Skip to content

P
pytensor
提交 799e97dd authored 作者: abergeron's avatar abergeron
浏览文件
操作

Merge pull request #1920 from nouiz/fix_tests

Fix tests in buildbot and memory leak with allow_gc=False
上级 d82eb54a 65b1d1ed
隐藏空白字符变更
内嵌 并排
正在显示 包含 75 行增加 和 123 行删除
theano/compile/tests/test_profiling.py
浏览文件 @ 799e97dd
......@@ -6,26 +6,33 @@ import theano
import theano.tensor as T
import StringIO
def test_profiling():
old1 = theano.config.profile
old2 = theano.config.profile_memory
theano.config.profile = True
theano.config.profile_memory = True
x = T.dvector("x")
y = T.dvector("y")
z = x + y
f = theano.function([x, y], z, profile=True, name="test_profiling")
output = f([1, 2, 3, 4],[1, 1, 1, 1])
buf = StringIO.StringIO()
f.profile.summary(buf)
theano.config.profile = old1
theano.config.profile_memory = old2
old1 = theano.config.profile
old2 = theano.config.profile_memory
try:
theano.config.profile = True
theano.config.profile_memory = True
x = T.dvector("x")
y = T.dvector("y")
z = x + y
p = theano.ProfileStats(False)
if theano.config.mode in ["DebugMode", "DEBUG_MODE"]:
m = "FAST_RUN"
else:
m = None
f = theano.function([x, y], z, profile=p, name="test_profiling",
mode=m)
output = f([1, 2, 3, 4], [1, 1, 1, 1])
buf = StringIO.StringIO()
f.profile.summary(buf)
finally:
theano.config.profile = old1
theano.config.profile_memory = old2
if __name__ == '__main__':
test_profiling()
\ No newline at end of file
test_profiling()
theano/gof/vm.py
浏览文件 @ 799e97dd
......@@ -305,7 +305,7 @@ class Stack(VM):
t0 = time.time()
rval = self.thunks[idx]()
self.node_executed_order.append(node)
# Some thunks on some computers run faster than the granularity
# of the time.time clock.
# Profile output looks buggy if a node has run but takes 0 time.
......@@ -313,11 +313,11 @@ class Stack(VM):
dt = max(time.time() - t0, 1e-10)
if self.callback is not None:
self.callback(
node=node,
thunk=self.thunks[idx],
storage_map=self.storage_map,
compute_map=self.compute_map,
)
node=node,
thunk=self.thunks[idx],
storage_map=self.storage_map,
compute_map=self.compute_map,
)
return rval, dt
def __call__(self):
......@@ -327,7 +327,7 @@ class Stack(VM):
dependencies = self.dependencies
self.node_executed_order = []
self.node_cleared_order = []
for k in self.storage_map:
compute_map[k][0] = (k.owner is None)
......
theano/sandbox/cuda/basic_ops.py
浏览文件 @ 799e97dd
......@@ -3289,7 +3289,7 @@ class GpuContiguous(GpuOp):
Py_INCREF(%(z)s);
} else if ((NULL == %(z)s)""" % locals()
for i in xrange(len(node.inputs[0].type.broadcastable)):
for i in xrange(node.inputs[0].type.ndim):
str += "\n|| (CudaNdarray_HOST_DIMS(%(input)s)[%(i)s] != CudaNdarray_HOST_DIMS(%(z)s)[%(i)s])" % locals()
str += """
|| !CudaNdarray_is_c_contiguous(%(z)s))
......
theano/tensor/opt.py
浏览文件 @ 799e97dd
......@@ -1409,12 +1409,13 @@ class Assert(T.Op):
check = "\n".join(check)
return """
%(check)s
Py_XDECREF(%(out)s);
%(out)s = %(value)s;
Py_INCREF(%(value)s);
""" % locals()
def c_code_cache_version(self):
return (1, 0)
return (1, 1)
def infer_shape(self, node, input_shapes):
return [input_shapes[0]]
......
theano/tensor/tests/test_gc.py
浏览文件 @ 799e97dd
......@@ -20,30 +20,35 @@ def test_no_reuse():
return
assert not 'should not get here'
def test_gc_never_pickles_temporaries():
x = T.dvector()
#print >> sys.stderr, 'BUILDING GRAPH'
for i in xrange(2): #TODO: 30 causes like LONG compilation due to MERGE
if i :
for i in xrange(2): # TODO: 30 causes like LONG compilation due to MERGE
if i:
r = r + r/10
else:
r = x
optimizer=None
optimizer='fast_run'
optimizer = None
optimizer = 'fast_run'
for f_linker, g_linker in [
(theano.PerformLinker(allow_gc = True), theano.PerformLinker(allow_gc=False)),
(theano.OpWiseCLinker(allow_gc = True), theano.OpWiseCLinker(allow_gc=False))]:
(theano.PerformLinker(allow_gc=True),
theano.PerformLinker(allow_gc=False)),
(theano.OpWiseCLinker(allow_gc=True),
theano.OpWiseCLinker(allow_gc=False))]:
#f_linker has garbage collection
#g_linker has no garbage collection
#print >> sys.stderr, 'COMPILING'
f = theano.function([x], r,mode=theano.Mode(optimizer=optimizer, linker=f_linker))
g = theano.function([x], r,mode=theano.Mode(optimizer=optimizer, linker=g_linker))
f = theano.function([x], r, mode=theano.Mode(optimizer=optimizer,
linker=f_linker))
g = theano.function([x], r, mode=theano.Mode(optimizer=optimizer,
linker=g_linker))
len_pre_f = len(cPickle.dumps(f))
len_pre_g = len(cPickle.dumps(g))
......@@ -55,21 +60,20 @@ def test_gc_never_pickles_temporaries():
def a(fn):
return len(cPickle.dumps(fn.maker))
assert a(f) == a(f) # some sanity checks on the pickling mechanism
assert a(g) == a(g) # some sanity checks on the pickling mechanism
assert a(f) == a(f) # some sanity checks on the pickling mechanism
assert a(g) == a(g) # some sanity checks on the pickling mechanism
def b(fn):
return len(
cPickle.dumps(
theano.compile.function_module._pickle_Function(
fn)))
assert b(f) == b(f) # some sanity checks on the pickling mechanism
cPickle.dumps(
theano.compile.function_module._pickle_Function(
fn)))
assert b(f) == b(f) # some sanity checks on the pickling mechanism
def c(fn):
return len(cPickle.dumps(fn))
assert c(f) == c(f) # some sanity checks on the pickling mechanism
assert c(g) == c(g) # some sanity checks on the pickling mechanism
assert c(f) == c(f) # some sanity checks on the pickling mechanism
assert c(g) == c(g) # some sanity checks on the pickling mechanism
# now run the function once to create temporaries within the no-gc
# linker
......@@ -86,28 +90,32 @@ def test_gc_never_pickles_temporaries():
# allow_gc should leave the function un-changed by calling
assert len_pre_f == len_post_f
#assert that g() didn't cause g to grow
# because temporaries that weren't collected shouldn't be pickled anyway
#assert that g() didn't cause g to grow because temporaries
# that weren't collected shouldn't be pickled anyway
assert len_post_f == len_post_g, (f_linker, len_post_f, len_post_g)
def test_merge_opt_runtime():
"""In the original merge optimization, the following graph took like caused the MERGE
optimizer to exhibit really bad performance (quadratic? exponential?)
"""In the original merge optimization, the following graph took
like caused the MERGE optimizer to exhibit really bad performance
(quadratic? exponential?)
Ironically, there is actually no merging to do in this graph.
"""
x = T.dvector()
for i in xrange(50):
if i :
if i:
r = r + r/10
else:
r = x
t = time.time()
f = theano.function([x], r, mode='FAST_COMPILE')
# FAST_RUN does in-place optimizer which requires a lot of toposorting, which is actually
# pretty slow at the moment. This test was designed to test MergeOptimizer... so I'm
# leaving toposort optimizations for a later date.
# FAST_RUN does in-place optimizer which requires a lot of
# toposorting, which is actually pretty slow at the moment. This
# test was designed to test MergeOptimizer... so I'm leaving
# toposort optimizations for a later date.
dt = time.time() - t
assert dt < 5.0 #it should never take longer than 5 seconds to compile this graph
# it should never take longer than 5 seconds to compile this graph
assert dt < 5.0
theano/tensor/tests/test_opt.py
浏览文件 @ 799e97dd
......@@ -502,9 +502,6 @@ class test_canonize(unittest.TestCase):
assert(out_dtype == out.dtype)
assert numpy.allclose(out, val_inputs[1])
topo = f.maker.fgraph.toposort()
print "ID TOPO", id, topo, sym_inputs
for r, t in f.maker.fgraph.shape_feature.shape_of.items():
print ' ', r, t
if topo and not(len(topo)==1 and topo[0].op==deep_copy_op):
for node in topo[:-1]:
assert isinstance(node.op, Shape_i)
......@@ -528,7 +525,6 @@ class test_canonize(unittest.TestCase):
out = f(*val_inputs)
assert numpy.allclose(out, (1 / val_inputs[1]))
topo = f.maker.fgraph.toposort()
print topo
elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
assert len(elem) == nb_elemwise
assert isinstance(elem[0].op, (T.Elemwise, ))
......@@ -727,7 +723,6 @@ class test_canonize(unittest.TestCase):
assert numpy.allclose(out, val_inputs[0] /
val_inputs[1] / val_inputs[2])
topo = f.maker.fgraph.toposort()
print topo
assert len(topo) == 2
assert isinstance(topo[0].op, (T.Elemwise, ))
assert isinstance(topo[0].op.scalar_op,
......@@ -746,7 +741,6 @@ class test_canonize(unittest.TestCase):
assert numpy.allclose(out, val_inputs[0] / (
val_inputs[1] / val_inputs[2]))
topo = f.maker.fgraph.toposort()
print topo
assert len(topo) == 2
assert isinstance(topo[0].op, (T.Elemwise, ))
assert isinstance(topo[0].op.scalar_op,
......@@ -798,13 +792,11 @@ def test_local_merge_abs():
f = theano.function([y, z], (abs(y * z * -2)), mode=mode)
f(y_val, z_val)
theano.printing.debugprint(f)
assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
assert len(f.maker.fgraph.toposort()) == 2
f = theano.function([x, y], abs(x / y), mode=mode)
f(x_val, y_val)
theano.printing.debugprint(f)
assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
assert len(f.maker.fgraph.toposort()) == 2
......@@ -1511,17 +1503,13 @@ def test_log1p():
# check trickier cases (and use different dtype)
y = fmatrix()
f = function([x, y], T.log(tensor.fill(y, 1) + (x)), mode=m)
print f.maker.fgraph.toposort()
# the first three ops are Shape_i, Shape_i, and Dimshuffle
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
T.log1p, tensor.alloc]
f = function([x, y], T.log(0 + (x) + tensor.fill(y, 1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
T.log1p, tensor.alloc]
f = function([x, y], T.log(2 + (x) - tensor.fill(y, 1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] \
== [T.log1p, tensor.alloc]
......@@ -1533,14 +1521,12 @@ def test_log1p():
# I was never sure if this optimization should work on complex numbers or not.
z = tensor.zmatrix()
f = function([z], T.log(1 + (z)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
if 1:
# should work for int
z = tensor.imatrix()
f = function([z], T.log(1 + (z)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
......@@ -1559,14 +1545,12 @@ def test_log_add():
y = dvector()
f = function([x, y], T.log(T.exp(x) + T.exp(y)), mode=m)
theano.printing.debugprint(f)
print f([10000], [10000]) # causes overflow if handled incorrectly
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.isfinite(f([10000], [10000]))
assert numpy.allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
#test that it give the same result when it don't overflow
print f([10], [10]) # don't causes overflow
f([10], [10]) # don't causes overflow
assert numpy.allclose(f([10], [10]), 10 + numpy.log1p(1))
# test that it also works with more than two args, (this currently fails)
......@@ -1574,10 +1558,9 @@ def test_log_add():
y = dvector()
f = function([x, y], T.log(T.exp(x) + T.exp(y) + T.exp(x - y) + T.exp(
x + y)), mode=m)
theano.printing.debugprint(f)
try:
print f([10000], [10000]) # causes overflow if handled incorrectly
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.allclose(f([10000], [10000]), 20000)
except AssertionError:
raise KnownFailureTest(('log(add(exp)) is not stabilized when adding '
......@@ -2192,8 +2175,8 @@ class test_local_subtensor_merge(unittest.TestCase):
n_ok += 1
f(x_val, b_v, e_v, s_v, i_v)
print 'shape: %s' % (x_s,)
print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
#print 'shape: %s' % (x_s,)
#print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
@attr('slow')
def test_none_slice(self):
......@@ -2873,41 +2856,30 @@ def test_local_mul_specialize():
f = function([v], v * 1, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
nodes == [deep_copy_op]
f = function([v], v * 0, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
assert nodes == [Shape_i(0), T.alloc]
f = function([v], v * (-1), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
assert nodes == [T.neg]
f = function([v, m], v * 1 * (-m), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
theano.printing.debugprint(f)
assert nodes == [T.mul]
f = function([v, m], v * 0 * (-m), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
theano.printing.debugprint(f)
assert nodes == [Shape_i(0), T.alloc]
f = function([v, m], v * (-1) * (-m), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
theano.printing.debugprint(f)
assert nodes == [T.mul]
f = function([v, m], v * (-1) * m, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
print nodes
theano.printing.debugprint(f)
assert nodes == [T.mul]
......@@ -3078,7 +3050,6 @@ class T_useless_elemwise(unittest.TestCase):
f2 = theano.function([x], T.eq(x, x), mode=self.mode)
assert numpy.all(f2(vx) == numpy.ones((5, 4)))
topo2 = f2.maker.fgraph.toposort()
print topo2
#Shape_i{1}(<TensorType(float64, matrix)>), Shape_i{0}(<TensorType(float64, matrix)>), Alloc([[1]], Shape_i{0}.0, Shape_i{1}.0
assert len(topo2) == 3
assert isinstance(topo2[-1].op, T.Alloc)
......@@ -3097,7 +3068,6 @@ class T_useless_elemwise(unittest.TestCase):
f2 = theano.function([x], T.neq(x, x), mode=self.mode)
assert numpy.all(f2(vx) == numpy.zeros((5, 4)))
topo2 = f2.maker.fgraph.toposort()
print topo2
assert len(topo2) == 3
assert isinstance(topo2[-1].op, T.Alloc)
......@@ -3114,7 +3084,6 @@ class T_useless_elemwise(unittest.TestCase):
f2 = theano.function([x, y], T.mul(x, y), mode=self.mode)
assert numpy.all(f2(vx, vy) == vx * vy)
topo2 = f2.maker.fgraph.toposort()
print topo2
assert len(topo2) == 1
assert isinstance(topo2[0].op, T.Elemwise)
assert isinstance(topo2[0].op.scalar_op, theano.scalar.Mul)
......@@ -3132,7 +3101,6 @@ class T_useless_elemwise(unittest.TestCase):
f2 = theano.function([x, y], T.add(x, y), mode=self.mode)
assert numpy.all(f2(vx, vy) == vx + vy)
topo2 = f2.maker.fgraph.toposort()
print topo2
assert len(topo2) == 1
assert isinstance(topo2[0].op, T.Elemwise)
assert isinstance(topo2[0].op.scalar_op, theano.scalar.Add)
......@@ -3264,20 +3232,17 @@ class T_local_erf(unittest.TestCase):
x = T.vector()
f = theano.function([x], 1 + T.erf(x), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [
T.mul, T.erfc], f.maker.fgraph.toposort()
f(val)
f = theano.function([x], T.erf(x) + 1, mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [
T.mul, T.erfc], f.maker.fgraph.toposort()
f(val)
f = theano.function([x], T.erf(x) + 2, mode=self.mode)
topo = f.maker.fgraph.toposort()
print topo
assert len(topo) == 2
assert topo[0].op == T.erf
assert isinstance(topo[1].op, T.Elemwise)
......@@ -3290,26 +3255,22 @@ class T_local_erf(unittest.TestCase):
x = T.vector()
f = theano.function([x], 1 - T.erf(x), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], 1 + (-T.erf(x)), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], (-T.erf(x)) + 1, mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], 2 - T.erf(x), mode=self.mode)
topo = f.maker.fgraph.toposort()
print topo
assert len(topo) == 2, f.maker.fgraph.toposort()
assert topo[0].op == T.erf, f.maker.fgraph.toposort()
assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
......@@ -3323,23 +3284,19 @@ class T_local_erf(unittest.TestCase):
x = T.vector()
f = theano.function([x], T.erf(x) - 1, mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
print f(val)
f = theano.function([x], T.erf(x) + (-1), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
print f(val)
f = theano.function([x], -1 + T.erf(x), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
print f(val)
f = theano.function([x], T.erf(x) - 2, mode=self.mode)
topo = f.maker.fgraph.toposort()
print topo
assert len(topo) == 2
assert topo[0].op == T.erf
assert isinstance(topo[1].op, T.Elemwise)
......@@ -3366,20 +3323,17 @@ class T_local_erfc(unittest.TestCase):
x = T.vector('x')
f = theano.function([x], 1 - T.erfc(x), mode=self.mode)
theano.printing.debugprint(f)
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], (-T.erfc(x)) + 1, mode=self.mode)
theano.printing.debugprint(f)
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], 2 - T.erfc(x), mode=self.mode)
topo = f.maker.fgraph.toposort()
theano.printing.debugprint(f)
assert len(topo) == 2, f.maker.fgraph.toposort()
assert topo[0].op == T.erfc, f.maker.fgraph.toposort()
assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
......@@ -3394,19 +3348,16 @@ class T_local_erfc(unittest.TestCase):
x = T.vector('x')
f = theano.function([x], -1 + T.erfc(-x), mode=self.mode)
theano.printing.debugprint(f)
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], T.erfc(-x) - 1, mode=self.mode)
theano.printing.debugprint(f)
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
, f.maker.fgraph.toposort()
print f(val)
f = theano.function([x], T.erfc(-x) + (-1), mode=self.mode)
theano.printing.debugprint(f)
assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
, f.maker.fgraph.toposort()
print f(val)
......@@ -3427,13 +3378,11 @@ class T_local_erfc(unittest.TestCase):
mode_fusion.check_isfinite = False
f = theano.function([x], T.log(T.erfc(x)), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
assert all(numpy.isfinite(f(val)))
f = theano.function([x], T.log(T.erfc(-x)), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 24, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
assert all(numpy.isfinite(f(-val)))
......@@ -3470,7 +3419,6 @@ class T_local_erfc(unittest.TestCase):
mode_fusion.check_isfinite = False
f = theano.function([x], T.grad(T.log(T.erfc(x)).sum(), x), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
assert all(numpy.isfinite(f(val)))
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
......@@ -3478,14 +3426,12 @@ class T_local_erfc(unittest.TestCase):
#test with a different mul constant
f = theano.function([x], T.mul(T.exp(T.neg(T.sqr(x))), -
10.12837917) / T.erfc(x), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
assert all(numpy.isfinite(f(val)))
#test that we work without the mul
f = theano.function([x], T.exp(T.neg(T.sqr(x))) / T.erfc(x), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
assert all(numpy.isfinite(f(val)))
......@@ -3493,14 +3439,12 @@ class T_local_erfc(unittest.TestCase):
#test that we don't work if x!=y
f = theano.function([x, y], T.exp(T.neg(T.sqr(x))) / T.erfc(
y), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 5, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
f(val, val - 3)
#test that we work without the sqr and neg
f = theano.function([x], T.exp(T.mul(-1, x, x)) / T.erfc(x), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 22, len(f.maker.fgraph.apply_nodes)
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
assert all(numpy.isfinite(f(val)))
......@@ -3508,7 +3452,6 @@ class T_local_erfc(unittest.TestCase):
#test that it work correctly if x is x*2 in the graph.
f = theano.function([x], T.grad(T.log(T.erfc(2 * x)).sum(),
x), mode=mode)
#theano.printing.debugprint(f)
assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
assert numpy.isfinite(f(val)).all()
assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
......@@ -3587,7 +3530,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
z = theano.tensor.switch(1, x, y)
f = theano.function([x, y], z, mode=self.mode)
#theano.printing.debugprint(f)
assert len([node.op for node in f.maker.fgraph.toposort() if
isinstance(node.op, theano.tensor.Elemwise) and
not isinstance(node.op.scalar_op,theano.scalar.basic.Cast)]) == 0
......@@ -3597,7 +3539,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
z = theano.tensor.switch(0, x, y)
f = theano.function([x, y], z, mode=self.mode)
#theano.printing.debugprint(f)
assert len([node.op for node in f.maker.fgraph.toposort() if
isinstance(node.op, theano.tensor.Elemwise)]) == 0
vx = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='int32')
......@@ -3912,9 +3853,7 @@ class T_local_sum_dimshuffle(unittest.TestCase):
print i
f = theano.function([a, b, c, d], s, mode=self.mode,
on_unused_input='ignore')
theano.printing.debugprint(f)
g = f.maker.fgraph.toposort()
#print 'g =', g
assert isinstance(g[-1].op.scalar_op,
theano.scalar.basic.TrueDiv)
f(a_val, b_val, c_val, d_val)
......@@ -4157,8 +4096,6 @@ def test_local_div_to_inv():
denom_m = denom_s.dimshuffle('x', 'x')
out = num_v / denom_m
theano.printing.debugprint(out, print_type=True)
print out.broadcastable
assert numpy.all(out.broadcastable == (True, False))
f = theano.function([num_len_s, denom_s], out)
......
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