[解決済み] カスタムデータセットをトレーニングデータとテストデータに分割するにはどうしたらいいですか？

質問

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):
        return len(self.data)

これは、他のリポジトリからの参照を利用することでなんとかできたことです。 しかし、このデータセットをtrainとtestに分けたいのです。

このクラスの中でどのようにそれを行うことができますか？それとも、そのために別のクラスを作成する必要がありますか？

どのように解決するのですか？

Pytorchの SubsetRandomSampler :

import torch
import numpy as np
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler

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):
        # This method should return only 1 sample and label 
        # (according to "index"), not the whole dataset
        # So probably something like this for you:
        pixel_sequence = self.data['pixels'][index]
        face = [int(pixel) for pixel in pixel_sequence.split(' ')]
        face = np.asarray(face).reshape(self.width, self.height)
        face = cv2.resize(face.astype('uint8'), (self.width, self.height))
        label = self.labels[index]

        return face, label

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


dataset = CustomDatasetFromCSV(my_path)
batch_size = 16
validation_split = .2
shuffle_dataset = True
random_seed= 42

# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset :
    np.random.seed(random_seed)
    np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]

# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)

train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, 
                                           sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                                sampler=valid_sampler)

# Usage Example:
num_epochs = 10
for epoch in range(num_epochs):
    # Train:   
    for batch_index, (faces, labels) in enumerate(train_loader):
        # ...