def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
super(BertLearningRate, self).__init__()
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
self.greater = P.Greater()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.cast = P.Cast()
def __init__(self, learning_rate, warmup_steps, multi_epochs, steps_per_epoch, factor=10):
super(CenterNetMultiEpochsDecayLR, self).__init__()
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = MultiEpochsDecayLR(learning_rate, multi_epochs, steps_per_epoch, factor)
self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
self.greater = ops.Greater()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.cast = ops.Cast()
def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
super(CenterNetPolynomialDecayLR, self).__init__()
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
self.greater = ops.Greater()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.cast = ops.Cast()
def __init__(self, learning_rate, end_learning_rate, warmup_steps,
decay_steps, power):
super(BertLearningRate, self).__init__()
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate,
decay_steps, power)
self.warmup_steps = ts.array([warmup_steps], dtype=ts.float32)
self.greater = P.Greater()
self.one = ts.array([1.0], dtype=ts.float32)
self.cast = P.Cast()
def __init__(self,
learning_rate,
end_learning_rate,
warmup_steps,
decay_steps,
power=1.0,
use_cosine=True):
super(LearningRate, self).__init__()
self.warmup_flag = False
if warmup_steps > 0:
self.warmup_flag = True
self.warmup_lr = WarmUpLR(learning_rate, warmup_steps)
self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate,
decay_steps, power)
self.cosine_decay_lr = CosineDecayLR(end_learning_rate, learning_rate,
decay_steps)
self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
self.greater = P.Greater()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.cast = P.Cast()
self.use_cosine = use_cosine
