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提交 86b4471c authored 作者: Frederic Bastien's avatar Frederic Bastien
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remove duplicate import with 2 names.

上级 73131c59
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正在显示 包含 107 行增加 和 108 行删除
theano/tensor/tests/test_opt.py
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......@@ -19,7 +19,6 @@ from theano.tensor.opt import Shape_i
from theano.tensor import scalar, iscalar, dscalar, lscalar, vectors, lvector, fvector, dvector, fmatrix, dmatrix, matrices, fmatrices, dmatrices, Subtensor, as_tensor_variable, Join, join
from theano import tensor #do not use, there is an import * below that hides it
from theano import tensor as TT #ugly but works for now...
from theano import tensor as T #ugly but works for now...
from theano.tensor import TensorType, inplace
from theano.gof import Env
......@@ -360,12 +359,12 @@ class test_canonize(unittest.TestCase):
if sym_inputs[0].broadcastable[0]:
assert len(topo)==2
assert isinstance(topo[0].op, Shape_i)
assert isinstance(topo[1].op, TT.Alloc)
assert isinstance(topo[1].op, tensor.Alloc)
else:
assert len(topo)==3
assert isinstance(topo[0].op, Shape_i)
assert isinstance(topo[1].op, Shape_i)
assert isinstance(topo[2].op, TT.Alloc)
assert isinstance(topo[2].op, tensor.Alloc)
assert(out_dtype==out.dtype)
#test (x * y) / x -> y
......@@ -392,7 +391,7 @@ class test_canonize(unittest.TestCase):
if topo and not(len(topo)==1 and topo[0].op==theano.compile.function_module.deep_copy_op):
for node in topo[:-1]:
assert isinstance(node.op, Shape_i)
assert isinstance(topo[-1].op, TT.Alloc)
assert isinstance(topo[-1].op, tensor.Alloc)
#test x / y / x -> 1 / y
for id,(g, sym_inputs, val_inputs, nb_elemwise, out_dtype) in enumerate([
......@@ -1147,16 +1146,16 @@ def test_log_add():
#TODO: (write and) test that the optimization works with Sum in addition to working with Add.
def test_local_useless_subtensor():
x = TT.matrix('x')
x = tensor.matrix('x')
# Test default
for dims in [(slice(0,None),),
(slice(0,None),slice(0,None)),
]:
f = function([x], TT.exp(x).__getitem__(dims), mode=mode_opt)
f = function([x], tensor.exp(x).__getitem__(dims), mode=mode_opt)
#theano.printing.debugprint(f)
prog=f.maker.env.toposort()
assert prog[0].op == TT.exp
assert prog[0].op == tensor.exp
assert len(prog)==1
f([[0,1,2],[3,4,5]]) # let debugmode test something
......@@ -1171,12 +1170,12 @@ def test_local_useless_subtensor():
((slice(0,1),slice(0,None)), False),
((slice(0,1),1), False),
]:
f = function([x], TT.exp(x_c).__getitem__(dims), mode=mode_opt)
f = function([x], tensor.exp(x_c).__getitem__(dims), mode=mode_opt)
#theano.printing.debugprint(f)
prog=f.maker.env.toposort()
if res:
assert isinstance(prog[0].op, theano.tensor.basic.SpecifyShape), dims
assert prog[1].op == TT.exp, dims
assert prog[1].op == tensor.exp, dims
assert len(prog)==2, dims
else:
assert any([isinstance(node.op, Subtensor) for node in prog])
......@@ -1193,11 +1192,11 @@ def test_local_useless_subtensor():
((slice(0,x.shape[1]),2), False),
((slice(0,x.shape[1]),slice(x.shape[0]-x.shape[0],x.shape[1]),), False),
]):
f = function([x], TT.exp(x).__getitem__(dims), mode=mode_opt)
f = function([x], tensor.exp(x).__getitem__(dims), mode=mode_opt)
#theano.printing.debugprint(f)
prog=f.maker.env.toposort()
if res:
assert prog[0].op == TT.exp, dims
assert prog[0].op == tensor.exp, dims
assert len(prog)==1, dims
else:
assert any([isinstance(node.op, Subtensor) for node in prog])
......@@ -1208,11 +1207,11 @@ def test_local_useless_subtensor():
((slice(0,x.shape[0]),slice(0,3)), False),
((slice(0,3),slice(0,x.shape[1])), False),
]):
f = function([x], TT.exp(x_c).__getitem__(dims), mode=mode_opt)
f = function([x], tensor.exp(x_c).__getitem__(dims), mode=mode_opt)
#theano.printing.debugprint(f)
prog=f.maker.env.toposort()
if res:
assert prog[0].op == TT.exp, dims
assert prog[0].op == tensor.exp, dims
assert len(prog)==1, dims
else:
assert any([isinstance(node.op, Subtensor) for node in prog])
......@@ -1223,54 +1222,54 @@ class test_local_subtensor_lift(unittest.TestCase):
def test0(self):
# basic test that the Op works
x = TT.matrix('x')
f = function([x], TT.exp(x)[0], mode=mode_opt)
x = tensor.matrix('x')
f = function([x], tensor.exp(x)[0], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[0].op, TT.Subtensor) #first subtensor
assert prog[1].op == TT.exp
assert isinstance(prog[0].op, tensor.Subtensor) #first subtensor
assert prog[1].op == tensor.exp
assert len(prog)==2
f([[0,1],[2,3]]) # let debugmode test something
def test0b(self):
# as test0, but we reuse the output of the elemwise
# So we should not lift the subtensor
x = TT.matrix('x')
f = function([x], [TT.exp(x)[0], TT.exp(x)], mode=mode_opt)
x = tensor.matrix('x')
f = function([x], [tensor.exp(x)[0], tensor.exp(x)], mode=mode_opt)
prog=f.maker.env.toposort()
assert prog[0].op == TT.exp
assert isinstance(prog[1].op, TT.Subtensor) #first subtensor
assert prog[0].op == tensor.exp
assert isinstance(prog[1].op, tensor.Subtensor) #first subtensor
assert isinstance(prog[2].op, theano.compile.function_module.DeepCopyOp)
assert len(prog)==3
f([[0,1],[2,3]]) # let debugmode test something
def test1(self):
# basic test that the optimization work with scalar broadcasted
x = TT.matrix('x')
y = TT.scalar('y')
z = TT.matrix('z')
f = function([x,y,z], TT.exp(x+y+z)[0], mode=mode_opt)
x = tensor.matrix('x')
y = tensor.scalar('y')
z = tensor.matrix('z')
f = function([x,y,z], tensor.exp(x+y+z)[0], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[1].op, TT.DimShuffle)
assert isinstance(prog[0].op, TT.Subtensor) #first subtensor
assert isinstance(prog[2].op, TT.Subtensor) #first subtensor
assert isinstance(prog[1].op, tensor.DimShuffle)
assert isinstance(prog[0].op, tensor.Subtensor) #first subtensor
assert isinstance(prog[2].op, tensor.Subtensor) #first subtensor
assert isinstance(prog[3].op.scalar_op, theano.scalar.Composite)#Composite{add,add}
assert len(prog)==4
f([[0,1],[2,3]], 4, [[4,5],[6,7]]) # let debugmode test something
def test2(self):
# as 1, but take a slice
x = TT.matrix('x')
y = TT.scalar('y')
z = TT.matrix('z')
f = function([x,y,z], TT.exp(x+y+z)[0:2], mode=mode_opt)
x = tensor.matrix('x')
y = tensor.scalar('y')
z = tensor.matrix('z')
f = function([x,y,z], tensor.exp(x+y+z)[0:2], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[1].op, TT.DimShuffle)
assert isinstance(prog[0].op, TT.Subtensor) #first subtensor
assert isinstance(prog[2].op, TT.Subtensor) #first subtensor
assert isinstance(prog[1].op, tensor.DimShuffle)
assert isinstance(prog[0].op, tensor.Subtensor) #first subtensor
assert isinstance(prog[2].op, tensor.Subtensor) #first subtensor
assert isinstance(prog[3].op.scalar_op, theano.scalar.Composite)#Composite{add,add}
assert len(prog)==4
f([[0,1],[2,3]], 4, [[4,5],[6,7]]) # let debugmode test something
......@@ -1278,13 +1277,13 @@ class test_local_subtensor_lift(unittest.TestCase):
def test3(self):
# basic test that the optimization does work with broadcasting
# for unary elemwise.
y = TT.vector('y')
f = function([y], TT.exp(y.dimshuffle(0,'x'))[0], mode=mode_opt)
y = tensor.vector('y')
f = function([y], tensor.exp(y.dimshuffle(0,'x'))[0], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[0].op, TT.DimShuffle)
assert isinstance(prog[1].op, TT.Subtensor)
assert prog[2].op == TT.exp
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op, tensor.Subtensor)
assert prog[2].op == tensor.exp
assert len(prog)==3
f([4,5]) # let debugmode test something
......@@ -1292,14 +1291,14 @@ class test_local_subtensor_lift(unittest.TestCase):
# basic test that the optimization doesn't work with broadcasting
# ... It *could* be extended to,
# ... but right now it doesn't, so it shouldn't try.
x = TT.matrix('x')
y = TT.vector('y')
f = function([x,y], TT.exp(x+y)[0], mode=mode_opt)
x = tensor.matrix('x')
y = tensor.vector('y')
f = function([x,y], tensor.exp(x+y)[0], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[0].op, TT.DimShuffle)
assert prog[1].op == TT.add
assert isinstance(prog[2].op, TT.Subtensor) #first subtensor
assert isinstance(prog[0].op, tensor.DimShuffle)
assert prog[1].op == tensor.add
assert isinstance(prog[2].op, tensor.Subtensor) #first subtensor
assert prog[3].op == inplace.exp_inplace
assert len(prog)==4
f([[0,1],[2,3]], [4,5]) # let debugmode test something
......@@ -1307,15 +1306,15 @@ class test_local_subtensor_lift(unittest.TestCase):
def test5(self):
# test that we don't lift when we reuse the output of the
# elemwise for other computation.
x = TT.matrix('x')
y = TT.vector('y')
f = function([x,y], [TT.exp(x+y)[0],TT.exp(x+y)+x], mode=mode_opt)
x = tensor.matrix('x')
y = tensor.vector('y')
f = function([x,y], [tensor.exp(x+y)[0],tensor.exp(x+y)+x], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[0].op, TT.DimShuffle)
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op.scalar_op, theano.scalar.Composite)#Composite{add,exp}
assert prog[2].op == TT.add
assert isinstance(prog[3].op, TT.Subtensor) #first subtensor
assert prog[2].op == tensor.add
assert isinstance(prog[3].op, tensor.Subtensor) #first subtensor
assert len(prog)==4
f([[0,1],[2,3]], [4,5]) # let debugmode test something
......@@ -1324,12 +1323,12 @@ class test_local_subtensor_lift(unittest.TestCase):
# and a scalar as output (no broadcasting of the scalar needed).
# The optimization used to fail and display an ERROR message.
x = TT.vector('x')
y = TT.scalar('y')
f = function([x,y], TT.exp(x+y)[0], mode=mode_opt)
x = tensor.vector('x')
y = tensor.scalar('y')
f = function([x,y], tensor.exp(x+y)[0], mode=mode_opt)
prog=f.maker.env.toposort()
assert isinstance(prog[0].op, TT.Subtensor)
assert isinstance(prog[0].op, tensor.Subtensor)
# Composite{add,exp}
assert isinstance(prog[1].op.scalar_op, theano.scalar.Composite)
assert len(prog)==2
......@@ -1343,13 +1342,13 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_const(self):
# var[const::][-1] -> var[-1]
x = TT.matrix('x')
x = tensor.matrix('x')
for idx in range(-7,6):
f = function([x], x[idx::][-1], mode=mode_opt)
g = function([x], x[idx::][-1], mode=mode_opt.excluding('local_subtensor_merge'))
topo=f.maker.env.toposort()
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
for x_s in self.x_shapes:
......@@ -1365,15 +1364,15 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_scalar(self):
# var[int::][-1] -> var[-1]
x = TT.matrix('x')
y = TT.iscalar('y')
x = tensor.matrix('x')
y = tensor.iscalar('y')
f = function([x,y], x[y::][-1], mode=mode_opt)
g = function([x,y], x[y::][-1], mode=mode_opt.excluding('local_subtensor_merge'))
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1390,15 +1389,15 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_const2(self):
# var[::-1][const] -> var[-1]
x = TT.matrix('x')
x = tensor.matrix('x')
for idx in range(-8,7):
f = function([x], x[::-1][idx], mode=mode_opt)
g = function([x], x[::-1][idx], mode=mode_opt.excluding('local_subtensor_merge'))
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1414,15 +1413,15 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_scalar2(self):
# var[::-1][int] -> var[-1]
x = TT.matrix('x')
y = TT.iscalar('y')
x = tensor.matrix('x')
y = tensor.iscalar('y')
f = function([x,y], x[::-1][y], mode=mode_opt)
g = function([x,y], x[::-1][y], mode=mode_opt.excluding('local_subtensor_merge'))
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1438,14 +1437,14 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_const3(self):
# var[::-1][:const] -> var[-1]
x = TT.matrix('x')
x = tensor.matrix('x')
for idx in range(-9,8):
f = function([x], x[::-1][:idx], mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1455,14 +1454,14 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_scalar3(self):
# var[::-1][:int] -> var[-1]
x = TT.matrix('x')
y = TT.iscalar('y')
x = tensor.matrix('x')
y = tensor.iscalar('y')
f = function([x,y], x[::-1][:y], mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1473,15 +1472,15 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_const4(self):
# var[const1::][:const2]
x = TT.matrix('x')
x = tensor.matrix('x')
for idx1 in range(-7,7):
for idx2 in range(-7,7):
f = function([x], x[idx1:][:idx2], mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1491,15 +1490,15 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_scalar4(self):
# var[int1:][:int2]
x = TT.matrix('x')
y = TT.iscalar('y')
z = TT.iscalar('y')
x = tensor.matrix('x')
y = tensor.iscalar('y')
z = tensor.iscalar('y')
f = function([x,y,z], x[y:][:z], mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -1516,7 +1515,7 @@ class test_local_subtensor_merge(unittest.TestCase):
((12, 1), (None, None, -4), (None, None, 1)),
((5,3), (1, 4, 2), (None, None, -1)),
]
x = TT.matrix('x')
x = tensor.matrix('x')
for shape, sl1, sl2 in cases:
z = x[slice(*sl1)][slice(*sl2)]
......@@ -1529,19 +1528,19 @@ class test_local_subtensor_merge(unittest.TestCase):
def test_scalar5(self):
# var[int1:][:int2]
x = TT.matrix('x')
b1 = TT.iscalar('b1')
e1 = TT.iscalar('e1')
s1 = TT.iscalar('s1')
b2 = TT.iscalar('b2')
e2 = TT.iscalar('e2')
s2 = TT.iscalar('s2')
x = tensor.matrix('x')
b1 = tensor.iscalar('b1')
e1 = tensor.iscalar('e1')
s1 = tensor.iscalar('s1')
b2 = tensor.iscalar('b2')
e2 = tensor.iscalar('e2')
s2 = tensor.iscalar('s2')
f = function([x,b1,e1,s1,b2,e2,s2], x[b1:e1:s1][b2:e2:s2], mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo=f.maker.env.toposort()
#print [t for t in topo if isinstance(t.op, TT.Subtensor)]
assert len([t for t in topo if isinstance(t.op, TT.Subtensor)]) == 1
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, theano.compile.function_module.DeepCopyOp)
......@@ -2694,7 +2693,7 @@ def test_make_vector():
def test_local_join_1():
#test for vector
a = TT.vector('a')
a = tensor.vector('a')
s = tensor.stack(a)
f = function([a], s, mode=mode_opt)
val = f([1])
......@@ -2704,7 +2703,7 @@ def test_local_join_1():
assert f.maker.env.outputs[0].dtype == config.floatX
#test for matrix join(0,a)
a = TT.matrix('a')
a = tensor.matrix('a')
s = join(0,a)
f = function([a], s, mode=mode_opt)
val = f([[1]])
......@@ -2745,13 +2744,13 @@ def test_local_mul_to_neg():
def test_local_add_specialize():
# test of non-zero dimension
a = TT.vector()
s = TT.add(TT.zeros_like(a))
a = tensor.vector()
s = tensor.add(tensor.zeros_like(a))
assert local_add_specialize.transform(s.owner)
# test of 0-d
a = TT.scalar()
s = TT.add(TT.zeros_like(a))
a = tensor.scalar()
s = tensor.add(tensor.zeros_like(a))
assert local_add_specialize.transform(s.owner)
def test_local_tensor_scalar_tensor():
......@@ -2764,14 +2763,14 @@ def test_local_tensor_scalar_tensor():
for dtype in dtypes:
t_type = TensorType(dtype=dtype, broadcastable=())
t = t_type()
s = TT.scalar_from_tensor(t)
t2 = TT.tensor_from_scalar(s)
s = tensor.scalar_from_tensor(t)
t2 = tensor.tensor_from_scalar(s)
f = function([t], t2, mode=mode_opt)
e = f.maker.env.toposort()
cast_nodes = [n for n in e
if isinstance(n.op, (TT.TensorFromScalar,
TT.ScalarFromTensor))]
if isinstance(n.op, (tensor.TensorFromScalar,
tensor.ScalarFromTensor))]
assert len(cast_nodes) == 0
f(0)
......@@ -2790,14 +2789,14 @@ def test_local_scalar_tensor_scalar():
for dtype in dtypes:
s_type = theano.scalar.Scalar(dtype=dtype)
s = s_type()
t = TT.tensor_from_scalar(s)
s2 = TT.scalar_from_tensor(t)
t = tensor.tensor_from_scalar(s)
s2 = tensor.scalar_from_tensor(t)
f = function([s], s2, mode=mode_opt)
e = f.maker.env.toposort()
cast_nodes = [n for n in e
if isinstance(n.op, (TT.TensorFromScalar,
TT.ScalarFromTensor))]
if isinstance(n.op, (tensor.TensorFromScalar,
tensor.ScalarFromTensor))]
assert len(cast_nodes) == 0
f(0)
......
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