How do I split a custom dataset into training and test datasets?

torch.utils.data.random_split(dataset, lengths)
pytorch random split
torch.utils.data.random_split example
pytorch random_split
subsetrandomsampler
pytorch random split example
torch.utils.data.random_split(dataset lengths) example
pytorch random_split example
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)

This is what I could manage to do by using references from other repositories. However, I want to split this dataset into train and test.

How can I do that inside this class? Or do I need to make a separate class to do that?

Using Pytorch's 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):
        # ...

How to split dataset into test and validation sets, I have a dataset in which the different images are classified into different folders. I want to split the data to test, train, valid sets. Please help. How to split your dataset to train and test datasets using SciKit Learn. When you’re working on a model and want to train it, you obviously have a dataset. But after training, we have to test the model on some test dataset. For this, you’ll a dataset which is different from the training set you used earlier.

Starting in PyTorch 0.4.1 you can use 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])

Building Efficient Custom Datasets in PyTorch, Managing data for neural network training can be hard at “scale”. In this article, I will be exploring the PyTorch Dataset object from the ground up Let's have a look at what it would look like if we sliced the dataset into a batch: Create validation sets by splitting your custom PyTorch datasets easily with  shuffle the whole matrix arr and then split the data to train and test. shuffle the indices and then assign it x and y to split the data. same as method 2, but in a more efficient way to do it. using pandas dataframe to split.

Current answers do random splits which has disadvantage that number of samples per class is not guaranteed to be balanced. This is especially problematic when you want to have small number of samples per class. For example, MNIST has 60,000 examples, i.e. 6000 per digit. Assume that you want only 30 examples per digit in your training set. In this case, random split may produce imbalance between classes (one digit with more training data then others). So you want to make sure each digit precisely has only 30 labels. This is called stratified sampling.

One way to do this is using sampler interface in Pytorch and sample code is here.

Another way to do this is just hack your way through :). For example, below is simple implementation for MNIST where ds is MNIST dataset and k is number of samples needed for each class.

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))

You can use this function like this:

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

Train, Validation and Test Split for torchvision Datasets · GitHub, Train, Validation and Test Split for torchvision Datasets - data_loader.py. num_workers: number of subprocesses to use when loading the dataset. - pin_memory: whether to copy tensors into CUDA pinned memory. Set it to. True if using  Test dataset now has first 1000 elements and the rest goes for training. You may use Dataset.take() and Dataset.skip(): For more generality, I gave an example using a 70/15/15 train/val/test split but if you don't need a test or a val set, just ignore the last 2 lines.

This is the PyTorch Subset class attached holding the random_split method. Note that this method is base for the SubsetRandomSampler.

For MNIST if we use 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)

We get:

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])

Our test_ds.indices and valid_ds.indices will be random from range (0, 600000). But if I would like to get sequence of indices from (0, 49999) and from (50000, 59999) I cannot do that at the moment unfortunately, except this way.

Handy in case you run the MNIST benchmark where it is predefined what should be the test and what should be the validation dataset.

Validation split with Dataloader/Dataset · Issue #1106 · pytorch , You can use a SubsetRandomSampler to achieve it, or a custom sampler which lets you create train, validation and test splits on torchvision datasets I check the example/mnist,it present the train and test ,not include valid. Try this snippet of mine if you need to split Dataset into training and validation. Splits and slicing. All DatasetBuilder s expose various data subsets defined as splits (eg: train, test ). When constructing a tf.data.Dataset instance using either tfds.load() or tfds.DatasetBuilder.as_dataset(), one can specify which split(s) to retrieve. It is also possible to retrieve slice(s) of split(s) as well as combinations of those.

Custom dataset has a special meaning in PyTorch, but I think you meant any dataset. Let's check out the MNIST dataset (this is probable the most famous dataset for the beginners).

import torch, torchvision
import torchvision.datasets as datasets
from torch.utils.data import DataLoader, Dataset, TensorDataset
train_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(train_loader.dataset.data.shape)

test_ds =  train_loader.dataset.data[:50000, :, :]
valid_ds =  train_loader.dataset.data[50000:, :, :]
print(test_ds.shape)
print(valid_ds.shape)

test_dst =  train_loader.dataset.targets.data[:50000]
valid_dst =  train_loader.dataset.targets.data[50000:]
print(test_dst, test_dst.shape)
print(valid_dst, valid_dst.shape)

What this will outupt, is size of the original [60000, 28, 28], then the splits [50000, 28, 28] for test and [10000, 28, 28] for validation:

torch.Size([60000, 28, 28])
torch.Size([50000, 28, 28])
torch.Size([10000, 28, 28])
tensor([5, 0, 4,  ..., 8, 4, 8]) torch.Size([50000])
tensor([3, 8, 6,  ..., 5, 6, 8]) torch.Size([10000])

Additional info if you actually plan to pair images and labels (targets) together

bs = 16
test_dl = DataLoader(TensorDataset(test_ds, test_dst), batch_size=bs, shuffle=True)

for xb, yb in test_dl:
    # Do your work

Splits and slicing, import pandas as pd import numpy as np import cv2 from torch.utils.data.dataset import Dataset class CustomDatasetFromCSV(Dataset): def  As we work with datasets, a machine learning algorithm works in two stages. We usually split the data around 20%-80% between testing and training stages. Under supervised learning, we split a dataset into a training data and test data in Python ML. Train and Test Set in Python Machine Learning. a. Prerequisites for Train and Test Data.

How to split my image datasets into training, validation and testing , All DatasetBuilder s expose various data subsets defined as splits (eg: train , test ). The full `train` split and the full `test` split as two distinct datasets. train_ds  One way to do this is using sampler interface in Pytorch and sample code is here. Another way to do this is just hack your way through :). For example, below is simple implementation for MNIST where ds is MNIST dataset and k is number of samples needed for each class.

PyTorch Tutorial: Dataset. Data preparetion stage., For train-test splits and cross validation, I strongly suggest using the SciKitLearn from sklearn import datasets; print('[INFO] loading MNIST full dataset. One common technique for validating models is to break the data to be analyzed into training and test subsamples, then fit the model using the training data and score it by predicting on the test data. Once you have split your original data set onto your cluster nodes, you can split the data on the individual nodes by calling rxSplit again within a call to rxExec.

A detailed example of data loaders with PyTorch, Simple Dataset; Splitting data into train and validation part; Using augmentation Dataset: An abstract class representing a Dataset. All other datasets should subclass it. 'test' if mode == 'train': self.mode = 'train' self.masks = pd.read_csv('. How do i split my dataset into 70% training , 30% testing ? Dear all , I have a dataset in csv format. I am looking for a way/tool to randomly done by dividing 70% of the database for training and 30% for testing , in order to guarantee that both subsets are random samples from the same d

Comments
  • What is num_train?
  • My bad, it has been renamed appropriately (dataset_size).
  • Also when I put this in model, the function forward takes the input data. And the shape of that data is 5D tensor - (32L, 35887L, 48L, 48L, 1L). 32 is the batch size, next is the length of dataset and then image height, width and channel.
  • Dataset.__getitem__() should return a single sample and label, not the whole dataset. I edited my post to give you an example how it should look.
  • @AnaClaudia: batch_size defines the number of samples stacked together into a mini-batch passed to the neural network each training iteration. See Dataloader documentation or this Cross-Validated thread for more info.
  • I followed your answer and got this problem while iterating throught the split train_loader stackoverflow.com/questions/53916594/…