李沐46_语义分割和数据集—

李沐46_语义分割和数据集——自学笔记

语义分割

语义分割将图片中的每个像素分类到对应的类别。

实例分割（目标检测的进化版本）

如果有物体，会区别同一类的不同物体。

语义分割重要数据集：Pascal VOC2012

%matplotlib inline
import os
import torch
import torchvision
from d2l import torch as d2l

下载数据集VOC，大小2GB，类型tar

d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar','4e443f8a2eca6b1dac8a6c57641b67dd40621a49')voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')

Downloading ../data/VOCtrainval_11-May-2012.tar from http://d2l-data.s3-accelerate.amazonaws.com/VOCtrainval_11-May-2012.tar...

读取输入的图像和标签

def read_voc_images(voc_dir, is_train=True):"""读取所有VOC图像并标注"""txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation','train.txt' if is_train else 'val.txt')mode = torchvision.io.image.ImageReadMode.RGBwith open(txt_fname, 'r') as f:images = f.read().split()features, labels = [], []for i, fname in enumerate(images):features.append(torchvision.io.read_image(os.path.join(voc_dir, 'JPEGImages', f'{fname}.jpg')))labels.append(torchvision.io.read_image(os.path.join(voc_dir, 'SegmentationClass' ,f'{fname}.png'), mode))return features, labelstrain_features, train_labels = read_voc_images(voc_dir, True)

下面我们绘制前5个输入图像及其标签。在标签图像中，白色和黑色分别表示边框和背景，而其他颜色则对应不同的类别

n = 5
imgs = train_features[0:n] + train_labels[0:n]
imgs = [img.permute(1,2,0) for img in imgs]
d2l.show_images(imgs, 2, n);

在这里插入图片描述

列举RGB颜色值和类名


VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],[0, 64, 128]]VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat','bottle', 'bus', 'car', 'cat', 'chair', 'cow','diningtable', 'dog', 'horse', 'motorbike', 'person','potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']

定义了voc_colormap2label函数来构建从上述RGB颜色值到类别索引的映射，而voc_label_indices函数将RGB值映射到在Pascal VOC2012数据集中的类别索引


def voc_colormap2label():"""构建从RGB到VOC类别索引的映射"""colormap2label = torch.zeros(256 ** 3, dtype=torch.long)for i, colormap in enumerate(VOC_COLORMAP):colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = ireturn colormap2labeldef voc_label_indices(colormap, colormap2label):"""将VOC标签中的RGB值映射到它们的类别索引"""colormap = colormap.permute(1, 2, 0).numpy().astype('int32')idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256+ colormap[:, :, 2])return colormap2label[idx]

第一张样本图像，飞机头部索引标签是1，背景索引是0

y = voc_label_indices(train_labels[0], voc_colormap2label())
y[105:115, 130:140], VOC_CLASSES[1]

(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],[0, 0, 0, 0, 0, 0, 0, 1, 1, 1],[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],[0, 0, 0, 0, 1, 1, 1, 1, 1, 1],[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],[0, 0, 0, 0, 0, 0, 1, 1, 1, 1],[0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]),'aeroplane')

数据预处理

使用图像增广中的随机裁剪，裁剪输入图像和标签的相同区域。


def voc_rand_crop(feature, label, height, width):"""随机裁剪特征和标签图像"""rect = torchvision.transforms.RandomCrop.get_params(feature, (height, width))feature = torchvision.transforms.functional.crop(feature, *rect)label = torchvision.transforms.functional.crop(label, *rect)return feature, labelimgs = []
for _ in range(n):imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)imgs = [img.permute(1, 2, 0) for img in imgs]
d2l.show_images(imgs[::2] + imgs[1::2], 2, n);

在这里插入图片描述

自定义语义分割数据集

通过继承高级API提供的Dataset类，自定义了一个语义分割数据集类VOCSegDataset。

通过实现__getitem__函数，我们可以任意访问数据集中索引为idx的输入图像及其每个像素的类别索引。

由于数据集中有些图像的尺寸可能小于随机裁剪所指定的输出尺寸，这些样本可以通过自定义的filter函数移除掉。

定义了normalize_image函数，从而对输入图像的RGB三个通道的值分别做标准化。

class VOCSegDataset(torch.utils.data.Dataset):"""一个用于加载VOC数据集的自定义数据集"""def __init__(self, is_train, crop_size, voc_dir):self.transform = torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])self.crop_size = crop_sizefeatures, labels = read_voc_images(voc_dir, is_train=is_train)self.features = [self.normalize_image(feature)for feature in self.filter(features)]self.labels = self.filter(labels)self.colormap2label = voc_colormap2label()print('read ' + str(len(self.features)) + ' examples')def normalize_image(self, img):return self.transform(img.float() / 255)def filter(self, imgs):return [img for img in imgs if (img.shape[1] >= self.crop_size[0] andimg.shape[2] >= self.crop_size[1])]def __getitem__(self, idx):feature, label = voc_rand_crop(self.features[idx], self.labels[idx],*self.crop_size)return (feature, voc_label_indices(label, self.colormap2label))def __len__(self):return len(self.features)

读取数据集

自定义的VOCSegDataset类来分别创建训练集和测试集的实例。假设我们指定随机裁剪的输出图像的形状为320X480，下面我们可以查看训练集和测试集所保留的样本个数。

crop_size = (320, 480)
voc_train = VOCSegDataset(True, crop_size, voc_dir)
voc_test = VOCSegDataset(False, crop_size, voc_dir)

read 1114 examples
read 1078 examples

设批量大小为64，我们定义训练集的迭代器。打印第一个小批量的形状会发现：与图像分类或目标检测不同，这里的标签是一个三维数组。

batch_size = 64
train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,drop_last=True,num_workers=d2l.get_dataloader_workers())
for X, Y in train_iter:print(X.shape)print(Y.shape)break

/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.warnings.warn(_create_warning_msg(
/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.self.pid = os.fork()torch.Size([64, 3, 320, 480])
torch.Size([64, 320, 480])

整合所有组件

定义以下load_data_voc函数来下载并读取Pascal VOC2012语义分割数据集。它返回训练集和测试集的数据迭代器

def load_data_voc(batch_size, crop_size):"""加载VOC语义分割数据集"""voc_dir = d2l.download_extract('voc2012', os.path.join('VOCdevkit', 'VOC2012'))num_workers = d2l.get_dataloader_workers()train_iter = torch.utils.data.DataLoader(VOCSegDataset(True, crop_size, voc_dir), batch_size,shuffle=True, drop_last=True, num_workers=num_workers)test_iter = torch.utils.data.DataLoader(VOCSegDataset(False, crop_size, voc_dir), batch_size,drop_last=True, num_workers=num_workers)return train_iter, test_iter