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Train, Validation and Test Split for torchvision Datasets
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""" | |
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 |
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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() |
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
trainnormalize = 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],
)
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also the pytorch tutorials use 0.5 as opposte to:
test:
and
train
why?