def test_conditional():
with pf.Graph() as graph:
x = pf.constant(4)
y = pf.placeholder(name='y')
condition = pf.placeholder(name='condition')
z = pf.conditional(condition, x, y)
assert graph(z, condition=True) == 4
assert graph(z, condition=False, y=5) == 5
# We expect a value error if we evaluate the other branch without a placeholder
with pytest.raises(ValueError):
graph(z, condition=False)
def test_conditional_with_length():
def f(a):
return a, a
with pf.Graph() as graph:
x = pf.constant(4)
y = pf.placeholder(name='y')
condition = pf.placeholder(name='condition')
z1, z2 = pf.conditional(condition, pf.func_op(f, x), pf.func_op(f, y), length=2)
assert graph([z1, z2], condition=True) == (4, 4)
assert graph([z1, z2], condition=False, y=5) == (5, 5)
def test_conditional_callback():
with pf.Graph() as graph:
a = pf.constant(1)
b = pf.constant(2)
c = pf.placeholder()
d = pf.conditional(c, a, b + 1)
# Check that we have "traced" the correct number of operation evaluations
tracer = pf.Profiler()
assert graph(d, {c: True}, callback=tracer) == 1
assert len(tracer.times) == 2
tracer = pf.Profiler()
assert graph(d, {c: False}, callback=tracer) == 3
assert len(tracer.times) == 3
def test_profiling():
with pf.Graph() as graph:
duration = pf.placeholder('duration')
# Use a conditional to make sure we also test the evaluation of conditionals with callbacks
positive_duration = pf.conditional(duration < 0, 0, duration)
sleep = pf.import_('time').sleep(positive_duration)
callback = pf.Profiler()
graph(sleep, callback=callback, duration=0.5)
assert callback.times[sleep] > 0.5
# We never evaluate duration because it is already provided in the context
assert duration not in callback.times
# Check that the slowest operation comes first
callback_str = str(callback)
assert callback_str.startswith(str(sleep))
assert len(callback_str.split('\n')) == 5
