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提交 2d13ec42 authored 作者: Frederic Bastien's avatar Frederic Bastien
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Better test error message.

上级 9ba08fd0
隐藏空白字符变更
内嵌 并排
正在显示 包含 63 行增加 和 64 行删除
theano/tensor/tests/test_opt.py
浏览文件 @ 2d13ec42
......@@ -564,7 +564,7 @@ class test_canonize(unittest.TestCase):
mode=mode)
out = f(*val_inputs)
assert(out_dtype == out.dtype)
assert numpy.allclose(out, val_inputs[1])
utt.assert_allclose(out, val_inputs[1])
topo = f.maker.fgraph.toposort()
if topo and not(len(topo) == 1 and topo[0].op == deep_copy_op):
for node in topo[:-1]:
......@@ -587,7 +587,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (1 / val_inputs[1]))
utt.assert_allclose(out, (1 / val_inputs[1]))
topo = f.maker.fgraph.toposort()
elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
assert len(elem) == nb_elemwise
......@@ -612,7 +612,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (val_inputs[0] / val_inputs[3]))
utt.assert_allclose(out, (val_inputs[0] / val_inputs[3]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op, (T.Elemwise, ))
......@@ -636,7 +636,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, (0.5 *
utt.assert_allclose(out, (0.5 *
val_inputs[0] / val_inputs[1]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -662,7 +662,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0])
utt.assert_allclose(out, val_inputs[0])
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
topo[0].op == deep_copy_op
......@@ -681,7 +681,7 @@ class test_canonize(unittest.TestCase):
mode=mode)
out = f(*val_inputs)
assert numpy.all(numpy.isfinite(out))
assert numpy.allclose(out, numpy.sign(val_inputs[0]))
utt.assert_allclose(out, numpy.sign(val_inputs[0]))
assert(out_dtype == out.dtype)
assert len(f.maker.fgraph.toposort()) == 1
......@@ -705,7 +705,7 @@ class test_canonize(unittest.TestCase):
topo = f.maker.fgraph.toposort()
out = f(*val_inputs)
assert numpy.all(numpy.isfinite(out))
assert numpy.allclose(out, numpy.sign(val_inputs[0]) * 2 / 3)
utt.assert_allclose(out, numpy.sign(val_inputs[0]) * 2 / 3)
assert(out_dtype == out.dtype)
def test_abs_mul_div(self):
......@@ -781,7 +781,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0] /
utt.assert_allclose(out, val_inputs[0] /
val_inputs[1] / val_inputs[2])
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -799,7 +799,7 @@ class test_canonize(unittest.TestCase):
f = compile.function(list(sym_inputs), g,
mode=mode)
out = f(*val_inputs)
assert numpy.allclose(out, val_inputs[0] / (
utt.assert_allclose(out, val_inputs[0] / (
val_inputs[1] / val_inputs[2]))
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -905,7 +905,7 @@ def test_const_type_in_mul_canonizer():
betaval = numpy.random.rand(5)
aval = numpy.random.rand(5)
assert numpy.allclose(
utt.assert_allclose(
f2(ival, wval, visbval, hidbval, betaval, aval),
f1(ival, wval, visbval, hidbval, betaval, aval))
......@@ -1608,11 +1608,11 @@ def test_log_add():
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.isfinite(f([10000], [10000]))
assert numpy.allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
utt.assert_allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
# test that it give the same result when it don't overflow
f([10], [10]) # don't causes overflow
assert numpy.allclose(f([10], [10]), 10 + numpy.log1p(1))
utt.assert_allclose(f([10], [10]), 10 + numpy.log1p(1))
# test that it also works with more than two args, (this currently fails)
x = dvector()
......@@ -1622,7 +1622,7 @@ def test_log_add():
try:
f([10000], [10000]) # causes overflow if handled incorrectly
assert numpy.allclose(f([10000], [10000]), 20000)
utt.assert_allclose(f([10000], [10000]), 20000)
except AssertionError:
raise SkipTest("log(add(exp)) is not stabilized when adding "
"more than 2 elements, see #623")
......@@ -2734,7 +2734,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose(dy, res)
utt.assert_allclose(dy, res)
topo = f.maker.fgraph.toposort()
if opt:
assert len(topo) == 1
......@@ -2748,7 +2748,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose((dx[didx] + dy), res)
utt.assert_allclose((dx[didx] + dy), res)
topo = f.maker.fgraph.toposort()
len(topo) == 2
......@@ -2758,7 +2758,7 @@ class test_local_adv_sub1_adv_inc_sub1(unittest.TestCase):
f = theano.function([x, y, idx], o, self.mode_no_assert)
res = f(dx, dy, didx)
assert numpy.allclose(dy, res)
utt.assert_allclose(dy, res)
topo = f.maker.fgraph.toposort()
if opt:
assert len(topo) == 1
......@@ -4178,22 +4178,22 @@ def test_local_pow_specialize():
f = function([v], v ** 0, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [Shape_i(0), T.alloc]
assert numpy.allclose(f(val), val ** 0)
utt.assert_allclose(f(val), val ** 0)
f = function([v], v ** 1, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
nodes == [deep_copy_op]
assert numpy.allclose(f(val), val ** 1)
utt.assert_allclose(f(val), val ** 1)
f = function([v], v ** (-1), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.inv]
assert numpy.allclose(f(val_no0), val_no0 ** (-1))
utt.assert_allclose(f(val_no0), val_no0 ** (-1))
f = function([v], v ** 2, mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.sqr]
assert numpy.allclose(f(val), val ** 2)
utt.assert_allclose(f(val), val ** 2)
f = function([v], v ** (-2), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4201,12 +4201,12 @@ def test_local_pow_specialize():
assert nodes[0] == T.sqr
assert isinstance(nodes[1].scalar_op, theano.scalar.basic.Inv)
# assert nodes == [T.sqr,T.inv]#Why this don't work?
assert numpy.allclose(f(val_no0), val_no0 ** (-2))
utt.assert_allclose(f(val_no0), val_no0 ** (-2))
f = function([v], v ** (.5), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert nodes == [T.sqrt]
assert numpy.allclose(f(val), val ** (.5))
utt.assert_allclose(f(val), val ** (.5))
f = function([v], v ** (-.5), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4214,7 +4214,7 @@ def test_local_pow_specialize():
assert nodes[0] == T.sqrt
assert isinstance(nodes[1].scalar_op, theano.scalar.basic.Inv)
# assert nodes == [T.sqrt,T.inv]#Why this don't work?
assert numpy.allclose(f(val_no0), val_no0 ** (-.5))
utt.assert_allclose(f(val_no0), val_no0 ** (-.5))
def test_local_pow_specialize_device_more_aggressive_on_cpu():
......@@ -4232,7 +4232,7 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(nodes) == 1
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 6
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert numpy.allclose(f(val), val ** 15)
utt.assert_allclose(f(val), val ** 15)
f = function([v], v ** (-15), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4240,14 +4240,14 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 6
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert isinstance(nodes[-1].scalar_op, theano.scalar.basic.Inv)
assert numpy.allclose(f(val_no0), val_no0 ** (-15))
utt.assert_allclose(f(val_no0), val_no0 ** (-15))
f = function([v], v ** (16), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
assert len(nodes) == 1
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 4
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert numpy.allclose(f(val), val ** 16)
utt.assert_allclose(f(val), val ** 16)
f = function([v], v ** (-16), mode=mode)
nodes = [node.op for node in f.maker.fgraph.toposort()]
......@@ -4255,7 +4255,7 @@ def test_local_pow_specialize_device_more_aggressive_on_cpu():
assert len(f.maker.fgraph.toposort()[0].op.scalar_op.fgraph.apply_nodes) == 4
assert isinstance(nodes[0].scalar_op, theano.scalar.Composite)
assert isinstance(nodes[-1].scalar_op, theano.scalar.basic.Inv)
assert numpy.allclose(f(val_no0), val_no0 ** (-16))
utt.assert_allclose(f(val_no0), val_no0 ** (-16))
class T_Rebroadcast(unittest.TestCase):
......@@ -5146,26 +5146,26 @@ class T_local_sum_prod(unittest.TestCase):
# test sum
f = theano.function([a], a.sum(), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
# test prod
f = theano.function([a], a.prod(), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
# test sum
f = theano.function([a], a.sum([0, 1, 2]), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
# test prod
f = theano.function([a], a.prod([0, 1, 2]), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
backup = config.warn.sum_sum_bug
config.warn.sum_sum_bug = False
try:
f = theano.function([a], a.sum(0).sum(0).sum(0), mode=self.mode)
assert len(f.maker.fgraph.apply_nodes) == 1
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
finally:
config.warn.sum_sum_bug = backup
......@@ -5216,19 +5216,19 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in dims:
expected = my_sum(input, d, dd)
f = theano.function([a], a.sum(d).sum(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 1
for d, dd in dims[:6]:
f = theano.function([a], a.sum(d).sum(dd).
sum(0), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).sum(dd).sum(0))
utt.assert_allclose(f(input), input.sum(d).sum(dd).sum(0))
assert len(f.maker.fgraph.apply_nodes) == 1
for d in [0, 1, 2]:
f = theano.function([a], a.sum(d).sum(None), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).sum())
utt.assert_allclose(f(input), input.sum(d).sum())
assert len(f.maker.fgraph.apply_nodes) == 1
f = theano.function([a], a.sum(None).sum(), mode=self.mode)
assert numpy.allclose(f(input), input.sum())
utt.assert_allclose(f(input), input.sum())
assert len(f.maker.fgraph.apply_nodes) == 1
finally:
config.warn.sum_sum_bug = backup
......@@ -5237,41 +5237,40 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in dims:
expected = my_prod(input, d, dd)
f = theano.function([a], a.prod(d).prod(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 1
for d, dd in dims[:6]:
f = theano.function([a], a.prod(d).prod(dd).
prod(0), mode=self.mode)
assert numpy.allclose(f(input), input.prod(d).prod(dd).prod(0))
utt.assert_allclose(f(input), input.prod(d).prod(dd).prod(0))
assert len(f.maker.fgraph.apply_nodes) == 1
for d in [0, 1, 2]:
f = theano.function([a], a.prod(d).prod(None), mode=self.mode)
assert numpy.allclose(f(input), input.prod(d).prod())
utt.assert_allclose(f(input), input.prod(d).prod())
assert len(f.maker.fgraph.apply_nodes) == 1
f = theano.function([a], a.prod(None).prod(), mode=self.mode)
assert numpy.allclose(f(input), input.prod())
utt.assert_allclose(f(input), input.prod())
assert len(f.maker.fgraph.apply_nodes) == 1
# test sum prod don't get opt.
for d, dd in dims:
expected = my_sum_prod(input, d, dd)
f = theano.function([a], a.sum(d).prod(dd), mode=self.mode)
assert numpy.allclose(f(input), expected)
utt.assert_allclose(f(input), expected)
assert len(f.maker.fgraph.apply_nodes) == 2
for d, dd in dims[:6]:
f = theano.function([a], a.sum(d).prod(dd).
prod(0), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).prod(dd).prod(0))
utt.assert_allclose(f(input), input.sum(d).prod(dd).prod(0))
assert len(f.maker.fgraph.apply_nodes) == 2
for d in [0, 1, 2]:
f = theano.function([a], a.sum(d).prod(None), mode=self.mode)
assert numpy.allclose(f(input), input.sum(d).prod())
utt.assert_allclose(f(input), input.sum(d).prod())
assert len(f.maker.fgraph.apply_nodes) == 2
f = theano.function([a], a.sum(None).prod(), mode=self.mode)
assert numpy.allclose(f(input), input.sum())
utt.assert_allclosey(f(input), input.sum())
assert len(f.maker.fgraph.apply_nodes) == 1
def test_local_sum_prod_alloc(self):
a = T.dtensor3()
input = numpy.asarray(numpy.arange(2 * 3 * 4).reshape(2, 3, 4),
......@@ -5283,23 +5282,23 @@ class T_local_sum_prod(unittest.TestCase):
# test sum
f = theano.function([a], t_like(a).sum(None), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum())
utt.assert_allclose(f(input), n_like(input).sum())
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
f = theano.function([a], t_like(a).sum([0, 1, 2]), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum())
utt.assert_allclose(f(input), n_like(input).sum())
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
for d in xrange(3):
f = theano.function([a], t_like(a).sum(d), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum(d))
utt.assert_allclose(f(input), n_like(input).sum(d))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[1]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
assert not any([isinstance(node.op, T.Sum) for node in topo])
for i in xrange(3):
f = theano.function([a], t_like(a).sum(i), mode=mode)
assert numpy.allclose(f(input), n_like(input).sum(i))
utt.assert_allclose(f(input), n_like(input).sum(i))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[2]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
......@@ -5307,23 +5306,23 @@ class T_local_sum_prod(unittest.TestCase):
# test prod
f = theano.function([a], t_like(a).prod(None), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod())
utt.assert_allclose(f(input), n_like(input).prod())
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
f = theano.function([a], t_like(a).prod([0, 1, 2]), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod())
utt.assert_allclose(f(input), n_like(input).prod())
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[0]
for d in range(3):
f = theano.function([a], t_like(a).prod(d), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod(d))
utt.assert_allclose(f(input), n_like(input).prod(d))
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[1]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
assert not any([isinstance(node.op, T.elemwise.Prod) for node in topo])
for i in range(3):
f = theano.function([a], t_like(a).prod(i), mode=mode)
assert numpy.allclose(f(input), n_like(input).prod(i))
utt.assert_allclose(f(input), n_like(input).prod(i))
#assert len(f.maker.fgraph.apply_nodes) == nb_nodes[2]
topo = f.maker.fgraph.toposort()
assert topo[-1].op == T.alloc
......@@ -5335,7 +5334,7 @@ class T_local_sum_prod(unittest.TestCase):
for d, dd in [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1)]:
f = theano.function([a], t_like(a).
sum(d).sum(dd), mode=mode)
assert numpy.allclose(f(input),
utt.assert_allclose(f(input),
n_like(input).sum(d).sum(dd))
assert len(f.maker.fgraph.apply_nodes) == nb_nodes[3]
topo = f.maker.fgraph.toposort()
......@@ -5459,7 +5458,7 @@ class T_local_reduce(unittest.TestCase):
A = theano.shared(numpy.array([1, 2, 3, 4, 5], dtype='int64'))
f = theano.function([], T.sum(T.stack([A, A]), axis=0), mode=self.mode)
assert numpy.allclose(f(), [2, 4, 6, 8, 10])
utt.assert_allclose(f(), [2, 4, 6, 8, 10])
topo = f.maker.fgraph.toposort()
assert isinstance(topo[-1].op, T.Elemwise)
......@@ -5471,7 +5470,7 @@ class T_local_reduce(unittest.TestCase):
mode=self.mode)
finally:
theano.config.warn.reduce_join = old
assert numpy.allclose(f(), [15, 15])
utt.assert_allclose(f(), [15, 15])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
......@@ -5479,14 +5478,14 @@ class T_local_reduce(unittest.TestCase):
A = theano.shared(numpy.array([1, 2, 3, 4, 5]).reshape(5, 1))
f = theano.function([], T.sum(T.concatenate((A, A), axis=1), axis=1),
mode=self.mode)
assert numpy.allclose(f(), [2, 4, 6, 8, 10])
utt.assert_allclose(f(), [2, 4, 6, 8, 10])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
A = theano.shared(numpy.array([1, 2, 3, 4, 5]).reshape(5, 1))
f = theano.function([], T.sum(T.concatenate((A, A), axis=1), axis=0),
mode=self.mode)
assert numpy.allclose(f(), [15, 15])
utt.assert_allclose(f(), [15, 15])
topo = f.maker.fgraph.toposort()
assert not isinstance(topo[-1].op, T.Elemwise)
......@@ -5718,7 +5717,7 @@ class TestMakeVector(utt.InferShapeTester):
if dtype.startswith('int'):
# The gradient should be 0
assert numpy.allclose(g_val, 0)
utt.assert_allclose(g_val, 0)
else:
for var, grval in zip((b, i, d), g_val):
float_inputs = []
......@@ -5977,7 +5976,7 @@ def test_local_div_to_inv():
f = theano.function([num_len_s, denom_s], out)
out_val = f(3, 2.)
assert out_val.shape == (1, 3)
assert numpy.allclose(out_val, 0.5)
utt.assert_allclose(out_val, 0.5)
def test_local_useless_split():
......@@ -6129,14 +6128,14 @@ class TestShape_i(utt.InferShapeTester):
advec_val = numpy.random.rand(3).astype(config.floatX)
f = function([advec], Shape_i(0)(advec))
out = f(advec_val)
assert numpy.allclose(out, advec_val.shape[0])
utt.assert_allclose(out, advec_val.shape[0])
admat = matrix()
admat_val = numpy.random.rand(4, 3).astype(config.floatX)
for i in xrange(2):
f = function([admat], Shape_i(i)(admat))
out = f(admat_val)
assert numpy.allclose(out, admat_val.shape[i])
utt.assert_allclose(out, admat_val.shape[i])
def test_infer_shape(self):
admat = matrix()
......@@ -6289,7 +6288,7 @@ def test_local_sumsqr2dot():
f_val = f(w_val, g_val)
f_test = numpy.dot(numpy.square(g_val), numpy.square(w_val).sum(axis=0))
assert numpy.allclose(f_val, f_test)
utt.assert_allclose(f_val, f_test)
assert any(isinstance(n.op, (tensor.basic.Dot, tensor.blas.Dot22,
tensor.blas.Gemv, tensor.blas_c.CGemv))
for n in f.maker.fgraph.toposort())
......@@ -6312,7 +6311,7 @@ def test_local_expm1():
f_val = f(x_val)
f_test = function([x], T.expm1(x), mode=MODE)
assert numpy.allclose(f_val, f_test(x_val))
utt.assert_allclose(f_val, f_test(x_val))
assert any(isinstance(n.op, T.Elemwise) and isinstance(n.op.scalar_op, theano.scalar.basic.Expm1)
for n in f.maker.fgraph.toposort())
......
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