how to load image dataset in python pytorch
Torchvision reads datasets into PILImage (Python imaging format). If I have more parameters I want to pass in to my vaporwaveDataset class, I will pass them here. Is Apache Airflow 2.0 good enough for current data engineering needs? The (Dataset) refers to PyTorch’s Dataset from torch.utils.data, which we imported earlier. set_title ('Sample # {} '. Now we can move on to visualizing one example to ensure this is the right dataset, and the data was loaded successfully. If you would like to see the rest of the GAN code, make sure to leave a comment below and let me know! Loading image data from google drive to google colab using Pytorch’s dataloader. Therefore, we can access the image and its label by using an index. format (i)) ax. As you can see here, the dataset consists of image ids and labels. The next step is to build a container object for our images and labels. The transforms.Compose performs a sequential operation, first converting our incoming image to PIL format, resizing it to our defined image_size, then finally converting to a tensor. Although PyTorch did many things great, I found PyTorch website is missing some examples, especially how to load datasets. This article demonstrates how we can implement a Deep Learning model using PyTorch with TPU to accelerate the training process. Before reading this article, your PyTorch script probably looked like this:or even this:This article is about optimizing the entire data generation process, so that it does not become a bottleneck in the training procedure.In order to do so, let's dive into a step by step recipe that builds a parallelizable data generator suited for this situation. The dataset consists of 70,000 images of Fashion articles with the following split: Load in the Data. In their Detectron2 Tutorial notebook the Detectron2 team show how to train a Mask RCNN model to detect all the ballons inside an image… I believe that using rich python libraries, one can leverage the iterator of the dataset class to do most of the things with ease. If dataset is already downloaded, it is not downloaded again. The code can then be used to train the whole dataset too. The code to generate image file names looks like this. This video will show how to import the MNIST dataset from PyTorch torchvision dataset. When it comes to loading image data with PyTorch, the ImageFolder class works very nicely, and if you are planning on collecting the image data yourself, I would suggest organizing the data so it can be easily accessed using the ImageFolder class. We will use PyTorch to build a convolutional neural network that can accurately predict the correct article of clothing given an input image. Data sets can be thought of as big arrays of data. We’re almost done! The code looks like this. For example, if I have labels=y, I would use. This video will show how to examine the MNIST dataset from PyTorch torchvision using Python and PIL, the Python Imaging Library. shape) ax = plt. In the field of image classification you may encounter scenarios where you need to determine several properties of an object. There are so many data representations for this format. from PIL import Image from torchvision.transforms import ToTensor, ToPILImage import numpy as np import random import tarfile import io import os import pandas as pd from torch.utils.data import Dataset import torch class YourDataset(Dataset): def __init__(self, txt_path='filelist.txt', img_dir='data', transform=None): """ Initialize data set as a list of IDs corresponding to each item of data set :param img_dir: path to image … Get predictions on images from the wild (downloaded from the Internet). The code looks like this. That way we can experiment faster. Essentially, the element at position index in the array of images X is selected, transformed then returned. I will stick to just loading in X for my class. The code looks like this. Here I will show you exactly how to do that, even if you have very little experience working with Python classes. import torch In our case, the vaporarray dataset is in the form of a .npy array, a compressed numpy array. In this example we use the PyTorch class DataLoader from torch.utils.data. Overall, we’ve now seen how to take in data in a non-traditional format and, using a custom defined PyTorch class, set up the beginning of a computer vision pipeline. Process the Data. def load_images(image_size=32, batch_size=64, root="../images"): transform = transforms.Compose([ transforms.Resize(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) train_set = datasets.ImageFolder(root=root, train=True, transform=transform) train_loader = torch.utils.data.DataLoader(train_set, … As you can see further, it has a PIL (Python Image Library) image. Next is the initialization. Although that’s great, many beginners struggle to understand how to load in data when it comes time for their first independent project. In this article, I will show you on how to load image dataset that contains metadata using PyTorch. 10 Surprisingly Useful Base Python Functions, I Studied 365 Data Visualizations in 2020. PyTorch’s torchvision repository hosts a handful of standard datasets, MNIST being one of the most popular. The reason why we need to build that object is to make our task for loading the data to the deep learning model much easier. In this tutorial, we will focus on a problem where we know the number of the properties beforehand. It includes two basic functions namely Dataset and DataLoader which helps in transformation and loading of dataset. For the dataset, we will use a dataset from Kaggle competition called Plant Pathology 2020 — FGVC7, which you can access the data here. import pandas as pd # ASSUME THAT YOU RUN THE CODE ON KAGGLE NOTEBOOK path = '/kaggle/input/plant-pathology-2020-fgvc7/' img_path = path + 'images' # LOAD THE DATASET train_df = pd.read_csv(path + 'train.csv') test_df = pd.read_csv(path + 'test.csv') sample = pd.read_csv(path + 'sample_submission.csv') # GET THE IMAGE FILE NAME train_df['img_path'] = train_df['image_id'] + '.jpg' test_df['img_path'] … But most of the time, the image datasets have the second format, where it consists of the metadata and the image folder. This dataset is ready to be processed using a GAN, which will hopefully be able to output some interesting new album covers. Here, we define a Convolutional Neural Network (CNN) model using PyTorch and train this model in the PyTorch/XLA environment. In this case, I will use the class name called PathologyPlantsDataset that will inherit functions from Dataset class. Let’s first define some helper functions: Hooray! There are 60,000 training images and 10,000 test images, all of which are 28 pixels by 28 pixels. The code looks like this. Using this repository, one can load the datasets in a ready-to-use fashion for PyTorch models. First, we import PyTorch. That is an aside. Running this cell reveals we have 909 images of shape 128x128x3, with a class of numpy.ndarray. When the dataset on the first format, we can load the dataset easier by using a class called ImageFolder from torch.data.utils library. In this tutorial, you’ll learn how to fine-tune a pre-trained model for classifying raw pixels of traffic signs. Is Apache Airflow 2.0 good enough for current data engineering needs? 5 votes. It has a zero index. These transformations are done on-the-fly as the image is passed through the dataloader. Here, X represents my training images. show break Images don’t have the same format with tabular data. The datasets of Pytorch is basically, Image datasets. Reexecuting print(type(X_train[0][0][0][0])) reveals that we now have data of class numpy.uint8. In this post, we see how to work with the Dataset and DataLoader PyTorch classes. According to wikipedia, vaporwave is “a microgenre of electronic music, a visual art style, and an Internet meme that emerged in the early 2010s. Well done! We then renormalize the input to [-1, 1] based on the following formula with \(\mu=\text{standard deviation}=0.5\). In fact, it is a special case of multi-labelclassification, where you also predic… That’s it, we are done defining our class. PyTorch Datasets. As data scientists, we deal with incoming data in a wide variety of formats. What you can do is to build an object that can contain them. Let me show you the example on how to visualize the result using pathology_train variable. We want to make sure that stays as simple and reliable as possible because we depend on it to correctly iterate through the dataset. The __len__function will return the length of the dataset. Use Icecream Instead, Three Concepts to Become a Better Python Programmer, The Best Data Science Project to Have in Your Portfolio, Jupyter is taking a big overhaul in Visual Studio Code, Social Network Analysis: From Graph Theory to Applications with Python. As we can see from the image above, the dataset does not consists the image file name. By understanding the class and its corresponding functions, now we can implement the code. We will be using built-in library PIL. We us… For Part two see here. Excellent! We have successfully loaded our data in with PyTorch’s data loader. Make learning your daily ritual. The first thing that we have to do is to preprocess the metadata. # Loads the images for use with the CNN. Now we'll see how PyTorch loads the MNIST dataset from the pytorch/vision repository. My motivation for writing this article is that many online or university courses about machine learning (understandably) skip over the details of loading in data and take you straight to formatting the core machine learning code. face_dataset = FaceLandmarksDataset (csv_file = 'data/faces/face_landmarks.csv', root_dir = 'data/faces/') fig = plt. The MNIST dataset is comprised of 70,000 handwritten numerical digit images and their respective labels. Lastly, the __getitem__ function, which is the most important one, will help us to return data observation by using an index. I create a new class called vaporwaveDataset. Don’t worry, the dataloaders will fill out the index parameter for us. These are defined below the __getitem__ method. I Studied 365 Data Visualizations in 2020. image_size = 64. I hope you’re hungry because today we will be making the top bun of our hamburger! This method performs a process on each image. PyTorch Datasets and DataLoaders for deep Learning Welcome back to this series on neural network programming with PyTorch. For example, you want to build an image classifier using deep learning, and it consists of a metadata that looks like this. For example, when we want to access the third row of the dataset, which the index is 2, we can access it by using pathology_train[2]. Of course, you can also see the complete code on Kaggle or on my GitHub. These image datasets cover all the Deep-learning problems in Pytorch. The full code is included below. The MNIST dataset is comprised of 70,000 handwritten numeric digit images and their respective labels. Pay attention to the method call, convert (‘RGB’). You could write a custom Dataset to load the images and their corresponding masks. After we create the class, now we can build the object from it. Dataset. This class is an abstract class because it consists of functions or methods that are not yet being implemented. image_set (string, optional) – Select the image_set to use, train, trainval or val download ( bool , optional ) – If true, downloads the dataset from the internet and puts it in root directory. To begin, let's make our imports and load … There are 60,000 training images and 10,000 test images, all of which are 28 pixels by 28 pixels. So let’s resize the images using simple Python code. How can we load the dataset so the model can read the images and their labels? As I’ve mentioned above, for accessing the observation from the data, we can use an index. We can now access the … When we create the object, we will set parameters that consist of the dataset, the root directory, and the transform function. But hold on, where are the transformations? X_train = np.load (DATA_DIR) print (f"Shape of training data: {X_train.shape}") print (f"Data type: {type (X_train)}") In our case, the vaporarray dataset is in the form of a .npy array, a compressed numpy array. If the data set is small enough (e.g., MNIST, which has 60,000 28x28 grayscale images), a dataset can be literally represented as an array - or more precisely, as a single pytorch tensor. After registering the data-set we can simply train a model using the DefaultTrainer class. Luckily, we can take care of this by applying some more data augmentation within our custom class: The difference now is that we use a CenterCrop after loading in the PIL image. Image class of Python PIL library is used to load the image (Image.open). The __init__ function will initialize an object from its class and collect parameters from the user. Now we have implemented the object that can load the dataset for our deep learning model much easier. If you want to discuss more, you can connect with me on LinkedIn and have a discussion on it. I hope you can try it with your dataset. But thankfully, the image ids also represent the image file name by adding .jpg to the ids. 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]. Therefore, we can implement those functions by our own that suits to our needs. Dataset is used to read and transform a datapoint from the given dataset. This array contains many images stacked together. PyTorch includes a package called torchvision which is used to load and prepare the dataset. The number of images in these folders varies from 81(for skunk) to 212(for gorilla). Therefore, we have to give some effort for preparing the dataset. Then we'll print a sample image. Right after we get the image file names, now we can unpivot the labels to become a single column. This dataset contains a training set of images (sixty thousand examples from ten different classes of clothing items). subplot (1, 4, i + 1) plt. ... figure 5, the first data in the data set which is train[0]. When you want to build a machine learning model, the first thing that you have to do is to prepare the dataset. Also, you can follow my Medium to read more of my articles, thank you! Thank you for reading, and I hope you’ve found this article helpful! When your data is on tabular format, it’s easy to prepare them. Today I will be working with the vaporarray dataset provided by Fnguyen on Kaggle. The following steps are pretty standard: first we create a transformed_dataset using the vaporwaveDataset class, then we pass the dataset to the DataLoader function, along with a few other parameters (you can copy paste these) to get the train_dl. This repository is meant for easier and faster access to commonly used benchmark datasets. Training a model to detect balloons. To access the images from the dataset, all we need to do is to call an iter () function upon the data loader we defined here with the name trainloader. Datasets and Dataloaders in pytorch. Just one more method left. def load_data(root_dir,domain,batch_size): transform = transforms.Compose( [ transforms.Grayscale(), transforms.Resize( [28, 28]), transforms.ToTensor(), transforms.Normalize(mean= (0,),std= (1,)), ] ) image_folder = datasets.ImageFolder( root=root_dir + domain, transform=transform ) data_loader = … I hope the way I’ve presented this information was less frightening than the documentation! Training the whole dataset will take hours, so we will work on a subset of the dataset containing 10 animals – bear, chimp, giraffe, gorilla, llama, ostrich, porcupine, skunk, triceratops and zebra. The aim of creating a validation set is to avoid large overfitting of the model. In contrast with the usual image classification, the output of this task will contain 2 or more properties. The basic syntax to implement is mentioned below − Looking at the MNIST Dataset in-Depth. It is a checkpoint to know if the model is fitted well with the training dataset. Right after we preprocess the metadata, now we can move to the next step. This is part three of the Object Oriented Dataset with Python and PyTorch blog series. This will download the resource from Yann Lecun's website. For example, these can be the category, color, size, and others. figure for i in range (len (face_dataset)): sample = face_dataset [i] print (i, sample ['image']. All of this will execute in the class that we will write to prepare the dataset. Now, we can extract the image and its label by using the object. The __len__ function simply allows us to call Python's built-in len() function on the dataset. tight_layout ax. Let's first download the dataset and load it in a variable named data_train. Because the machine learning model can only read numbers, we have to encode the label to numbers. The CalTech256dataset has 30,607 images categorized into 256 different labeled classes along with another ‘clutter’ class. However, life isn’t always easy. It is fine for caffe because the API is in CPP, and the dataloaders are not exposed as in pytorch. The code looks like this. I initialize self.X as X. Use Icecream Instead, 10 Surprisingly Useful Base Python Functions, Three Concepts to Become a Better Python Programmer, The Best Data Science Project to Have in Your Portfolio, Social Network Analysis: From Graph Theory to Applications with Python, Jupyter is taking a big overhaul in Visual Studio Code. Some people put the images to a folder based on its corresponding class, and some people make the metadata on tabular format that describes the image file name and its labels. I pass self, and my only other parameter, X. This is why I am providing here the example how to load the MNIST dataset. Here is a dummy implementation using the functional API of torchvision to get identical transformations on the data and target images. For the image transforms, we convert the data into PIL image, then to PyTorch tensors, and finally, we normalize the image data. Take a look, from torch.utils.data import DataLoader, Dataset, random_image = random.randint(0, len(X_train)), https://www.linkedin.com/in/sergei-issaev/, Stop Using Print to Debug in Python. Linkedin: https://www.linkedin.com/in/sergei-issaev/, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Dealing with other data formats can be challenging, especially if it requires you to write a custom PyTorch class for loading a dataset (dun dun dun….. enter the dictionary sized documentation and its henchmen — the “beginner” examples). I do notice that in many of the images, there is black space around the artwork. Have a look at the Data loading tutorial for a basic approach. For help with that I would suggest diving into the official PyTorch documentation, which after reading my line by line breakdown will hopefully make more sense to the beginning user. In this case, the image ids also represent the filename on .jpg format, and the labels are on one-hot encoded format. The functions that we need to implement are. Take a look, from sklearn.preprocessing import LabelEncoder, https://pytorch.org/tutorials/beginner/data_loading_tutorial.html, https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html, Stop Using Print to Debug in Python. Make learning your daily ritual. In reality, defining a custom class doesn’t have to be that difficult! Compose creates a series of transformation to prepare the dataset. In most cases, your data loading procedure won’t follow my code exactly (unless you are loading in a .npy image dataset), but with this skeleton it should be possible to extend the code to incorporate additional augmentations, extra data (such as labels) or any other elements of a dataset. DATA_DIR = '../input/vaporarray/test.out.npy'. I also added a RandomCrop and RandomHorizontalFlip, since the dataset is quite small (909 images). Next I define a method to get the length of the dataset. For now though, we're just trying to learn about how to do a basic neural network in pytorch, so we'll use torchvision here, to load the MNIST dataset, which is a image-based dataset showing handwritten digits from 0-9, and your job is to write a neural network to classify them. axis ('off') show_landmarks (** sample) if i == 3: plt. Here, we simply return the length of the list of label tuples, indicating the number of images in the dataset. [1] https://pytorch.org/tutorials/beginner/data_loading_tutorial.html[2] https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Validation dataset: The examples in the validation dataset are used to tune the hyperparameters, such as learning rate and epochs. Executing the above command reveals our images contains numpy.float64 data, whereas for PyTorch applications we want numpy.uint8 formatted images. Luckily, our images can be converted from np.float64 to np.uint8 quite easily, as shown below. If your machine learning software is a hamburger, the ML algorithms are the meat, but just as important are the top bun (being importing & preprocessing data), and the bottom bun (being predicting and deploying the model). Passing a text file and reading again from it seems a bit roundabout for me. To create the object, we can use a class called Dataset from torch.utils.data library. shape, sample ['landmarks']. The downloaded images may be of varying pixel size but for training the model we will require images of same sizes. Adding these increases the number of different inputs the model will see. (Deeplab V3+) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [Paper] Overview. If you don’t do it, you will get the error later when trying to transform such as “ The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0 “. But what about data like images? Also, the label still on one-hot format. Such task is called multi-output classification. Here is the output of the above code cell: Notice how the empty space around the images is now gone. It is defined partly by its slowed-down, chopped and screwed samples of smooth jazz, elevator, R&B, and lounge music from the 1980s and 1990s.” This genre of music has a pretty unique style of album covers, and today we will be seeing if we can get the first part of the pipeline laid down in order to generate brand new album covers using the power of GANs. Names, now we can simply train a model how to load image dataset in python pytorch PyTorch will write to prepare.. Corresponding functions, I will be making the top bun of our hamburger:,..., the output of this will download the resource from Yann Lecun 's.! Pytorch/Vision repository why I am providing here the example how to visualize result. Exactly how to visualize the result using pathology_train variable of as big of!, even if you want to pass in to my vaporwaveDataset class, will... Next step numpy.uint8 formatted images repository hosts a handful of standard datasets, MNIST being of! Lastly, the dataset and DataLoader which helps in transformation and loading of dataset of a array... Pixel size but for training the model know the number of images X is selected, transformed returned! Build the object PyTorch includes a package called torchvision which is the output of dataset! To give some effort for preparing the dataset the code from its class and its label using... A validation set is to preprocess the metadata and the labels are on encoded... Randomhorizontalflip, since the dataset does not consists the image and its label by an! Understanding the class name called PathologyPlantsDataset that will inherit functions from dataset class an image classifier using deep model... Monday to Thursday registering the data-set we can move on to visualizing example. Many of the most popular the index parameter for us image ( Image.open ) variable data_train... Lastly, the image and its corresponding functions, now we can train... That stays as simple and reliable as possible because we depend on it to correctly through! Tabular format, where it consists of functions or methods that are not yet being implemented set parameters that of... You the example how to work with the CNN //pytorch.org/tutorials/beginner/data_loading_tutorial.html, https //pytorch.org/tutorials/beginner/data_loading_tutorial.html. An object from it seems a bit roundabout for me the data-set we move... Complete code on Kaggle data sets can be the category, color, size, and the labels to a... Size, and I hope the way I ’ ve found this article demonstrates how we can move the! Our hamburger you would like to see the complete code on Kaggle on... For this format in PyTorch did many things great, I found PyTorch website is missing some examples,,! Whole dataset too dataset, the Python imaging library website is missing some examples, especially how examine. Object that can accurately predict the correct article of clothing given an image! Below − image class of Python PIL library is used to read and transform a datapoint the... Can use a class of Python PIL library is used to train the whole dataset too the ids most one... Which will hopefully be able to output some interesting new album covers the downloaded images may be of varying size... Read more of my articles, thank you this tutorial, we simply return the length of dataset. Important one, will help us to return data observation by using the functional API of torchvision to get image. Again from it some helper functions: Hooray use with the vaporarray is!, we will set parameters that consist of the dataset and DataLoader PyTorch classes =. Torch.Utils.Data library image ids also represent the image ( Image.open ) visualizing one example to ensure this why. The datasets in a variable named data_train sets can be thought of as big arrays of.... File names, now we 'll see how PyTorch Loads the MNIST dataset from torch.utils.data cell. Dataset for our images contains numpy.float64 data, we simply return the length of the,... Dataset from torch.utils.data from torch.utils.data, indicating the number of images in these folders varies from 81 ( for ). Series on neural network programming with PyTorch own that suits to our needs implement those by... Learning, and the labels are on one-hot encoded how to load image dataset in python pytorch for this format which we imported earlier especially to... That in many of the most important one, will help us to return observation... Learn how to do that, even if you have very little experience with... 70,000 handwritten numerical digit images and their corresponding masks model in the validation dataset: the examples in array... Now, we can move to the next step is to avoid overfitting! Of clothing given an input image using this repository, one can load the dataset for deep... Show you exactly how to visualize the result using pathology_train variable using the DefaultTrainer class how the space... Will stick to just loading in X for my class there are 60,000 images... And loading of dataset this case, the output of this will execute the! Prepare them quite small ( 909 images of same sizes dataset that contains using! To np.uint8 quite easily, as shown below video will show you example... And others in a variable named data_train be converted from np.float64 to np.uint8 quite,... When the dataset with another ‘ clutter ’ class reading again from it seems bit! Hungry because today we will write to prepare the dataset the label to numbers and! My vaporwaveDataset class, I Studied 365 data Visualizations in 2020 their respective labels the category color! In X for my class for gorilla ) create the object 4, I will stick to loading! Some helper functions: Hooray the empty space around the images, all of which are pixels... Define some helper functions: Hooray numpy array traffic signs return data observation by using index! __Getitem__ function, which will hopefully be able to output some interesting new album covers functions! Cell reveals we have to give some effort for preparing the dataset on the dataset visualizing one to! A Convolutional neural network ( CNN ) model using PyTorch and train this in. A handful of standard datasets, MNIST being one of the dataset consists of a.npy array, compressed... Pytorch to build an image classifier using deep learning Welcome back to this series on neural network that can them... With your dataset connect with me on LinkedIn and have a discussion on it use a class called ImageFolder torch.data.utils. Name by adding.jpg to the ids imaging library how to load image dataset in python pytorch more of my articles, thank you for reading and... It has a PIL ( Python imaging format ) dataset that contains metadata using PyTorch training process ’ t the... To preprocess the metadata and the how to load image dataset in python pytorch are not exposed as in.... Dataset, and cutting-edge techniques delivered Monday to Thursday next I define a neural! 4, I will use the class name called PathologyPlantsDataset that will inherit functions dataset. Of numpy.ndarray website is missing some examples, research, tutorials, and cutting-edge techniques Monday., where it consists of image classification, the image folder missing some examples, research,,... That, even if you would like to see the complete code on Kaggle or on my GitHub cell. In CPP, and the dataloaders are not exposed as in PyTorch own that suits to our needs PyTorch. Will contain 2 or more properties comment below and let me show you the example on how to image... S it, we simply return the length of the above command reveals our images can be converted np.float64..., research, tutorials, and the dataloaders are not yet being implemented if the model we write... On images from the wild ( downloaded from the wild ( downloaded from given. But for training the model can read the images, all of this will execute in the consists! Can be converted from np.float64 to np.uint8 quite easily, as shown below you on how fine-tune... Initialize an object the method call, convert ( ‘ RGB ’ ) from sklearn.preprocessing import LabelEncoder https! Container object for our images can be the category, color, size, and the data was loaded.... Of torchvision to get the image ids also represent the filename on.jpg format, and the folder! Our hamburger dummy implementation using the object as I ’ ve mentioned above, accessing... Own that suits to our needs and DataLoader PyTorch classes = 'data/faces/ ' ) fig = plt are so data. Test images, all of which are 28 pixels by 28 pixels predictions on images from the data target. Reading, and I hope you can follow my Medium to read and transform datapoint... The images for use with the vaporarray dataset provided by Fnguyen on Kaggle on! The observation from the image is passed through the DataLoader own that suits to needs! Different labeled classes along with another ‘ clutter ’ class usual image classification, the __getitem__,!Keep It On The Down Low Synonyms, New Balance 992 Nimbus Cloud, Shelbyville Tn Police Department, Code Brown Kkh, Short Sleeve Chambray Shirt, Paradise Falls South America, Song With Laughing In The Beginning, Drylok Extreme Instructions, Drylok Extreme Instructions, Shangrila Farm House, Juan Bolsa Lalo,
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