def __init__(self, mul_weight, strategy1=None, strategy2=None):
super().__init__()
self.mul = P.Mul().shard(strategy1)
self.dropout_do_mask = P.DropoutDoMask().shard(strategy2)
self.dropout_gen_mask = P.DropoutGenMask()
self.get_shape = P.Shape()
self.cast = P.Cast()
self.mul_weight = Parameter(mul_weight, "w1")
self.keep_prob = Tensor(0.9)
def test_dropout():
dropOutGenMask = P.DropoutGenMask()
dropoutDoMask = P.DropoutDoMask()
shape = P.Shape()
def get_dropout(x, prob):
mask = dropOutGenMask(shape(x), prob)
y = dropoutDoMask(x, mask, prob)
return y
return get_dropout
def __init__(self, keep_prob=0.5, seed0=0, seed1=0, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
self.keep_prob = Tensor(keep_prob)
self.seed0 = seed0
self.seed1 = seed1
self.dtype = dtype
self.get_shape = P.Shape()
self.dropout_gen_mask = P.DropoutGenMask(Seed0=seed0, Seed1=seed1)
self.dropout_do_mask = P.DropoutDoMask()
self.cast = P.Cast()
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
self.keep_prob = keep_prob
seed0, seed1 = _get_graph_seed(0, "dropout")
self.seed0 = seed0
self.seed1 = seed1
self.dtype = dtype
self.get_shape = P.Shape()
self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0, Seed1=self.seed1)
self.dropout_do_mask = P.DropoutDoMask()
self.cast = P.Cast()
self.is_gpu = context.get_context('device_target') in ["GPU"]
self.dropout = P.Dropout(keep_prob)
'desc_bprop': [[8192,1024]],
'skip': ['backward']}),
('UnsortedSegmentSum_1', {
'block': P.UnsortedSegmentSum(),
'desc_const': [4],
'desc_inputs': [[3, 2, 1, 3], Tensor(np.array([[0, 1], [0, 1], [0, 1]]).astype(np.int32))],
'desc_bprop': [[4, 1, 3]],
'skip': ['backward']}),
('DropoutGenMask', {
'block': P.DropoutGenMask(),
'desc_const': [(2, 2), Tensor(0.5, mstype.float32)],
'desc_inputs': [],
'desc_bprop': [Tensor(np.ones(1).astype(np.int8))],
'skip': ['backward']}),
('DropoutDoMask', {
'block': P.DropoutDoMask(),
'desc_const': [Tensor(0.5)],
'desc_inputs': [[64, 12, 128, 128], Tensor(np.ones(1572864).astype(np.uint8))],
'desc_bprop': [[64, 12, 128, 128]]}),
('Dropout', {
'block': nn.Dropout(0.5),
'desc_inputs': [[64, 12, 128, 128]],
'desc_bprop': [[64, 12, 128, 128]]}),
('ReduceMean0', {
'block': P.ReduceMean(),
'desc_const': [(2,)],
'desc_inputs': [[3, 2, 2]],
'desc_bprop': [[3, 2]]}),
('ReduceMean1', {
'block': P.ReduceMean(),
'desc_const': [2],
def __init__(self):
super(Net, self).__init__()
self.dropoutdomask = P.DropoutDoMask()
'desc_inputs':
[[3, 2, 1, 3],
Tensor(np.array([[0, 1], [0, 1], [0, 1]]).astype(np.int32))],
'desc_bprop': [[4, 1, 3]],
'skip': ['backward']
}),
('DropoutGenMask', {
'block': P.DropoutGenMask(),
'desc_const': [(2, 2), Tensor(0.5, mstype.float32)],
'desc_inputs': [],
'desc_bprop': [Tensor(np.ones(1).astype(np.int8))],
'skip': ['backward']
}),
('DropoutDoMask', {
'block':
P.DropoutDoMask(),
'desc_const': [Tensor(0.5)],
'desc_inputs': [[64, 12, 128, 128],
Tensor(np.ones(1572864).astype(np.uint8))],
'desc_bprop': [[64, 12, 128, 128]]
}),
('Dropout', {
'block': nn.Dropout(0.5),
'desc_inputs': [[64, 12, 128, 128]],
'desc_bprop': [[64, 12, 128, 128]]
}),
('ReduceMean0', {
'block': P.ReduceMean(),
'desc_const': [(2, )],
'desc_inputs': [[3, 2, 2]],
'desc_bprop': [[3, 2]]