def __init__(self, sharpness=1.0, name='Softplus'):
"""
Constructor of Softplus Bijector.
"""
param = dict(locals())
param['param_dict'] = {'sharpness': sharpness}
super(Softplus, self).__init__(name=name, dtype=None, param=param)
self._sharpness = self._add_parameter(sharpness, 'sharpness')
self.exp = exp_generic
self.log = log_generic
self.expm1 = P.Expm1()
self.abs = P.Abs()
self.dtypeop = P.DType()
self.cast = P.Cast()
self.fill = P.Fill()
self.greater = P.Greater()
self.less = P.Less()
self.log_sigmoid = LogSigmoid()
self.logicalor = P.LogicalOr()
self.select = P.Select()
self.shape = P.Shape()
self.sigmoid = P.Sigmoid()
self.softplus = self._softplus
self.inverse_softplus = self._inverse_softplus
self.threshold = np.log(np.finfo(np.float32).eps) + 1
self.tiny = np.exp(self.threshold)
def __init__(self, sharpness=1.0, name='Softplus'):
"""
Constructor of Softplus Bijector.
"""
param = dict(locals())
validator.check_value_type('sharpness', sharpness, [int, float],
type(self).__name__)
super(Softplus, self).__init__(name=name, param=param)
self._sharpness = cast_to_tensor(sharpness)
self.exp = exp_generic
self.log = log_generic
self.expm1 = expm1_generic
self.abs = P.Abs()
self.dtypeop = P.DType()
self.fill = P.Fill()
self.greater = P.Greater()
self.less = P.Less()
self.log_sigmoid = LogSigmoid()
self.logicalor = P.LogicalOr()
self.select = P.Select()
self.shape = P.Shape()
self.sigmoid = P.Sigmoid()
self.softplus = self._softplus
self.inverse_softplus = self._inverse_softplus
self.threshold = np.log(np.finfo(np.float32).eps) + 1
self.tiny = np.exp(self.threshold)
def __init__(self):
super(IGamma, self).__init__()
# const numbers
# If more data types are supported, this float max value need to be selected.
self.log_maxfloat32 = Tensor(np.log(np.finfo(np.float32).max),
mstype.float32)
# operations
self.logicaland = P.LogicalAnd()
self.logicalor = P.LogicalOr()
self.logicalnot = P.LogicalNot()
self.equal = P.Equal()
self.greater = P.Greater()
self.less = P.Less()
self.neg = P.Neg()
self.log = P.Log()
self.exp = P.Exp()
self.select = P.Select()
self.zeroslike = P.ZerosLike()
self.fill = P.Fill()
self.shape = P.Shape()
self.dtype = P.DType()
self.lgamma = LGamma()
self.const = P.ScalarToArray()
self.cast = P.Cast()
def __init__(self, batch_size=4):
super(DiceLoss, self).__init__()
self.threshold0 = Tensor(0.5, mstype.float32)
self.zero_float32 = Tensor(0.0, mstype.float32)
self.k = int(640 * 640)
self.negative_one_int32 = Tensor(-1, mstype.int32)
self.batch_size = batch_size
self.concat = P.Concat()
self.less_equal = P.LessEqual()
self.greater = P.Greater()
self.reduce_sum = P.ReduceSum()
self.reduce_sum_keep_dims = P.ReduceSum(keep_dims=True)
self.reduce_mean = P.ReduceMean()
self.reduce_min = P.ReduceMin()
self.cast = P.Cast()
self.minimum = P.Minimum()
self.expand_dims = P.ExpandDims()
self.select = P.Select()
self.fill = P.Fill()
self.topk = P.TopK(sorted=True)
self.shape = P.Shape()
self.sigmoid = P.Sigmoid()
self.reshape = P.Reshape()
self.slice = P.Slice()
self.logical_and = P.LogicalAnd()
self.logical_or = P.LogicalOr()
self.equal = P.Equal()
self.zeros_like = P.ZerosLike()
self.add = P.TensorAdd()
self.gather = P.Gather()
def __init__(self,
network,
optimizer,
scale_update_cell=None,
accumulation_steps=1,
enable_global_norm=False):
super(BertTrainAccumulateStepsWithLossScaleCell,
self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.weights = optimizer.parameters
self.optimizer = optimizer
self.accumulation_steps = accumulation_steps
self.enable_global_norm = enable_global_norm
self.one = Tensor(np.array([1]).astype(np.int32))
self.zero = Tensor(np.array([0]).astype(np.int32))
self.local_step = Parameter(initializer(0, [1], mstype.int32),
name="local_step")
self.accu_grads = self.weights.clone(prefix="accu_grads", init='zeros')
self.accu_overflow = Parameter(initializer(0, [1], mstype.int32),
name="accu_overflow")
self.loss = Parameter(initializer(0, [1], mstype.float32),
name="accu_loss")
self.grad = C.GradOperation(get_by_list=True, sens_param=True)
self.reducer_flag = False
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
if self.parallel_mode in [
ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL
]:
self.reducer_flag = True
self.grad_reducer = F.identity
self.degree = 1
if self.reducer_flag:
self.degree = get_group_size()
self.grad_reducer = DistributedGradReducer(optimizer.parameters,
False, self.degree)
self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
self.overflow_reducer = F.identity
if self.is_distributed:
self.overflow_reducer = P.AllReduce()
self.cast = P.Cast()
self.alloc_status = P.NPUAllocFloatStatus()
self.get_status = P.NPUGetFloatStatus()
self.clear_before_grad = P.NPUClearFloatStatus()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = P.LessEqual()
self.logical_or = P.LogicalOr()
self.not_equal = P.NotEqual()
self.select = P.Select()
self.reshape = P.Reshape()
self.hyper_map = C.HyperMap()
self.loss_scale = None
self.loss_scaling_manager = scale_update_cell
if scale_update_cell:
self.loss_scale = Parameter(Tensor(
scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
def __init__(self):
super(MathBinaryNet2, self).__init__()
self.less_equal = P.LessEqual()
self.greater = P.Greater()
self.logic_or = P.LogicalOr()
self.logic_and = P.LogicalAnd()
self.number = 3
self.flag = True
def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.uint_type + mstype.int_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Categorical, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self.probs is not None:
check_rank(self.probs)
check_prob(self.probs)
check_sum_equal_one(probs)
# update is_scalar_batch and broadcast_shape
# drop one dimension
if self.probs.shape[:-1] == ():
self._is_scalar_batch = True
self._broadcast_shape = self._broadcast_shape[:-1]
self.argmax = P.ArgMaxWithValue(axis=-1)
self.broadcast = broadcast_to
self.cast = P.Cast()
self.clip_by_value = C.clip_by_value
self.concat = P.Concat(-1)
self.cumsum = P.CumSum()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expand_dim = P.ExpandDims()
self.fill = P.Fill()
self.gather = P.GatherNd()
self.greater = P.Greater()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = log_generic
self.log_softmax = P.LogSoftmax()
self.logicor = P.LogicalOr()
self.logicand = P.LogicalAnd()
self.multinomial = P.Multinomial(seed=self.seed)
self.reshape = P.Reshape()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select = P.Select()
self.shape = P.Shape()
self.softmax = P.Softmax()
self.squeeze = P.Squeeze()
self.squeeze_first_axis = P.Squeeze(0)
self.squeeze_last_axis = P.Squeeze(-1)
self.square = P.Square()
self.transpose = P.Transpose()
self.is_nan = P.IsNan()
self.index_type = mstype.int32
self.nan = np.nan
def test_logicalor():
op = P.LogicalOr()
op_wrapper = OpNetWrapper(op)
input_x = Tensor(np.array([True, False, False]))
input_y = Tensor(np.array([True, True, False]))
outputs = op_wrapper(input_x, input_y)
assert np.allclose(outputs.asnumpy(), (True, True, False))
def __init__(self,
loc=None,
scale=None,
seed=None,
dtype=mstype.float32,
name="Logistic"):
"""
Constructor of Logistic.
"""
param = dict(locals())
param['param_dict'] = {'loc': loc, 'scale': scale}
valid_dtype = mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Logistic, self).__init__(seed, dtype, name, param)
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
if self._scale is not None:
check_greater_zero(self._scale, "scale")
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.consttensor = P.ScalarToTensor()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expm1 = P.Expm1()
self.fill = P.Fill()
self.less = P.Less()
self.log = log_generic
self.log1p = P.Log1p()
self.logicalor = P.LogicalOr()
self.erf = P.Erf()
self.greater = P.Greater()
self.sigmoid = P.Sigmoid()
self.squeeze = P.Squeeze(0)
self.select = P.Select()
self.shape = P.Shape()
self.softplus = self._softplus
self.sqrt = P.Sqrt()
self.uniform = C.uniform
self.threshold = np.log(np.finfo(np.float32).eps) + 1.
self.tiny = np.finfo(np.float).tiny
self.sd_const = np.pi / np.sqrt(3)
def __init__(self):
super(NetOr, self).__init__()
self.logicalor = P.LogicalOr()
'desc_inputs': [[2, 3, 4, 1], [4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))]}),
('GreaterEqual', {
'block': P.GreaterEqual(),
'desc_inputs': [[2, 3, 4, 1], [4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))]}),
('LogicalNot', {
'block': P.LogicalNot(),
'desc_inputs': [Tensor(np.zeros((3, 4, 5), np.bool_))],
'desc_bprop': [Tensor(np.ones((3, 4, 5), np.bool_))]}),
('LogicalAnd', {
'block': P.LogicalAnd(),
'desc_inputs': [Tensor(np.zeros((2, 3, 4), np.bool_)), Tensor(np.ones((1), np.bool_))],
'desc_bprop': [Tensor(np.zeros((2, 3, 4), np.bool_))]}),
('LogicalOr', {
'block': P.LogicalOr(),
'desc_inputs': [Tensor(np.zeros((3, 4, 5), np.bool_)), Tensor(np.ones((3, 1, 1), np.bool_))],
'desc_bprop': [Tensor(np.zeros((3, 4, 5), np.bool_))]}),
('NpuAllocFloatStatus', {
'block': P.NPUAllocFloatStatus(),
'desc_inputs': [],
'add_fack_input': True,
'fack_input_type': np.float32,
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']}),
('NpuGetFloatStatus', {
'block': P.NPUGetFloatStatus(),
'desc_inputs': [Tensor(np.zeros([8]).astype(np.float32))],
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']}),
('NpuClearFloatStatus', {
def __init__(self):
super(NetConditionLackBranch, self).__init__()
self.logicaland = P.LogicalAnd()
self.logicalor = P.LogicalOr()
'skip': ['backward']}),
# type of x and y not match
('LogicalAnd1', {
'block': (P.LogicalAnd(), {'exception': TypeError, 'error_keywords': ['LogicalAnd']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# shape of x and y not match
('LogicalAnd2', {
'block': (P.LogicalAnd(), {'exception': ValueError, 'error_keywords': ['LogicalAnd']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
'skip': ['backward']}),
# type of x and y not match
('LogicalOr1', {
'block': (P.LogicalOr(), {'exception': TypeError, 'error_keywords': ['LogicalOr']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.bool_))],
'skip': ['backward']}),
# shape of x and y not match
('LogicalOr2', {
'block': (P.LogicalOr(), {'exception': ValueError, 'error_keywords': ['LogicalOr']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.bool_)), Tensor(np.ones([3, 2]).astype(np.bool_))],
'skip': ['backward']}),
# input is not tensor
('NPUGetFloatStatus0', {
'block': (P.NPUGetFloatStatus(), {'exception': TypeError, 'error_keywords': ['NPUGetFloatStatus']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input is Tensor(int32), not Tensor(float32)
('NPUGetFloatStatus1', {
# shape of x and y not match
('LogicalAnd2', {
'block': (P.LogicalAnd(), {
'exception': ValueError,
'error_keywords': ['LogicalAnd']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.bool_)),
Tensor(np.ones([3, 2]).astype(np.bool_))
],
'skip': ['backward']
}),
# type of x and y not match
('LogicalOr1', {
'block': (P.LogicalOr(), {
'exception': TypeError,
'error_keywords': ['LogicalOr']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.int32)),
Tensor(np.ones([3, 4]).astype(np.bool_))
],
'skip': ['backward']
}),
# shape of x and y not match
('LogicalOr2', {
'block': (P.LogicalOr(), {
'exception': ValueError,
'error_keywords': ['LogicalOr']
}),
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().shard(strategy1)
self.equal = P.Equal().shard(strategy2)
self.notequal = P.NotEqual().shard(strategy2)
self.logical = P.LogicalOr().shard(strategy2)
'block': P.LogicalNot(),
'desc_inputs': [Tensor(np.zeros((3, 4, 5), np.bool_))],
'desc_bprop': [Tensor(np.ones((3, 4, 5), np.bool_))]
}),
('LogicalAnd', {
'block':
P.LogicalAnd(),
'desc_inputs': [
Tensor(np.zeros((2, 3, 4), np.bool_)),
Tensor(np.ones((1), np.bool_))
],
'desc_bprop': [Tensor(np.zeros((2, 3, 4), np.bool_))]
}),
('LogicalOr', {
'block':
P.LogicalOr(),
'desc_inputs': [
Tensor(np.zeros((3, 4, 5), np.bool_)),
Tensor(np.ones((3, 1, 1), np.bool_))
],
'desc_bprop': [Tensor(np.zeros((3, 4, 5), np.bool_))]
}),
('NpuAllocFloatStatus', {
'block': P.NPUAllocFloatStatus(),
'desc_inputs': [],
'add_fack_input': True,
'fack_input_type': np.float32,
'desc_bprop': [Tensor(np.zeros([8]).astype(np.float32))],
'skip': ['backward']
}),
('NpuGetFloatStatus', {
