Skip to content

P
pytensor
提交 9fcdcdba authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
浏览文件
操作

Use op instances instead of op classes for test classes.

上级 7fd59ec5
隐藏空白字符变更
内嵌 并排
正在显示 包含 60 行增加 和 49 行删除
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ 9fcdcdba
......@@ -945,14 +945,14 @@ class TestAlloc(theano.tensor.tests.test_basic.TestAlloc):
dtype = "float32"
mode = mode_with_gpu
shared = staticmethod(cuda.shared_constructor)
allocs = [B.GpuAlloc, B.GpuAlloc, tensor.Alloc]
allocs = [B.GpuAlloc(), B.GpuAlloc(), tensor.Alloc()]
class T_Join_and_Split(theano.tensor.tests.test_basic.T_Join_and_Split):
def setUp(self):
utt.seed_rng()
self.mode = mode_with_gpu.excluding('constant_folding')
self.join_op = cuda.GpuJoin
self.join_op = cuda.GpuJoin()
# No gpu split.
self.split_op = tensor.Split
# No Make vector on the gpu, Join used instead
......
theano/sandbox/gpuarray/tests/test_basic_ops.py
浏览文件 @ 9fcdcdba
......@@ -305,7 +305,7 @@ class TestAlloc(theano.tensor.tests.test_basic.TestAlloc):
dtype = "float32"
mode = mode_with_gpu
shared = staticmethod(gpuarray_shared_constructor)
allocs = [GpuAlloc, GpuAlloc, T.Alloc]
allocs = [GpuAlloc(), GpuAlloc(), T.Alloc]
def test_shape():
......@@ -363,7 +363,7 @@ class G_Join_and_Split(test_basic.T_Join_and_Split):
def setUp(self):
super(G_Join_and_Split, self).setUp()
self.mode = mode_with_gpu.excluding('constant_folding')
self.join_op = GpuJoin
self.join_op = GpuJoin()
self.split_op = GpuSplit
# Use join instead of MakeVector since there is no MakeVector on GPU
self.make_vector_op = GpuJoin
......
theano/tensor/tests/test_basic.py
浏览文件 @ 9fcdcdba
......@@ -2099,7 +2099,7 @@ class TestAlloc(unittest.TestCase):
dtype = config.floatX
mode = mode_opt
shared = staticmethod(theano.shared)
allocs = [tensor.Alloc] * 3
allocs = [tensor.Alloc()] * 3
def setUp(self):
self.rng = numpy.random.RandomState(seed=utt.fetch_seed())
......@@ -2131,13 +2131,13 @@ class TestAlloc(unittest.TestCase):
#<= is needed as the GPU currently don't implement
# AdvancedIncSubtensor. When this is the case it can be
# replaced with ==.
assert numpy.sum([isinstance(node.op, alloc)
assert numpy.sum([isinstance(node.op, type(alloc))
for node in topo_obj]) <= 1
topo_grad = fgrad.maker.fgraph.toposort()
# print subtensor
# theano.printing.debugprint(fgrad)
assert numpy.sum([isinstance(node.op, alloc)
assert numpy.sum([isinstance(node.op, type(alloc))
for node in topo_grad]) == n_alloc, (
alloc, subtensor, n_alloc, topo_grad)
fobj(test_params)
......@@ -2148,46 +2148,51 @@ class TestAlloc(unittest.TestCase):
for alloc in self.allocs:
# The output is the result of the alloc operation,
# we do not want it to be constant-folded
out = alloc()(val, 50, 60)
out = alloc(val, 50, 60)
f = theano.function([], out)
f = theano.function([], out, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert numpy.sum([isinstance(node.op, alloc)
assert numpy.sum([isinstance(node.op, type(alloc))
for node in topo]) == 1
assert not isinstance(topo[0].op, DeepCopyOp)
def test_ones(self):
for shp in [[], 1, [1], [1, 2], [1, 2, 3]]:
ones = theano.function([], [tensor.ones(shp)])
ones = theano.function([], [tensor.ones(shp)], mode=self.mode)
assert numpy.allclose(ones(), numpy.ones(shp))
# scalar doesn't have to be provided as input
x = scalar()
shp = []
ones_scalar = theano.function([], [tensor.ones(x.shape)])
ones_scalar = theano.function([], [tensor.ones(x.shape)],
mode=self.mode)
assert numpy.allclose(ones_scalar(), numpy.ones(shp))
for (typ, shp) in [(vector, [3]), (matrix, [3, 4])]:
x = typ()
ones_tensor = theano.function([x], [tensor.ones(x.shape)])
ones_tensor = theano.function([x], [tensor.ones(x.shape)],
mode=self.mode)
inp = numpy.zeros(shp, dtype=config.floatX)
assert numpy.allclose(ones_tensor(inp),
numpy.ones(shp))
def test_zeros(self):
for shp in [[], 1, [1], [1, 2], [1, 2, 3]]:
zeros = theano.function([], [tensor.zeros(shp)])
zeros = theano.function([], [tensor.zeros(shp)],
mode=self.mode)
assert numpy.allclose(zeros(), numpy.zeros(shp))
# scalar doesn't have to be provided as input
x = scalar()
shp = []
zeros_scalar = theano.function([], [tensor.zeros(x.shape)])
zeros_scalar = theano.function([], [tensor.zeros(x.shape)],
mode=self.mode)
assert numpy.allclose(zeros_scalar(), numpy.zeros(shp))
for (typ, shp) in [(vector, [3]), (matrix, [3, 4])]:
x = typ()
zeros_tensor = theano.function([x], [tensor.zeros(x.shape)])
zeros_tensor = theano.function([x], [tensor.zeros(x.shape)],
mode=self.mode)
inp = numpy.zeros(shp, dtype=config.floatX)
assert numpy.allclose(zeros_tensor(inp),
numpy.zeros(shp))
......@@ -3187,7 +3192,7 @@ class T_Join_and_Split(unittest.TestCase):
self.mode = theano.compile.get_default_mode().excluding(
'constant_folding'
)
self.join_op = Join
self.join_op = Join()
self.split_op = Split
self.make_vector_op = opt.MakeVector
self.floatX = config.floatX
......@@ -3199,7 +3204,8 @@ class T_Join_and_Split(unittest.TestCase):
def eval_outputs_and_check_join(self, outputs):
f = theano.function([], outputs, self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
variables = f()
if isinstance(variables, (tuple, list)) and len(variables) == 1:
return variables[0]
......@@ -3259,7 +3265,7 @@ class T_Join_and_Split(unittest.TestCase):
self.assertTrue(numpy.all(val == [1, 2, 1, 2]))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0
assert f.maker.fgraph.outputs[0].dtype == self.floatX
def test_stack_scalar_make_vector_dtype(self):
......@@ -3273,7 +3279,7 @@ class T_Join_and_Split(unittest.TestCase):
self.assertTrue(numpy.all(val == [1, 2, 1, 2]))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0
assert f.maker.fgraph.outputs[0].dtype == 'int64'
def test_stack_scalar_make_vector_constant(self):
......@@ -3289,7 +3295,7 @@ class T_Join_and_Split(unittest.TestCase):
self.assertTrue(numpy.all(val == [10, 1, 2, 3]))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, opt.MakeVector)]) > 0
assert len([n for n in topo if isinstance(n, self.join_op)]) == 0
assert len([n for n in topo if isinstance(n, type(self.join_op))]) == 0
assert f.maker.fgraph.outputs[0].dtype == 'int64'
def test_stack_hessian(self):
......@@ -3459,8 +3465,8 @@ class T_Join_and_Split(unittest.TestCase):
out = self.eval_outputs_and_check_join([s])
self.assertTrue((out == want).all())
assert (grad(s.sum(), b).eval() == 0).all()
assert (grad(s.sum(), a).eval() == 0).all()
assert (numpy.asarray(grad(s.sum(), b).eval()) == 0).all()
assert (numpy.asarray(grad(s.sum(), a).eval()) == 0).all()
def test_join_matrix1_using_vertical_stack(self):
a = self.shared(numpy.array([[1, 2, 3], [4, 5, 6]], dtype=self.floatX))
......@@ -3499,7 +3505,8 @@ class T_Join_and_Split(unittest.TestCase):
f = inplace_func([ax], [s], mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
want = numpy.array([[.1, .2, .3], [.4, .5, .6],
[.1, .2, .3], [.4, .5, .6]])
......@@ -3540,25 +3547,26 @@ class T_Join_and_Split(unittest.TestCase):
a = self.shared(a_val, broadcastable=(False, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(1, a, b)
c = self.join_op(1, a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# Opt can remplace the int by a Theano constant
c = self.join_op()(theano.tensor.constant(1), a, b)
c = self.join_op(theano.tensor.constant(1), a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
# In case futur opt insert other useless stuff
c = self.join_op()(theano.tensor.cast(theano.tensor.constant(1),
dtype="int32"),
a, b)
c = self.join_op(theano.tensor.cast(theano.tensor.constant(1),
dtype="int32"),
a, b)
assert c.type.broadcastable[0] and c.type.broadcastable[2]
assert not c.type.broadcastable[1]
f = function([], c, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
f()
utt.verify_grad((lambda a, b: join(1, a, b)), [a_val, b_val], rng=rng,
......@@ -3580,12 +3588,13 @@ class T_Join_and_Split(unittest.TestCase):
a = self.shared(a_val, broadcastable=(False, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(0, a, b)
c = self.join_op(0, a, b)
assert not c.type.broadcastable[0]
f = function([], c, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
f()
utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng,
......@@ -3596,7 +3605,7 @@ class T_Join_and_Split(unittest.TestCase):
rng.rand(3, 4, 1).astype(self.floatX))
a = TensorType(dtype=self.floatX, broadcastable=[0, 0, 1])()
b = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1])()
c = join(0, a, b)
c = self.join_op(0, a, b)
f = function([a, b], c, mode=self.mode)
bad_b_val = rng.rand(3, 4, 1).astype(self.floatX)
self.assertRaises(TypeError, f, a_val, bad_b_val)
......@@ -3613,12 +3622,13 @@ class T_Join_and_Split(unittest.TestCase):
a = self.shared(a_val, broadcastable=(True, False, True))
b = self.shared(b_val, broadcastable=(True, False, True))
c = self.join_op()(0, a, b)
c = self.join_op(0, a, b)
assert not c.type.broadcastable[0]
f = function([], c, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
f()
utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng,
......@@ -3630,7 +3640,7 @@ class T_Join_and_Split(unittest.TestCase):
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(True, False, True))
b = self.join_op()(0, a)
b = self.join_op(0, a)
assert b.type.broadcastable[0]
assert b.type.broadcastable[2]
assert not b.type.broadcastable[1]
......@@ -3638,8 +3648,8 @@ class T_Join_and_Split(unittest.TestCase):
f = function([], b, mode=self.mode)
topo = f.maker.fgraph.toposort()
if theano.config.mode != 'FAST_COMPILE':
assert not [True for node in topo if isinstance(
node.op, self.join_op)]
assert not [True for node in topo
if isinstance(node.op, type(self.join_op))]
f()
utt.verify_grad((lambda a: join(0, a)), [a_val], rng=rng,
......@@ -3657,19 +3667,20 @@ class T_Join_and_Split(unittest.TestCase):
c = TensorType(dtype=self.floatX, broadcastable=[1, 0, 0, 0, 0, 0])()
d = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 1, 0, 1])()
e = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 1])()
f = join(0, a, b, c, d, e)
f = self.join_op(0, a, b, c, d, e)
fb = f.type.broadcastable
assert not fb[0] and fb[1] and fb[2] and fb[3] and not fb[4] and fb[5]
g = join(1, a, b, c, d, e)
g = self.join_op(1, a, b, c, d, e)
gb = g.type.broadcastable
assert gb[0] and not gb[1] and gb[2] and gb[3] and not gb[4] and gb[5]
h = join(4, a, b, c, d, e)
h = self.join_op(4, a, b, c, d, e)
hb = h.type.broadcastable
assert hb[0] and hb[1] and hb[2] and hb[3] and not hb[4] and hb[5]
f = function([a, b, c, d, e], f, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [True for node in topo if isinstance(node.op, self.join_op)]
assert [True for node in topo
if isinstance(node.op, type(self.join_op))]
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 1, 1, 1, 2, 1).astype(self.floatX)
......@@ -3710,7 +3721,7 @@ class T_Join_and_Split(unittest.TestCase):
dtype=self.floatX)
# Test dim 0
z = join(0, x1, x2, x3)
z = self.join_op(0, x1, x2, x3)
f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
topo = f.maker.fgraph.toposort()
......@@ -3719,10 +3730,10 @@ class T_Join_and_Split(unittest.TestCase):
if theano.config.mode != 'FAST_COMPILE':
for node in f.maker.fgraph.toposort():
assert not isinstance(node.op, tensor.Join)
assert not isinstance(node.op, type(self.join_op))
# Test dim 1
z = join(1, x1, x2, x3)
z = self.join_op(1, x1, x2, x3)
f = theano.function([x1, x2, x3], z.shape, mode=self.mode)
topo = f.maker.fgraph.toposort()
......@@ -3731,7 +3742,7 @@ class T_Join_and_Split(unittest.TestCase):
if theano.config.mode != 'FAST_COMPILE':
for node in topo:
assert not isinstance(node.op, tensor.Join)
assert not isinstance(node.op, type(self.join_op))
# Test hide error
if not self.hide_error:
......@@ -3757,8 +3768,8 @@ class T_Join_and_Split(unittest.TestCase):
f = function([], Tout, mode=self.mode)
out = f()
if theano.config.mode != 'FAST_COMPILE':
assert [True for node in f.maker.fgraph.toposort() if isinstance(
node.op, self.join_op)]
assert [True for node in f.maker.fgraph.toposort()
if isinstance(node.op, type(self.join_op))]
assert numpy.allclose(out,
numpy.concatenate([T_shared.get_value(),
T_shared.get_value()]))
......@@ -3767,7 +3778,7 @@ class T_Join_and_Split(unittest.TestCase):
rng = numpy.random.RandomState(seed=utt.fetch_seed())
v = self.shared(rng.rand(4).astype(self.floatX))
m = self.shared(rng.rand(4, 4).astype(self.floatX))
self.assertRaises(TypeError, self.join_op(), 0, v, m)
self.assertRaises(TypeError, self.join_op, 0, v, m)
def test_split_0elem(self):
rng = numpy.random.RandomState(seed=utt.fetch_seed())
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论