如何将自定义数据集拆分为训练和测试数据集？

Question

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)

这是我可以通过使用来自其他存储库的引用来做到的。 但是，我想将此数据集拆分为训练和测试。

我怎样才能在这堂课中做到这一点？还是我需要单独开设一个班级来做到这一点？

Answer 1

从 PyTorch 0.4.1 开始，您可以使用 random_split:

train_size = int(0.8 * len(full_dataset))
test_size = len(full_dataset) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [train_size, test_size])

Answer 2

使用 Pytorch 的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):
        # ...

Answer 3

当前的答案是随机拆分，其缺点是不能保证每个类的样本数量是平衡的。当您希望每个类有少量样本时，这尤其成问题。例如，MNIST 有 60,000 个示例，即每个数字 6000 个。假设您只需要训练集中每个数字 30 个示例。在这种情况下，随机拆分可能会在类之间产生不平衡（一个数字的训练数据比其他数字多）。因此，您要确保每个数字精确地只有 30 个标签。这称为分层抽样。

一种方法是使用 Pytorch 中的采样器接口和sample code is here。

另一种方法是破解你的方式:)。例如，下面是 MNIST 的简单实现，其中ds 是 MNIST 数据集，k 是每个类所需的样本数。

def sampleFromClass(ds, k):
    class_counts = {}
    train_data = []
    train_label = []
    test_data = []
    test_label = []
    for data, label in ds:
        c = label.item()
        class_counts[c] = class_counts.get(c, 0) + 1
        if class_counts[c] <= k:
            train_data.append(data)
            train_label.append(torch.unsqueeze(label, 0))
        else:
            test_data.append(data)
            test_label.append(torch.unsqueeze(label, 0))
    train_data = torch.cat(train_data)
    for ll in train_label:
        print(ll)
    train_label = torch.cat(train_label)
    test_data = torch.cat(test_data)
    test_label = torch.cat(test_label)

    return (TensorDataset(train_data, train_label), 
        TensorDataset(test_data, test_label))

你可以这样使用这个函数：

def main():
    train_ds = datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor()
                       ]))
    train_ds, test_ds = sampleFromClass(train_ds, 3)

Answer 4

如果您想确保您的分组有平衡的班级，您可以使用sklearn 中的train_test_split。

假设您已将 data 包装在 custom Dataset object 中：

from torch.utils.data import DataLoader, Subset
from sklearn.model_selection import train_test_split

TEST_SIZE = 0.1
BATCH_SIZE = 64
SEED = 42

# generate indices: instead of the actual data we pass in integers instead
train_indices, test_indices, _, _ = train_test_split(
    range(len(data)),
    data.targets,
    stratify=data.targets,
    test_size=TEST_SIZE,
    random_state=SEED
)

# generate subset based on indices
train_split = Subset(data, train_indices)
test_split = Subset(data, test_indices)

# create batches
train_batches = DataLoader(train_split, batch_size=BATCH_SIZE, shuffle=True)
test_batches = DataLoader(test_split, batch_size=BATCH_SIZE, shuffle=True)

Answer 5

这是 PyTorch Subset 附加的类，包含 random_split 方法。请注意，此方法是 SubsetRandomSampler 的基础。

对于 MNIST，如果我们使用 random_split:

loader = DataLoader(
  torchvision.datasets.MNIST('/data/mnist', train=True, download=True,
                             transform=torchvision.transforms.Compose([
                               torchvision.transforms.ToTensor(),
                               torchvision.transforms.Normalize(
                                 (0.5,), (0.5,))
                             ])),
  batch_size=16, shuffle=False)

print(loader.dataset.data.shape)
test_ds, valid_ds = torch.utils.data.random_split(loader.dataset, (50000, 10000))
print(test_ds, valid_ds)
print(test_ds.indices, valid_ds.indices)
print(test_ds.indices.shape, valid_ds.indices.shape)

我们得到：

torch.Size([60000, 28, 28])
<torch.utils.data.dataset.Subset object at 0x0000020FD1880B00> <torch.utils.data.dataset.Subset object at 0x0000020FD1880C50>
tensor([ 1520,  4155, 45472,  ..., 37969, 45782, 34080]) tensor([ 9133, 51600, 22067,  ...,  3950, 37306, 31400])
torch.Size([50000]) torch.Size([10000])

我们的test_ds.indices 和valid_ds.indices 将在(0, 600000) 范围内随机出现。但是，如果我想从(0, 49999) 和(50000, 59999) 获取索引序列，不幸的是，除了this 方式之外，我目前不能这样做。

如果您运行 the MNIST benchmark 会很方便，因为它预先定义了测试数据集和验证数据集。

Answer 6

请记住，大多数典型示例已经被否定了。例如在this page 你会找到MNIST。一种普遍的看法是它有 60.000 张图像。砰!错误的！它有 60.000 张训练图像和 10.000 张验证（测试）图像中的 70.000 张图像。

因此，对于规范数据集，PyTorch 的风格是为您提供已经受到攻击的数据集。

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, TensorDataset
from torch.optim import *
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import os
import numpy as np
import random

bs=512

t = transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize(mean=(0), std=(1))]
                       )

dl_train = DataLoader( torchvision.datasets.MNIST('/data/mnist', download=True, train=True, transform=t), 
                batch_size=bs, drop_last=True, shuffle=True)
dl_valid = DataLoader( torchvision.datasets.MNIST('/data/mnist', download=True, train=False, transform=t), 
                batch_size=bs, drop_last=True, shuffle=True)

Answer 7

如果您希望训练数据集中每个类别最多有 X 个样本，您可以使用以下代码：

def stratify_split(dataset: Dataset, train_samples_per_class: int):
        import collections
        train_indices = []
        val_indices = []
        TRAIN_SAMPLES_PER_CLASS = 10
        target_counter = collections.Counter()
        for idx, data in enumerate(dataset):
            target = data['target']
            target_counter[target] += 1
            if target_counter[target] <= train_samples_per_class:
                train_indices.append(idx)
            else:
                val_indices.append(idx)
        train_dataset = Subset(dataset, train_indices)
        val_dataset = Subset(dataset, val_indices)
        return train_dataset, val_dataset