相关疑难解决方法(0)

import pandas as pd
import numpy as np
import cv2
from torch.utils.data.dataset import Dataset

class CustomDatasetFromCSV(Dataset):
    def __init__(self, csv_path, transform=None):
        self.data = pd.read_csv(csv_path)
        self.labels = pd.get_dummies(self.data['emotion']).as_matrix()
        self.height = 48
        self.width = 48
        self.transform = transform

    def __getitem__(self, index):
        pixels = self.data['pixels'].tolist()
        faces = []
        for pixel_sequence in pixels:
            face = [int(pixel) for pixel in pixel_sequence.split(' ')]
            # print(np.asarray(face).shape)
            face = np.asarray(face).reshape(self.width, self.height)
            face = cv2.resize(face.astype('uint8'), (self.width, self.height))
            faces.append(face.astype('float32'))
        faces = np.asarray(faces)
        faces = np.expand_dims(faces, -1)
        return faces, self.labels

    def __len__(self): …

在 Pytorch 中，有没有办法使用类加载特定的torch.utils.data.DataLoader单个样本？我想用它做一些测试。

教程使用​

trainloader = torch.utils.data.DataLoader(...)
images, labels = next(iter(trainloader))

获取一批随机样本。有没有办法使用DataLoader来获取特定样本？

干杯