Torchvision Download Dataset

All datasets are subclasses of torch.utils.data.Dataseti.e, they have __getitem__ and __len__ methods implemented.Hence, they can all be passed to a torch.utils.data.DataLoaderwhich can load multiple samples parallelly using torch.multiprocessing workers.For example:

HMDB51 is an action recognition video dataset.This dataset consider every video as a collection of video clips of fixed size, specifiedby frames_per_clip, where the step in frames between each clip is given bystep_between_clips.

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To give an example, for 2 videos with 10 and 15 frames respectively, if frames_per_clip=5and step_between_clips=5, the dataset size will be (2 + 3) = 5, where the first twoelements will come from video 1, and the next three elements from video 2.Note that we drop clips which do not have exactly frames_per_clip elements, so not allframes in a video might be present.

Kinetics-400 is an action recognition video dataset.This dataset consider every video as a collection of video clips of fixed size, specifiedby frames_per_clip, where the step in frames between each clip is given bystep_between_clips.

SVHN Dataset.Note: The SVHN dataset assigns the label 10 to the digit 0. However, in this Dataset,we assign the label 0 to the digit 0 to be compatible with PyTorch loss functions whichexpect the class labels to be in the range [0, C-1]

UCF101 is an action recognition video dataset.This dataset consider every video as a collection of video clips of fixed size, specifiedby frames_per_clip, where the step in frames between each clip is given bystep_between_clips.

All datasets are subclasses of torch.utils.data.Dataseti.e, they have __getitem__ and __len__ methods implemented.Hence, they can all be passed to a torch.utils.data.DataLoaderwhich can load multiple samples in parallel using torch.multiprocessing workers.For example:

All the datasets have almost similar API. They all have two common arguments:transform and target_transform to transform the input and target respectively.You can also create your own datasets using the provided base classes.

I am training a GANS on the Cifar-10 dataset in PyTorch (and hence don't need train/val/test splits), and I want to be able to combine the torchvision.datasets.CIFAR10 in the snippet below to form one single torch.utils.data.DataLoader iterator. My current solution is something like :

We all know the common MNIST dataset, included in the torchvision.datasets package. Imagine that I want to create a reduced version of this dataset containing only 1 and 0 to classify only this two numbers instead all 10 values.

After torchvision is imported, the provided datasets can be downloaded with a single line of code. Here is an example of downloading the MNIST dataset, which consists of 60,000 train and 10,000 test images of handwritten digits. Each image is grayscale and 28 x 28 pixels:

You can specify a particular subset of a downloaded dataset (e.g. train, val, or test). The syntax is simple and varies only a little depending on the dataset you are using. The necessary arguments to specify a particular subset of a downloaded dataset are all documented here, on the torchvision datasets page, for each dataset separately.

You can make a PyTorch dataset for any collection of images that you want, e.g. medical data, random images you pulled off the Internet, or photos you took. Examples of various machine learning data sets can be found here.

We can define this new kind of label within our custom dataset class. That is done in the custom_pascal.py function get_label_vector(), which takes in the default segmentation map label and transforms it into a multi-hot presence/absence vector. Then, __getitem__ makes use of get_label_vector() to transform the segmentation map label into the classification label that we want to use.

Compose creates a series of transformation to prepare the dataset. Torchvision reads datasets into PILImage (Python imaging format). ToTensor converts the PIL Image from range [0, 255] to a FloatTensor of shape (C x H x W) with range [0.0, 1.0]. We then renormalize the input to [-1, 1] based on the following formula with \(\mu=\text{standard deviation}=0.5\).

Dataset read and transform a datapoint in a dataset. Since we often read datapoints in batches, we use DataLoader to shuffle and batch data. Then it load the data in parallel using multiprocessing workers. .datasets.CIFAR10 below is responsible for loading the CIFAR datapoint and transform it.

PyTorch is a great library for machine learning. You can in a few lines of codes retrieve a dataset, define your model, add a cost function and then train your model. It's quite magic to copy and paste code from the internet and get the LeNet network working in a few seconds to achieve more than 98% accuracy.

However, it can be tedious sometimes to extend existing objects and here, I will manipulate some ways to define the right dataset for your application. In particular I will modify the call to a standard dataset (MNIST) to place the characters at random places in a large image.

The TorchVision datasets subpackage is a convenient utility for accessing well-known public image and video datasets. You can use these tools to start training new computer vision models very quickly.

The TorchVision datasets implement __len__() and __getitem__() methods, which means that in addition to getting specific elements by index, you can also get the number of samples with the len() function:

Hi ! I think this is because the torchvision dataset stores the array of pixel values, while the HF dataset stores encoded images in PNG. Therefore the HF dataset uses significantly less disk space, but there is an extra decoding step to get the image.

Or if we were trying to build a model to classify whether or not a text-based review on a website was positive or negative, our custom dataset might be examples of existing customer reviews and their ratings.

What we're going to build. We'll use torchvision.datasets as well as our own custom Dataset class to load in images of food and then we'll build a PyTorch computer vision model to hopefully be able to classify them.

Note: The dataset we're about to use has been pre-formatted for what we'd like to use it for. However, you'll often have to format your own datasets for whatever problem you're working on. This is a regular practice in the machine learning world.

Note: The structure of the data you work with will vary depending on the problem you're working on. But the premise still remains: become one with the data, then find a way to best turn it into a dataset compatible with PyTorch.

vision.datasets : Data loaders for popular visiondatasetsvision.models : Definitions for popular modelarchitectures, such as AlexNet, VGG, and ResNet and pre-trainedmodels.vision.transforms : Common image transformationssuch as random crop, rotations etc.vision.utils : Useful stuff such as saving tensor (3 x Hx W) as image to disk, given a mini-batch creating a grid of images,etc.InstallationAnaconda:

root : root directory of dataset where there is foldercifar-10-batches-pytrain : True = Training set, False = Test setdownload : True = downloads the dataset from the internet andputs it in root directory. If dataset is already downloaded, does not doanything.STL10dset.STL10(root, split='train', transform=None, target_transform=None, download=False)

root : root directory of dataset where there is folder SVHNsplit : 'train' = Training set, 'test' = Test set, 'extra' = Extra training setdownloadTrue = downloads the dataset from the internet andputs it in root directory. If dataset is already downloaded, does not doanything.

self.classes - The class names as a listself.class_to_idx - Corresponding class indicesself.imgs - The list of (image path, class-index) tuplesImagenet-12This is simply implemented with an ImageFolder dataset.

The problem I am facing is as follows: I want to evaluate the performance pretrained models (pretrained on Imagenet} available in Pytorch model zoo (torchvision.models) on the Imagenet validation set (afterwards I want to code some adversarial attacks and test their impact but that is once I have solved current issues).

As the validation set is available as a single folder and in order to use one of the predefined way to load dataset in Pytorch (specifically torchvision.datasets.ImageFolder (as this seemed to be the best choice)) I used the validation_set_creator notebook . It basically uses the ILSVRC2012_validation_ground_truth.txt to split the validation set into 1000 subfolders, each corresponding to one class. This code works fine and I also verified that the folder names (which end up becoming class labels when torchvision.datasets.ImageFolder is used as far as I understood) correspond to the class labels used by Imagenet (verified using the map_clsloc.txt (second column matches the folder names in my directory structure)). I verified manually by checking many folders that the folders contain the images of only the true category corresponding to that label.

as the target labels to compare to but I realized (by plotting the figures in the minibatch and the corresponding values of target ) that torchvision.datasets.ImageFolder does some internal relabelling so this did not correspond to the actual folder numbers (which is equal to the second column of map_clsloc.txt ). After some digging I found that the folder names (which are also the target labels) can be found via 2351a5e196