Note
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Developing Custom PyTorch Dataloaders¶
A significant amount of the effort applied to developing machine learning algorithms is related to data preparation. PyTorch provides many tools to make data loading easy and hopefully, makes your code more readable. In this recipe, you will learn how to:
- Create a custom dataset leveraging the PyTorch dataset APIs;
- Create callable custom transforms that can be composable; and
- Put these components together to create a custom dataloader.
Please note, to run this tutorial, ensure the following packages are installed:
scikit-image
: For image io and transformspandas
: For easier csv parsing
As a point of attribution, this recipe is based on the original tutorial from Sasank Chilamkurthy and was later edited by Joe Spisak.
Setup¶
First let’s import all of the needed libraries for this recipe.
from __future__ import print_function, division
import os
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
# Ignore warnings
import warnings
warnings.filterwarnings("ignore")
plt.ion() # interactive mode
Part 1: The Dataset¶
The dataset we are going to deal with is that of facial pose. Overall, 68 different landmark points are annotated for each face.
As a next step, please download the dataset from here so that the images are in a directory named ‘data/faces/’.
Note: This dataset was actually generated by applying dlib’s pose estimation on images from the imagenet dataset containing the ‘face’ tag.
!wget https://download.pytorch.org/tutorial/faces.zip
!mkdir data/faces/
import zipfile
with zipfile.ZipFile("faces.zip","r") as zip_ref:
zip_ref.extractall("/data/faces/")
%cd /data/faces/
The dataset comes with a csv file with annotations which looks like this:
image_name,part_0_x,part_0_y,part_1_x,part_1_y,part_2_x, ... ,part_67_x,part_67_y
0805personali01.jpg,27,83,27,98, ... 84,134
1084239450_e76e00b7e7.jpg,70,236,71,257, ... ,128,312
Let’s quickly read the CSV and get the annotations in an (N, 2) array where N is the number of landmarks.
landmarks_frame = pd.read_csv('faces/face_landmarks.csv')
n = 65
img_name = landmarks_frame.iloc[n, 0]
landmarks = landmarks_frame.iloc[n, 1:]
landmarks = np.asarray(landmarks)
landmarks = landmarks.astype('float').reshape(-1, 2)
print('Image name: {}'.format(img_name))
print('Landmarks shape: {}'.format(landmarks.shape))
print('First 4 Landmarks: {}'.format(landmarks[:4]))
1.1 Write a simple helper function to show an image¶
Next let’s write a simple helper function to show an image, its landmarks and use it to show a sample.
def show_landmarks(image, landmarks):
"""Show image with landmarks"""
plt.imshow(image)
plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r')
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
show_landmarks(io.imread(os.path.join('faces/', img_name)),
landmarks)
plt.show()
1.2 Create a dataset class¶
Now lets talk about the PyTorch dataset class
torch.utils.data.Dataset
is an abstract class representing a
dataset. Your custom dataset should inherit Dataset
and override the
following methods:
__len__
so thatlen(dataset)
returns the size of the dataset.__getitem__
to support indexing such thatdataset[i]
can be used to get :math:i
th sample
Let’s create a dataset class for our face landmarks dataset. We will
read the csv in __init__
but leave the reading of images to
__getitem__
. This is memory efficient because all the images are not
stored in the memory at once but read as required.
Here we show a sample of our dataset in the forma of a dict
{'image': image, 'landmarks': landmarks}
. Our dataset will take an
optional argument transform
so that any required processing can be
applied on the sample. We will see the usefulness of transform
in
another recipe.
class FaceLandmarksDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, csv_file, root_dir, transform=None):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.landmarks_frame = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.landmarks_frame)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = os.path.join(self.root_dir,
self.landmarks_frame.iloc[idx, 0])
image = io.imread(img_name)
landmarks = self.landmarks_frame.iloc[idx, 1:]
landmarks = np.array([landmarks])
landmarks = landmarks.astype('float').reshape(-1, 2)
sample = {'image': image, 'landmarks': landmarks}
if self.transform:
sample = self.transform(sample)
return sample
1.3 Iterate through data samples¶
Next let’s instantiate this class and iterate through the data samples. We will print the sizes of first 4 samples and show their landmarks.
face_dataset = FaceLandmarksDataset(csv_file='faces/face_landmarks.csv',
root_dir='faces/')
fig = plt.figure()
for i in range(len(face_dataset)):
sample = face_dataset[i]
print(i, sample['image'].shape, sample['landmarks'].shape)
ax = plt.subplot(1, 4, i + 1)
plt.tight_layout()
ax.set_title('Sample #{}'.format(i))
ax.axis('off')
show_landmarks(**sample)
if i == 3:
plt.show()
break
Part 2: Data Tranformations¶
Now that we have a dataset to work with and have done some level of customization, we can move to creating custom transformations. In computer vision, these come in handy to help generalize algorithms and improve accuracy. A suite of transformations used at training time is typically referred to as data augmentation and is a common practice for modern model development.
One issue common in handling datasets is that the samples may not all be the same size. Most neural networks expect the images of a fixed size. Therefore, we will need to write some prepocessing code. Let’s create three transforms:
Rescale
: to scale the imageRandomCrop
: to crop from image randomly. This is data augmentation.ToTensor
: to convert the numpy images to torch images (we need to swap axes).
We will write them as callable classes instead of simple functions so
that parameters of the transform need not be passed everytime it’s
called. For this, we just need to implement __call__
method and if
required, __init__
method. We can then use a transform like this:
tsfm = Transform(params)
transformed_sample = tsfm(sample)
Observe below how these transforms had to be applied both on the image and landmarks.
2.1 Create callable classes¶
Let’s start with creating callable classes for each transform
class Rescale(object):
"""Rescale the image in a sample to a given size.
Args:
output_size (tuple or int): Desired output size. If tuple, output is
matched to output_size. If int, smaller of image edges is matched
to output_size keeping aspect ratio the same.
"""
def __init__(self, output_size):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
h, w = image.shape[:2]
if isinstance(self.output_size, int):
if h > w:
new_h, new_w = self.output_size * h / w, self.output_size
else:
new_h, new_w = self.output_size, self.output_size * w / h
else:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
img = transform.resize(image, (new_h, new_w))
# h and w are swapped for landmarks because for images,
# x and y axes are axis 1 and 0 respectively
landmarks = landmarks * [new_w / w, new_h / h]
return {'image': img, 'landmarks': landmarks}
class RandomCrop(object):
"""Crop randomly the image in a sample.
Args:
output_size (tuple or int): Desired output size. If int, square crop
is made.
"""
def __init__(self, output_size):
assert isinstance(output_size, (int, tuple))
if isinstance(output_size, int):
self.output_size = (output_size, output_size)
else:
assert len(output_size) == 2
self.output_size = output_size
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
h, w = image.shape[:2]
new_h, new_w = self.output_size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[top: top + new_h,
left: left + new_w]
landmarks = landmarks - [left, top]
return {'image': image, 'landmarks': landmarks}
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((2, 0, 1))
return {'image': torch.from_numpy(image),
'landmarks': torch.from_numpy(landmarks)}
2.2 Compose transforms and apply to a sample¶
Next let’s compose these transforms and apply to a sample
Let’s say we want to rescale the shorter side of the image to 256 and
then randomly crop a square of size 224 from it. i.e, we want to compose
Rescale
and RandomCrop
transforms.
torchvision.transforms.Compose
is a simple callable class which
allows us to do this.
scale = Rescale(256)
crop = RandomCrop(128)
composed = transforms.Compose([Rescale(256),
RandomCrop(224)])
# Apply each of the above transforms on sample.
fig = plt.figure()
sample = face_dataset[65]
for i, tsfrm in enumerate([scale, crop, composed]):
transformed_sample = tsfrm(sample)
ax = plt.subplot(1, 3, i + 1)
plt.tight_layout()
ax.set_title(type(tsfrm).__name__)
show_landmarks(**transformed_sample)
plt.show()
2.3 Iterate through the dataset¶
Next we will iterate through the dataset
Let’s put this all together to create a dataset with composed transforms. To summarize, every time this dataset is sampled:
- An image is read from the file on the fly
- Transforms are applied on the read image
- Since one of the transforms is random, data is augmentated on sampling
We can iterate over the created dataset with a for i in range
loop
as before.
transformed_dataset = FaceLandmarksDataset(csv_file='faces/face_landmarks.csv',
root_dir='faces/',
transform=transforms.Compose([
Rescale(256),
RandomCrop(224),
ToTensor()
]))
for i in range(len(transformed_dataset)):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['landmarks'].size())
if i == 3:
break
Part 3: The Dataloader¶
By operating on the dataset directly, we are losing out on a lot of
features by using a simple for
loop to iterate over the data. In
particular, we are missing out on:
- Batching the data
- Shuffling the data
- Load the data in parallel using
multiprocessing
workers.
torch.utils.data.DataLoader
is an iterator which provides all these
features. Parameters used below should be clear. One parameter of
interest is collate_fn
. You can specify how exactly the samples need
to be batched using collate_fn
. However, default collate should work
fine for most use cases.
dataloader = DataLoader(transformed_dataset, batch_size=4,
shuffle=True, num_workers=4)
# Helper function to show a batch
def show_landmarks_batch(sample_batched):
"""Show image with landmarks for a batch of samples."""
images_batch, landmarks_batch = \
sample_batched['image'], sample_batched['landmarks']
batch_size = len(images_batch)
im_size = images_batch.size(2)
grid = utils.make_grid(images_batch)
plt.imshow(grid.numpy().transpose((1, 2, 0)))
for i in range(batch_size):
plt.scatter(landmarks_batch[i, :, 0].numpy() + i * im_size,
landmarks_batch[i, :, 1].numpy(),
s=10, marker='.', c='r')
plt.title('Batch from dataloader')
for i_batch, sample_batched in enumerate(dataloader):
print(i_batch, sample_batched['image'].size(),
sample_batched['landmarks'].size())
# observe 4th batch and stop.
if i_batch == 3:
plt.figure()
show_landmarks_batch(sample_batched)
plt.axis('off')
plt.ioff()
plt.show()
break
Now that you’ve learned how to create a custom dataloader with PyTorch,
we recommend diving deeper into the docs and customizing your workflow
even further. You can learn more in the torch.utils.data
docs
here.
Total running time of the script: ( 0 minutes 0.000 seconds)