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pytensor
提交 57c025ae authored 作者: Frederic Bastien's avatar Frederic Bastien
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fix a bug when empty ndarray in python code. This don't affect normal behavior.

上级 e5657f9a
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正在显示 包含 86 行增加 和 73 行删除
theano/tensor/elemwise.py
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...@@ -626,6 +626,8 @@ class Elemwise(Op): ...@@ -626,6 +626,8 @@ class Elemwise(Op):
raise raise
if ufunc.nout == 1: variables = [variables] if ufunc.nout == 1: variables = [variables]
for variable, storage in zip(variables, output_storage): for variable, storage in zip(variables, output_storage):
if hasattr(variable,'shape') and storage[0].shape != variable.shape:
storage[0].resize(variable.shape)
if storage[0].shape: if storage[0].shape:
storage[0][:] = variable storage[0][:] = variable
else: else:
......
theano/tensor/tests/test_basic.py
浏览文件 @ 57c025ae
...@@ -233,7 +233,9 @@ _good_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)), ...@@ -233,7 +233,9 @@ _good_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
column = (rand(2, 3), rand(2, 1)), column = (rand(2, 3), rand(2, 1)),
integers = (randint(2, 3), randint(2, 3)), integers = (randint(2, 3), randint(2, 3)),
dtype_mixup_1 = (rand(2, 3), randint(2, 3)), dtype_mixup_1 = (rand(2, 3), randint(2, 3)),
dtype_mixup_2 = (randint(2, 3), rand(2, 3))) dtype_mixup_2 = (randint(2, 3), rand(2, 3)),
empty = (numpy.asarray([]),numpy.asarray([1])),
)
_bad_build_broadcast_binary_normal = dict()#not_same_dimensions = (rand(2), rand(2, 2))) _bad_build_broadcast_binary_normal = dict()#not_same_dimensions = (rand(2), rand(2, 2)))
...@@ -243,7 +245,10 @@ _bad_runtime_broadcast_binary_normal = dict(bad_shapes = (rand(2, 3), rand(3, 2) ...@@ -243,7 +245,10 @@ _bad_runtime_broadcast_binary_normal = dict(bad_shapes = (rand(2, 3), rand(3, 2)
_grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)), _grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)), row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))) column = (rand(2, 3), rand(2, 1)),
#This don't work as verify grad don't support that
#empty = (numpy.asarray([]), numpy.asarray([1]))
)
AddTester = makeBroadcastTester(op = add, AddTester = makeBroadcastTester(op = add,
...@@ -324,6 +329,29 @@ MulInplaceTester = makeBroadcastTester(op = inplace.mul_inplace, ...@@ -324,6 +329,29 @@ MulInplaceTester = makeBroadcastTester(op = inplace.mul_inplace,
bad_runtime = _bad_runtime_broadcast_binary_normal, bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal, grad = _grad_broadcast_binary_normal,
inplace = True) inplace = True)
_good_broadcast_div_mod_normal_float_inplace = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3)),
#integers_positive = (randint_ranged(4, 10, (2, 3)), randint_ranged(1, 6, (2, 3))),
#integers_known_to_fail = (numpy.array(-1), numpy.array(5))
empty1 = (numpy.asarray([]), numpy.asarray([1])),
#empty2 = (numpy.asarray([0]), numpy.asarray([])),
)
_good_broadcast_div_mod_normal_float = dict(empty2 = (numpy.asarray([0]), numpy.asarray([])),
**_good_broadcast_div_mod_normal_float_inplace
)
_grad_broadcast_div_mod_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
#empty1 = (numpy.asarray([]), numpy.asarray([1.])),
#empty2 = (numpy.asarray([0]), numpy.asarray([])),
)
div_grad_rtol=None div_grad_rtol=None
if config.floatX=='float32': if config.floatX=='float32':
#We raise the relative tolerence for the grad as their is error in float32 #We raise the relative tolerence for the grad as their is error in float32
...@@ -331,90 +359,59 @@ if config.floatX=='float32': ...@@ -331,90 +359,59 @@ if config.floatX=='float32':
div_grad_rtol=0.025 div_grad_rtol=0.025
DivTester = makeBroadcastTester(op = true_div, DivTester = makeBroadcastTester(op = true_div,
expected = lambda x, y: x / y, expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = _good_broadcast_div_mod_normal_float,
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3)),
# integers_positive = (randint_ranged(4, 10, (2, 3)), randint_ranged(1, 6, (2, 3))),
# integers_known_to_fail = (numpy.array(-1), numpy.array(5))
),
# integers = (randint(2, 3), randint_nonzero(2, 3)), # integers = (randint(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)), # dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))), # dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))),
grad = dict(same_shapes = (rand(2, 3), rand(2, 3)), grad = _grad_broadcast_div_mod_normal,
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))),
grad_rtol=div_grad_rtol, grad_rtol=div_grad_rtol,
) )
DivInplaceTester = makeBroadcastTester(op = inplace.true_div_inplace, DivInplaceTester = makeBroadcastTester(op = inplace.true_div_inplace,
expected = lambda x, y: x / y, expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = _good_broadcast_div_mod_normal_float_inplace,
scalar = (rand(2, 3), rand(1, 1)), grad = _grad_broadcast_div_mod_normal,
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))
),
grad = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))),
grad_rtol=div_grad_rtol, grad_rtol=div_grad_rtol,
inplace = True) inplace = True)
ModTester = makeBroadcastTester(op = mod, ModTester = makeBroadcastTester(op = mod,
expected = lambda x, y: x % y, expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = _good_broadcast_div_mod_normal_float,
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3)),
# integers_positive = (randint_ranged(4, 10, (2, 3)), randint_ranged(1, 6, (2, 3))),
# integers_known_to_fail = (numpy.array(-1), numpy.array(5))
),
# integers = (randint(2, 3), randint_nonzero(2, 3)), # integers = (randint(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)), # dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))), # dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))),
) )
ModInplaceTester = makeBroadcastTester(op = inplace.mod_inplace, ModInplaceTester = makeBroadcastTester(op = inplace.mod_inplace,
expected = lambda x, y: x % y, expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = _good_broadcast_div_mod_normal_float_inplace,
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))
),
inplace = True) inplace = True)
_good_broadcast_pow_normal_float = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))),
dtype_mixup = (rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3))),
empty1 = (numpy.asarray([]), numpy.asarray([1])),
empty2 = (numpy.asarray([0]), numpy.asarray([])),)
_grad_broadcast_pow_normal = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))),
#empty1 = (numpy.asarray([]), numpy.asarray([1])),
#empty2 = (numpy.asarray([0]), numpy.asarray([])),
)
#empty2 case is not supported by numpy.
_good_broadcast_pow_normal_float_pow = copy(_good_broadcast_pow_normal_float)
del _good_broadcast_pow_normal_float_pow["empty2"]
PowTester = makeBroadcastTester(op = pow, PowTester = makeBroadcastTester(op = pow,
expected = lambda x, y: x ** y, expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), good = _good_broadcast_pow_normal_float,
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))), grad = _grad_broadcast_pow_normal)
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))),
dtype_mixup = (rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3)))),
grad = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))))
)
PowInplaceTester = makeBroadcastTester(op = inplace.pow_inplace, PowInplaceTester = makeBroadcastTester(op = inplace.pow_inplace,
expected = lambda x, y: x ** y, expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), good = _good_broadcast_pow_normal_float_pow,
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))), grad = _grad_broadcast_pow_normal,
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))), inplace = True)
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))),
dtype_mixup = (rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3)))),
grad = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1)))),
inplace = True)
#Those are corner case when rounding. Their is many rounding algo. #Those are corner case when rounding. Their is many rounding algo.
#c round() fct and numpy round are not the same! #c round() fct and numpy round are not the same!
...@@ -422,14 +419,18 @@ corner_case = numpy.asarray([-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0, 0.49, 0. ...@@ -422,14 +419,18 @@ corner_case = numpy.asarray([-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0, 0.49, 0.
#we remove 0 here as the grad is not always computable numerically. #we remove 0 here as the grad is not always computable numerically.
corner_case_grad = numpy.asarray([-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0.49, 0.5, 0.9, 1, 1.5, 2, 2.5], dtype=config.floatX) corner_case_grad = numpy.asarray([-2.5, -2., -1.5, -1., -0.5, -.51, -.49, 0.49, 0.5, 0.9, 1, 1.5, 2, 2.5], dtype=config.floatX)
_good_broadcast_unary_normal_float = dict(normal = (rand_ranged(-5, 5, (2, 3)),), _good_broadcast_unary_normal_float = dict(normal = (rand_ranged(-5, 5, (2, 3)),),
corner_case = (corner_case,)) corner_case = (corner_case,),
empty = (numpy.asarray([]),))
_good_broadcast_unary_normal = dict(normal = (numpy.asarray(rand_ranged(-5, 5, (2, 3)),dtype=config.floatX),), _good_broadcast_unary_normal = dict(normal = (numpy.asarray(rand_ranged(-5, 5, (2, 3)),dtype=config.floatX),),
integers = (randint_ranged(-5, 5, (2, 3)),), integers = (randint_ranged(-5, 5, (2, 3)),),
corner_case = (corner_case,)) corner_case = (corner_case,),
empty = (numpy.asarray([]),))
_grad_broadcast_unary_normal = dict(normal = (numpy.asarray(rand_ranged(-5, 5, (2, 3)),dtype=config.floatX),), _grad_broadcast_unary_normal = dict(normal = (numpy.asarray(rand_ranged(-5, 5, (2, 3)),dtype=config.floatX),),
corner_case = (corner_case_grad,)) corner_case = (corner_case_grad,),
#empty = (numpy.asarray([]),)
)
...@@ -516,9 +517,13 @@ ExpInplaceTester = makeBroadcastTester(op = inplace.exp_inplace, ...@@ -516,9 +517,13 @@ ExpInplaceTester = makeBroadcastTester(op = inplace.exp_inplace,
_good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),), _good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
integers = (randint_ranged(1, 5, (2, 3)),)) integers = (randint_ranged(1, 5, (2, 3)),),
empty = (numpy.asarray([]),),
)
_grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),)) _grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
#empty = (numpy.asarray([]),),
)
LogTester = makeBroadcastTester(op = log, LogTester = makeBroadcastTester(op = log,
expected = numpy.log, expected = numpy.log,
...@@ -574,9 +579,12 @@ SqrtInplaceTester = makeBroadcastTester(op = inplace.sqrt_inplace, ...@@ -574,9 +579,12 @@ SqrtInplaceTester = makeBroadcastTester(op = inplace.sqrt_inplace,
_good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),), _good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
integers = (randint_ranged(-1000, 1000, (2, 3)),)) integers = (randint_ranged(-1000, 1000, (2, 3)),),
empty = (numpy.asarray([]),),)
_grad_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),)) _grad_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
#empty = (numpy.asarray([]),),
)
SinTester = makeBroadcastTester(op = sin, SinTester = makeBroadcastTester(op = sin,
...@@ -666,7 +674,8 @@ else: ...@@ -666,7 +674,8 @@ else:
0.96610515, 0.99532227, 0.99959305]) 0.96610515, 0.99532227, 0.99959305])
normal = numpy.array([[-1. , 0.99991358, 0.70314729], normal = numpy.array([[-1. , 0.99991358, 0.70314729],
[ 0.9977147 , -0.99999884, 0.33409098]]) [ 0.9977147 , -0.99999884, 0.33409098]])
expected = dict(integers=integers,corner_case=corner_case,normal = normal) expected = dict(integers=integers, corner_case=corner_case, normal = normal,
empty = (numpy.asarray([]),),)
ErfTester = makeBroadcastTester(op = erf, ErfTester = makeBroadcastTester(op = erf,
expected = expected, expected = expected,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
...@@ -693,7 +702,8 @@ else: ...@@ -693,7 +702,8 @@ else:
3.38948535e-02, 4.67773498e-03, 4.06952017e-04]) 3.38948535e-02, 4.67773498e-03, 4.06952017e-04])
normal = numpy.array([[ 2.00000000e+00, 8.64228449e-05, 2.96852710e-01], normal = numpy.array([[ 2.00000000e+00, 8.64228449e-05, 2.96852710e-01],
[ 2.28530326e-03, 1.99999884e+00, 6.65909025e-01]]) [ 2.28530326e-03, 1.99999884e+00, 6.65909025e-01]])
expected = dict(integers=integers,corner_case=corner_case,normal = normal) expected = dict(integers=integers, corner_case=corner_case, normal = normal,
empty = (numpy.asarray([]),),)
ErfcTester = makeBroadcastTester(op = erfc, ErfcTester = makeBroadcastTester(op = erfc,
expected = expected, expected = expected,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
...@@ -715,7 +725,8 @@ DotTester = makeTester(name = 'DotTester', ...@@ -715,7 +725,8 @@ DotTester = makeTester(name = 'DotTester',
checks = {}, checks = {},
good = dict(correct1 = (rand(5, 7), rand(7, 5)), good = dict(correct1 = (rand(5, 7), rand(7, 5)),
correct2 = (rand(5, 7), rand(7, 9)), correct2 = (rand(5, 7), rand(7, 9)),
correct3 = (rand(5, 7), rand(7))), correct3 = (rand(5, 7), rand(7)),
empty = (numpy.asarray([]),numpy.asarray([])),),
bad_build = dict(), bad_build = dict(),
bad_runtime = dict(bad1 = (rand(5, 7), rand(5, 7)), bad_runtime = dict(bad1 = (rand(5, 7), rand(5, 7)),
bad2 = (rand(5, 7), rand(8, 3)))) bad2 = (rand(5, 7), rand(8, 3))))
......
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