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提交 eaf14709 authored 作者: Brandon T. Willard's avatar Brandon T. Willard
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Use change_flags instead of try...finally statements in test_compute_test_value.py

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tests/gof/test_compute_test_value.py
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...@@ -3,15 +3,14 @@ import sys ...@@ -3,15 +3,14 @@ import sys
import traceback import traceback
import warnings import warnings
import numpy as np
import pytest import pytest
import numpy as np
import theano import theano
from theano import config import theano.tensor as tt
from theano import scalar
from theano import tensor as T from theano import config, scalar
from theano.gof import Apply, Op from theano.gof import Apply, Op, utils
from theano.gof import utils
from theano.tensor.basic import _allclose from theano.tensor.basic import _allclose
...@@ -38,299 +37,234 @@ class IncOneC(Op): ...@@ -38,299 +37,234 @@ class IncOneC(Op):
class TestComputeTestValue: class TestComputeTestValue:
@theano.change_flags(compute_test_value="raise")
def test_variable_only(self): def test_variable_only(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.matrix("x")
try: x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
theano.config.compute_test_value = "raise" y = tt.matrix("y")
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
x = T.matrix("x")
x.tag.test_value = np.random.rand(3, 4).astype(config.floatX) # should work
y = T.matrix("y") z = tt.dot(x, y)
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX) assert hasattr(z.tag, "test_value")
f = theano.function([x, y], z)
# should work assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value)
z = T.dot(x, y)
assert hasattr(z.tag, "test_value") # this test should fail
f = theano.function([x, y], z) y.tag.test_value = np.random.rand(6, 5).astype(config.floatX)
assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value) with pytest.raises(ValueError):
tt.dot(x, y)
# this test should fail
y.tag.test_value = np.random.rand(6, 5).astype(config.floatX) @theano.change_flags(compute_test_value="raise")
with pytest.raises(ValueError):
T.dot(x, y)
finally:
theano.config.compute_test_value = orig_compute_test_value
def test_compute_flag(self): def test_compute_flag(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.matrix("x")
y = tt.matrix("y")
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
# should skip computation of test value
theano.config.compute_test_value = "off"
z = tt.dot(x, y)
assert not hasattr(z.tag, "test_value")
# should fail when asked by user
theano.config.compute_test_value = "raise"
with pytest.raises(ValueError):
tt.dot(x, y)
# test that a warning is raised if required
theano.config.compute_test_value = "warn"
warnings.simplefilter("error", UserWarning)
try: try:
x = T.matrix("x") with pytest.raises(UserWarning):
y = T.matrix("y") tt.dot(x, y)
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
# should skip computation of test value
theano.config.compute_test_value = "off"
z = T.dot(x, y)
assert not hasattr(z.tag, "test_value")
# should fail when asked by user
theano.config.compute_test_value = "raise"
with pytest.raises(ValueError):
T.dot(x, y)
# test that a warning is raised if required
theano.config.compute_test_value = "warn"
warnings.simplefilter("error", UserWarning)
try:
with pytest.raises(UserWarning):
T.dot(x, y)
finally:
# Restore the default behavior.
# TODO There is a cleaner way to do this in Python 2.6, once
# Theano drops support of Python 2.4 and 2.5.
warnings.simplefilter("default", UserWarning)
finally: finally:
theano.config.compute_test_value = orig_compute_test_value # Restore the default behavior.
# TODO There is a cleaner way to do this in Python 2.6, once
# Theano drops support of Python 2.4 and 2.5.
warnings.simplefilter("default", UserWarning)
@theano.change_flags(compute_test_value="raise")
def test_string_var(self): def test_string_var(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.matrix("x")
try: x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
theano.config.compute_test_value = "raise" y = tt.matrix("y")
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
x = T.matrix("x") z = theano.shared(np.random.rand(5, 6).astype(config.floatX))
x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
y = T.matrix("y")
y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
z = theano.shared(np.random.rand(5, 6).astype(config.floatX)) # should work
out = tt.dot(tt.dot(x, y), z)
assert hasattr(out.tag, "test_value")
tf = theano.function([x, y], out)
assert _allclose(tf(x.tag.test_value, y.tag.test_value), out.tag.test_value)
# should work def f(x, y, z):
out = T.dot(T.dot(x, y), z) return tt.dot(tt.dot(x, y), z)
assert hasattr(out.tag, "test_value")
tf = theano.function([x, y], out)
assert _allclose(tf(x.tag.test_value, y.tag.test_value), out.tag.test_value)
def f(x, y, z): # this test should fail
return T.dot(T.dot(x, y), z) z.set_value(np.random.rand(7, 6).astype(config.floatX))
with pytest.raises(ValueError):
# this test should fail f(x, y, z)
z.set_value(np.random.rand(7, 6).astype(config.floatX))
with pytest.raises(ValueError):
f(x, y, z)
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_shared(self): def test_shared(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.matrix("x")
try: x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
theano.config.compute_test_value = "raise" y = theano.shared(np.random.rand(4, 6).astype(config.floatX), "y")
x = T.matrix("x") # should work
x.tag.test_value = np.random.rand(3, 4).astype(config.floatX) z = tt.dot(x, y)
y = theano.shared(np.random.rand(4, 6).astype(config.floatX), "y") assert hasattr(z.tag, "test_value")
f = theano.function([x], z)
# should work assert _allclose(f(x.tag.test_value), z.tag.test_value)
z = T.dot(x, y)
assert hasattr(z.tag, "test_value") # this test should fail
f = theano.function([x], z) y.set_value(np.random.rand(5, 6).astype(config.floatX))
assert _allclose(f(x.tag.test_value), z.tag.test_value) with pytest.raises(ValueError):
tt.dot(x, y)
# this test should fail
y.set_value(np.random.rand(5, 6).astype(config.floatX)) @theano.change_flags(compute_test_value="raise")
with pytest.raises(ValueError):
T.dot(x, y)
finally:
theano.config.compute_test_value = orig_compute_test_value
def test_ndarray(self): def test_ndarray(self):
orig_compute_test_value = theano.config.compute_test_value x = np.random.rand(2, 3).astype(config.floatX)
try: y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
theano.config.compute_test_value = "raise"
x = np.random.rand(2, 3).astype(config.floatX)
y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
# should work # should work
z = T.dot(x, y) z = tt.dot(x, y)
assert hasattr(z.tag, "test_value") assert hasattr(z.tag, "test_value")
f = theano.function([], z) f = theano.function([], z)
assert _allclose(f(), z.tag.test_value) assert _allclose(f(), z.tag.test_value)
# this test should fail # this test should fail
x = np.random.rand(2, 4).astype(config.floatX) x = np.random.rand(2, 4).astype(config.floatX)
with pytest.raises(ValueError): with pytest.raises(ValueError):
T.dot(x, y) tt.dot(x, y)
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_empty_elemwise(self): def test_empty_elemwise(self):
orig_compute_test_value = theano.config.compute_test_value x = theano.shared(np.random.rand(0, 6).astype(config.floatX), "x")
try:
theano.config.compute_test_value = "raise"
x = theano.shared(np.random.rand(0, 6).astype(config.floatX), "x") # should work
z = (x + 2) * 3
# should work assert hasattr(z.tag, "test_value")
z = (x + 2) * 3 f = theano.function([], z)
assert hasattr(z.tag, "test_value") assert _allclose(f(), z.tag.test_value)
f = theano.function([], z)
assert _allclose(f(), z.tag.test_value)
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_constant(self): def test_constant(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.constant(np.random.rand(2, 3), dtype=config.floatX)
try: y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
theano.config.compute_test_value = "raise"
x = T.constant(np.random.rand(2, 3), dtype=config.floatX)
y = theano.shared(np.random.rand(3, 6).astype(config.floatX), "y")
# should work # should work
z = T.dot(x, y) z = tt.dot(x, y)
assert hasattr(z.tag, "test_value") assert hasattr(z.tag, "test_value")
f = theano.function([], z) f = theano.function([], z)
assert _allclose(f(), z.tag.test_value) assert _allclose(f(), z.tag.test_value)
# this test should fail # this test should fail
x = T.constant(np.random.rand(2, 4), dtype=config.floatX) x = tt.constant(np.random.rand(2, 4), dtype=config.floatX)
with pytest.raises(ValueError): with pytest.raises(ValueError):
T.dot(x, y) tt.dot(x, y)
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_incorrect_type(self): def test_incorrect_type(self):
orig_compute_test_value = theano.config.compute_test_value x = tt.fmatrix("x")
try: # Incorrect dtype (float64) for test_value
theano.config.compute_test_value = "raise" x.tag.test_value = np.random.rand(3, 4)
y = tt.dmatrix("y")
y.tag.test_value = np.random.rand(4, 5)
x = T.fmatrix("x") with pytest.raises(TypeError):
# Incorrect dtype (float64) for test_value tt.dot(x, y)
x.tag.test_value = np.random.rand(3, 4)
y = T.dmatrix("y")
y.tag.test_value = np.random.rand(4, 5)
with pytest.raises(TypeError):
T.dot(x, y)
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_overided_function(self): def test_overided_function(self):
# We need to test those as they mess with Exception # We need to test those as they mess with Exception
# And we don't want the exception to be changed. # And we don't want the exception to be changed.
orig_compute_test_value = theano.config.compute_test_value x = tt.matrix()
try: x.tag.test_value = np.zeros((2, 3), dtype=config.floatX)
config.compute_test_value = "raise" y = tt.matrix()
x = T.matrix() y.tag.test_value = np.zeros((2, 2), dtype=config.floatX)
x.tag.test_value = np.zeros((2, 3), dtype=config.floatX) with pytest.raises(ValueError):
y = T.matrix() x.__mul__(y)
y.tag.test_value = np.zeros((2, 2), dtype=config.floatX)
with pytest.raises(ValueError): @theano.change_flags(compute_test_value="raise")
x.__mul__(y)
finally:
theano.config.compute_test_value = orig_compute_test_value
def test_scan(self): def test_scan(self):
# Test the compute_test_value mechanism Scan. # Test the compute_test_value mechanism Scan.
k = tt.iscalar("k")
orig_compute_test_value = theano.config.compute_test_value A = tt.vector("A")
try: k.tag.test_value = 3
theano.config.compute_test_value = "raise" A.tag.test_value = np.random.rand(5).astype(config.floatX)
# theano.config.compute_test_value = 'warn'
k = T.iscalar("k") def fx(prior_result, A):
A = T.vector("A") return prior_result * A
k.tag.test_value = 3
A.tag.test_value = np.random.rand(5).astype(config.floatX) # Symbolic description of the result
result, updates = theano.scan(
def fx(prior_result, A): fn=fx, outputs_info=tt.ones_like(A), non_sequences=A, n_steps=k
return prior_result * A )
# Symbolic description of the result # We only care about A**k, but scan has provided us with A**1 through A**k.
result, updates = theano.scan( # Discard the values that we don't care about. Scan is smart enough to
fn=fx, outputs_info=T.ones_like(A), non_sequences=A, n_steps=k # notice this and not waste memory saving them.
) final_result = result[-1]
assert hasattr(final_result.tag, "test_value")
# We only care about A**k, but scan has provided us with A**1 through A**k.
# Discard the values that we don't care about. Scan is smart enough to @theano.change_flags(compute_test_value="raise")
# notice this and not waste memory saving them.
final_result = result[-1]
assert hasattr(final_result.tag, "test_value")
finally:
theano.config.compute_test_value = orig_compute_test_value
def test_scan_err1(self): def test_scan_err1(self):
# This test should fail when building fx for the first time # This test should fail when building fx for the first time
orig_compute_test_value = theano.config.compute_test_value k = tt.iscalar("k")
try: A = tt.matrix("A")
theano.config.compute_test_value = "raise" k.tag.test_value = 3
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
k = T.iscalar("k")
A = T.matrix("A")
k.tag.test_value = 3
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
def fx(prior_result, A):
return T.dot(prior_result, A)
# Since we have to inspect the traceback,
# we cannot simply use self.assertRaises()
try:
theano.scan(
fn=fx, outputs_info=T.ones_like(A), non_sequences=A, n_steps=k
)
assert False
except ValueError:
# Get traceback
tb = sys.exc_info()[2]
frame_infos = traceback.extract_tb(tb)
# We should be in the "fx" function defined above
expected = "test_compute_test_value.py"
assert any(
(
os.path.split(frame_info[0])[1] == expected
and frame_info[2] == "fx"
)
for frame_info in frame_infos
), frame_infos
finally: def fx(prior_result, A):
theano.config.compute_test_value = orig_compute_test_value return tt.dot(prior_result, A)
# Since we have to inspect the traceback,
# we cannot simply use self.assertRaises()
try:
theano.scan(fn=fx, outputs_info=tt.ones_like(A), non_sequences=A, n_steps=k)
assert False
except ValueError:
# Get traceback
tb = sys.exc_info()[2]
frame_infos = traceback.extract_tb(tb)
# We should be in the "fx" function defined above
expected = "test_compute_test_value.py"
assert any(
(os.path.split(frame_info[0])[1] == expected and frame_info[2] == "fx")
for frame_info in frame_infos
), frame_infos
@theano.change_flags(compute_test_value="raise")
def test_scan_err2(self): def test_scan_err2(self):
# This test should not fail when building fx for the first time, # This test should not fail when building fx for the first time,
# but when calling the scan's perform() # but when calling the scan's perform()
orig_compute_test_value = theano.config.compute_test_value k = tt.iscalar("k")
try: A = tt.matrix("A")
theano.config.compute_test_value = "raise" k.tag.test_value = 3
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
k = T.iscalar("k")
A = T.matrix("A")
k.tag.test_value = 3
A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
def fx(prior_result, A):
return T.dot(prior_result, A)
with pytest.raises(ValueError):
theano.scan(
fn=fx, outputs_info=T.ones_like(A.T), non_sequences=A, n_steps=k
)
# Since we have to inspect the traceback,
# we cannot simply use self.assertRaises()
try:
theano.scan(
fn=fx, outputs_info=T.ones_like(A.T), non_sequences=A, n_steps=k
)
assert False
except ValueError as e:
assert str(e).startswith("could not broadcast input"), str(e)
finally: def fx(prior_result, A):
theano.config.compute_test_value = orig_compute_test_value return tt.dot(prior_result, A)
with pytest.raises(ValueError):
theano.scan(
fn=fx, outputs_info=tt.ones_like(A.T), non_sequences=A, n_steps=k
)
# Since we have to inspect the traceback,
# we cannot simply use self.assertRaises()
try:
theano.scan(
fn=fx, outputs_info=tt.ones_like(A.T), non_sequences=A, n_steps=k
)
assert False
except ValueError as e:
assert str(e).startswith("could not broadcast input"), str(e)
@theano.change_flags(compute_test_value="raise")
def test_no_c_code(self): def test_no_c_code(self):
class IncOnePython(Op): class IncOnePython(Op):
""" """
...@@ -349,60 +283,41 @@ class TestComputeTestValue: ...@@ -349,60 +283,41 @@ class TestComputeTestValue:
(output,) = outputs (output,) = outputs
output[0] = input + 1 output[0] = input + 1
orig_compute_test_value = theano.config.compute_test_value i = scalar.int32("i")
try: i.tag.test_value = 3
theano.config.compute_test_value = "raise"
i = scalar.int32("i")
i.tag.test_value = 3
o = IncOnePython()(i) o = IncOnePython()(i)
# Check that the c_code function is not implemented # Check that the c_code function is not implemented
with pytest.raises((NotImplementedError, utils.MethodNotDefined)): with pytest.raises((NotImplementedError, utils.MethodNotDefined)):
o.owner.op.c_code(o.owner, "o", ["x"], "z", {"fail": ""}) o.owner.op.c_code(o.owner, "o", ["x"], "z", {"fail": ""})
assert hasattr(o.tag, "test_value") assert hasattr(o.tag, "test_value")
assert o.tag.test_value == 4 assert o.tag.test_value == 4
finally:
theano.config.compute_test_value = orig_compute_test_value
@pytest.mark.skipif( @pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test." not theano.config.cxx, reason="G++ not available, so we need to skip this test."
) )
@theano.change_flags(compute_test_value="raise")
def test_no_perform(self): def test_no_perform(self):
orig_compute_test_value = theano.config.compute_test_value i = scalar.int32("i")
try: i.tag.test_value = 3
theano.config.compute_test_value = "raise"
i = scalar.int32("i") # Class IncOneC is defined outside of the TestComputeTestValue
i.tag.test_value = 3 # so it can be pickled and unpickled
o = IncOneC()(i)
# Class IncOneC is defined outside of the TestComputeTestValue # Check that the perform function is not implemented
# so it can be pickled and unpickled with pytest.raises((NotImplementedError, utils.MethodNotDefined)):
o = IncOneC()(i) o.owner.op.perform(o.owner, 0, [None])
# Check that the perform function is not implemented assert hasattr(o.tag, "test_value")
with pytest.raises((NotImplementedError, utils.MethodNotDefined)): assert o.tag.test_value == 4
o.owner.op.perform(o.owner, 0, [None])
assert hasattr(o.tag, "test_value")
assert o.tag.test_value == 4
finally:
theano.config.compute_test_value = orig_compute_test_value
@theano.change_flags(compute_test_value="raise")
def test_disabled_during_compilation(self): def test_disabled_during_compilation(self):
# We test that it is disabled when we include deep copy in the code # We test that it is disabled when we include deep copy in the code
# This don't test that it is disabled during optimization, but the code do it. # This don't test that it is disabled during optimization, but the code do it.
orig_compute_test_value = theano.config.compute_test_value init_Mu1 = theano.shared(np.zeros((5,), dtype=config.floatX)).dimshuffle("x", 0)
try:
theano.config.compute_test_value = "raise"
init_Mu1 = theano.shared(np.zeros((5,), dtype=config.floatX)).dimshuffle(
"x", 0
)
theano.function([], outputs=[init_Mu1]) theano.function([], outputs=[init_Mu1])
finally:
theano.config.compute_test_value = orig_compute_test_value
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