cdsprites.py

gistfile1.txt

import os
import io
import subprocess
import numpy as np
from scipy.sparse.linalg import svds
from scipy.io import loadmat

import torch
from torchvision import datasets, transforms

CDspritesDataSize = torch.Size([3, 64, 64])
class CDspritesDataset(torch.utils.data.Dataset):
    """2D shapes dataset, WITH COLORS.
    More info here:
    https://github.com/deepmind/dsprites-dataset/blob/master/dsprites_reloading_example.ipynb
    """
    data_root = '../data'
    filename = 'dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz'
    npz_file = data_root + '/' + filename
    npzc_file = data_root + '/' + 'color_' + filename
    npzc_train_file = data_root + '/train_color_' + filename
    npzc_test_file = data_root + '/test_color_' + filename
    pca_filename = data_root + '/pca_color_' + filename

    def download_dataset(self, npz_file):
        from urllib import request
        url = 'https://github.com/deepmind/dsprites-dataset/blob/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz?raw=true'
        print('Downloading ' + url)
        data = request.urlopen(url)
        with open(npz_file, 'wb') as f:
            f.write(data.read())

    def compute_pca(self, path, X):
        print('Compute singular values...')
        _, s, _ = svds(X.reshape(X.shape[0], -1).astype(np.float32), k=20)
        s = s**2
        np.savez(path, pca=s)

    @classmethod
    def load_pca(cls, latent_dim):
        pca = np.load(cls.pca_filename, mmap_mode='r')['pca']
        return torch.Tensor(pca[:latent_dim])

    def gen_and_split_dataset(self, npz_file, npzc_train_file, npzc_test_file, npzc_file,
                              train_fract):
        print('Computing dataset split')
        rdataset = np.load(npz_file, encoding='latin1', mmap_mode='r')
        dataset = {'latents': rdataset['latents_values'][:, 1:],  # drop colour
                   'images': rdataset['imgs']}
        latents = []
        for r in range(3):
            colors = np.random.choice(3, (len(dataset['latents']),1))
            latents.append(np.concatenate([dataset['latents'], colors], axis=-1))


        images = np.tile(dataset['images'], (3, 1, 1))
        latents = np.concatenate(latents, axis=0)

        split_idx = np.int(train_fract * len(latents))
        shuffled_range = np.random.permutation(len(latents))
        train_idx = shuffled_range[range(0, split_idx)]
        test_idx = shuffled_range[range(split_idx, len(latents))]

        np.savez(npzc_train_file, images=images[train_idx], latents=latents[train_idx])
        np.savez(npzc_test_file, images=images[test_idx], latents=latents[test_idx])
        np.savez(npzc_file, images=images, latents=latents)

    def __init__(self, data_root, train=True, train_fract=0.8, split=True):
        """
        Args:
            npz_file (string): Path to the npz file.
        """
        if not os.path.isfile(self.npz_file):
            self.download_dataset(self.npz_file)

        if not (os.path.isfile(self.npzc_train_file) or os.path.isfile(self.npzc_test_file)
                or os.path.isfile(self.npzc_file)):
            self.gen_and_split_dataset(self.npz_file, self.npzc_train_file, self.npzc_test_file,
                                       self.npzc_file, train_fract)
        if split:
            dataset = np.load(self.npzc_train_file if train else self.npzc_test_file, mmap_mode='r')
        else:
            dataset = np.load(self.npzc_file, mmap_mode='r')
        self.latents, self.images = dataset['latents'], dataset['images']

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

    def __getitem__(self, idx):
        image = torch.Tensor(self.images[idx])
        latent = torch.Tensor(self.latents[idx])
        canvas = torch.zeros_like(image).unsqueeze(0).repeat(3,1,1)
        canvas[latent[-1].long(),...] = image
        return (canvas, latent)