def __init__(self):
# with tf.gfile.GFile(SHAPES3D_PATH, "rb") as f:
# # Data was saved originally using python2, so we need to set the encoding.
# data = np.load(f, encoding="latin1")
# images = data["images"]
# labels = data["labels"]
# n_samples = np.prod(images.shape[0:6])
with h5py.File(SHAPES3D_PATH, 'r') as dataset:
images = dataset['images'][()]
labels = dataset['labels'][()]
n_samples = images.shape[0]
# with tf.gfile.GFile(SHAPES3D_PATH, "rb") as f:
# data = pickle.load(f)
# images = data["images"]
# labels = data["labels"]
# n_samples = images.shape[0]
self.images = (
images.reshape([n_samples, 64, 64, 3]).astype(np.float32) / 255.)
features = labels.reshape([n_samples, 6])
self.factor_sizes = [10, 10, 10, 8, 4, 15]
self.latent_factor_indices = list(range(6))
self.num_total_factors = features.shape[1]
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
def __init__(self, latent_factor_granularity=64):
self.latent_factor_indices = list(range(6))
self.data_shape = [64, 64, 1]
self.latent_factor_granularity = latent_factor_granularity
self.factor_sizes = [latent_factor_granularity] * 6
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
def __init__(self, mode="mpi3d_toy"):
self.factor_sizes = [4, 4, 2, 3, 3, 40, 40]
self.latent_factor_indices = [0, 1, 2, 3, 4, 5, 6]
self.num_total_factors = 7
if mode == "mpi3d_toy":
mpi3d_path = os.path.join(
os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "mpi3d_toy",
"mpi3d_toy.npz")
if not tf.io.gfile.exists(mpi3d_path):
raise ValueError(
"Dataset '{}' not found. Make sure the dataset is publicly available and downloaded correctly."
.format(mode))
else:
with tf.io.gfile.GFile(mpi3d_path, "rb") as f:
data = np.load(f)
elif mode == "mpi3d_realistic":
mpi3d_path = os.path.join(
os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "mpi3d_realistic",
"mpi3d_realistic.npz")
if not tf.io.gfile.exists(mpi3d_path):
raise ValueError(
"Dataset '{}' not found. Make sure the dataset is publicly available and downloaded correctly."
.format(mode))
else:
with tf.io.gfile.GFile(mpi3d_path, "rb") as f:
data = np.load(f)
elif mode == "mpi3d_real":
mpi3d_path = os.path.join(
os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "mpi3d_real",
"mpi3d_real.npz")
if not tf.io.gfile.exists(mpi3d_path):
raise ValueError(
"Dataset '{}' not found. Make sure the dataset is publicly available and downloaded correctly."
.format(mode))
else:
with tf.io.gfile.GFile(mpi3d_path, "rb") as f:
data = np.load(f)
elif mode == "mpi3d_real_subset":
self.factor_sizes = [2, 2, 2, 3, 3, 40, 40]
mpi3d_path = os.path.join(
os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "mpi3d_real",
"mpi3d_real_subset.npz")
if not tf.io.gfile.exists(mpi3d_path):
raise ValueError(
"Dataset '{}' not found. Make sure the dataset is publicly available and downloaded correctly."
.format(mode))
else:
with tf.io.gfile.GFile(mpi3d_path, "rb") as f:
data = np.load(f)
else:
raise ValueError("Unknown mode provided.")
self.images = data["images"]
self.state_space = util.SplitDiscreteStateSpace(self.factor_sizes,
self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
def __init__(self):
self.images, features = _load_small_norb_chunks(
SMALLNORB_TEMPLATE, SMALLNORB_CHUNKS)
self.factor_sizes = [5, 10, 9, 18, 6]
# Instances are not part of the latent space.
self.latent_factor_indices = [0, 2, 3, 4]
self.num_total_factors = features.shape[1]
self.index = util.StateSpaceAtomIndex(self.factor_sizes, features)
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
def __init__(self):
self.factor_sizes = [4, 24, 183]
features = cartesian([np.array(list(range(i))) for i in self.factor_sizes])
self.latent_factor_indices = [0, 1, 2]
self.num_total_factors = features.shape[1]
self.index = util.StateSpaceAtomIndex(self.factor_sizes, features)
self.state_space = util.SplitDiscreteStateSpace(self.factor_sizes,
self.latent_factor_indices)
self.data_shape = [64, 64, 3]
self.images = self._load_data()
def __init__(self):
with gfile.Open(CUSTOMDATA_PATH, "rb") as f:
# load data
data = np.load(file=f, allow_pickle=True)
self.images = data["imgs"]
self.data_shape = list(self.images.shape[1:]) # first dimension [dim0] is dataset size; as list [] because gaussian_encoder_model.py requires it
self.factor_sizes = data["factor_sizes"]
self.latent_factor_indices = list(range(len(self.factor_sizes)))
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(self.factor_sizes)
self.state_space = util.SplitDiscreteStateSpace(self.factor_sizes, self.latent_factor_indices)
def __init__(self):
base_path = Path(SHAPES3D_PATH)
self.images_path = base_path/'images'
self.factors_path = base_path/'factors'
self.factor_sizes = [10, 10, 10, 8, 4, 15]
self.latent_factor_indices = list(range(6))
self.num_total_factors = 6
self.state_space = util.SplitDiscreteStateSpace(self.factor_sizes,
self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
def __init__(self):
with h5py.File(SHAPES3D_PATH, 'r') as dataset:
images = dataset['images'][:]
labels = dataset['labels'][:]
# images: shape [480000,64,64,3], uint8 in range(256)
# labels: shape [480000,6], float64
self.images = images.astype('float32') / 255.0
self.factor_sizes = [10, 10, 10, 8, 4, 15]
self.latent_factor_indices = list(range(6))
self.num_total_factors = labels.shape[1]
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
def __init__(self):
with h5py.File(SHAPES3D_PATH, 'r') as dataset:
images = dataset['images'][()]
labels = dataset['labels'][()]
n_samples = images.shape[0]
self.images = (
images.reshape([n_samples, 64, 64, 3]).astype(np.float16) / 255.)
features = labels.reshape([n_samples, 6])
self.factor_sizes = [10, 10, 10, 8, 4, 15]
self.latent_factor_indices = list(range(6))
self.num_total_factors = features.shape[1]
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
def __init__(self, latent_factor_indices=None):
# By default, all factors (including shape) are considered ground truth
# factors.
if latent_factor_indices is None:
latent_factor_indices = list(range(6))
self.latent_factor_indices = latent_factor_indices
self.data_shape = [64, 64, 3]
# Load the data so that we can sample from it.
with gfile.Open(DSPRITES_PATH, "rb") as data_file:
# Data was saved originally using python2, so we need to set the encoding.
data = np.load(data_file, encoding="latin1", allow_pickle=True)
self.images = np.array(data["imgs"])
self.factor_sizes = np.array(data["metadata"][()]["latents_sizes"],
dtype=np.int64)
self.full_factor_sizes = [1, 3, 6, 40, 32, 32]
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
def __init__(self, latent_factor_indices=None):
# By default, all factors (including shape) are considered ground truth
# factors.
if latent_factor_indices is None:
latent_factor_indices = list(range(6))
self.latent_factor_indices = latent_factor_indices
self.data_shape = [64, 64, 1]
# Load the data so that we can sample from it.
with gfile.Open(
"/home/adarsh/Desktop/Disentangled-Representation-Learning-Playground/data/dsprites/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz",
"rb") as data_file:
# Data was saved originally using python2, so we need to set the encoding.
data = np.load(data_file, encoding="latin1", allow_pickle=True)
self.images = np.array(data["imgs"])
self.factor_sizes = np.array(data["metadata"][()]["latents_sizes"],
dtype=np.int64)
self.full_factor_sizes = [1, 3, 6, 40, 32, 32]
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
def __init__(self, name):
SHAPES3D_PATH = os.path.join(
os.environ.get("DISENTANGLEMENT_LIB_DATA", "."), "3dshapes",
name + ".h5")
self.name = name
with h5py.File(SHAPES3D_PATH, 'r') as dataset:
images = dataset['images'][()]
labels = dataset['labels'][()]
n_samples = images.shape[0]
self.data_size = n_samples
self.images = (
images.reshape([n_samples, 64, 64, 3]).astype(np.float16) / 255.)
features = labels.reshape([n_samples, 6])
self.labels = features
self.factor_sizes = [10, 10, 10, 8, 4, 15]
self.latent_factor_indices = list(range(6))
self.num_total_factors = features.shape[1]
self.state_space = util.SplitDiscreteStateSpace(
self.factor_sizes, self.latent_factor_indices)
self.factor_bases = np.prod(self.factor_sizes) / np.cumprod(
self.factor_sizes)
