Train, Validation and Test Split for torchvision MNIST Dataset

get_train_valid_loader.py

# https://gist.github.com/kevinzakka/d33bf8d6c7f06a9d8c76d97a7879f5cb#file-data_loader-py
# This is an example for the MNIST dataset (formerly CIFAR-10).
# There's a function for creating a train and validation iterator.
# There's also a function for creating a test iterator.
# Inspired by https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4

# Adapted for MNIST by github.com/MatthewKleinsmith

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

from utils import plot_images

def get_train_valid_loader(data_dir,
                           batch_size,
                           random_seed,
                           augment=False,
                           valid_size=0.2,
                           shuffle=True,
                           show_sample=False,
                           num_workers=1,
                           pin_memory=True):
    """
    Utility function for loading and returning train and valid 
    multi-process iterators over the MNIST dataset. A sample 
    9x9 grid of the images can be optionally displayed.

    If using CUDA, num_workers should be set to 1 and pin_memory to True.

    Params
    ------
    - data_dir: path directory to the dataset.
    - batch_size: how many samples per batch to load.
    - augment: whether to apply the data augmentation scheme
      mentioned in the paper. Only applied on the train split.
    - random_seed: fix seed for reproducibility.
    - valid_size: percentage split of the training set used for
      the validation set. Should be a float in the range [0, 1].
    - shuffle: whether to shuffle the train/validation indices.
    - show_sample: plot 9x9 sample grid of the dataset.
    - 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 GPU.

    Returns
    -------
    - train_loader: training set iterator.
    - valid_loader: validation set iterator.
    """
    error_msg = "[!] valid_size should be in the range [0, 1]."
    assert ((valid_size >= 0) and (valid_size <= 1)), error_msg

    normalize = transforms.Normalize((0.1307,), (0.3081,))  # MNIST

    # define transforms
    valid_transform = transforms.Compose([
            transforms.ToTensor(),
            normalize
        ])
    if augment:
        train_transform = transforms.Compose([
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize
        ])
    else:
        train_transform = transforms.Compose([
            transforms.ToTensor(),
            normalize
        ])

    # load the dataset
    train_dataset = datasets.MNIST(root=data_dir, train=True, 
                download=True, transform=train_transform)

    valid_dataset = datasets.MNIST(root=data_dir, train=True, 
                download=True, transform=valid_transform)

    num_train = len(train_dataset)
    indices = list(range(num_train))
    split = int(np.floor(valid_size * num_train))

    if shuffle == True:
        np.random.seed(random_seed)
        np.random.shuffle(indices)

    train_idx, valid_idx = indices[split:], indices[:split]

    train_sampler = SubsetRandomSampler(train_idx)
    valid_sampler = SubsetRandomSampler(valid_idx)

    train_loader = torch.utils.data.DataLoader(train_dataset, 
                    batch_size=batch_size, sampler=train_sampler, 
                    num_workers=num_workers, pin_memory=pin_memory)

    valid_loader = torch.utils.data.DataLoader(valid_dataset, 
                    batch_size=batch_size, sampler=valid_sampler, 
                    num_workers=num_workers, pin_memory=pin_memory)


    # visualize some images
    if show_sample:
        sample_loader = torch.utils.data.DataLoader(train_dataset, 
                                                    batch_size=9, 
                                                    shuffle=shuffle, 
                                                    num_workers=num_workers,
                                                    pin_memory=pin_memory)
        data_iter = iter(sample_loader)
        images, labels = data_iter.next()
        X = images.numpy()
        plot_images(X, labels)

    return (train_loader, valid_loader)
    
def get_test_loader(data_dir, 
                    batch_size,
                    shuffle=True,
                    num_workers=1,
                    pin_memory=True):
    """
    Utility function for loading and returning a multi-process 
    test iterator over the MNIST dataset.

    If using CUDA, num_workers should be set to 1 and pin_memory to True.

    Params
    ------
    - data_dir: path directory to the dataset.
    - batch_size: how many samples per batch to load.
    - shuffle: whether to shuffle the dataset after every epoch.
    - 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 GPU.

    Returns
    -------
    - data_loader: test set iterator.
    """
    normalize = transforms.Normalize((0.1307,), (0.3081,))  # MNIST

    # define transform
    transform = transforms.Compose([
        transforms.ToTensor(),
        normalize
    ])

    dataset = datasets.MNIST(root=data_dir, 
                               train=False, 
                               download=True,
                               transform=transform)

    data_loader = torch.utils.data.DataLoader(dataset, 
                                              batch_size=batch_size, 
                                              shuffle=shuffle, 
                                              num_workers=num_workers,
                                              pin_memory=pin_memory)

    return data_loader

utils.py

# https://gist.github.com/kevinzakka/d33bf8d6c7f06a9d8c76d97a7879f5cb#file-utils-py

import matplotlib.pyplot as plt

def plot_images(images, labels, preds=None):

    assert len(images) == len(labels) == 9

    # Create figure with sub-plots.
    fig, axes = plt.subplots(3, 3)

    for i, ax in enumerate(axes.flat):
        ax.imshow(images[i, 0, :, :], interpolation='spline16', cmap='gray')
            
        label = str(labels[i])
        if preds is None:
            xlabel = label
        else:
            pred = str(preds[i])
            xlabel = "True: {0}\nPred: {1}".format(label, pred)
            
        ax.set_xlabel(xlabel)
        ax.set_xticks([])
        ax.set_yticks([])

    plt.show()

why not read training set and use torch.utils.data.random_split?

@zhiqihuang share your code perhaps?

@MattKleinsmith isn't:

    train_idx, valid_idx = indices[split_idx:], indices[:split_idx]

backwards?