def test_RepeaterPipe():
dumbo = one_way_iter_dummy()
robert = pl.RepeaterPipe(dumbo)
numbaz = Message()
assert len(numbaz) == 0
for numba in robert:
numbaz = numbaz.append(numba)
assert len(numbaz) == robert.repetitions * 20
dumbo = one_way_dummy()
robert = pl.RepeaterPipe(dumbo)
numbaz = Message()
robert.reset()
i = 0
assert len(numbaz) == 0
while True:
try:
numbaz = numbaz.append(robert.__next__())
i += 1
if i > 1000: # If something goes horribly wrong, cancel test
assert False
except StopIteration:
break
assert len(numbaz) == robert.repetitions * 20
def test_append():
t = tensors
v = vectors
m1 = Message(t, v)
m2 = Message(t, v)
m3 = Message(t)
m4 = TensorMessage(t)
m5 = Message(pd.DataFrame(v))
m6 = pd.DataFrame(v)
m0 = Message()
assert(len(m0) == 0)
m = m0.append(Message(t))
assert m == Message(t)
m = m0.append(Message(v))
assert m == Message(v)
m = m0.append(Message(t,v))
assert m == Message(t,v)
m = m1.append(m2)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])}, {'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m3.append(t)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m3.append(m3)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m3.append(m4)
assert len(m) == 6
assert m == Message({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(t)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(m3)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m4.append(m4)
assert len(m) == 6
assert m == TensorMessage({'a': torch.Tensor([1,2,3,1,2,3]), 'b': torch.Tensor([4,5,6,4,5,6])})
m = m5.append(v)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m5.append(m5)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
m = m5.append(m6)
assert len(m) == 6
assert m == Message({'c': np.array([7,8,9,7,8,9]), 'd': np.array([10,11,12,10,11,12])})
# Test type conversions on appending to TensorMessage
m = m4.append({'a': np.array([42]), 'b': np.array([24])})
assert len(m) == 4
assert m == TensorMessage({'a': torch.Tensor([1,2,3,42]), 'b': torch.Tensor([4,5,6,24])})
def test_junction():
dumbo = one_way_dummy()
bumbo = one_way_dummy()
gumbo = one_way_dummy()
angry = jn.RandomHubJunction(components={
'dumbo': dumbo,
'bumbo': bumbo,
'gumbo': gumbo
})
angry.reset()
assert angry._available_inputs == set(['dumbo', 'bumbo', 'gumbo'])
numbaz = Message()
counter = lambda l, i: sum([1 for x in l
if x == i]) # Counts how often i appears in l
while True:
try:
numbaz = numbaz.append(angry.__next__())
except StopIteration:
break
assert dumbo.count == 21
assert bumbo.count == 21
assert gumbo.count == 21
assert len(numbaz) == 60
counts = {i: counter(numbaz['count'], i) for i in range(20)}
for count in counts.values():
assert count == 3 # Make sure each element showed up 3 times, corresponding to the 3 inputs
mangry = jn.ClockworkHubJunction(components={
'dumbo': dumbo,
'bumbo': bumbo,
'gumbo': gumbo
})
bumbaz = Message()
for nextone in mangry:
bumbaz = bumbaz.append(nextone)
assert dumbo.count == 21
assert bumbo.count == 21
assert gumbo.count == 21
assert len(bumbaz) == 60
for x, i in zip(bumbaz, itertools.count()):
assert x['count'][0] == math.floor(i / 3)
counts = {i: counter(bumbaz['count'], i) for i in range(20)}
for count in counts.values():
assert count == 3 # Make sure each element showed up 3 times, corresponding to the 3 inputs
class ModelSaverMetric(Metric):
def __init__(self, output_transform=lambda x: x, log_interval=100):
self.model_state = Message()
Metric.__init__(self, output_transform=output_transform)
self.log_interval = log_interval
def iteration_completed(self, engine):
iter = (engine.state.iteration - 1)
if iter % self.log_interval == 0:
current_state = Message.from_objects(
deepcopy(engine.state.output['state']))
current_state['iteration'] = [iter]
self.model_state = self.model_state.append(current_state)
def compute(self):
# Return most recent model state
l = len(self.model_state)
return self.model_state[l - 1]
def reset(self):
pass
def update(self, output):
pass