test_backend.py
from concurrent.futures import ThreadPoolExecutor
import pytest
import numpy as np
from scipy.linalg import svd
import tensorly as tl
from ..backend import numpy_backend
from .. import backend as T
from ..base import fold, unfold
from ..base import partial_fold, partial_unfold
from ..base import tensor_to_vec, vec_to_tensor
from ..base import partial_tensor_to_vec, partial_vec_to_tensor
from ..testing import (assert_array_equal, assert_equal, assert_,
assert_array_almost_equal, assert_raises)
# Author: Jean Kossaifi
def test_set_backend():
torch = pytest.importorskip('torch')
toplevel_backend = tl.get_backend()
# Set in context manager
with tl.backend_context('numpy'):
assert tl.get_backend() == 'numpy'
assert isinstance(tl.tensor([1, 2, 3]), np.ndarray)
assert isinstance(T.tensor([1, 2, 3]), np.ndarray)
assert tl.float32 is T.float32 is np.float32
with tl.backend_context('pytorch'):
assert tl.get_backend() == 'pytorch'
assert torch.is_tensor(tl.tensor([1, 2, 3]))
assert torch.is_tensor(T.tensor([1, 2, 3]))
assert tl.float32 is T.float32 is torch.float32
# Sets back to numpy
assert tl.get_backend() == 'numpy'
assert isinstance(tl.tensor([1, 2, 3]), np.ndarray)
assert isinstance(T.tensor([1, 2, 3]), np.ndarray)
assert tl.float32 is T.float32 is np.float32
# Reset back to initial backend
assert tl.get_backend() == toplevel_backend
# Set not in context manager
tl.set_backend('pytorch')
assert tl.get_backend() == 'pytorch'
tl.set_backend(toplevel_backend)
assert tl.get_backend() == toplevel_backend
# Improper name doesn't reset backend
with assert_raises(ValueError):
tl.set_backend('not-a-real-backend')
assert tl.get_backend() == toplevel_backend
def test_set_backend_local_threadsafe():
pytest.importorskip('torch')
global_default = tl.get_backend()
with ThreadPoolExecutor(max_workers=1) as executor:
with tl.backend_context('numpy', local_threadsafe=True):
assert tl.get_backend() == 'numpy'
# Changes only happen locally in this thread
assert executor.submit(tl.get_backend).result() == global_default
# Set the global default backend
try:
tl.set_backend('pytorch', local_threadsafe=False)
# Changed toplevel default in all threads
assert executor.submit(tl.get_backend).result() == 'pytorch'
with tl.backend_context('numpy', local_threadsafe=True):
assert tl.get_backend() == 'numpy'
def check():
assert tl.get_backend() == 'pytorch'
with tl.backend_context('numpy', local_threadsafe=True):
assert tl.get_backend() == 'numpy'
assert tl.get_backend() == 'pytorch'
executor.submit(check).result()
finally:
tl.set_backend(global_default, local_threadsafe=False)
executor.submit(tl.set_backend,global_default).result()
assert tl.get_backend() == global_default
assert executor.submit(tl.get_backend).result() == global_default
def test_backend_and_tensorly_module_attributes():
for dtype in ['int32', 'int64', 'float32', 'float64']:
assert dtype in dir(tl)
assert dtype in dir(T)
assert getattr(T, dtype) is getattr(tl, dtype)
with assert_raises(AttributeError):
tl.not_a_real_attribute
def test_tensor_creation():
tensor = T.tensor(np.arange(12).reshape((4, 3)))
tensor2 = tl.tensor(np.arange(12).reshape((4, 3)))
assert T.is_tensor(tensor)
assert T.is_tensor(tensor2)
def test_unfold():
"""Test for unfold
1. We do an exact test.
2. Second, a test inspired by the example in Kolda's paper:
Even though we use a different definition of the unfolding,
it should only differ by the ordering of the columns
"""
X = T.tensor([[[1, 13],
[4, 16],
[7, 19],
[10, 22]],
[[2, 14],
[5, 17],
[8, 20],
[11, 23]],
[[3, 15],
[6, 18],
[9, 21],
[12, 24]]])
X = T.reshape(T.arange(24), (3, 4, 2))
unfoldings = [T.tensor([[0, 1, 2, 3, 4, 5, 6, 7],
[8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]]),
T.tensor([[0, 1, 8, 9, 16, 17],
[2, 3, 10, 11, 18, 19],
[4, 5, 12, 13, 20, 21],
[6, 7, 14, 15, 22, 23]]),
T.tensor([[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22],
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23]])]
for mode in range(T.ndim(X)):
unfolding = unfold(X, mode=mode)
assert_array_equal(unfolding, unfoldings[mode])
assert_array_equal(T.reshape(unfolding, (-1, )),
T.reshape(unfoldings[mode], (-1,)))
def test_fold():
"""Test for fold
"""
X = T.reshape(T.arange(24), (3, 4, 2))
unfoldings = [T.tensor([[0, 1, 2, 3, 4, 5, 6, 7],
[8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]]),
T.tensor([[0, 1, 8, 9, 16, 17],
[2, 3, 10, 11, 18, 19],
[4, 5, 12, 13, 20, 21],
[6, 7, 14, 15, 22, 23]]),
T.tensor([[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22],
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23]])]
# hard coded example
for mode in range(T.ndim(X)):
assert_array_equal(fold(unfoldings[mode], mode, X.shape), X)
# check dims
for i in range(T.ndim(X)):
assert_array_equal(X, fold(unfold(X, i), i, X.shape))
# chain unfolding and folding
X = T.tensor(np.random.random(2 * 3 * 4 * 5).reshape(2, 3, 4, 5))
for i in range(T.ndim(X)):
assert_array_equal(X, fold(unfold(X, i), i, X.shape))
def test_tensor_to_vec():
"""Test for tensor_to_vec"""
X = T.tensor([[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11],
[12, 13],
[14, 15]],
[[16, 17],
[18, 19],
[20, 21],
[22, 23]]])
true_res = T.tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])
assert_array_equal(tensor_to_vec(X), true_res)
def test_vec_to_tensor():
"""Test for tensor_to_vec"""
X = T.tensor([[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11],
[12, 13],
[14, 15]],
[[16, 17],
[18, 19],
[20, 21],
[22, 23]]])
vec = T.tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])
assert_array_equal(X, vec_to_tensor(vec, X.shape))
# Convert to vector and back to tensor
X = T.tensor(np.random.random((3, 4, 5, 2)))
vec = tensor_to_vec(X)
reconstructed = vec_to_tensor(vec, X.shape)
assert_array_equal(X, reconstructed)
def test_partial_unfold():
"""Test for partial_unfold
Notes
-----
Assumes that the standard unfold is correct!
"""
X = T.reshape(T.arange(24), (3, 4, 2))
n_samples = 3
###################################
# Samples are the first dimension #
###################################
tensor = T.tensor(np.concatenate([np.arange(24).reshape((1, 3, 4, 2))+i\
for i in range(n_samples)]))
t = T.tensor(X)
# We created here a tensor with 3 samples, each sample being similar to X
for i in range(T.ndim(X)): # test for each mode
unfolded = partial_unfold(tensor, i, skip_begin=1)
unfolded_X = unfold(t, i)
for j in range(n_samples): # test for each sample
assert_array_equal(unfolded[j], unfolded_X+j)
# Test for raveled tensor
for i in range(T.ndim(X)): # test for each mode
unfolded = partial_unfold(tensor, mode=i, skip_begin=1, ravel_tensors=True)
unfolded_X = T.reshape(unfold(t, i), (-1, ))
for j in range(n_samples): # test for each sample
assert_array_equal(unfolded[j], unfolded_X + j)
##################################
# Samples are the last dimension #
##################################
tensor = T.tensor(np.concatenate([np.arange(24).reshape((3, 4, 2, 1))+i\
for i in range(n_samples)], axis=-1))
for i in range(T.ndim(X)): # test for each mode
unfolded = partial_unfold(tensor, mode=i, skip_end=1, skip_begin=0)
unfolded_X = unfold(t, i)
for j in range(n_samples): # test for each sample
assert_array_equal(T.transpose(T.transpose(unfolded)[j]), unfolded_X+j)
# Test for raveled tensor
for i in range(T.ndim(X)): # test for each mode
unfolded = partial_unfold(tensor, mode=i, skip_end=1, skip_begin=0, ravel_tensors=True)
unfolded_X = T.reshape(unfold(t, i), (-1, ))
for j in range(n_samples): # test for each sample
assert_array_equal(T.transpose(unfolded)[j], unfolded_X+j)
def test_partial_fold():
"""Test for partial_fold
Assumes partial unfolding works and check that
refolding partially folded tensors results in
the original tensor.
"""
X = T.reshape(T.arange(24), (3, 4, 2))
unfolded = T.tensor([[[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]],
[[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]],
[[ 0, 1, 2, 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23]]])
folded = partial_fold(unfolded, 0, (3, 3, 4, 2), skip_begin=1)
for i in range(3):
assert_array_equal(folded[i], X)
shape = [3, 4, 5, 6]
X = T.tensor(np.random.random(shape))
for i in [0, 1]:
for mode in range(len(shape)-1):
unfolded = partial_unfold(X, mode=mode, skip_begin=i, skip_end=(1-i))
refolded = partial_fold(unfolded, mode=mode, shape=shape, skip_begin=i, skip_end=(1-i))
assert_array_equal(refolded, X)
# Test for raveled_tensor=True
for i in [0, 1]:
for mode in range(len(shape)-1):
unfolded = partial_unfold(X, mode=mode, skip_begin=i, skip_end=(1-i), ravel_tensors=True)
refolded = partial_fold(unfolded, mode=mode, shape=shape, skip_begin=i, skip_end=(1-i))
assert_array_equal(refolded, X)
def test_partial_tensor_to_vec():
"""Test for partial_tensor_to_vec """
X = np.arange(24).reshape((3, 4, 2))
n_samples = 3
###################################
# Samples are the first dimension #
###################################
tensor = T.tensor(np.concatenate([X[None, ...]+i for i in range(n_samples)]))
#we created here a tensor with 3 samples, each sample being similar to X
vectorised = partial_tensor_to_vec(tensor, skip_begin=1)
vec_X = tensor_to_vec(T.tensor(X))
for j in range(n_samples): # test for each sample
assert_array_equal(vectorised[j], vec_X+j)
##################################
# Samples are the last dimension #
##################################
tensor = T.tensor(np.concatenate([X[..., None]+i for i in range(n_samples)], axis=-1))
vectorised = partial_tensor_to_vec(tensor, skip_end=1, skip_begin=0)
vec_X = tensor_to_vec(T.tensor(X))
for j in range(n_samples): # test for each sample
assert_array_equal(T.transpose(vectorised)[j], vec_X+j)
def test_partial_vec_to_tensor():
"""Test for partial_vec_to_tensor
"""
X = np.arange(24).reshape((3, 4, 2))
vectorised = T.tensor([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23],
[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24],
[ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25]])
folded = partial_vec_to_tensor(vectorised, (3, 3, 4, 2), skip_begin=1)
for i in range(3):
assert_array_equal(folded[i], X+i)
shape = [3, 4, 5, 6]
X = T.tensor(np.random.random(shape))
for i in [0, 1]:
vec = partial_tensor_to_vec(X, skip_begin=i, skip_end=(1-i))
ten = partial_vec_to_tensor(vec, shape=shape, skip_begin=i, skip_end=(1-i))
assert_array_equal(X, ten)
def test_svd():
"""Test for the SVD functions"""
tol = 0.1
tol_orthogonality = 0.01
for name, svd_fun in T.SVD_FUNS.items():
sizes = [(100, 100), (100, 5), (10, 10), (10, 4), (5, 100)]
n_eigenvecs = [90, 4, 5, 4, 5]
for s, n in zip(sizes, n_eigenvecs):
matrix = np.random.random(s)
matrix_backend = T.tensor(matrix)
fU, fS, fV = svd_fun(matrix_backend, n_eigenvecs=n)
U, S, V = svd(matrix)
U, S, V = U[:, :n], S[:n], V[:n, :]
assert_array_almost_equal(np.abs(S), T.abs(fS), decimal=3,
err_msg='eigenvals not correct for "{}" svd fun VS svd and backend="{}, for {} eigenenvecs, and size {}".'.format(
name, tl.get_backend(), n, s))
# True reconstruction error (based on numpy SVD)
true_rec_error = np.sum((matrix - np.dot(U, S.reshape((-1, 1))*V))**2)
# Reconstruction error with the backend's SVD
rec_error = T.sum((matrix_backend - T.dot(fU, T.reshape(fS, (-1, 1))*fV))**2)
# Check that the two are similar
assert_(true_rec_error - rec_error <= tol,
msg='Reconstruction not correct for "{}" svd fun VS svd and backend="{}, for {} eigenenvecs, and size {}".'.format(
name, tl.get_backend(), n, s))
# Check for orthogonality when relevant
if name != 'symeig_svd':
left_orthogonality_error = T.norm(T.dot(T.transpose(fU), fU) - T.eye(n))
assert_(left_orthogonality_error <= tol_orthogonality,
msg='Left eigenvecs not orthogonal for "{}" svd fun VS svd and backend="{}, for {} eigenenvecs, and size {}".'.format(
name, tl.get_backend(), n, s))
right_orthogonality_error = T.norm(T.dot(T.transpose(fU), fU) - T.eye(n))
assert_(right_orthogonality_error <= tol_orthogonality,
msg='Right eigenvecs not orthogonal for "{}" svd fun VS svd and backend="{}, for {} eigenenvecs, and size {}".'.format(
name, tl.get_backend(), n, s))
# Should fail on non-matrices
with assert_raises(ValueError):
tensor = T.tensor(np.random.random((3, 3, 3)))
svd_fun(tensor)
def test_shape():
A = T.arange(3*4*5)
shape1 = (3*4,5)
A1 = T.reshape(A, shape1)
assert_equal(T.shape(A1), shape1)
shape2 = (3,4,5)
A2 = T.reshape(A, shape2)
assert_equal(T.shape(A2), shape2)
def test_ndim():
A = T.arange(3*4*5)
assert_equal(T.ndim(A), 1)
shape1 = (3*4,5)
A1 = T.reshape(A, shape1)
assert_equal(T.ndim(A1), 2)
shape2 = (3,4,5)
A2 = T.reshape(A, shape2)
assert_equal(T.ndim(A2), 3)
def test_norm():
v = T.tensor([1., 2., 3.])
assert_equal(T.norm(v,1), 6)
A = T.reshape(T.arange(6), (3,2))
assert_equal(T.norm(A, 1), 15)
column_norms1 = T.norm(A, 1, axis=0)
row_norms1 = T.norm(A, 1, axis=1)
assert_array_equal(column_norms1, T.tensor([6., 9]))
assert_array_equal(row_norms1, T.tensor([1, 5, 9]))
column_norms2 = T.norm(A, 2, axis=0)
row_norms2 = T.norm(A, 2, axis=1)
assert_array_almost_equal(column_norms2, T.tensor([4.47213602, 5.91608]))
assert_array_almost_equal(row_norms2, T.tensor([1., 3.60555124, 6.40312433]))
# limit as order->oo is the oo-norm
column_norms10 = T.norm(A, 10, axis=0)
row_norms10 = T.norm(A, 10, axis=1)
assert_array_almost_equal(column_norms10, T.tensor([4.00039053, 5.00301552]))
assert_array_almost_equal(row_norms10, T.tensor([1., 3.00516224, 5.05125666]))
column_norms_oo = T.norm(A, 'inf', axis=0)
row_norms_oo = T.norm(A, 'inf', axis=1)
assert_array_equal(column_norms_oo, T.tensor([4, 5]))
assert_array_equal(row_norms_oo, T.tensor([1, 3, 5]))
def test_where():
# 1D
shape = (2*3*4,); N = np.prod(shape)
X = T.arange(N)
zeros = T.zeros(X.shape)
ones = T.ones(X.shape)
out = T.where(X < 2*3, zeros, ones)
for i in range(N):
if i < 2*3:
assert_equal(out[i], 0, 'Unexpected result on vector for element {}'.format(i))
else:
assert_equal(out[i], 1, 'Unexpected result on vector for element {}'.format(i))
# 2D
shape = (2*3,4); N = np.prod(shape)
X = T.reshape(T.arange(N), shape)
zeros = T.zeros(X.shape)
ones = T.ones(X.shape)
out = T.where(X < 2*3, zeros, ones)
for i in range(shape[0]):
for j in range(shape[1]):
index = i*shape[1] + j
if index < 2*3:
assert_equal(out[i,j], 0, 'Unexpected result on matrix')
else:
assert_equal(out[i,j], 1, 'Unexpected result on matrix')
# 3D
shape = (2,3,4); N = np.prod(shape)
X = T.reshape(T.arange(N), shape)
zeros = T.zeros(X.shape)
ones = T.ones(X.shape)
out = T.where(X < 2*3, zeros, ones)
for i in range(shape[0]):
for j in range(shape[1]):
for k in range(shape[2]):
index = (i*shape[1] + j)*shape[2] + k
if index < 2*3:
assert_equal(out[i,j, k], 0, 'Unexpected result on matrix')
else:
assert_equal(out[i,j, k], 1, 'Unexpected result on matrix')
# random testing against Numpy's output
shapes = (16,8,4,2)
for order in range(1,5):
shape = shapes[:order]
tensor = T.tensor(np.random.randn(*shape))
args = (tensor < 0, T.zeros(shape), T.ones(shape))
result = T.where(*args)
expected = np.where(*map(T.to_numpy, args))
assert_array_equal(result, expected)
def test_qr():
M = 8; N = 5
A = T.tensor(np.random.random((M,N)))
Q, R = T.qr(A)
assert T.shape(Q) == (M,N), 'Unexpected shape'
assert T.shape(R) == (N,N), 'Unexpected shape'
# assert that the columns of Q are orthonormal
Q_column_norms = T.norm(Q, 2, axis=0)
assert_array_almost_equal(Q_column_norms, T.ones(N))
for i in range(N):
for j in range(i):
dot_product = T.to_numpy(T.dot(Q[:,i], Q[:,j]))
assert abs(dot_product) < 1e-6, 'Columns of Q not orthogonal'
A_reconstructed = T.dot(Q, R)
assert_array_almost_equal(A, A_reconstructed)
def test_prod():
v = T.tensor([3, 4, 5])
x = T.to_numpy(T.prod(v))
assert_equal(x, 60)
