img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")]

这段代码是用来获取文件夹中所有以 ".jpg" 结尾的图片文件的路径，并将它们存储在一个列表中。其中，imgs_root 是文件夹的路径，os.listdir(imgs_root) 返回该文件夹中所有文件和文件夹的名称列表，os.path.join() 函数用于将文件夹路径和文件名拼接成完整的文件路径。最后，列表推导式过滤出所有以 ".jpg" 结尾的文件名，生成最终的图片路径列表。

相关问题

import os import json import torch from PIL import Image from torchvision import transforms from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image # 指向需要遍历预测的图像文件夹 imgs_root = "../dataset/val" assert os.path.exists(imgs_root), f"file: '{imgs_root}' dose not exist." # 读取指定文件夹下所有jpg图像路径 img_path_list = [os.path.join(imgs_root, i) for i in os.listdir(imgs_root) if i.endswith(".jpg")] # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), f"file: '{json_path}' dose not exist." json_file = open(json_path, "r") class_indict = json.load(json_file) # create model model = resnet34(num_classes=16).to(device) # load model weights weights_path = "./newresNet34.pth" assert os.path.exists(weights_path), f"file: '{weights_path}' dose not exist." model.load_state_dict(torch.load(weights_path, map_location=device)) # prediction model.eval() batch_size = 8 # 每次预测时将多少张图片打包成一个batch with torch.no_grad(): for ids in range(0, len(img_path_list) // batch_size): img_list = [] for img_path in img_path_list[ids * batch_size: (ids + 1) * batch_size]: assert os.path.exists(img_path), f"file: '{img_path}' dose not exist." img = Image.open(img_path) img = data_transform(img) img_list.append(img) # batch img # 将img_list列表中的所有图像打包成一个batch batch_img = torch.stack(img_list, dim=0) # predict class output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1) for idx, (pro, cla) in enumerate(zip(probs, classes)): print("image: {} class: {} prob: {:.3}".format(img_path_list[ids * batch_size + idx], class_indict[str(cla.numpy())], pro.numpy())) if __name__ == '__main__': main()

这段代码实现了导入必要的包和模块，包括操作系统、JSON、PyTorch、PIL及其转换模块、还有定义的resnet34模型。在主函数中，首先根据可用GPU情况使用cuda或cpu作为设备，然后定义数据的处理流程，包括缩放、剪裁、转换为Tensor并进行标准化。

基于pytorch的arcface的lfw_dataset = LFW(lfw_path, transform)中LFW的具体函数代码

LFW的函数代码如下：

class LFW(Dataset):

    def __init__(self, root, transform=None, pairs_path=None, download=False):
        self.root = root
        self.transform = transform
        self.pairs_path = pairs_path
        self.download = download

        if download:
            self.download_lfw()

        if not self._check_integrity():
            raise RuntimeError('Dataset not found or corrupted.' +
                               ' You can use download=True to download it')

        self.imgs, self.pairs = self._load_metadata()

    def __getitem__(self, index):
        path1, path2, label = self.pairs[index]
        img1 = self._load_image(os.path.join(self.root, path1))
        img2 = self._load_image(os.path.join(self.root, path2))
        if self.transform is not None:
            img1 = self.transform(img1)
            img2 = self.transform(img2)
        return img1, img2, label

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

    def _load_metadata(self):
        pairs = []
        if self.pairs_path is None:
            pairs = self._generate_pairs()
        else:
            with open(self.pairs_path, 'r') as f:
                for line in f.readlines()[1:]:
                    pair = line.strip().split()
                    if len(pair) == 3:
                        path1 = os.path.join(pair[0], pair[0] + '_' + '%04d' % int(pair[1]) + '.jpg')
                        path2 = os.path.join(pair[0], pair[0] + '_' + '%04d' % int(pair[2]) + '.jpg')
                        label = 1
                    elif len(pair) == 4:
                        path1 = os.path.join(pair[0], pair[0] + '_' + '%04d' % int(pair[1]) + '.jpg')
                        path2 = os.path.join(pair[2], pair[2] + '_' + '%04d' % int(pair[3]) + '.jpg')
                        label = -1
                    else:
                        raise ValueError('Pair {} do not have length of 3 or 4'.format(pair))
                    pairs.append((path1, path2, label))

        root = os.path.expanduser(self.root)
        imgs = {os.path.join(root, img): None for img in os.listdir(root)}

        return imgs, pairs

    def _generate_pairs(self):
        root = os.path.expanduser(self.root)
        if not os.path.exists(os.path.join(root, 'lfw_funneled')):
            print('Please download the Funneled version of the LFW dataset from the official website'
                  'and place it in: ' + root)
            exit(0)

        imgs = glob.glob(os.path.join(root, 'lfw_funneled', '**/*.jpg'))
        imgs = {os.path.relpath(x, root): None for x in imgs}

        pairs = []
        people = set()
        for img in imgs:
            people.add('_'.join(img.split('_')[:-1]))

        people = list(people)
        n = len(people)

        for i, name in enumerate(people):
            same = [(name, x) for x in people[i+1:]]
            for s in same:
                pairs.append((s[0], s[1], 1))

        for i, name in enumerate(people):
            diff = [name, random.choice(list(set(people) - set([name])))]
            pairs.append((diff[0], diff[1], -1))

        return pairs

    def _load_image(self, path):
        if self.imgs[path] is None:
            self.imgs[path] = pil_loader(path)
        return self.imgs[path]

    def _check_integrity(self):
        root = os.path.expanduser(self.root)
        if not os.path.isdir(root):
            return False
        return True

    def download_lfw(self):
        if self._check_integrity():
            print('Files already downloaded and verified')
            return
        download_and_extract_archive(LFW_URL, self.root, filename=LFW_FILENAME, md5=LFW_MD5)

其中，该函数接受四个参数：`root`表示LFW数据集的根目录，`transform`表示数据预处理函数，`pairs_path`表示pairs.txt文件的路径，`download`表示是否自动下载LFW数据集。

该函数主要实现了以下功能：

- 加载LFW数据集的元数据，包括人脸图像路径、同/异类标签等信息；

- 加载LFW数据集的人脸图像；

- 对人脸图像进行预处理，如裁剪、归一化等；

- 按照pairs.txt文件中的信息，计算出每对人脸图像的同/异类标签；

- 返回每对人脸图像的数据和标签。