.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_beginner_data_loading_tutorial.py: Data Loading and Processing Tutorial ==================================== **Author**: `Sasank Chilamkurthy `_ A lot of effort in solving any machine learning problem goes in to preparing the data. PyTorch provides many tools to make data loading easy and hopefully, to make your code more readable. In this tutorial, we will see how to load and preprocess/augment data from a non trivial dataset. To run this tutorial, please make sure the following packages are installed: - ``scikit-image``: For image io and transforms - ``pandas``: For easier csv parsing .. code-block:: python from __future__ import print_function, division import os import torch import pandas as pd from skimage import io, transform import numpy as np import matplotlib.pyplot as plt from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utils # Ignore warnings import warnings warnings.filterwarnings("ignore") plt.ion() # interactive mode The dataset we are going to deal with is that of facial pose. This means that a face is annotated like this: .. figure:: /_static/img/landmarked_face2.png :width: 400 Over all, 68 different landmark points are annotated for each face. .. note:: Download the dataset from `here `_ so that the images are in a directory named 'faces/'. This dataset was actually generated by applying excellent `dlib's pose estimation `__ on a few images from imagenet tagged as 'face'. Dataset comes with a csv file with annotations which looks like this: :: image_name,part_0_x,part_0_y,part_1_x,part_1_y,part_2_x, ... ,part_67_x,part_67_y 0805personali01.jpg,27,83,27,98, ... 84,134 1084239450_e76e00b7e7.jpg,70,236,71,257, ... ,128,312 Let's quickly read the CSV and get the annotations in an (N, 2) array where N is the number of landmarks. .. code-block:: python 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) print('Image name: {}'.format(img_name)) print('Landmarks shape: {}'.format(landmarks.shape)) print('First 4 Landmarks: {}'.format(landmarks[:4])) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Image name: person-7.jpg Landmarks shape: (68, 2) First 4 Landmarks: [[32. 65.] [33. 76.] [34. 86.] [34. 97.]] Let's write a simple helper function to show an image and its landmarks and use it to show a sample. .. code-block:: python def show_landmarks(image, landmarks): """Show image with landmarks""" plt.imshow(image) plt.scatter(landmarks[:, 0], landmarks[:, 1], s=10, marker='.', c='r') plt.pause(0.001) # pause a bit so that plots are updated plt.figure() show_landmarks(io.imread(os.path.join('faces/', img_name)), landmarks) plt.show() .. image:: /beginner/images/sphx_glr_data_loading_tutorial_001.png :class: sphx-glr-single-img Dataset class ------------- ``torch.utils.data.Dataset`` is an abstract class representing a dataset. Your custom dataset should inherit ``Dataset`` and override the following methods: - ``__len__`` so that ``len(dataset)`` returns the size of the dataset. - ``__getitem__`` to support the indexing such that ``dataset[i]`` can be used to get :math:`i`\ th sample Let's create a dataset class for our face landmarks dataset. We will read the csv in ``__init__`` but leave the reading of images to ``__getitem__``. This is memory efficient because all the images are not stored 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 an optional argument ``transform`` so that any required processing can be applied on the sample. We will see the usefulness of ``transform`` in the next section. .. code-block:: python class FaceLandmarksDataset(Dataset): """Face Landmarks dataset.""" def __init__(self, csv_file, root_dir, transform=None): """ Args: csv_file (string): Path to the csv file with annotations. root_dir (string): Directory with all the images. transform (callable, optional): Optional transform to be applied on a sample. """ self.landmarks_frame = pd.read_csv(csv_file) self.root_dir = root_dir self.transform = transform def __len__(self): return len(self.landmarks_frame) def __getitem__(self, idx): img_name = os.path.join(self.root_dir, self.landmarks_frame.iloc[idx, 0]) image = io.imread(img_name) landmarks = self.landmarks_frame.iloc[idx, 1:].as_matrix() landmarks = landmarks.astype('float').reshape(-1, 2) sample = {'image': image, 'landmarks': landmarks} if self.transform: sample = self.transform(sample) return sample Let's instantiate this class and iterate through the data samples. We will print the sizes of first 4 samples and show their landmarks. .. code-block:: python face_dataset = FaceLandmarksDataset(csv_file='faces/face_landmarks.csv', root_dir='faces/') fig = plt.figure() for i in range(len(face_dataset)): sample = face_dataset[i] print(i, sample['image'].shape, sample['landmarks'].shape) ax = plt.subplot(1, 4, i + 1) plt.tight_layout() ax.set_title('Sample #{}'.format(i)) ax.axis('off') show_landmarks(**sample) if i == 3: plt.show() break .. image:: /beginner/images/sphx_glr_data_loading_tutorial_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 0 (324, 215, 3) (68, 2) 1 (500, 333, 3) (68, 2) 2 (250, 258, 3) (68, 2) 3 (434, 290, 3) (68, 2) Transforms ---------- One issue we can see from the above is that the samples are not of the same 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 image - ``RandomCrop``: to crop from image randomly. This is data augmentation. - ``ToTensor``: to convert the numpy images to torch images (we need to swap axes). We will write them as callable classes instead of simple functions so that parameters of the transform need not be passed everytime it's called. For this, we just need to implement ``__call__`` method and if required, ``__init__`` method. We can then use a transform like this: :: tsfm = Transform(params) transformed_sample = tsfm(sample) Observe below how these transforms had to be applied both on the image and landmarks. .. code-block:: python class Rescale(object): """Rescale the image in a sample to a given size. Args: output_size (tuple or int): Desired output size. If tuple, output is matched to output_size. If int, smaller of image edges is matched to output_size keeping aspect ratio the same. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] if isinstance(self.output_size, int): if h > w: new_h, new_w = self.output_size * h / w, self.output_size else: new_h, new_w = self.output_size, self.output_size * w / h else: new_h, new_w = self.output_size new_h, new_w = int(new_h), int(new_w) img = transform.resize(image, (new_h, new_w)) # h and w are swapped for landmarks because for images, # x and y axes are axis 1 and 0 respectively landmarks = landmarks * [new_w / w, new_h / h] return {'image': img, 'landmarks': landmarks} class RandomCrop(object): """Crop randomly the image in a sample. Args: output_size (tuple or int): Desired output size. If int, square crop is made. """ def __init__(self, output_size): assert isinstance(output_size, (int, tuple)) if isinstance(output_size, int): self.output_size = (output_size, output_size) else: assert len(output_size) == 2 self.output_size = output_size def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] h, w = image.shape[:2] new_h, new_w = self.output_size top = np.random.randint(0, h - new_h) left = np.random.randint(0, w - new_w) image = image[top: top + new_h, left: left + new_w] landmarks = landmarks - [left, top] return {'image': image, 'landmarks': landmarks} class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): image, landmarks = sample['image'], sample['landmarks'] # swap color axis because # numpy image: H x W x C # torch image: C X H X W image = image.transpose((2, 0, 1)) return {'image': torch.from_numpy(image), 'landmarks': torch.from_numpy(landmarks)} 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 and then 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 us to do this. .. code-block:: python scale = Rescale(256) crop = RandomCrop(128) composed = transforms.Compose([Rescale(256), RandomCrop(224)]) # Apply each of the above transforms on sample. fig = plt.figure() sample = face_dataset[65] for i, tsfrm in enumerate([scale, crop, composed]): transformed_sample = tsfrm(sample) ax = plt.subplot(1, 3, i + 1) plt.tight_layout() ax.set_title(type(tsfrm).__name__) show_landmarks(**transformed_sample) plt.show() .. image:: /beginner/images/sphx_glr_data_loading_tutorial_003.png :class: sphx-glr-single-img Iterating through the dataset ----------------------------- Let's put this all together to create a dataset with composed transforms. 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 on sampling We can iterate over the created dataset with a ``for i in range`` loop as before. .. code-block:: python transformed_dataset = FaceLandmarksDataset(csv_file='faces/face_landmarks.csv', root_dir='faces/', transform=transforms.Compose([ Rescale(256), RandomCrop(224), ToTensor() ])) for i in range(len(transformed_dataset)): sample = transformed_dataset[i] print(i, sample['image'].size(), sample['landmarks'].size()) if i == 3: break .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 0 torch.Size([3, 224, 224]) torch.Size([68, 2]) 1 torch.Size([3, 224, 224]) torch.Size([68, 2]) 2 torch.Size([3, 224, 224]) torch.Size([68, 2]) 3 torch.Size([3, 224, 224]) torch.Size([68, 2]) However, we are losing a lot of features by using a simple ``for`` loop to iterate 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 these features. Parameters used below should be clear. One parameter of interest is ``collate_fn``. You can specify how exactly the samples need to be batched using ``collate_fn``. However, default collate should work fine for most use cases. .. code-block:: python dataloader = DataLoader(transformed_dataset, batch_size=4, shuffle=True, num_workers=4) # Helper function to show a batch def show_landmarks_batch(sample_batched): """Show image with landmarks for a batch of samples.""" images_batch, landmarks_batch = \ sample_batched['image'], sample_batched['landmarks'] batch_size = len(images_batch) im_size = images_batch.size(2) grid = utils.make_grid(images_batch) plt.imshow(grid.numpy().transpose((1, 2, 0))) for i in range(batch_size): plt.scatter(landmarks_batch[i, :, 0].numpy() + i * im_size, landmarks_batch[i, :, 1].numpy(), s=10, marker='.', c='r') plt.title('Batch from dataloader') for i_batch, sample_batched in enumerate(dataloader): print(i_batch, sample_batched['image'].size(), sample_batched['landmarks'].size()) # observe 4th batch and stop. if i_batch == 3: plt.figure() show_landmarks_batch(sample_batched) plt.axis('off') plt.ioff() plt.show() break .. image:: /beginner/images/sphx_glr_data_loading_tutorial_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 0 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 1 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 2 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) 3 torch.Size([4, 3, 224, 224]) torch.Size([4, 68, 2]) Afterword: torchvision ---------------------- In this tutorial, we have seen how to write and use datasets, transforms and dataloader. ``torchvision`` package provides some common datasets and transforms. You might not even have to write custom classes. One of the more generic datasets available in torchvision is ``ImageFolder``. It assumes that images are organized in the following way: :: root/ants/xxx.png root/ants/xxy.jpeg root/ants/xxz.png . . . root/bees/123.jpg root/bees/nsdf3.png root/bees/asd932_.png where 'ants', 'bees' etc. are class labels. Similarly generic transforms which operate on ``PIL.Image`` like ``RandomHorizontalFlip``, ``Scale``, are also available. You can use these to write a dataloader like this: :: import torch from torchvision import transforms, datasets data_transform = transforms.Compose([ transforms.RandomSizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) hymenoptera_dataset = datasets.ImageFolder(root='hymenoptera_data/train', transform=data_transform) dataset_loader = torch.utils.data.DataLoader(hymenoptera_dataset, batch_size=4, shuffle=True, num_workers=4) For an example with training code, please see :doc:`transfer_learning_tutorial`. **Total running time of the script:** ( 0 minutes 58.231 seconds) .. _sphx_glr_download_beginner_data_loading_tutorial.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: data_loading_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: data_loading_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_