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• semi-supervised-pytorch-master
  • semi-supervised
    • layers
      • stochastic.py
      • __init__.py
      • flow.py
    • models
      • vae.py
      • __init__.py
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    • utils.py
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  • examples
    • betavae.py
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    • mnist_sslvae.py
    • notebooks
      • Ladder Variational Autoencoder.ipynb
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      • Gumbel Softmax.ipynb
      • Ladder Deep Generative Model.ipynb
      • datautils.py
  • LICENSE.md
  • README.md
  • .gitignore

import torch
import numpy as np
import sys
from urllib import request
from torch.utils.data import Dataset
sys.path.append("../semi-supervised")
n_labels = 10
cuda = torch.cuda.is_available()


class SpriteDataset(Dataset):
    """
    A PyTorch wrapper for the dSprites dataset by
    Matthey et al. 2017. The dataset provides a 2D scene
    with a sprite under different transformations:
    * color
    * shape
    * scale
    * orientation
    * x-position
    * y-position
    """
    def __init__(self, transform=None):
        self.transform = transform
        url = "https://github.com/deepmind/dsprites-dataset/raw/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz"
        
        try:
            self.dset = np.load("./dsprites.npz", encoding="bytes")["imgs"]
        except FileNotFoundError:
            request.urlretrieve(url, "./dsprites.npz")
            self.dset = np.load("./dsprites.npz", encoding="bytes")["imgs"]

    def __len__(self):
        return len(self.dset)

    def __getitem__(self, idx):
        sample = self.dset[idx]
                
        if self.transform:
            sample = self.transform(sample)
            
        return sample


def get_mnist(location="./", batch_size=64, labels_per_class=100):
    from functools import reduce
    from operator import __or__
    from torch.utils.data.sampler import SubsetRandomSampler
    from torchvision.datasets import MNIST
    import torchvision.transforms as transforms
    from utils import onehot

    flatten_bernoulli = lambda x: transforms.ToTensor()(x).view(-1).bernoulli()

    mnist_train = MNIST(location, train=True, download=True,
                        transform=flatten_bernoulli, target_transform=onehot(n_labels))
    mnist_valid = MNIST(location, train=False, download=True,
                        transform=flatten_bernoulli, target_transform=onehot(n_labels))

    def get_sampler(labels, n=None):
        # Only choose digits in n_labels
        (indices,) = np.where(reduce(__or__, [labels == i for i in np.arange(n_labels)]))

        # Ensure uniform distribution of labels
        np.random.shuffle(indices)
        indices = np.hstack([list(filter(lambda idx: labels[idx] == i, indices))[:n] for i in range(n_labels)])

        indices = torch.from_numpy(indices)
        sampler = SubsetRandomSampler(indices)
        return sampler

    # Dataloaders for MNIST
    labelled = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, num_workers=2, pin_memory=cuda,
                                           sampler=get_sampler(mnist_train.train_labels.numpy(), labels_per_class))
    unlabelled = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, num_workers=2, pin_memory=cuda,
                                             sampler=get_sampler(mnist_train.train_labels.numpy()))
    validation = torch.utils.data.DataLoader(mnist_valid, batch_size=batch_size, num_workers=2, pin_memory=cuda,
                                             sampler=get_sampler(mnist_valid.test_labels.numpy()))

    return labelled, unlabelled, validation