Because many of the pre-processing steps you will need to do before beginning training a model, finding ways to standardize these processes is critical for the readability and maintainability of your code. I create a Dataloader using this dataset as follows: batch_size = 6 dl_train = torch. It provides Tensors a. Pytorchメモ: DatasetとDataLoaderを使ったミニバッチ処理 クイックスタート書き換え # 訓練に際して、可能であればGPU（cuda）を設定します。 GPUが搭載されていない場合はCPUを使用します device = "cuda" if torch. DataLoader and torch. DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None,. If given as a list, will sample the same amount of nodes for each node type. Jun 12, 2022 by Sebastian Raschka. One of the advantages over Tensorflow is PyTorch avoids static graphs. The issue is when using num_workers > 0 the Datasets are created and then passed to the DataLoader’s worker processes, which requires any data sent to be pickleable unlike h5py. Published by Vahid Khalkhali on July 8, 2020. Cancel Next. Batching the data: batch_size refers to the number of training samples used in one iteration. Waymo-pytorch-dataloader is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. Updated: May 20, 2020. Dataset, and understand how the pre-loaded datasets work and how to create our own DataLoader and Datasets by subclassing these modules. Author: PL team License: CC BY-SA Generated: 2022-04-22T04:57:04. dataset_loader = DataLoader (dataset, batch_size=4, shuffle=True) data, labels = next (iter (dataset_loader)) data. In feature extraction, we start with a pre-trained model and only update the final layer weights from which we derive predictions. PyTorch is a deep learning framework that puts Python first. Finally, built-ins. The basic syntax to implement is mentioned below −. But in many practical applications, loading data is very challenging. Unfortunately, PyTorch can not detect such. DataLoader DataLoader will reseed workers following Randomness in multi-process data loading algorithm. 通过 DataLoader，使得我们在准备 mini-batch 时可以多线程并行处理，这样可以加快准备数据的速度。. In almost all machine learning tasks, the first step belong to data loading. Jun 15, 2021 · DataLoader (H5Dataset ("/some/path. Pytorch DataLoader Syntax. DataLoader が返すミニバッチのサイズを設定します。 batchsize=None とした場合、ミニバッチの代わりにサンプル1つを返します。 この場合、バッチ次元はありません。 batchsize に1以上の整数を指定した場合、複数のサンプルから作成したミニバッチを返しま. dataloader = torch. >>> tensor ( [ [724, 232, 501, 555, 369. PyTorch: Dataloader for time series task I have a Pandas dataframe with n rows and k columns loaded into memory. let's discuss it in bits and pieces. DataLoader( dataset_test, batch_size=2, shuffle=False, num_workers=0, collate_fn=utils. Each line represents a person: sex (male = 1 0, female = 0 1), normalized age, region (east = 1 0 0, west = 0. This brings substantial performance advantage in many compute environments, and it is essential for very large scale training. Load inside Dataset. How is the best way to get a tensor from my. Source File: dataloaders. Available Next Working Day when ordered before 3PM. PyTorch script. Because your labels are already on 'cuda:1' Pytorch will be able to calculate the loss and perform backpropagation without any further modifications. ¥500 cookie转换openid系列数据 ¥300 jeesite怎么通过链接地址传递用户名直接登录，不需要用户名密码登录 ¥100 Camstar 协助工具，请求收费共享 ¥100 求高斯坐标正反算，要求如图 ¥100 解析VMDK，碰到一些问题 ¥100 已知某公众号的微信号但该公众号长时间没有登陆已被系统注销，求怎样获取该. Training Our Model. class pytorch_lightning. , the number of workers num_workers. WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives and uses only sequential/streaming data access. I wanted to run some experiments with Victor Sanh's implementation of movement pruning so that I could compare against a custom Trainer I had implemented. Jun 26, 2022 · Use Saved PyTorch model to predict single and multiple images. We see the impact of several of the constructor parameters and see how the batch is built. Note that we clear cache at a regular interval. Published by Vahid Khalkhali on July 8, 2020. In order to better explain, we first give the flow line diagram of the above, this article will refine this figure. It consists of a variety of methods for deep learning on graphs from various published papers. Batch size of 1. Log In My Account ru. i = iter (iris_loader) and then next (i). import torch. Source File: dataloaders. PyTorchのExampleの確認 PyTorchを使っていれば、当然DataLoaderを見たことがあると思います。 誰もが機械学習で使うMNISTのPyTorchのExampleでもこんな記述があります。 train_loader = torch. I honestly. 3, Parallelization of DataLoader. Many articles are introduced from the top of DataSet, but for beginners, this is not well understood, because sometimes it will not consciously fall into some In the end of the sub-branches, you can't grasp the focus, so this article willTop-downIntroduce the Pytorch data reading method. These are built-in functions of python, they are used for working with iterables. class DataLoader (Generic [T_co]): r """ Data loader. training_data_loader = DataLoader ( dataset=train. collate_fn 方法，这个是干吗用的呢?在介绍前咱们须要知道每一个参数的意义：. on the Shuffle Parameter 05:35 Debugging next(iter(dataloader)) . I honestly. Before we look at the class, there are a couple of helpers we’ll need to define. A datamodule encapsulates the five steps involved in data processing in PyTorch: Download / tokenize / process. shuffle=False, num_workers=0) 3. A PyTorch DataLoader accepts a batch_size so that it can divide the dataset into chunks of samples. The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. Although the PIL-SIMD library does improve the situation a bit. Last modified 2yr ago. 此外，我的多图还参考了 dali git 中的这个 issue 1984 ，他介绍了 自己如何使用 多图（就是图片序列） ，但是 实操起来还是很难 的，所以我就总结了一下自己的版本，方便其他人学习使用。. Waymo-pytorch-dataloader has no vulnerabilities and it has low support. It should be a DataLoader problem for a long time, a solution: method 1: Num_Workers is set to 0 which is. wg; sd. For example, if you had 100 training. This approach yields a litany of benefits. DataLoader is used to shuffle and batch data. This means that it can't pull image samples for training, the. pytorch open image. Dataloader的处理逻辑是先通过Dataset类里面的 __getitem__ 函数获取单个的数据，然后组合成batch，再使用collate_fn所指定的函数对这个batch做一些操作，比如padding啊之类的。. DataLoader, 也可以使用类似 next (iter (DataLoader)) 的方式遍历地读取dataset的数据。 再次，使用yield关键字，也可以起到“遍历”的效果。 那么问题来了。 这iter，next，yield几个东西之间有什么关联，又有什么区别呢？ pytorch的DataLoader又是用的什么样的方式呢？ 分析 先看一个简单例子： fruit = ["apple", "banana",. ① DataLoader本质上就是一个iterable（跟python的内置类型list等一样），并利用多进程来加速batch data的处理，使用yield来使用有限的内存 ② Queue的. Batching the data: batch_size refers to the number of training samples used in one iteration. Publisher (s): O'Reilly Media, Inc. In the next part, you’ll see how to load custom labels for the PyTorch model. data import DataLoader. data import Dataset,DataLoader import cv2 import pytorch_lightning as pl import torchvision. PyTorch's DataLoader is a useful feature that allows us to iterate the data, manage batches, and shuffle the samples to avoid overfitting. Categories: ML. PyTorch DataLoader num_workers Test - Speed Things Up Welcome to this neural network programming series. PyTorch Dataset and DataLoader Python · Digit Recognizer. DataLoader ( dataset, batch_size=1, shuffle=False, num_workers=0, collate_fn=None, pin_memory=False, ). Dataset is used to read and transform a datapoint from the given dataset. This can result in improved performance, achieving +3X speedups on modern GPUs. In PyTorch this can be achieved using a weighted random sampler. For the sake of simplicity, we will. A Computer Science portal for geeks. 4 Open in Colab. PyTorch DataLoader: Working With Batches Of Data. The dataloader is created from PyTorch DataLoader which takes the object created from MovieReviewsDataset class and puts each example in batches. This demo shows how to make embedding with pretrained seq_encoder. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples. Models (Beta) Discover, publish, and reuse pre-trained models. force_edgecolor"] = True. , 5. Determined provides a high-level framework APIs for PyTorch, Keras, and Estimators that let users describe their model without boilerplate code. Hulk의 개인 공부용 블로그 : pytorch dataset 정리: 핵심적인 함수의 사용법들과 커스텀 클래스 선언이 궁금하신 분들에게 추천합니다. Dataset: The first parameter in the DataLoader class is the dataset. PyTorch will only load what is needed to the memory. PyTorch DataLoader: iterate through a subset of data instead of a the whole everytime it is called. Tags: collate_fn, dataloader, num_workers, parameter, pin_memory, pytorch, sampler. COCO (Captioning and Detection) Dataset includes majority of two types of functions given below −. format( device)) # modelを定義します class NeuralNetwork( nn. The directory is defined as the collection of files or subdirectories. Pytorchメモ: DatasetとDataLoaderを使ったミニバッチ処理. Whether you are studying maths, science, marketing or business, Challenger offers a 12-month structured program in several areas starting early February. The key point to notice here or to take note of is the. This way we can feed our model batches of data! The optimizer_ and scheduler_ are very common in PyTorch. PyTorch DataLoader num_workers Test - Speed Things Up Welcome to this neural network programming series. Compose ( [ transforms. fit(coles_module, datamodule) Result will be the same. Prefetching, that is, while GPU crunches the current batch, Dataloader can load the next batch into memory in meantime. ai has done for image recognition and natural language processing. def __init__ (self): -> 데이터셋의 다운로드와 데이터셋을 읽어오는 작업을. Sampler class, i. Artificial Intelligence 📦 72. For example, if you had 100 training. Categories: python, pytorch. format( device)) # modelを定義します class NeuralNetwork( nn. 0 램 메모리 누수 현상 발견 및 해결 2021. Feed the data into a distributed hyperparameter tuning function. July 7, 2022. The DataLoader is a function that iterates through all our available data and returns it in the form of batches. PyTorch on XLA Devices. I create a Dataloader using this dataset as follows: batch_size = 6 dl_train = torch. set_style(style = 'whitegrid') plt. It simply iterates over each evaluation dataloader from one to the next by . PyTorch provides two data primitives: torch. In almost all machine learning tasks, the first step belong to data loading. num_workers == 0: indices = next (self. Wrap inside a DataLoader. In a single insert, update, upsert, or delete operation, records moving to or from Salesforce are processed in increments of this size. Log In My Account ru. data_set = batchsamplerdataset (xdata, ydata) is used to define the dataset. DataLoader (transformed_dataset, batch_size=4, shuffle=True, num_workers=0) for i_batch, image in enumerate (dataloader): print (image [1]) batch_size: number of images that will come in a single batch. We will start from creating a new data loader, the data loader's batch size is smaller,. - 一般我们实现一个datasets对象，传入到dataloader中；然后内部使用yeild返回每一次batch的数据；. 在 Nvidia 提出的分布式框架 Apex 里面，我们在源码里面找到了一个简单的解决方案：. The batch sampler is defined below the batch. DataDrivenInvestor OpenAI Quietly Released GPT-3. The errors originate from the pytorch Dataloader. 우선 필요한 패키지들을. DataLoader (trainset, batchsize = batchsize, sampler=sampler) Since the pytorch doc says that the weights don't have to sum to 1, I think you can also just use the ratio which between the imbalanced classes. next() forループ以外のデータの取り出し方法 例えば、MNISTの. The way I understand your question is that you want to retrieve all batches to train the network with. 1307,), (0. The example we use in this notebook is based on the transfer. PyTorch Lightning is built on top of ordinary (vanilla) PyTorch. Pytorch 기반의 딥러닝 모델 사용 도중, torch. The PyTorch DataLoader class gives you an iterable over a Dataset. Data Loading and Processing Tutorial. Nov 25, 2018 · An Iterator is an object which is used to iterate over an iterable object using the __next__ method, which returns the next item of the object. 몇몇 분들과 얘기를 하다보면 그 중에서도 꽤나 많은 비중을 차지하는 이슈가 바로 DataLoader. The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. Now coles_module with seq_encoder are trained. 【Pytorch神经网络实战案例】10 搭建深度卷积神经网络_LiBiGo的博客-程序员宝宝. Let's now load an image dataset and create a PyTorch dataloader with the. pytorch dataloader tutorial. Construct word-to-index and index-to-word dictionaries, tokenize words and convert words to indexes. It can be used to load the data in parallel with multiprocessing workers. pytorch data loader large dataset parallel By Afshine Amidi and Shervine Amidi Motivation Have you ever had to load a dataset that was so memory consuming that you wished a magic trick could seamlessly take care of that? Large datasets are increasingly becoming part of our lives, as we are able to harness an ever-growing quantity of data. Edit the fields as needed. The example we use in this notebook is based on the transfer. These three methods are __init__ () , __len__. PyTorch is a deep learning framework that puts Python first. Author: PL team License: CC BY-SA Generated: 2022-05-05T03:23:24. Microsoft is a top contributor to the PyTorch ecosystem with recent contributions such as. Now that we have the data, we will go to the next step. DataLoader (trainset, batchsize = batchsize, sampler=sampler) Since the pytorch doc says that the weights don't have to sum to 1, I think you can also just use the ratio which between the imbalanced classes. We also create a variable self. Categories: ML. sampler = WeightedRandomSampler (weights=weights, num_samples=, replacement=True) trainloader = data. data import DataLoader. Here we discuss How to create a PyTorch DataLoader along with the examples in detail to. But even after following through this great tutorial, I still wasn’t sure how exactly DataLoader gathered the data returned in Dataset into a. num_workers == 0: indices = next (self. Feed the data into a single-node PyTorch model for training. qx; su. It allows us to load the dataset we want to use for our project. model_selection import train_test_split import glob import torch from torch import nn import torchvision from torch. It can be used to load the data in parallel with multiprocessing workers. import torchvision. In this section, we provide a segmentation training wrapper that extends the LightningModule. One solution is to inherit from the Dataset class and define a custom class that implements __len__() and __get__(), where you pass X and y to the __init__(self,X,y). 功能： DataLoader类位于Pytorch的utils类中，构建可迭代的数据装载器。 我们在训练的时候，每一个for循环，每一次iteration，就是从DataLoader中获取一个batch_size大小的数据的。 • dataset： Dataset类，决定数据从哪读取及如何读取 • batchsize： 批大小 • num_works： 是否多进程读取数据,可以减少数据读取时间，加快训练速度（一般设为4,8,16） • shuffle： 每个epoch是否乱序 • drop_last： 当样本数不能被batchsize整除时，是否舍弃最后一批数据 注意： Epoch、Iteration和Batchsize之间的关系 Epoch： 所有训练样本都已输入到模型中，称为一个epoch. The :class:`~torch. DataLoader This is main vehicle to help us to sample data from our data source, with my limited understanding, these are the key points: Manage multi-process fetching Sample data from dataset as. DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None,. 4: sequence length. I would like to get batches for a forecasting task where the first training example of a batch should have shape (q, k) with q referring to the number of rows from the original dataframe (e. step() The core research logic is simply shifted to the LightningModule:. DataLoader for PyTorch, or a tf. It consists of a variety of methods for deep learning on graphs from various published papers. This section we will learn more about it. 0 open source license. 이 튜토리얼에서 일반적이지 않은 데이터셋으로부터 데이터를 읽어오고 전처리하고. Now, let’s initialize the dataset class and prepare the data loader. (UK Mainland excl. Lightning Team Bolts Community. PyTorch on the other hand uses a data loader written in Python on top of the PIL library — great for ease of use and flexibility, not so great for speed. Here is the code for our training phase. WebDataset is a PyTorch Dataset (IterableDataset) implementation providing efficient access to datasets stored in POSIX tar archives and uses only sequential/streaming data access. # create dataloader-iterator. You could use persistent_workers=True to avoid this behavior). pin_memory_batch (batch) return batch 咱们仔细看能够发现，前面还有一个 self. It should be a DataLoader problem for a long time, a solution: method 1: Num_Workers is set to 0 which is. PyTorch provides many tools to make data loading easy and hopefully, to make your code more readable. It runs a training loop and trains the model. Dataset is responsible for data production, and DataLoader is responsible for data batch_size, sampler and transmission Python version: 1. Dataset is used to read and transform a datapoint from the given dataset. The dataloader constructor resides in the torch. In almost all machine learning tasks, the first step belong to data loading. DataLoader and DataSets. Dataset that allow you to use pre-loaded datasets as well as your own data. brookhaven script 2022 pastebin
Cancel Next. . Pytorch dataloader next
, 5. Pytorchメモ: DatasetとDataLoaderを使ったミニバッチ処理. PyTorch Lightning DataModules¶. DataLoader( dataset_test, batch_size=2, shuffle=False, num_workers=0, collate_fn=utils. The core of the pytorch lightning is the LightningModule that provides a warpper for the training framework. If the data to be loaded is unstructured, we should be careful in using proper libraries for loading the same. for us: we can use PyTorch's DataLoader that does precisely that. data to the variable b before training my model. The source data is a tiny 8-item file. Basically iter() calls the __iter__() method on the iris_loader which returns an iterator. PyTorch provides the torch. 위에서 언급한 Dataset의 구성. The validation loader is created for the initial checks and later on recreated for actual training. PyTorch offers two classes for data processing: torch. Since each epoch of training on SQuAD takes around 2 hours on a single GPU, I wanted to speed-up the comparison by. 【Pytorch神经网络实战案例】10 搭建深度卷积神经网络_LiBiGo的博客-程序员宝宝. This will print the next item until the end of the list is reached. Lightning Team Bolts Community. In ML resources, people usually wouldn’t talk so much about handling the data and they usually focus on preprocessing or classification tasks. PyTorch Dataloader. 193004 This notebook will use HuggingFace’s datasets library to get data, which will be wrapped in a LightningDataModule. DataLoader can be imported as follows: from torch. collate_fn 方法，这个是干吗用的呢?在介绍前咱们须要知道每一个参数的意义：. dy ro. 1 index_ The subscript of the data to be processed by the queue 3. The next step is to set the dataset in a PyTorch DataLoader , which will draw minibatches of data for us. To not break transformers that use random values, then reset the random seed each time the DataLoader is initialized. from torch. With DataLoader, a optional argument num_workers can be passed in to set how many threads to create for loading data. Evaluation after training. python读取文件 数据 生成图像_ pytorch ImageFolder和 Dataloader 加载自制图像 数据 集. class DataLoader(object):. For the sake of simplicity, we will. DataLoader (transformed_dataset, batch_size=4, shuffle=True, num_workers=0) for i_batch, image in enumerate (dataloader): print (image [1]) batch_size: number of images that will come in a single batch. Load inside Dataset. pytorch dataloader variable length sequences. time () - t0) 0. Tags: collate_fn, dataloader, num_workers, parameter, pin_memory, pytorch, sampler. A good way to see where this article is headed is to take a look at the screenshot of a demo program in Figure 1. next() print(images. Dataset: The first parameter in the DataLoader class is the dataset. Hulk의 개인 공부용 블로그 : pytorch dataset 정리: 핵심적인 함수의 사용법들과 커스텀 클래스 선언이 궁금하신 분들에게 추천합니다. when reading a damaged image file). 0x01 prefangry review. PyTorch offers a solution for parallelizing the data loading process with automatic batching by using DataLoader. history 4 of 4. DataLoader supports automatically collating individual fetched data samples into batches via arguments batch_size. I thought this code will give me a size of (batch_num, channel_num, image_size1, image_size2) but this isn’t true, instead next (iter (dataloader)) [0] (or fixing it with any index) gives me what I thought it should. DataLoader, which is very similar to torch. – ScootCork. That is, create a custom Dataset and DataLoader to preprocess the time series like data into a matrix-like shape. DataLoader and torch. This article will discuss about PyTorch’s DataLoader implementation. Photo by Chris Welch / The Verge. backward() optimizer. To Train model in Lightning:-. DataLoader (transformed_dataset, batch_size=4, shuffle=True, num_workers=0) for i_batch, image in enumerate (dataloader): print (image [1]) batch_size: number of images that will come in a single batch. Data loading in PyTorch can be separated in. Size ( [4, 3]) tensor ( [4. This article will discuss about PyTorch’s DataLoader implementation. 12 release. For example, if you had 100 training. - DataLoader 함수는 데이터셋의 feature을 가져오고 하나의 샘플에 label을 지정하는 일을 한 번에 합니다. For more information on model parallelism, see this article. Jun 24, 2020. data — PyTorch 1. Each line represents a person: sex (male = 1 0, female = 0 1), normalized age, region (east = 1 0 0, west = 0. Assume you had input and output data as -. The best part about PyTorch lightning is that you can set the number of gpus by simply setting “ gpus = [number of gpus]“ %%time # Checking the amount of time the cell takes to run from pytorch_lightning import Trainer model = Vehicle_Model() module = Vehicle_DataModule() trainer = Trainer(max_epochs=1,gpus = 1,callbacks = [checkpoint. In the next part, you'll see how to load custom labels for the PyTorch model. Pytorch lightning is a high-level pytorch wrapper that simplifies a lot of boilerplate code. Developer Resources. PyTorch provides two data primitives: torch. PyTorch has revolutionized the approach to computer vision or NLP problems. DataLoader( dataset_test, batch_size=2, shuffle=False, num_workers=0, collate_fn=utils. Digit Recognizer. Cell link copied. PyTorch's DataLoader is a useful feature that allows us to iterate the data, manage batches, and shuffle the samples to avoid overfitting. DataLoader and torch. Jun 13, 2022 · The PyTorch DataLoader class is an important tool to help you prepare, manage, and serve your data to your deep learning networks. class DataLoader (Generic [T_co]): r """ Data loader. DataLoader that overwrites its collate() functionality, i. See how we can write our own Dataset class and use available built-in datasets. 더 나은 가독성과 모듈성을 위해 데이터셋. By the end of this tutorial, you’ll have learned:. 我们能看到 Nvidia 是在读取每次数据返回给网络的时候，预读取下一次迭代需要的数据，那么对我们自己的训练代码只需要做下面的改造：. Available Next Working Day when ordered before 3PM. DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None,. According to the documentation: pin_memory (bool, optional) – If True, the data loader will copy tensors into CUDA pinned memory before returning them. shape) # [32, 3, 224, 224]. But even after following through this great tutorial, I still wasn’t sure how exactly DataLoader gathered the data returned in Dataset into a. To achieve this, we will do the following : Use DataLoader module from Pytorch to load our dataset and Transform It; We will implement Neural Net, with input, hidden & output Layer. Note that now you can simply take a batch from the dataloader, select a required attribute, do something with it if needed and pass to your loss function. Unfortunatly, PyTorch does not provide a handy tools to do it. DataLoader( dataset_test, batch_size=2, shuffle=False, num_workers=0, collate_fn=utils. Note that now you can simply take a batch from the dataloader, select a required attribute, do something with it if needed and pass to your loss function. DataLoader and is a. Log In My Account ap. Run the profiler. Jun 24, 2021 · First attempt. labels = dataiter. The core of the pytorch lightning is the LightningModule that provides a warpper for the training framework. # 訓練に際して、可能であればGPU（cuda）を設定します。. We are going to generate a simple data set and then we will read it For balanced classification problems, where all the classes have a likely accuracy, ROC and Area under the curve (AUC) are common metrics Build the DataLoader pip install split-folders tqdm Usage Bases: pytorch_lightning Bases: pytorch_lightning. dataset_loader = DataLoader (dataset, batch_size=4, shuffle=True) data, labels = next (iter (dataset_loader)) data. Evaluation after training. Because many of the pre-processing steps you will need to do before beginning training a model, finding ways to standardize these processes is critical for the readability and maintainability of your code. h5"), num_workers = 2) batch = next (iter (loader)) And then TypeError: h5py objects cannot be pickled. Data sets can be thought of as big arrays of data. PYTORCH DATA LOADERS — 4 Types. . jpmorgan chase wiki, tuff shed boise, marvel strike forcecom, flawless widescreen skyrim special edition, hairymilf, leaked photos of nude celebs, sexmex lo nuevo, flmbokep, vixen pornosu, la follo dormida, gina wilson all things algebra unit 1 algebra basics answer key, analcreampie porn co8rr