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()

@amobiny I think you have sampler and dataloader confused. The dataloader traverses the entire data set in batches. It selects the samples from the batch using the sampler. The sampler can be sequential so say for a batch of 4 and a dataset of size 32 you'd have [0, 1, 2, 3], [4, 5, 6, 7], etc until [28, 29, 30, 31]. In our case, the sampler is random and without replacement, in which case you'd have possibly something like [17, 1, 12, 31], [2, 8, 18, 28], etc. that would still cover the whole validation set. Does that make sense?

why does train & val not have same statistics usually for normalizing?

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],
    )