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提交 a4d57432 authored 作者: Frederic Bastien's avatar Frederic Bastien
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Make tensor/tests/test_basic.py tests run in floatX.

One test was forced to float64, as otherwise the test crash. I make an issue to fix it.
上级 9be8ca6a
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正在显示 包含 74 行增加 和 65 行删除
theano/tensor/tests/test_basic.py
浏览文件 @ a4d57432
......@@ -1488,7 +1488,7 @@ class T_max_and_argmax(unittest.TestCase):
assert len(v) == 0
def test2(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1], None), ([1, 0], None)]:
......@@ -1500,7 +1500,7 @@ class T_max_and_argmax(unittest.TestCase):
assert tuple(v_shape) == numpy.max(data, np_axis).shape
def test2_invalid(self):
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
......@@ -1515,7 +1515,7 @@ class T_max_and_argmax(unittest.TestCase):
_logger.setLevel(oldlevel)
def test2_invalid_neg(self):
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
......@@ -1528,7 +1528,7 @@ class T_max_and_argmax(unittest.TestCase):
sys.stderr = old_stderr
def test2_valid_neg(self):
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
v, i = eval_outputs(max_and_argmax(n, -1))
assert i.dtype == 'int64'
self.assertTrue(v.shape == (2,))
......@@ -1547,7 +1547,7 @@ class T_max_and_argmax(unittest.TestCase):
assert v == (3)
def test3(self):
data = numpy.random.rand(2, 3, 4)
data = rand(2, 3, 4)
n = as_tensor_variable(data)
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
([0, 1, 2], None), ([1, 2, 0], None)]:
......@@ -1559,7 +1559,7 @@ class T_max_and_argmax(unittest.TestCase):
assert tuple(v) == numpy.max(data, np_axis).shape
def test_grad(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
def check_grad_max(data, max_grad_data, axis=None):
......@@ -1595,8 +1595,17 @@ class T_max_and_argmax(unittest.TestCase):
check_grad_max(data, eval_outputs(grad(
max_and_argmax(n.flatten())[0], n)))
# Test 3d inner dimensions
data = rand(3, 4, 5)
for i in [0, 1, 2]:
utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
# Test 4d inner dimensions
data = numpy.random.rand(2, 3, 4, 5)
# Use float64 as otherwise the test don't pass.
data = rand(2, 3, 4, 5).astype("float64")
for i in [0, 1, 2, 3]:
utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
......@@ -1629,7 +1638,7 @@ class T_argmin_argmax(unittest.TestCase):
assert len(v) == 0
def test2(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
......@@ -1641,7 +1650,7 @@ class T_argmin_argmax(unittest.TestCase):
def test2_invalid(self):
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
......@@ -1657,7 +1666,7 @@ class T_argmin_argmax(unittest.TestCase):
def test2_invalid_neg(self):
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
......@@ -1671,7 +1680,7 @@ class T_argmin_argmax(unittest.TestCase):
def test2_valid_neg(self):
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
i = eval_outputs(fct(n, -1))
self.assertTrue(i.shape == (2,))
self.assertTrue(numpy.all(i == nfct(n.value, -1)))
......@@ -1685,7 +1694,7 @@ class T_argmin_argmax(unittest.TestCase):
assert v == (3)
def test3(self):
data = numpy.random.rand(2, 3, 4)
data = rand(2, 3, 4)
n = as_tensor_variable(data)
for fct, nfct in [(argmax, numpy.argmax), (argmin, numpy.argmin)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (2, 2),
......@@ -1697,7 +1706,7 @@ class T_argmin_argmax(unittest.TestCase):
assert tuple(v_shape) == nfct(data, np_axis).shape
def test_grad_argmin(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
#test grad of argmin
......@@ -1716,7 +1725,7 @@ class T_argmin_argmax(unittest.TestCase):
pass
def test_grad_argmax(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
#test grad of argmax
......@@ -1759,7 +1768,7 @@ class T_min_max(unittest.TestCase):
assert len(v) == 0
def test2(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (None, None),
......@@ -1771,7 +1780,7 @@ class T_min_max(unittest.TestCase):
def test2_invalid(self):
for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
# Silence expected error messages
_logger = logging.getLogger('theano.gof.opt')
oldlevel = _logger.level
......@@ -1787,7 +1796,7 @@ class T_min_max(unittest.TestCase):
def test2_invalid_neg(self):
for fct in [max, min]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
old_stderr = sys.stderr
sys.stderr = StringIO.StringIO()
try:
......@@ -1801,7 +1810,7 @@ class T_min_max(unittest.TestCase):
def test2_valid_neg(self):
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
n = as_tensor_variable(numpy.random.rand(2, 3))
n = as_tensor_variable(rand(2, 3))
v = eval_outputs(fct(n, -1))
self.assertTrue(v.shape == (2,))
self.assertTrue(numpy.all(v == nfct(n.value, -1)))
......@@ -1816,7 +1825,7 @@ class T_min_max(unittest.TestCase):
def test3(self):
# Test with 1 axis or all axis out of 3 dims
data = numpy.random.rand(2, 3, 4)
data = rand(2, 3, 4)
n = as_tensor_variable(data)
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
for (axis, np_axis) in [(-1, -1), (0, 0), (1, 1), (2, 2),
......@@ -1829,7 +1838,7 @@ class T_min_max(unittest.TestCase):
def test3b(self):
# Test with 2 axis out of 3 dims
data = numpy.random.rand(2, 3, 4)
data = rand(2, 3, 4)
n = as_tensor_variable(data)
for fct, nfct in [(max, numpy.max), (min, numpy.min)]:
for axis in [[0, 1], [1, 2], [0, 2]]:
......@@ -1840,7 +1849,7 @@ class T_min_max(unittest.TestCase):
assert tuple(v_shape) == np_v.shape
def test_grad_max(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
def check_grad_max(data, max_grad_data, axis=None):
......@@ -1874,7 +1883,7 @@ class T_min_max(unittest.TestCase):
check_grad_max(data, eval_outputs(grad(max(n.flatten()), n)))
def test_grad_min(self):
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
def check_grad_min(data, min_grad_data, axis=None):
......@@ -1914,7 +1923,7 @@ class T_min_max(unittest.TestCase):
This not implemented, so we disable the test. See ticket:
http://trac-hg.assembla.com/theano/ticket/511
"""
data = numpy.random.rand(2, 3)
data = rand(2, 3)
n = as_tensor_variable(data)
for fct in [max_and_argmax, max, min]:
utt.verify_grad(lambda v: fct(v, axis=[0, 1]), [data])
......@@ -2191,7 +2200,7 @@ class T_subtensor(unittest.TestCase):
def test_grad_1d(self):
subi = 0
data = numpy.asarray(numpy.random.rand(2,3), dtype=self.dtype)
data = numpy.asarray(rand(2,3), dtype=self.dtype)
n = self.shared(data)
z = scal.constant(subi)
t = n[z:,z]
......@@ -2211,7 +2220,7 @@ class T_subtensor(unittest.TestCase):
self.assertTrue(numpy.allclose(gval, good), (gval, good))
def test_grad_0d(self):
data = numpy.asarray(numpy.random.rand(2,3), dtype=self.dtype)
data = numpy.asarray(rand(2,3), dtype=self.dtype)
n = self.shared(data)
t = n[1,0]
gn = grad(sum(exp(t)), n)
......@@ -2229,16 +2238,16 @@ class T_subtensor(unittest.TestCase):
self.assertTrue(numpy.allclose(gval, good), (gval, good))
def test_ok_list(self):
for data, idx in [(numpy.random.rand(4), [1,0]),
(numpy.random.rand(4,5), [2,3]),
(numpy.random.rand(4,2,3), [0,3]),
(numpy.random.rand(4,2,3), [3,3,1,1,2,2,0,0]),
(numpy.random.rand(4,2,3), [3,3,1,1,2,2,0,0,-1,-2,-3,-4]),
for data, idx in [(rand(4), [1,0]),
(rand(4,5), [2,3]),
(rand(4,2,3), [0,3]),
(rand(4,2,3), [3,3,1,1,2,2,0,0]),
(rand(4,2,3), [3,3,1,1,2,2,0,0,-1,-2,-3,-4]),
# Test 4 dims as gpu code use another algo in that case
# This new algo is not as much optimized for that case.
(numpy.random.rand(4,4,2,3), [3,3,1,1,2,2,0,0,-1,-2,-3,-4]),
(rand(4,4,2,3), [3,3,1,1,2,2,0,0,-1,-2,-3,-4]),
# Test with TensorConstant index.
(numpy.random.rand(4,2,3), constant([3,3,1,1,2,2,0,0])),
(rand(4,2,3), constant([3,3,1,1,2,2,0,0])),
]:
data = numpy.asarray(data, dtype=self.dtype)
n = self.shared(data)
......@@ -2566,7 +2575,7 @@ class T_subtensor(unittest.TestCase):
pass
def test_grad_list(self):
data = numpy.random.rand(4)
data = rand(4)
data = numpy.asarray(data, dtype=self.dtype)
idxs = [[i] for i in range(data.shape[0])]
for i in range(data.shape[0]):
......@@ -2574,20 +2583,20 @@ class T_subtensor(unittest.TestCase):
idxs.append([i,j,(i+1)%data.shape[0]])
self.grad_list_(idxs, data)
data = numpy.random.rand(4,3)
data = rand(4,3)
data = numpy.asarray(data, dtype=self.dtype)
self.grad_list_(idxs, data)
data = numpy.random.rand(4,3,2)
data = rand(4,3,2)
data = numpy.asarray(data, dtype=self.dtype)
self.grad_list_(idxs, data)
def test_shape_list(self):
#TODO for all type of subtensor shape
for data, idx in [(numpy.random.rand(4), [1,0]),
(numpy.random.rand(4,2), [2,3]),
(numpy.random.rand(4,2,3), [0,3]),
(numpy.random.rand(4,2,3), [3,3,1,2,2,]),
for data, idx in [(rand(4), [1,0]),
(rand(4,2), [2,3]),
(rand(4,2,3), [0,3]),
(rand(4,2,3), [3,3,1,2,2,]),
]:
data = numpy.asarray(data, dtype=self.dtype)
n = self.shared(data)
......@@ -3264,13 +3273,13 @@ class T_add(unittest.TestCase):
self.assertTrue(a.type.values_eq_approx(fn(a.data, b.data), f(a.data, b.data)))
def test_grad_scalar_l(self):
utt.verify_grad(add, [numpy.asarray([3.0]), numpy.random.rand(3)])
utt.verify_grad(add, [numpy.asarray([3.0]), rand(3)])
def test_grad_scalar_r(self):
utt.verify_grad(add, [numpy.random.rand(3), numpy.asarray([3.0])])
utt.verify_grad(add, [rand(3), numpy.asarray([3.0])])
def test_grad_row(self):
utt.verify_grad(add, [numpy.random.rand(3, 5), numpy.random.rand(1, 5)])
utt.verify_grad(add, [rand(3, 5), rand(1, 5)])
def test_grad_col(self):
utt.verify_grad(add, [numpy.random.rand(3, 5), numpy.random.rand(3, 1)])
utt.verify_grad(add, [rand(3, 5), rand(3, 1)])
class T_ceil(unittest.TestCase):
def test_complex(self):
......@@ -3336,7 +3345,7 @@ class T_mean(unittest.TestCase):
#Simple test...
x = tensor.vector()
f = theano.function([x],tensor.mean(x))
data = numpy.asarray(numpy.random.rand(50), dtype=config.floatX)
data = numpy.asarray(rand(50), dtype=config.floatX)
assert numpy.allclose(f(data), numpy.mean(data))
......@@ -3368,8 +3377,8 @@ class test_matinv(unittest.TestCase):
fn = inplace_func([a,b], [ssdiff,g_b])
# use the function
x = numpy.random.rand(dim,dim)+0.1 # Initialized s.t. x is not too tiny
w = numpy.random.rand(dim,dim)
x = rand(dim,dim)+0.1 # Initialized s.t. x is not too tiny
w = rand(dim,dim)
x = numpy.asarray(x, dtype=config.floatX)
w = numpy.asarray(w, dtype=config.floatX)
......@@ -3388,8 +3397,8 @@ class test_matinv(unittest.TestCase):
utt.seed_rng()
# hand-coded numpy implementation for verification
x = numpy.random.rand(3,3)+0.1
w = numpy.random.rand(3,3)
x = rand(3,3)+0.1
w = rand(3,3)
x = numpy.asarray(x, dtype=config.floatX)
w = numpy.asarray(w, dtype=config.floatX)
ones = numpy.ones((3,3), dtype=config.floatX)
......@@ -3408,7 +3417,7 @@ class t_dot(unittest.TestCase):
utt.seed_rng()
@staticmethod
def rand(*args):
return numpy.random.rand(*args)
return rand(*args)
def cmp_dot(self,x,y):
#x, y are matrices or numbers
......@@ -3712,7 +3721,7 @@ class T_op_cache(unittest.TestCase):
fn_py = inplace_func([v], gv)
fn_c_or_py = inplace_func([v], gv)
a = numpy.random.rand(5,2).astype(config.floatX)
a = rand(5,2).astype(config.floatX)
self.assertTrue(numpy.all(fn_py(a) == fn_c_or_py(a)))
class T_reshape(unittest.TestCase):
......@@ -3801,7 +3810,7 @@ class T_reshape(unittest.TestCase):
f = function([a, shapes], z.shape)
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.uniform(size=(3,4)).astype(config.floatX)
a_val = rng.uniform(size=(3, 4)).astype(config.floatX)
self.assertTrue((f(a_val, [4, 3]) == [4, 3]).all())
self.assertTrue((f(a_val, [-1, 3]) == [4, 3]).all())
......@@ -4281,13 +4290,13 @@ class TestPermuteRowElements(unittest.TestCase):
def test_2_1(self):
"""Test broadcasting in PermuteRowElements(matrix, vector)"""
input = dmatrix()
input = matrix()
p = ivector()
out = permute_row_elements(input, p)
permute = function([input, p], out)
rng = numpy.random.RandomState(utt.fetch_seed())
input_val = rng.uniform(size=(3,5))
input_val = rng.uniform(size=(3, 5)).astype(config.floatX)
p_val = rng.permutation(5).astype('int32')
out_val = permute(input_val, p_val)
......@@ -4303,13 +4312,13 @@ class TestPermuteRowElements(unittest.TestCase):
def test_2_2(self):
"""Test PermuteRowElements(matrix, matrix)"""
input = dmatrix()
input = matrix()
p = imatrix()
out = permute_row_elements(input, p)
permute = function([input, p], out)
rng = numpy.random.RandomState(utt.fetch_seed())
input_val = rng.uniform(size=(3,5))
input_val = rng.uniform(size=(3, 5)).astype(config.floatX)
p_val = numpy.asarray([rng.permutation(5) for i in range(3)],
dtype='int32')
out_val = permute(input_val, p_val)
......@@ -4328,13 +4337,13 @@ class TestPermuteRowElements(unittest.TestCase):
def test_1_2(self):
"""Test PermuteRowElements(vector, matrix)
Different permutations will be applied to the same input vector"""
input = dvector()
input = vector()
p = imatrix()
out = permute_row_elements(input, p)
permute = function([input, p], out)
rng = numpy.random.RandomState(utt.fetch_seed())
input_val = rng.uniform(size=(5,))
input_val = rng.uniform(size=(5,)).astype(config.floatX)
p_val = numpy.asarray([rng.permutation(5) for i in range(3)], dtype='int32')
out_val = permute(input_val, p_val)
......@@ -4353,13 +4362,13 @@ class TestPermuteRowElements(unittest.TestCase):
input.type.broadcastable = (False, True, False),
p.type.broadcastable = (False, False)."""
input = TensorType('float64', (False, True, False))()
input = TensorType('floatX', (False, True, False))()
p = imatrix()
out = permute_row_elements(input, p)
permute = function([input, p], out)
rng = numpy.random.RandomState(utt.fetch_seed())
input_val = rng.uniform(size=(4,1,5))
input_val = rng.uniform(size=(4, 1, 5)).astype(config.floatX)
p_val = numpy.asarray([rng.permutation(5) for i in range(3)],
dtype='int32')
out_val = permute(input_val, p_val)
......@@ -4381,7 +4390,7 @@ class test_tensordot(unittest.TestCase):
utt.seed_rng()
def rand(self, *shape):
return numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX)
return numpy.asarray(rand(*shape), dtype=config.floatX)
def test0(self):
......@@ -4509,7 +4518,7 @@ class test_tensordot(unittest.TestCase):
bmat = dmatrix()# We let at float64 to test mix of float32 and float64.
axes = 1
aval = self.rand(4,5).astype('float32')
bval = numpy.random.rand(5,3)
bval = rand(5,3)
c = tensordot(amat, bmat, axes)
f3 = inplace_func([amat,bmat],c)
self.assertTrue(numpy.allclose(numpy.tensordot(aval,bval,axes),
......@@ -5087,8 +5096,8 @@ def test_unalign():
b = numpy.empty(1e4, dtype=dtype)['f1']
assert not a.flags.aligned
assert not b.flags.aligned
a[:] = numpy.random.rand(len(a))
b[:] = numpy.random.rand(len(b))
a[:] = rand(len(a))
b[:] = rand(len(b))
out_numpy = 2*a + 3*b
av,bv = tensor.vectors('ab')
......@@ -5149,10 +5158,10 @@ class T_get_constant_value(unittest.TestCase):
assert get_constant_value(v.shape[0])==1
def test_subtensor_of_constant(self):
c = constant(numpy.random.rand(5))
c = constant(rand(5))
for i in range(c.value.shape[0]):
assert get_constant_value(c[i]) == c.value[i]
c = constant(numpy.random.rand(5,5))
c = constant(rand(5,5))
for i in range(c.value.shape[0]):
for j in range(c.value.shape[1]):
assert get_constant_value(c[i,j]) == c.value[i,j]
......
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