def get_named_ground_truth_data(name):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "shapes3d":
return shapes3d.Shapes3D()
elif name == "dummy_data":
return dummy_data.DummyData()
else:
raise ValueError("Invalid data set name.")
def get_named_ground_truth_data(name,
corr_type='plane',
corr_indices=[3, 4],
col=None):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "correlated_dsprites_full":
return dsprites.CorrelatedDSprites([1, 2, 3, 4, 5], corr_indices,
corr_type)
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "correlated_dsprites_noshape":
return dsprites.CorrelatedDSprites([2, 3, 4, 5], corr_indices,
corr_type)
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "backgroundcolor_dsprites":
return dsprites.BackgroundColorDSprites([1, 2, 3, 4, 5, 6], col)
elif name == "correlated_backgroundcolor_dsprites":
return dsprites.CorrelatedBackgroundColorDSprites([1, 2, 3, 4, 5, 6],
corr_indices,
corr_type)
elif name == "correlated_color_dsprites":
return dsprites.CorrelatedColorDSprites([1, 2, 3, 4, 5], corr_indices,
corr_type)
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "correlated_noisy_dsprites":
return dsprites.CorrelatedNoisyDSprites([1, 2, 3, 4, 5], corr_indices,
corr_type)
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "correlated_scream_dsprites":
return dsprites.CorrelatedScreamDSprites([1, 2, 3, 4, 5], corr_indices,
corr_type)
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "shapes3d":
return shapes3d.Shapes3D()
elif name == "dummy_data":
return dummy_data.DummyData()
else:
raise ValueError("Invalid data set name: " + name + ".")
def get_dlib_data(task):
ut.log("Loading {}".format(task))
if task == "dsprites":
# 5 factors
return dsprites.DSprites(list(range(1, 6)))
elif task == "shapes3d":
# 6 factors
return shapes3d.Shapes3D()
elif task == "norb":
# 4 factors + 1 nuisance (which we'll handle via n_dim=2)
return norb.SmallNORB()
elif task == "cars3d":
# 3 factors
return cars3d.Cars3D()
elif task == "scream":
# 5 factors + 2 nuisance (handled as n_dim=2)
return dsprites.ScreamDSprites(list(range(1, 6)))
def get_named_ground_truth_data(name):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "mpi3d_toy":
return mpi3d.MPI3D(mode="mpi3d_toy")
elif name == "mpi3d_realistic":
return mpi3d.MPI3D(mode="mpi3d_realistic")
elif name == "mpi3d_real":
return mpi3d.MPI3D(mode="mpi3d_real")
elif name == "shapes3d":
return shapes3d.Shapes3D()
elif name == "dummy_data":
return dummy_data.DummyData()
elif name == "modelnet":
return DisLibGroundTruthData(**MODELNET_PARAMS)
elif name == "arrow":
return DisLibGroundTruthData(**ARROW_PARAMS)
elif name == "pixel4":
return DisLibGroundTruthData(**WRAPPED_PIXEL4_PARAMS)
elif name == "pixel8":
return DisLibGroundTruthData(**WRAPPED_PIXEL8_PARAMS)
else:
raise ValueError("Invalid data set name.")
def get_named_ground_truth_data(name):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "threeDotsCache":
# a large random sample from ThreeDots
return threeDots.ThreeDotsTrainingCache()
elif name == "threeDots":
return threeDots.ThreeDots()
elif name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "mpi3d_toy":
return mpi3d.MPI3D(mode="mpi3d_toy")
elif name == "mpi3d_realistic":
return mpi3d.MPI3D(mode="mpi3d_realistic")
elif name == "mpi3d_real":
return mpi3d.MPI3D(mode="mpi3d_real")
elif name == "mpi3d_multi_real":
return mpi3d_multi.MPI3DMulti(mode="mpi3d_real")
elif name == "shapes3d":
return shapes3d.Shapes3D()
elif name == "dummy_data":
return dummy_data.DummyData()
else:
raise ValueError("Invalid data set name.")
def get_named_ground_truth_data(name):
"""Returns ground truth data set based on name.
Args:
name: String with the name of the dataset.
Raises:
ValueError: if an invalid data set name is provided.
"""
if name == "dsprites_full":
return dsprites.DSprites([1, 2, 3, 4, 5])
elif name == "dsprites_noshape":
return dsprites.DSprites([2, 3, 4, 5])
elif name == "color_dsprites":
return dsprites.ColorDSprites([1, 2, 3, 4, 5])
elif name == "noisy_dsprites":
return dsprites.NoisyDSprites([1, 2, 3, 4, 5])
elif name == "scream_dsprites":
return dsprites.ScreamDSprites([1, 2, 3, 4, 5])
elif name == "smallnorb":
return norb.SmallNORB()
elif name == "cars3d":
return cars3d.Cars3D()
elif name == "mpi3d_toy":
return mpi3d.MPI3D(mode="mpi3d_toy")
elif name == "mpi3d_realistic":
return mpi3d.MPI3D(mode="mpi3d_realistic")
elif name == "mpi3d_real":
return mpi3d.MPI3D(mode="mpi3d_real")
elif name == "3dshapes":
return shapes3d.Shapes3D()
elif name == "3dshapes_holdout" or name == "3dshapes_pca_holdout_s5000":
return shapes3d_partial.Shapes3DPartial(name)
elif name == "3dshapes_model_all":
return shapes3d_partial.Shapes3DPartial(name), None
elif name[:8] == "3dshapes":
return shapes3d_partial.Shapes3DPartial(
name + '_train'), shapes3d_partial.Shapes3DPartial(name + '_valid')
elif name == "dummy_data":
return dummy_data.DummyData()
else:
raise ValueError("Invalid data set name.")
def create_data(factors):
"""
:param factors: underlying factors of variation
:return: data: obervational data from underlying factors
"""
if FLAGS.data_type == "dsprites":
dsp = dsprites.DSprites()
elif FLAGS.data_type == "smallnorb":
snb = norb.SmallNORB()
elif FLAGS.data_type == "cars3d":
cars = cars3d.Cars3D()
elif FLAGS.data_type == "shapes3d":
shp = shapes3d.Shapes3D()
random_state = np.random.RandomState(FLAGS.seed)
factors_train = np.transpose(factors['factors_train'], (0, 2, 1))
factors_test = np.transpose(factors['factors_test'], (0, 2, 1))
N_train = factors_train.shape[0]
N_test = factors_test.shape[0]
time_len = factors_train.shape[1]
if FLAGS.data_type in ["dsprites", "smallnorb"]:
data_train = np.zeros([N_train, time_len, 64 * 64])
data_test = np.zeros([N_test, time_len, 64 * 64])
elif FLAGS.data_type in ["cars3d", "shapes3d"]:
data_train = np.zeros([N_train, time_len, 64 * 64 * 3])
data_test = np.zeros([N_test, time_len, 64 * 64 * 3])
# Training data
for i in range(N_train):
if FLAGS.data_type == "dsprites":
data_point_train = np.squeeze(
dsp.sample_observations_from_factors_no_color(
factors=factors_train[i, :, :], random_state=random_state))
data_train_reshape = data_point_train.reshape(
data_point_train.shape[0], 64 * 64)
elif FLAGS.data_type == "smallnorb":
data_point_train = np.squeeze(
snb.sample_observations_from_factors(
factors=factors_train[i, :, :], random_state=random_state))
data_train_reshape = data_point_train.reshape(
data_point_train.shape[0], 64 * 64)
elif FLAGS.data_type == "cars3d":
data_point_train = cars.sample_observations_from_factors(
factors=factors_train[i, :, :], random_state=random_state)
data_train_reshape = data_point_train.reshape(
data_point_train.shape[0], 64 * 64 * 3)
elif FLAGS.data_type == "shapes3d":
data_point_train = shp.sample_observations_from_factors(
factors=factors_train[i, :, :], random_state=random_state)
data_train_reshape = data_point_train.reshape(
data_point_train.shape[0], 64 * 64 * 3)
data_train[i, :, :] = data_train_reshape
# Test data
for i in range(N_test):
if FLAGS.data_type == "dsprites":
data_point_test = np.squeeze(
dsp.sample_observations_from_factors_no_color(
factors=factors_test[i, :, :], random_state=random_state))
data_test_reshape = data_point_test.reshape(
data_point_test.shape[0], 64 * 64)
elif FLAGS.data_type == "smallnorb":
data_point_test = np.squeeze(
snb.sample_observations_from_factors(
factors=factors_test[i, :, :], random_state=random_state))
data_test_reshape = data_point_test.reshape(
data_point_test.shape[0], 64 * 64)
elif FLAGS.data_type == "cars3d":
data_point_test = cars.sample_observations_from_factors(
factors=factors_test[i, :, :], random_state=random_state)
data_test_reshape = data_point_test.reshape(
data_point_test.shape[0], 64 * 64 * 3)
elif FLAGS.data_type == "shapes3d":
data_point_test = shp.sample_observations_from_factors(
factors=factors_test[i, :, :], random_state=random_state)
data_test_reshape = data_point_test.reshape(
data_point_test.shape[0], 64 * 64 * 3)
data_test[i, :, :] = data_test_reshape
return data_train.astype('float32'), data_test.astype('float32')
else:
pass
if settings.showplot or settings.saveplot:
import matplotlib
if not settings.showplot:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# get ground truth factors
if args.dataset == 'cars3d':
dta = cars3d.Cars3D()
factors = get_cars_factors()
elif args.dataset == 'norb':
dta = norb.SmallNORB()
factors = get_norb_factors()
else:
raise Error
with hub.eval_function_for_module(dlib_model_path) as f:
# Save reconstructions.
inputs = dta.images
if inputs.ndim < 4:
inputs = np.expand_dims(inputs, 3)
targets = f(dict(images=inputs), signature="reconstructions",
as_dict=True)["images"]
if settings.dataname == 'faces' or settings.dataname == 'faces2' or settings.dataname == 'planes' or settings.dataname == 'cars' or settings.dataname == 'chairs' or settings.dataname == 'dlib_cars3d' or settings.dataname == 'dlib_faces3d':
input_shape = [64, 64, 3]
def get_dataset(dataname, dataroot, numsamples):
'''
inputs: x
targets: c or y (y if using dlib)
'''
if numsamples == -1:
inds = slice(0, None, 1)
else:
inds = slice(0, numsamples, 1)
if dataname == 'dSprites':
# Dataroot is the directory containing the dsprites .npz file
dataset_zip = np.load(os.path.join(
dataroot, 'dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz'),
encoding='bytes')
inputs = np.expand_dims(dataset_zip['imgs'], axis=4)[inds]
targets = dataset_zip['latents_values'][inds]
elif dataname == 'faces':
# Dataroot is the directory containing the images
filenames = np.array(
json.load(open(os.path.join(dataroot, 'img_store')))[inds])
inputs = np.array([
os.path.join(dataroot, '/'.join(filenames[i].split('/')[1:]))
for i in range(len(filenames))
])
targets = np.load(os.path.join(dataroot, 'z_store'))[inds]
elif dataname == 'faces2':
# Dataroot is the directory containing the images
filenames = np.array(
json.load(open(os.path.join(dataroot, 'img_store')))[inds])
inputs = np.array([
os.path.join(dataroot, '/'.join(filenames[i].split('/')[1:]))
for i in range(len(filenames))
])
targets = np.load(os.path.join(dataroot, 'z_store_full'))[inds]
elif dataname == 'planes':
# Dataroot is the directory containing the images
filenames = np.array(
json.load(open(os.path.join(dataroot, 'img_store_planes')))[inds])
inputs = np.array([
os.path.join(dataroot, 'planes',
'/'.join(filenames[i].split('/')[-2:]))
for i in range(len(filenames))
])
targets = np.load(os.path.join(dataroot, 'z_store_trans'))[inds]
elif dataname == 'cars':
# Dataroot is the directory containing the images
filenames = np.array(
json.load(open(os.path.join(dataroot, 'img_store_cars')))[inds])
inputs = np.array([
os.path.join(dataroot, 'cars_for_transfer',
'/'.join(filenames[i].split('/')[-2:]))
for i in range(len(filenames))
])
targets = np.load(os.path.join(dataroot, 'z_store_new_cars'))[inds]
elif dataname == 'chairs':
# Dataroot is the directory containing the images
filenames = np.array(
json.load(open(os.path.join(dataroot, 'img_store_chairs')))[inds])
inputs = np.array([
os.path.join(dataroot, 'chairs_for_transfer',
'/'.join(filenames[i].split('/')[-2:]))
for i in range(len(filenames))
])
targets = np.load(os.path.join(dataroot, 'z_store_new_chairs'))[inds]
elif dataname == 'dlib_cars3d':
## dataroot: path to the tensorflow_hub directory
dta = cars3d.Cars3D()
with hub.eval_function_for_module(dataroot) as f:
# Save reconstructions.
inputs = dta.images
targets = f(dict(images=inputs),
signature="reconstructions",
as_dict=True)["images"]
elif dataname == 'dlib_smallnorb':
## dataroot: path to the tensorflow_hub directory
dta = norb.SmallNORB()
with hub.eval_function_for_module(dataroot) as f:
# Save reconstructions.
inputs1 = np.expand_dims(dta.images, 3)[:25000]
targets1 = f(dict(images=inputs1),
signature="reconstructions",
as_dict=True)["images"]
inputs2 = np.expand_dims(dta.images, 3)[25000:]
targets2 = f(dict(images=inputs2),
signature="reconstructions",
as_dict=True)["images"]
inputs = np.concatenate((inputs1, inputs2), axis=0)
targets = np.concatenate((targets1, targets2), axis=0)
print(inputs.shape)
print(targets.shape)
elif dataname == 'dlib_faces3d':
## dataroot: path to the tensorflow_hub directory
dta = faces3d.Faces3D()
with hub.eval_function_for_module(dataroot) as f:
# Save reconstructions.
inputs = dta.images
targets = f(dict(images=inputs),
signature="reconstructions",
as_dict=True)["images"]
print(dataname)
print(inputs.shape)
print(targets.shape)
else:
raise NotImplementedError
return (inputs, targets)
