def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
"""Check the type of inputs."""
validator.check_value_type("start_learning_rate", start_learning_rate, [float], prim_name)
validator.check_number_range("start_learning_rate rate", start_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
prim_name)
validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GE, prim_name)
validator.check_value_type("beta1", beta1, [float], prim_name)
validator.check_value_type("beta2", beta2, [float], prim_name)
validator.check_value_type("eps", eps, [float], prim_name)
validator.check_value_type(
"weight_dacay", weight_decay, [float], prim_name)
validator.check_number_range(
"beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch,
decay_epoch, is_stair):
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_positive('decay_rate', decay_rate, None)
validator.check_value_type('is_stair', is_stair, [bool], None)
def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):
"""Check the type of inputs."""
validator.check_value_type("learning_rate", learning_rate, [float], prim_name)
validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch,
decay_epoch, is_stair):
validator.check_positive_int(total_step, 'total_step')
validator.check_positive_int(step_per_epoch, 'step_per_epoch')
validator.check_positive_int(decay_epoch, 'decay_epoch')
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_legal_value('learning_rate', learning_rate, None)
validator.check_float_positive('decay_rate', decay_rate, None)
validator.check_float_legal_value('decay_rate', decay_rate, None)
validator.check_value_type('is_stair', is_stair, [bool], None)
def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
r"""
Calculate learning rate base on cosine decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
(1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
Args:
min_lr (float): The minimum value of learning rate.
max_lr (float): The maximum value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
[0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
"""
if not isinstance(min_lr, float):
raise TypeError("min_lr must be float.")
validator.check_number_range("min_lr", min_lr, 0.0, float("inf"),
Rel.INC_LEFT, None)
validator.check_float_positive('max_lr', max_lr, None)
validator.check_float_legal_value('max_lr', max_lr, None)
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
if min_lr >= max_lr:
raise ValueError('`max_lr` should be greater than `min_lr`.')
delta = 0.5 * (max_lr - min_lr)
lr = []
for i in range(total_step):
tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
lr.append(min_lr + delta *
(1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
return lr
def __init__(self,
max_val=1.0,
power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03):
super(MSSSIM, self).__init__()
validator.check_value_type('max_val', max_val, [int, float],
self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
validator.check_value_type('power_factors', power_factors,
[tuple, list], self.cls_name)
self.filter_size = validator.check_integer('filter_size', filter_size,
1, Rel.GE, self.cls_name)
self.filter_sigma = validator.check_float_positive(
'filter_sigma', filter_sigma, self.cls_name)
self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name)
self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name)
window = _create_window(filter_size, filter_sigma)
self.level = len(power_factors)
self.conv = []
for i in range(self.level):
self.conv.append(_conv2d(1, 1, filter_size, Tensor(window)))
self.conv[i].weight.requires_grad = False
self.multi_convs_list = CellList(self.conv)
self.weight_tensor = Tensor(power_factors, mstype.float32)
self.avg_pool = AvgPool2d(kernel_size=2, stride=2, pad_mode='valid')
self.relu = ReLU()
self.reduce_mean = P.ReduceMean()
self.prod = P.ReduceProd()
self.pow = P.Pow()
self.pack = P.Pack(axis=-1)
self.concat = P.Concat(axis=1)
def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
super(SSIM, self).__init__()
validator.check_value_type('max_val', max_val, [int, float], self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name)
self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name)
validator.check_value_type('k1', k1, [float], self.cls_name)
self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
validator.check_value_type('k2', k2, [float], self.cls_name)
self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
update_decay_epoch=False):
r"""
Calculate learning rate base on polynomial decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
(1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate
Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`, and
:math:`tmp\_decay\_epoch = decay\_epoch`. If `update_decay_epoch` is true, update the value of `tmp_decay_epoch`
every epoch. The formula is :math:`tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
power (float): A value used to calculate decayed learning rate. This parameter should be greater than 0.
update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> power = 0.5
>>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
[0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
"""
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_legal_value('learning_rate', learning_rate, None)
validator.check_float_positive('end_learning_rate', end_learning_rate, None)
validator.check_float_legal_value('end_learning_rate', end_learning_rate, None)
validator.check_float_positive('power', power, None)
validator.check_float_legal_value('power', power, None)
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
validator.check_value_type('update_decay_epoch', update_decay_epoch, [bool], None)
origin_decay_epoch = decay_epoch
function = lambda x, y: (x, min(x, y))
if update_decay_epoch:
function = lambda x, y: (origin_decay_epoch * max(math.ceil(y / origin_decay_epoch), 1), y)
lr = []
delta = learning_rate - end_learning_rate
for i in range(total_step):
current_epoch = math.floor(i / step_per_epoch)
decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
lr.append(delta * (1 - tmp_epoch / decay_epoch) ** power + end_learning_rate)
return lr
def __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
super(SSIM, self).__init__()
validator.check_value_type('max_val', max_val, [int, float], self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name)
self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name)
self.k1 = validator.check_value_type('k1', k1, [float], self.cls_name)
self.k2 = validator.check_value_type('k2', k2, [float], self.cls_name)
window = _create_window(filter_size, filter_sigma)
self.conv = _conv2d(1, 1, filter_size, Tensor(window))
self.conv.weight.requires_grad = False
self.reduce_mean = P.ReduceMean()
self.concat = P.Concat(axis=1)
def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):
"""Check the type of inputs."""
validator.check_float_positive('learning_rate', learning_rate, prim_name)
validator.check_float_legal_value('learning_rate', learning_rate, prim_name)
validator.check_float_positive('end_learning_rate', end_learning_rate, prim_name)
validator.check_float_legal_value('end_learning_rate', end_learning_rate, prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
