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_type('max_val', max_val, [int, float])
validator.check('max_val', max_val, '', 0.0, Rel.GT)
self.max_val = max_val
self.filter_size = validator.check_integer('filter_size', filter_size,
1, Rel.GE)
self.filter_sigma = validator.check_float_positive(
'filter_sigma', filter_sigma)
validator.check_type('k1', k1, [float])
self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0,
Rel.INC_NEITHER)
validator.check_type('k2', k2, [float])
self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0,
Rel.INC_NEITHER)
self.mean = P.DepthwiseConv2dNative(channel_multiplier=1,
kernel_size=filter_size)
def _check_param(initial_accum,
learning_rate,
lr_power,
l1,
l2,
use_locking,
loss_scale=1.0,
weight_decay=0.0):
validator.check_type("initial_accum", initial_accum, [float])
validator.check("initial_accum", initial_accum, "", 0.0, Rel.GE)
validator.check_type("learning_rate", learning_rate, [float])
validator.check("learning_rate", learning_rate, "", 0.0, Rel.GT)
validator.check_type("lr_power", lr_power, [float])
validator.check("lr_power", lr_power, "", 0.0, Rel.LE)
validator.check_type("l1", l1, [float])
validator.check("l1", l1, "", 0.0, Rel.GE)
validator.check_type("l2", l2, [float])
validator.check("l2", l2, "", 0.0, Rel.GE)
validator.check_type("use_locking", use_locking, [bool])
validator.check_type("loss_scale", loss_scale, [float])
validator.check("loss_scale", loss_scale, "", 1.0, Rel.GE)
validator.check_type("weight_decay", weight_decay, [float])
validator.check("weight_decay", weight_decay, "", 0.0, Rel.GE)
def __init__(self, max_val=1.0):
super(PSNR, self).__init__()
validator.check_type('max_val', max_val, [int, float])
validator.check('max_val', max_val, '', 0.0, Rel.GT)
self.max_val = max_val
