets

data_transform = transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])
hymenoptera_dataset = datasets.ImageFolder(root='hymenoptera_data/train',
                                           transform=data_transform)
dataset_loader = torch.utils.data.DataLoader(hymenoptera_dataset,
                                             batch_size=4, shuffle=True,
                                             num_workers=4)

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