""" Testing and training events. There are: * TestResult * BeginIteration * EndIteration * BeginPass * EndPass """ __all__ = [ 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult', 'EndForwardBackward' ] class WithMetric(object): def __init__(self, evaluator): import py_paddle.swig_paddle as api if not isinstance(evaluator, api.Evaluator): raise TypeError("Evaluator should be api.Evaluator type") self.__evaluator__ = evaluator @property def metrics(self): names = self.__evaluator__.getNames() retv = dict() for each_name in names: val = self.__evaluator__.getValue(each_name) retv[each_name] = val return retv class TestResult(WithMetric): """ Result that trainer.test return. """ def __init__(self, evaluator, cost): super(TestResult, self).__init__(evaluator) self.cost = cost class BeginPass(object): """ Event On One Pass Training Start. """ def __init__(self, pass_id): self.pass_id = pass_id class EndPass(WithMetric): """ Event On One Pass Training Complete. To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')" in your event_handler call back """ def __init__(self, pass_id, evaluator, gm): self.pass_id = pass_id self.gm = gm WithMetric.__init__(self, evaluator) class BeginIteration(object): """ Event On One Batch Training Start. """ def __init__(self, pass_id, batch_id): self.pass_id = pass_id self.batch_id = batch_id class EndForwardBackward(object): """ Event On One Batch ForwardBackward Complete. """ def __init__(self, pass_id, batch_id, gm): self.pass_id = pass_id self.batch_id = batch_id self.gm = gm class EndIteration(WithMetric): """ Event On One Batch Training Complete. To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')" in your event_handler call back """ def __init__(self, pass_id, batch_id, cost, evaluator, gm): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost self.gm = gm WithMetric.__init__(self, evaluator)