Train, Validation and Test Split for torchvision Datasets

data_loader.py

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

from utils import plot_images
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler


def get_train_valid_loader(data_dir,
                           batch_size,
                           augment,
                           random_seed,
                           valid_size=0.1,
                           shuffle=True,
                           show_sample=False,
                           num_workers=4,
                           pin_memory=False):
    """
    Utility function for loading and returning train and valid
    multi-process iterators over the CIFAR-10 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(
        mean=[0.4914, 0.4822, 0.4465],
        std=[0.2023, 0.1994, 0.2010],
    )

    # 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.CIFAR10(
        root=data_dir, train=True,
        download=True, transform=train_transform,
    )

    valid_dataset = datasets.CIFAR10(
        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:
        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().transpose([0, 2, 3, 1])
        plot_images(X, labels)

    return (train_loader, valid_loader)


def get_test_loader(data_dir,
                    batch_size,
                    shuffle=True,
                    num_workers=4,
                    pin_memory=False):
    """
    Utility function for loading and returning a multi-process
    test iterator over the CIFAR-10 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(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    )

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

    dataset = datasets.CIFAR10(
        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

import matplotlib.pyplot as plt

label_names = [
    'airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck'
]


def plot_images(images, cls_true, cls_pred=None):
    """
    Adapted from https://github.com/Hvass-Labs/TensorFlow-Tutorials/
    """
    fig, axes = plt.subplots(3, 3)

    for i, ax in enumerate(axes.flat):
        # plot img
        ax.imshow(images[i, :, :, :], interpolation='spline16')

        # show true & predicted classes
        cls_true_name = label_names[cls_true[i]]
        if cls_pred is None:
            xlabel = "{0} ({1})".format(cls_true_name, cls_true[i])
        else:
            cls_pred_name = label_names[cls_pred[i]]
            xlabel = "True: {0}\nPred: {1}".format(
                cls_true_name, cls_pred_name
            )
        ax.set_xlabel(xlabel)
        ax.set_xticks([])
        ax.set_yticks([])

    plt.show()

also the pytorch tutorials use 0.5 as opposte to:

test:

    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    )

and
train

    normalize = transforms.Normalize(
        mean=[0.4914, 0.4822, 0.4465],
        std=[0.2023, 0.1994, 0.2010],
    )

why?

why get_train_valid_loader() return None-Type ?

also the pytorch tutorials use 0.5 as opposte to:

test:

    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    )

and
train

    normalize = transforms.Normalize(
        mean=[0.4914, 0.4822, 0.4465],
        std=[0.2023, 0.1994, 0.2010],
    )

why?

I can't speak the choice of transform used here, but from my own testing I will say that the transform applied to the train set should be the same as that of the test set. Prior to doing this, I was getting inconsistent accuracies on the test set when compared to the validation set. I chose to set both to

    normalize = transforms.Normalize(
        mean=[0.4914, 0.4822, 0.4465],
        std=[0.2023, 0.1994, 0.2010],
    )