- PyTorch Tutorial
- PyTorch - Home
- PyTorch - Introduction
- PyTorch - Installation
- Mathematical Building Blocks of Neural Networks
- PyTorch - Neural Network Basics
- Universal Workflow of Machine Learning
- Machine Learning vs. Deep Learning
- Implementing First Neural Network
- Neural Networks to Functional Blocks
- PyTorch - Terminologies
- PyTorch - Loading Data
- PyTorch - Linear Regression
- PyTorch - Convolutional Neural Network
- PyTorch - Recurrent Neural Network
- PyTorch - Datasets
- PyTorch - Introduction to Convents
- Training a Convent from Scratch
- PyTorch - Feature Extraction in Convents
- PyTorch - Visualization of Convents
- Sequence Processing with Convents
- PyTorch - Word Embedding
- PyTorch - Recursive Neural Networks
- PyTorch Useful Resources
- PyTorch - Quick Guide
- PyTorch - Useful Resources
- PyTorch - Discussion
PyTorch - Loading Data
PyTorch includes a package called torchvision which is used to load and prepare the dataset. It includes two basic functions namely Dataset and DataLoader which helps in transformation and loading of dataset.
Dataset
Dataset is used to read and transform a datapoint from the given dataset. The basic syntax to implement is mentioned below −
trainset = torchvision.datasets.CIFAR10(root = './data', train = True, download = True, transform = transform)
DataLoader is used to shuffle and batch data. It can be used to load the data in parallel with multiprocessing workers.
trainloader = torch.utils.data.DataLoader(trainset, batch_size = 4, shuffle = True, num_workers = 2)
Example: Loading CSV File
We use the Python package Panda to load the csv file. The original file has the following format: (image name, 68 landmarks - each landmark has a x, y coordinates).
landmarks_frame = pd.read_csv('faces/face_landmarks.csv')
n = 65
img_name = landmarks_frame.iloc[n, 0]
landmarks = landmarks_frame.iloc[n, 1:].as_matrix()
landmarks = landmarks.astype('float').reshape(-1, 2)
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