def test_linearfilter(ctx, n_per_kind, rng):
kinds = (
nengo.synapses.LinearFilter((2.,), (1.,), analog=False),
nengo.synapses.Lowpass(0.005),
nengo.synapses.Alpha(0.005),
)
assert len(n_per_kind) == len(kinds)
kinds_n = [(kind, n) for kind, n in zip(kinds, n_per_kind) if n > 0]
dt = 0.001
steps = [kind.make_step((n,), (n,), dt, None, dtype=np.float32)
for kind, n in kinds_n]
A = RA([step.den for step in steps])
B = RA([step.num for step in steps])
X = RA([rng.normal(size=n) for kind, n in kinds_n])
Y = RA([np.zeros(n) for kind, n in kinds_n])
Xbuf = RA([np.zeros(shape) for shape in zip(B.sizes, X.sizes)])
Ybuf = RA([np.zeros(shape) for shape in zip(A.sizes, Y.sizes)])
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
clB = CLRA(queue, B)
clX = CLRA(queue, X)
clY = CLRA(queue, Y)
clXbuf = CLRA(queue, Xbuf)
clYbuf = CLRA(queue, Ybuf)
n_calls = 3
plans = plan_linearfilter(queue, clX, clY, clA, clB, clXbuf, clYbuf)
with Timer() as timer:
for _ in range(n_calls):
[plan() for plan in plans]
print(timer.duration)
for i, [kind, n] in enumerate(kinds_n):
n = min(n, 100)
step = kind.make_step((n, 1), (n, 1), dt, None, dtype=np.float32)
x = X[i][:n]
y = np.zeros_like(x)
for _ in range(n_calls):
y[:] = step(0, x)
z = clY[i][:n]
assert np.allclose(z, y, atol=1e-7, rtol=1e-5), kind
def test_linearfilter(n_per_kind, rng):
kinds = (
nengo.synapses.LinearFilter((2.,), (1.,), analog=False),
nengo.synapses.Lowpass(0.005),
nengo.synapses.Alpha(0.005),
)
assert len(n_per_kind) == len(kinds)
kinds_n = [(kind, n) for kind, n in zip(kinds, n_per_kind) if n > 0]
dt = 0.001
steps = [kind.make_step((n,), (n,), dt, None, dtype=np.float32)
for kind, n in kinds_n]
A = RA([step.den for step in steps])
B = RA([step.num for step in steps])
X = RA([rng.normal(size=n) for kind, n in kinds_n])
Y = RA([np.zeros(n) for kind, n in kinds_n])
Xbuf = RA([np.zeros(shape) for shape in zip(B.sizes, X.sizes)])
Ybuf = RA([np.zeros(shape) for shape in zip(A.sizes, Y.sizes)])
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
clB = CLRA(queue, B)
clX = CLRA(queue, X)
clY = CLRA(queue, Y)
clXbuf = CLRA(queue, Xbuf)
clYbuf = CLRA(queue, Ybuf)
n_calls = 3
plan = plan_linearfilter(queue, clX, clY, clA, clB, clXbuf, clYbuf)
with Timer() as timer:
for _ in range(n_calls):
plan()
print(timer.duration)
for i, [kind, n] in enumerate(kinds_n):
n = min(n, 100)
step = kind.make_step((n, 1), (n, 1), dt, None, dtype=np.float32)
x = X[i][:n]
y = np.zeros_like(x)
for _ in range(n_calls):
y[:] = step(0, x)
z = clY[i][:n]
assert np.allclose(z, y, atol=1e-7, rtol=1e-5), kind
def _plan_LinearFilter(self, ops):
steps = [op.process.make_step(op.input.shape, op.output.shape,
self.model.dt, rng=None) for op in ops]
A = self.RaggedArray([f.den for f in steps], dtype=np.float32)
B = self.RaggedArray([f.num for f in steps], dtype=np.float32)
X = self.all_data[[self.sidx[op.input] for op in ops]]
Y = self.all_data[[self.sidx[op.output] for op in ops]]
Xbuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(B.sizes, X.sizes)],
dtype=np.float32)
Ybuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(A.sizes, Y.sizes)],
dtype=np.float32)
Xbuf = CLRaggedArray(self.queue, Xbuf0)
Ybuf = CLRaggedArray(self.queue, Ybuf0)
self._raggedarrays_to_reset[Xbuf] = Xbuf0
self._raggedarrays_to_reset[Ybuf] = Ybuf0
return plan_linearfilter(self.queue, X, Y, A, B, Xbuf, Ybuf)
def _plan_LinearFilter(self, ops):
steps = [
op.process.make_step(op.input.shape,
op.output.shape,
self.model.dt,
rng=None) for op in ops
]
A = self.RaggedArray([f.den for f in steps], dtype=np.float32)
B = self.RaggedArray([f.num for f in steps], dtype=np.float32)
X = self.all_data[[self.sidx[op.input] for op in ops]]
Y = self.all_data[[self.sidx[op.output] for op in ops]]
Xbuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(B.sizes, X.sizes)],
dtype=np.float32)
Ybuf0 = RaggedArray(
[np.zeros(shape) for shape in zip(A.sizes, Y.sizes)],
dtype=np.float32)
Xbuf = CLRaggedArray(self.queue, Xbuf0)
Ybuf = CLRaggedArray(self.queue, Ybuf0)
self._raggedarrays_to_reset[Xbuf] = Xbuf0
self._raggedarrays_to_reset[Ybuf] = Ybuf0
return plan_linearfilter(self.queue, X, Y, A, B, Xbuf, Ybuf)
def test_linearfilter(ctx, n_per_kind, rng):
kinds = (
nengo.synapses.LinearFilter((2.0, ), (1.0, ), analog=False),
nengo.synapses.Lowpass(0.005),
nengo.synapses.Alpha(0.005),
)
assert len(n_per_kind) == len(kinds)
kinds_n = [(kind, n) for kind, n in zip(kinds, n_per_kind) if n > 0]
dt = 0.001
steps = list()
for kind, n in kinds_n:
state = kind.make_state((n, ), (n, ), dt, dtype=np.float32)
step = kind.make_step((n, ), (n, ), dt, rng=None, state=state)
steps.append(step)
# Nengo 3 uses state space filters. For now, convert back to transfer function.
# Getting rid of this conversion would require a new plan_linearfilter.
dens = list()
nums = list()
for f in steps:
if type(f).__name__ == "NoX": # special case for a feedthrough
den = np.array([1.0])
num = f.D
else:
num, den = ss2tf(f.A, f.B, f.C, f.D)
# This preprocessing copied out of nengo2.8/synapses.LinearFilter.make_step
num = num.flatten()
assert den[0] == 1.0
num = num[1:] if num[0] == 0 else num
den = den[1:] # drop first element (equal to 1)
num, den = num.astype(np.float32), den.astype(np.float32)
dens.append(den)
nums.append(num)
A = RA(dens)
B = RA(nums)
X = RA([rng.normal(size=n) for kind, n in kinds_n])
Y = RA([np.zeros(n) for kind, n in kinds_n])
Xbuf = RA([np.zeros(shape) for shape in zip(B.sizes, X.sizes)])
Ybuf = RA([np.zeros(shape) for shape in zip(A.sizes, Y.sizes)])
queue = cl.CommandQueue(ctx)
clA = CLRA(queue, A)
clB = CLRA(queue, B)
clX = CLRA(queue, X)
clY = CLRA(queue, Y)
clXbuf = CLRA(queue, Xbuf)
clYbuf = CLRA(queue, Ybuf)
n_calls = 3
plans = plan_linearfilter(queue, clX, clY, clA, clB, clXbuf, clYbuf)
with Timer() as timer:
for _ in range(n_calls):
for plan in plans:
plan()
print(timer.duration)
for i, [kind, n] in enumerate(kinds_n):
n = min(n, 100)
state = kind.make_state((n, ), (n, ), dt, dtype=np.float32)
step = kind.make_step((n, ), (n, ), dt, rng=None, state=state)
x = X[i][:n].T
y = np.zeros_like(x)
for _ in range(n_calls):
y[:] = step(0, x)
z = clY[i][:n].T
assert np.allclose(z, y, atol=1e-7, rtol=1e-5), kind
