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)
'block': ArgminNet(),
'desc_inputs': [Tensor(np.array([[128, 32, 32, 64],[128, 32, 32, 64]]).astype(np.float16))],
'desc_bprop': [Tensor(np.array([[128, 32, 32, 64],[128, 32, 32, 64]]).astype(np.float16))],
'skip': ['backward']}),
('CumSumNet', {
'block': CumSumNet(),
'desc_const': [0],
'desc_inputs': [Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float16))],
'desc_bprop': [Tensor(np.array([[3, 4, 6, 10],[1, 6, 7, 9],[4, 3, 8, 7],[1, 3, 7, 9]]).astype(np.float16))]}),
('OneHot', {
'block': P.OneHot(),
'desc_const': [3, Tensor(1.0, mstype.float32), Tensor(0.0, mstype.float32)],
'desc_inputs': [Tensor(np.array([64]).astype(np.int32))],
'desc_bprop': [[64, 2]]}),
('ReduceProd_0', {
'block': P.ReduceProd(),
'desc_const': [0],
'desc_inputs': [[3, 2]],
'desc_bprop': [[2]]}),
('ReduceProd_1', {
'block': P.ReduceProd(keep_dims=True),
'desc_const': [0],
'desc_inputs': [[3, 2]],
'desc_bprop': [[1, 2]]}),
('CumProd', {
'block': P.CumProd(),
'desc_const': [0],
'desc_inputs': [[3, 2]],
'desc_bprop': [[3, 2]]}),
('ApplyFtrl', {
'block': P.ApplyFtrl(),
