def __init__(self, workflow, **kwargs):
super(EnsembleWorkflowBase, self).__init__(workflow, **kwargs)
self.ensemble = \
self.MANAGER_UNIT(self, **kwargs) # pylint: disable=E1102
self.repeater = Repeater(self)
self.repeater.link_from(self.start_point)
self.ensemble.link_from(self.repeater)
self.repeater.link_from(self.ensemble)
self.end_point.link_from(self.ensemble)
self.end_point.gate_block = ~self.ensemble.complete
self.repeater.gate_block = self.ensemble.complete
class EnsembleWorkflowBase(Workflow):
MANAGER_UNIT = None
def __init__(self, workflow, **kwargs):
super(EnsembleWorkflowBase, self).__init__(workflow, **kwargs)
self.ensemble = \
self.MANAGER_UNIT(self, **kwargs) # pylint: disable=E1102
self.repeater = Repeater(self)
self.repeater.link_from(self.start_point)
self.ensemble.link_from(self.repeater)
self.repeater.link_from(self.ensemble)
self.end_point.link_from(self.ensemble)
self.end_point.gate_block = ~self.ensemble.complete
self.repeater.gate_block = self.ensemble.complete
def __init__(self, workflow, **kwargs):
super(OptimizationWorkflow, self).__init__(workflow, **kwargs)
self.optimizer = GeneticsOptimizer(self, **kwargs)
self.repeater = Repeater(self)
self.repeater.link_from(self.start_point)
self.optimizer.link_from(self.repeater)
self.plotter_max = AccumulatingPlotter(
self, name="Genetic Optimization Fitness",
plot_style="g-", redraw_plot=False, clear_plot=True)
if self.plotters_are_enabled:
self.plotter_max.link_attrs(self.optimizer,
("input", "max_fitness"))
else:
self.plotter_max.input = 0
self.plotter_max.link_from(self.optimizer)
self.plotter_max.gate_skip = ~self.optimizer.generation_changed
self.plotter_avg = AccumulatingPlotter(
self, name="Genetic Optimization Fitness",
plot_style="b-", redraw_plot=True)
if self.plotters_are_enabled:
self.plotter_avg.link_attrs(self.optimizer,
("input", "avg_fitness"))
else:
self.plotter_avg.input = 0
self.plotter_avg.link_from(self.plotter_max)
self.plotter_avg.gate_skip = ~self.optimizer.generation_changed
self.repeater.link_from(self.plotter_avg)
self.end_point.link_from(self.plotter_avg)
self.end_point.gate_block = ~self.optimizer.complete
self.repeater.gate_block = self.optimizer.complete
self.json_encoder = ConfigJSONEncoder
class BenchmarkWorkflow(StandardWorkflow):
def create_workflow(self):
self.loader = self.real_loader = BenchmarkLoader(
self, minibatch_size=BenchmarkLoader.BATCH,
force_numpy=True) # do not preload all dataset to device
self.loader.link_from(self.start_point)
self.t0 = Timer(self, name="Timer 0",
sync_iterations=(1, )).link_from(self.loader)
self.repeater.link_from(self.t0)
self.link_forwards(("input", "minibatch_data"), self.loader)
self.forwards[0].unlink_before()
self.forwards[0].link_from(self.repeater)
self.t1 = Timer(self, name="Timer 1",
sync_iterations=(1, )).link_from(self.forwards[-2])
self.repeater.link_from(self.t1)
self.forwards[-1].gate_block = ~self.t1.complete
self.forwards[0].gate_block = self.t1.complete
self.link_evaluator(self.forwards[-1])
self.link_decision(self.evaluator)
self.decision.gate_skip = Bool(True)
last_gd = self.link_gds(self.decision)
self.t2 = Timer(self, name="Timer 2",
sync_iterations=(1, )).link_from(self.gds[-1])
self.repeater2 = Repeater(self).link_from(self.t2)
self.gds[-2].unlink_before()
self.gds[-2].link_from(self.repeater2)
self.t3 = Timer(self, name="Timer 3",
sync_iterations=(1, )).link_from(last_gd)
self.repeater2.link_from(self.t3)
self.end_point.link_from(self.t3)
self.end_point.gate_block = ~self.t3.complete
self.repeater2.gate_block = self.t3.complete
def initialize(self, device, **kwargs):
super(BenchmarkWorkflow, self).initialize(device, **kwargs)
self.forwards[-1].unlink_before()
self.forwards[-1].link_from(self.repeater)
class BenchmarkWorkflow(StandardWorkflow):
def create_workflow(self):
self.loader = self.real_loader = BenchmarkLoader(
self, minibatch_size=BenchmarkLoader.BATCH,
force_numpy=True) # do not preload all dataset to device
self.loader.link_from(self.start_point)
self.t0 = Timer(self, name="Timer 0",
sync_iterations=(1,)).link_from(self.loader)
self.repeater.link_from(self.t0)
self.link_forwards(("input", "minibatch_data"), self.loader)
self.forwards[0].unlink_before()
self.forwards[0].link_from(self.repeater)
self.t1 = Timer(self, name="Timer 1",
sync_iterations=(1,)).link_from(self.forwards[-2])
self.repeater.link_from(self.t1)
self.forwards[-1].gate_block = ~self.t1.complete
self.forwards[0].gate_block = self.t1.complete
self.link_evaluator(self.forwards[-1])
self.link_decision(self.evaluator)
self.decision.gate_skip = Bool(True)
last_gd = self.link_gds(self.decision)
self.t2 = Timer(self, name="Timer 2",
sync_iterations=(1,)).link_from(self.gds[-1])
self.repeater2 = Repeater(self).link_from(self.t2)
self.gds[-2].unlink_before()
self.gds[-2].link_from(self.repeater2)
self.t3 = Timer(self, name="Timer 3",
sync_iterations=(1,)).link_from(last_gd)
self.repeater2.link_from(self.t3)
self.end_point.link_from(self.t3)
self.end_point.gate_block = ~self.t3.complete
self.repeater2.gate_block = self.t3.complete
def initialize(self, device, **kwargs):
super(BenchmarkWorkflow, self).initialize(device, **kwargs)
self.forwards[-1].unlink_before()
self.forwards[-1].link_from(self.repeater)
class OptimizationWorkflow(AcceleratedWorkflow):
KWATTRS = set(GeneticsOptimizer.KWATTRS)
def __init__(self, workflow, **kwargs):
super(OptimizationWorkflow, self).__init__(workflow, **kwargs)
self.optimizer = GeneticsOptimizer(self, **kwargs)
self.repeater = Repeater(self)
self.repeater.link_from(self.start_point)
self.optimizer.link_from(self.repeater)
self.plotter_max = AccumulatingPlotter(
self,
name="Genetic Optimization Fitness",
plot_style="g-",
redraw_plot=False,
clear_plot=True)
if self.plotters_are_enabled:
self.plotter_max.link_attrs(self.optimizer,
("input", "max_fitness"))
else:
self.plotter_max.input = 0
self.plotter_max.link_from(self.optimizer)
self.plotter_max.gate_skip = ~self.optimizer.generation_changed
self.plotter_avg = AccumulatingPlotter(
self,
name="Genetic Optimization Fitness",
plot_style="b-",
redraw_plot=True)
if self.plotters_are_enabled:
self.plotter_avg.link_attrs(self.optimizer,
("input", "avg_fitness"))
else:
self.plotter_avg.input = 0
self.plotter_avg.link_from(self.plotter_max)
self.plotter_avg.gate_skip = ~self.optimizer.generation_changed
self.repeater.link_from(self.plotter_avg)
self.end_point.link_from(self.plotter_avg)
self.end_point.gate_block = ~self.optimizer.complete
self.repeater.gate_block = self.optimizer.complete
self.json_encoder = ConfigJSONEncoder
