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_inputs': [0],
}),
('Softmax_ValueError_1', {
'block': (lambda _: P.Softmax("1"), {'exception': TypeError}),
'desc_inputs': [0],
}),
('Softmax_ValueError_2', {
'block': (lambda _: P.Softmax(1.1), {'exception': TypeError}),
'desc_inputs': [0],
}),
('Softmax_ValueError_3', {
'block': (lambda _: P.Softmax(axis="1"), {'exception': TypeError}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_1', {
'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {'exception': TypeError}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_2', {
'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {'exception': TypeError}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_3', {
'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {'exception': TypeError}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_4', {
'block': (lambda _: P.DropoutGenMask(Seed1=2.0), {'exception': TypeError}),
'desc_inputs': [0],
}),
('MaxPool2d_ValueError_1', {
def __init__(self):
super(Net, self).__init__()
self.mask = P.DropoutGenMask(10, 28)
self.shape = P.Shape()
'desc_inputs': [[1152], Tensor(np.array(10).astype(np.int32))],
'desc_bprop': [Tensor(np.array(10).astype(np.float32))]}),
('UnsortedSegmentSum', {
'block': P.UnsortedSegmentSum(),
'desc_const': [1280],
'desc_inputs': [[1280,1024], Tensor(np.ones(1280).astype(np.int32))],
'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_inputs': [0],
}),
('Softmax_ValueError_2', {
'block': (lambda _: P.Softmax(1.1), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('Softmax_ValueError_3', {
'block': (lambda _: P.Softmax(axis="1"), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_1', {
'block': (lambda _: P.DropoutGenMask(Seed0="seed0"), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_2', {
'block': (lambda _: P.DropoutGenMask(Seed0=1.0), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('DropoutGenMask_ValueError_3', {
'block': (lambda _: P.DropoutGenMask(Seed1="seed1"), {
'exception': TypeError
}),
'desc_inputs': [0],
def __init__(self, shape):
super(DropoutGenMaskNet, self).__init__()
self.shape = shape
self.dropout_gen_mask = P.DropoutGenMask(Seed0=0, Seed1=0)
backend_reshape = Primitive('Reshape')
cast = P.Cast()
backend_cast = Primitive('Cast')
transpose = P.Transpose()
backend_transpose = Primitive('Transpose')
onehot1 = P.OneHot()
onehot2 = P.OneHot()
backend_onehot1 = Primitive('OneHot')
backend_onehot2 = Primitive('OneHot')
stridedslicegrad = G.StridedSliceGrad()
backend_stridedslicegrad = Primitive('StridedSliceGrad')
on_value = Tensor(1.0, mstype.float32)
off_value = Tensor(0.0, mstype.float32)
depth = Tensor(2, mstype.int32)
shape = (2, 4, 2, 2)
dropout_gen_mask = P.DropoutGenMask()
class FnDict:
def __init__(self):
self.fnDict = {}
def __call__(self, fn):
self.fnDict[fn.__name__] = fn
def __getitem__(self, name):
return self.fnDict[name]
def test_convert_reshape_input_to_attr(tag):
fns = FnDict()
