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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 |
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() |
Isn't it pointless to set a fixed random seed? It does help to generate the same order of indices for splitting the training set and validation set. But the SubsetRandomSampler does not use the seed, thus each batch sampled for training will be different every time.
@songkangsg I'm setting the seed exactly for that purpose: to have the same validation set all the time. I don't care about the order in which I receive the validation images. The goal is to compute a mean validation accuracy and loss.
The mean and std you adopted in this script are for ImageNet not CIFAR10 or CIFAR100
@sunkevin1214 nice catch! Fixed it now.
Using this I have len(train_loader.dataset) = len(val_loader.dataset)=60000, which is wrong.
@tan1889 that's because they both use the same underlying dataset, but a different sampler. You need to do len(train_loader.sampler)
instead.
@kevinzakka
im trying the pytorch firstly.
i used to use the keras and the dataset has 3 parts , train,valid,test.
but when i check the https://github.com/pytorch/examples/blob/master/mnist/main.py, it has train
function
and test
function
.
I cannot find the valid_dataset
,only the train_loader
and test_loader
So i think that the valid_dataset
doesn't to exist.
It confused me now.
Do you give me some explainations? thanks
The normalisation should only be done on the training set.But here the normalization is on the whole set. It should be a problem
@huangchaoxing validation and test sets should be normalized with train set statistics.
You need train=False in below line:
https://gist.github.com/kevinzakka/d33bf8d6c7f06a9d8c76d97a7879f5cb#file-data_loader-py-L86
@sytelus the validation data is taken from the training set. The test set is untouched at all times.
@kevinzakka
Hey Kevin and thanks for the gist.
I had a quick question about the valid_loader. How do you make sure that the validation sampler sweeps all the samples in the validation set exactly once? My understanding is that it takes batches of the provided indices randomly! so if we execute
for images, labels in valid_loader: ...
to for example compute the loss and accuracy over the validation (feed batch by batch and average), it will not do it correctly as it doesn't sweep the whole set once. Am I correct?
@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
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],
)
Isn't it pointless to set a fixed random seed? It does help to generate the same order of indices for splitting the training set and validation set. But the SubsetRandomSampler does not use the seed, thus each batch sampled for training will be different every time.