def test_ensure_sorted_indices():
x = 2000
y = 2000
sparsity = 1000
for i in range(2):
# testing both csc and csr
if i is 0:
# csc
input_tensor = theano.sparse.csc_dmatrix()
sample = scipy.sparse.csc_matrix(random_lil((x, y), "float64", sparsity))
else:
# csr
input_tensor = theano.sparse.csr_dmatrix()
sample = scipy.sparse.csr_matrix(random_lil((x, y), "float64", sparsity))
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
sorted_scipy = sample.sorted_indices()
sorted_theano = f(sample)
assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
def test_ensure_sorted_indices():
x = 2000
y = 2000
sparsity = 1000
for i in range(2):
# testing both csc and csr
if i is 0:
# csc
input_tensor = theano.sparse.csc_dmatrix()
sample = scipy.sparse.csc_matrix(
random_lil((x, y), 'float64', sparsity))
else:
# csr
input_tensor = theano.sparse.csr_dmatrix()
sample = scipy.sparse.csr_matrix(
random_lil((x, y), 'float64', sparsity))
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
sorted_scipy = sample.sorted_indices()
sorted_theano = f(sample)
assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
def test_local_csm_properties_csm():
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
mode = mode.including("specialize", "local_csm_properties_csm")
for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
f = theano.function([data, indices, indptr, shape],
sparse.csm_properties(
CS(data, indices, indptr, shape)),
mode=mode)
assert not any(
isinstance(node.op, (sparse.CSM, sparse.CSMProperties))
for node in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40), config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
def test_local_csm_properties_csm():
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
mode = mode.including("specialize", "local_csm_properties_csm")
for CS, cast in [(CSC, sp.csc_matrix), (CSR, sp.csr_matrix)]:
f = theano.function([data, indices, indptr, shape],
csm_properties(CS(data, indices, indptr, shape)),
mode=mode)
#theano.printing.debugprint(f)
assert not any(isinstance(node.op, (CSM, CSMProperties)) for node
in f.maker.env.toposort())
v = cast(random_lil((10, 40),
config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
def test_local_csm_grad_c():
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
if theano.config.mode == 'FAST_COMPILE':
mode = theano.compile.Mode(linker='c|py', optimizer='fast_compile')
mode = mode.including("specialize", "local_csm_grad_c")
for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
cost = tensor.sum(sparse.DenseFromSparse()(CS(data, indices, indptr, shape)))
f = theano.function(
[data, indices, indptr, shape],
tensor.grad(cost, data),
mode=mode)
assert not any(isinstance(node.op, sparse.CSMGrad) for node
in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40),
config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
def test_local_csm_grad_c():
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
if theano.config.mode == 'FAST_COMPILE':
mode = theano.compile.Mode(linker='c|py', optimizer='fast_compile')
mode = mode.including("specialize", "local_csm_grad_c")
for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
cost = tensor.sum(sparse.DenseFromSparse()(CS(data, indices, indptr, shape)))
f = theano.function(
[data, indices, indptr, shape],
tensor.grad(cost, data),
mode=mode)
assert not any(isinstance(node.op, sparse.CSMGrad) for node
in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40),
config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
