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Author: Sasank Chilamkurthy
A lot of effort in solving any machine learning problem goes in topreparing the data. PyTorch provides many tools to make data loadingeasy and hopefully, to make your code more readable. In this tutorial,we will see how to load and preprocess/augment data from a non trivialdataset.
To run this tutorial, please make sure the following packages areinstalled:
scikit-image
: For image io and transformspandas
: For easier csv parsing
The dataset we are going to deal with is that of facial pose.This means that a face is annotated like this:
Over all, 68 different landmark points are annotated for each face.
Note
Download the dataset from hereso that the images are in a directory named ‘faces/’.This dataset was actuallygenerated by applying excellent dlib’s poseestimationon a few images from imagenet tagged as ‘face’.
Dataset comes with a csv file with annotations which looks like this:
Let’s quickly read the CSV and get the annotations in an (N, 2) array where Nis the number of landmarks.
Let’s write a simple helper function to show an image and its landmarksand use it to show a sample.
Dataset class¶
torch.utils.data.Dataset
is an abstract class representing adataset.Your custom dataset should inherit Dataset
and override the followingmethods: Call of the ninja! mac os.__len__
so thatlen(dataset)
returns the size of the dataset.__getitem__
to support the indexing such thatdataset[i]
canbe used to get (i)th sample
Let’s create a dataset class for our face landmarks dataset. We willread the csv in
__init__
but leave the reading of images to__getitem__
. This is memory efficient because all the images are notstored in the memory at once but read as required.Sample of our dataset will be a dict
{'image':image,'landmarks':landmarks}
. Our datset will take anoptional argument transform
so that any required processing can beapplied on the sample. We will see the usefulness of transform
in thenext section.Let’s instantiate this class and iterate through the data samples. Wewill print the sizes of first 4 samples and show their landmarks.
Transforms¶
One issue we can see from the above is that the samples are not of thesame 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 dataaugmentation.ToTensor
: to convert the numpy images to torch images (we need toswap axes).
We will write them as callable classes instead of simple functions sothat parameters of the transform need not be passed everytime it’scalled. For this, we just need to implement
__call__
method andif required, __init__
method. We can then use a transform like this:Matlab r2019a. Observe below how these transforms had to be applied both on the image andlandmarks.
Compose transforms¶
Now, we apply the transforms on an sample.
Let’s say we want to rescale the shorter side of the image to 256 andthen 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 usto do this.Iterating through the dataset¶
Let’s put this all together to create a dataset with composedtransforms.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 onsampling
We can iterate over the created dataset with a
foriinrange
loop as before.However, we are losing a lot of features by using a simple
for
loop toiterate 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 thesefeatures. Parameters used below should be clear. One parameter ofinterest is collate_fn
. You can specify how exactly the samples needto be batched using collate_fn
. However, default collate should workfine for most use cases.Afterword: torchvision¶
In this tutorial, we have seen how to write and use datasets, transformsand dataloader. Keep it together mac os.
torchvision
package provides some common datasets andtransforms. You might not even have to write custom classes. One of themore generic datasets available in torchvision is ImageFolder
.It assumes that images are organized in the following way:where ‘ants’, ‘bees’ etc. are class labels. Similarly generic transformswhich operate on
PIL.Image
like RandomHorizontalFlip
, Scale
https://cooljfile876.weebly.com/chomper-deluxe-mac-os.html. ,are also avaiable. You can use these to write a dataloader like this:For an example with training code, please seeTransfer Learning tutorial.
![Multi Multi](https://nmac.to/wp-content/uploads/2016/04/PhotoMill-X1-65x65.png)
Total running time of the script: ( 0 minutes 0.000 seconds)
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