def _plan_PresentInput(self, ops):
ps = [op.process for op in ops]
Y = self.all_data[[self.sidx[op.output] for op in ops]]
t = self.all_data[[self.sidx[self._step] for _ in ops]]
inputs = self.RaggedArray([p.inputs.reshape(p.inputs.shape[0], -1)
for p in ps], dtype=np.float32)
pres_t = self.Array([p.presentation_time for p in ps])
dt = self.model.dt
return [plan_presentinput(self.queue, Y, t, inputs, dt, pres_t=pres_t)]
def _plan_PresentInput(self, ops):
ps = [op.process for op in ops]
Y = self.all_data[[self.sidx[op.output] for op in ops]]
t = self.all_data[[self.sidx[self.model.step] for _ in ops]]
inputs = self.RaggedArray(
[p.inputs.reshape(p.inputs.shape[0], -1) for p in ps],
dtype=np.float32)
pres_t = self.Array([p.presentation_time for p in ps])
dt = self.model.dt
return [plan_presentinput(self.queue, Y, t, inputs, dt, pres_t=pres_t)]
def _plan_WhiteSignal(self, ops):
Y = self.all_data[[self.sidx[op.output] for op in ops]]
t = self.all_data[[self.sidx[self._step] for _ in ops]]
dt = self.model.dt
signals = []
for op in ops:
assert op.input is None and op.output is not None
f = op.process.make_step(0, op.output.size, dt, self.rng)
signals.append(get_closures(f)['signal'])
signals = self.RaggedArray(signals, dtype=np.float32)
return [plan_presentinput(self.queue, Y, t, signals, dt)]
def _plan_WhiteSignal(self, ops):
Y = self.all_data[[self.sidx[op.output] for op in ops]]
t = self.all_data[[self.sidx[self._step] for _ in ops]]
dt = self.model.dt
signals = []
for op in ops:
assert op.input is None and op.output is not None
f = op.process.make_step(0, op.output.size, dt, self.rng)
signals.append(get_closures(f)['signal'])
signals = self.RaggedArray(signals, dtype=np.float32)
return [plan_presentinput(self.queue, Y, t, signals, dt)]
