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modify MetaTensor and Tensor

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pull/10325/head
lilei 4 years ago
parent 2dc275be06
commit b858097ae4
25 changed files with 260 additions and 238 deletions
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8
mindspore/common/initializer.py
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@ -21,7 +21,7 @@ import numpy as np
from scipy.stats import truncnorm
from .seed import get_seed, _get_graph_seed
from . import dtype as mstype
from .tensor import Tensor, MetaTensor
from .tensor import Tensor
from .._c_expression import random_normal
_INITIALIZER_ALIAS = dict()
@ -416,8 +416,8 @@ def initializer(init, shape=None, dtype=mstype.float32):
dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32.
Returns:
Union[Tensor, MetaTensor], When `init` is Tensor, the return is Tensor object,
otherwise the return is Initialize object.
Union[Tensor], return is Tensor object.
Examples:
>>> tensor = initializer('ones', [1, 2, 3], mindspore.float32)
@ -451,7 +451,7 @@ def initializer(init, shape=None, dtype=mstype.float32):
elif isinstance(init, numbers.Number):
init = Constant(init)
shape = shape if shape is not None else init.shape
init_obj = MetaTensor(dtype, shape, init)
init_obj = Tensor(dtype=dtype, shape=shape, init=init)
return init_obj
__all__ = [

96
mindspore/common/parameter.py
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@ -17,11 +17,11 @@
from copy import copy
import numbers
from .._c_expression import ParamInfo
from .._c_expression import MetaTensor as MetaTensor_
from . import dtype as mstype
from .initializer import initializer
from .tensor import Tensor, MetaTensor
from .tensor import Tensor
from .._checkparam import Validator
from .._c_expression import Tensor as Tensor_
from ..parallel._tensor import _get_slice_index
from ..parallel._auto_parallel_context import auto_parallel_context
from ..parallel._ps_context import _is_role_worker, _is_role_pserver, _is_role_sched, _clone_hash_table
@ -52,15 +52,15 @@ def init_to_value(init):
raise ValueError("init should be number or string")
class Parameter(MetaTensor_):
class Parameter(Tensor_):
"""
Parameter types of cell models.
After initialized `Parameter` is a subtype of `Tensor`.
In auto_parallel mode of "semi_auto_parallel" and "auto_parallel", if init `Parameter` by
an `MetaTensor`, the type of Parameter will be `MetaTensor` not `Tensor`. `MetaTensor_`
only saves the shape and type info of a tensor with no memory usage. The shape can be changed while
an `Tensor`, the type of Parameter will be `Tensor`. `Tensor`
will save the shape and type info of a tensor with no memory usage. The shape can be changed while
compiling for auto-parallel. Call `init_data` will return a Tensor Parameter with initialized data.
Note:
@ -72,7 +72,7 @@ class Parameter(MetaTensor_):
otherwise, the parameter name may be different than expected.
Args:
default_input (Union[Tensor, MetaTensor, Number]): Parameter data, to be set initialized.
default_input (Union[Tensor, Number]): Parameter data, to be set initialized.
name (str): Name of the child parameter. Default: None.
requires_grad (bool): True if the parameter requires gradient. Default: True.
layerwise_parallel (bool): When layerwise_parallel is true in data parallel mode,
@ -116,12 +116,12 @@ class Parameter(MetaTensor_):
new_type = Parameter._get_base_class(input_class)
obj = input_class.__new__(new_type)
input_class.__init__(obj, *class_init_args)
# it's better to make the Initializer a kind of metatensor.
# it's better to make the Initializer a kind of tensor.
obj.init_mode = None
obj.is_default_input_meta = False
if isinstance(default_input, MetaTensor):
obj.is_default_input_meta = True
if not isinstance(obj, Tensor):
obj.is_default_input_init = False
if isinstance(default_input, Tensor) and default_input.has_init:
obj.is_default_input_init = True
if obj.has_init:
obj.init_mode = default_input
return obj
@ -154,12 +154,18 @@ class Parameter(MetaTensor_):
self._cast_type = None
self._unique = False
self.is_in_parallel = _is_in_parallel_mode()
if isinstance(default_input, (MetaTensor, Tensor)):
MetaTensor_.__init__(self, default_input.dtype, default_input.shape)
if isinstance(default_input, Tensor):
Tensor_.__init__(self, default_input.dtype, default_input.shape)
elif isinstance(default_input, int):
MetaTensor_.__init__(self, mstype.int64, ())
Tensor_.__init__(self, mstype.int64, ())
elif isinstance(default_input, float):
MetaTensor_.__init__(self, mstype.float32, ())
Tensor_.__init__(self, mstype.float32, ())
def __deepcopy__(self, memodict):
new_obj = Parameter(self)
new_obj.name = self.name
new_obj._inited_param = self._inited_param # pylint: disable=W0212
return new_obj
@staticmethod
def _get_base_class(input_class):
@ -176,12 +182,12 @@ class Parameter(MetaTensor_):
"""Set `set_data` of current `Parameter`."""
if isinstance(data, bool):
raise ValueError('Parameter data can not be `bool`')
if isinstance(data, MetaTensor):
if isinstance(data, Tensor) and data.has_init:
if _is_in_parallel_mode() or _is_role_worker() or _is_role_sched():
# do not init data while in auto parallel.
return (MetaTensor_, data.dtype, data.shape)
data = data.to_tensor()
if isinstance(data, Tensor):
return (Tensor, None, data.dtype, data.shape, data.init)
data = data.init_data().asnumpy()
elif isinstance(data, Tensor):
# make a copy of Tensor to init the parameter
return (Tensor, data.asnumpy(),)
if isinstance(data, int):
@ -322,7 +328,7 @@ class Parameter(MetaTensor_):
Clone the parameter.
Args:
init (Union[Tensor, str, MetaTensor, numbers.Number]): Initialize the shape of the parameter.
init (Union[Tensor, str, numbers.Number]): Initialize the shape of the parameter.
Default: 'same'.
Returns:
@ -332,6 +338,7 @@ class Parameter(MetaTensor_):
# pylint: disable=protected-access
x._param_info = self._param_info.clone()
x.is_init = False
x.init = self.init
x.is_param_ps = self.is_param_ps
x.init_in_server = self.init_in_server
x.cache_enable = self.cache_enable
@ -382,6 +389,7 @@ class Parameter(MetaTensor_):
if isinstance(self, Tensor):
# for Tensor same shape:
self.init_flag = False
self.init = None
return self.assign_value(data)
# create a new tensor
return Parameter(data, self.name, self.requires_grad)
@ -391,7 +399,7 @@ class Parameter(MetaTensor_):
Set `set_data` of current `Parameter`.
Args:
data (Union[Tensor, MetaTensor, int, float]): new data.
data (Union[Tensor, int, float]): new data.
slice_shape (bool): If slice the parameter is set to true, the shape is not checked for consistency.
Default: False.
@ -403,20 +411,20 @@ class Parameter(MetaTensor_):
f"Current dtype is {self.dtype}, and incoming is {incoming}. "
f"Use .set_dtype(xxx) to change the dtype.")
if not isinstance(data, (MetaTensor_, int, float)):
raise TypeError(f"Parameter data must be [`MetaTensor`, `int`, `float`] or a kind of `MetaTensor_` "
f"(like `Tensor` or `MetaTensor_`). But with type {type(data)}.")
if not isinstance(data, (Tensor, int, float)):
raise TypeError(f"Parameter data must be [`Tensor`, `int`, `float`] or a kind of `Tensor` "
f"(like `Tensor`). But with type {type(data)}.")
if isinstance(data, (int, float)):
if self.dtype in mstype.int_type and isinstance(data, float):
raise_type_error(mstype.float_)
data = Tensor(data, self.dtype)
# both not init.
is_incoming_tensor = isinstance(data, Tensor)
is_current_tensor = isinstance(self, Tensor)
incoming_tensor_is_init = isinstance(data, Tensor) and not data.has_init
current_tensor_is_init = isinstance(self, Tensor) and not self.has_init
if is_incoming_tensor and not is_current_tensor:
raise TypeError("Parameter is a `MetaTensor_` and not initializered, `data` for `set_data`"
"should be a MetaTensor. If you want to update it by Tensor, call method"
if incoming_tensor_is_init and not current_tensor_is_init:
raise TypeError("Parameter is a `Tensor` and not initializered, `data` for `set_data`"
"should be a Tensor. If you want to update it by Tensor, call method"
"`init_parameters_data` of `Cell` to init and replace all the Parameter of"
"network, then call this method.")
if tuple(self.shape) != tuple(data.shape):
@ -429,16 +437,16 @@ class Parameter(MetaTensor_):
raise_type_error(data.dtype)
else:
data = Tensor(data, self.dtype)
if isinstance(data, MetaTensor):
if isinstance(data, Tensor) and data.has_init:
# The parameter has been initializered, directly update by the data
if is_current_tensor:
self._update_tensor_data(data.to_tensor())
if current_tensor_is_init:
self._update_tensor_data(data.init_data())
else:
# also update the related inited parameter data
if self.inited_param is not None:
self.inited_param.set_data(data)
self.init_mode = data
elif is_incoming_tensor or is_current_tensor:
elif incoming_tensor_is_init or current_tensor_is_init:
self._update_tensor_data(data)
else:
raise ValueError(f"Not support to update the Parameter by {data}")
@ -465,10 +473,10 @@ class Parameter(MetaTensor_):
Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before,
returns the same initialized `Parameter`.
"""
if self.is_default_input_meta:
if self.is_default_input_init:
is_current_in_parallel = _is_in_parallel_mode()
if self.is_in_parallel != is_current_in_parallel:
raise RuntimeError("Must set or change parallel mode before any MetaTensor created.")
raise RuntimeError("Must set or change parallel mode before any Tensor created.")
if self.init_mode is None:
return self
if self.inited_param is not None:
@ -482,21 +490,23 @@ class Parameter(MetaTensor_):
if len(layout) < 3:
raise ValueError("The length of layout must be larger than 3! layout is {}.".format(layout))
slice_index = int(_get_slice_index(layout[0], layout[1]))
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, MetaTensor)):
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Tensor)
and self.init_mode.init is not None):
if _is_role_worker() or _is_role_sched():
data = self.init_mode.to_tensor(0, [1])
data = self.init_mode.init_data(0, [1])
else:
data = self.init_mode.to_tensor(slice_index, layout[2], layout[5])
data = self.init_mode.init_data(slice_index, layout[2], layout[5])
else:
data = self.init_mode.to_tensor(slice_index, layout[2], layout[5])
data = self.init_mode.init_data(slice_index, layout[2], layout[5])
else:
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, MetaTensor)):
if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Tensor)
and self.init_mode.init is not None):
if _is_role_worker() or _is_role_sched():
data = self.init_mode.to_tensor(0, [1])
data = self.init_mode.init_data(0, [1])
else:
data = self.init_mode.to_tensor()
data = self.init_mode.init_data()
else:
data = self.init_mode.to_tensor()
data = self.init_mode.init_data()
obj = self._update_tensor_data(data)
if id(obj) != id(self):

192
mindspore/common/tensor.py
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@ -19,7 +19,7 @@ from mindspore import log as logger
from mindspore.communication.management import get_rank, get_group_size
from . import dtype as mstype
from ._register_for_tensor import tensor_operator_registry
from .._c_expression import MetaTensor as MetaTensor_
from .._c_expression import MetaTensor
from .._c_expression import Tensor as Tensor_
from .._checkparam import Validator as validator
@ -60,32 +60,46 @@ class Tensor(Tensor_):
>>> assert t2.dtype == mindspore.float64
"""
def __init__(self, input_data, dtype=None):
def __init__(self, input_data=None, dtype=None, shape=None, init=None):
# If input data is numpy number, convert it to np array
if isinstance(input_data, np_types):
input_data = np.array(input_data)
if ((input_data is not None and init is None) or (input_data is None and init is not None)) is False:
raise TypeError("input_data and init can not be None at the same time.")
# If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
validator.check_value_type('input_data', input_data, (Tensor_, np.ndarray, list, tuple, float, int, bool),
'Tensor')
valid_dtypes = (np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64, np.bool_)
if isinstance(input_data, np.ndarray) and input_data.dtype not in valid_dtypes:
raise TypeError(f"For Tensor, the input_data is a numpy array, "
f"but it's data type is not in supported list: {list(i.__name__ for i in valid_dtypes)}.")
if isinstance(input_data, (tuple, list)):
if np.array(input_data).dtype not in valid_dtypes:
raise TypeError(f"For Tensor, the input_data is {input_data} that contain unsupported element.")
if dtype is not None:
validator.check_type_name('dtype', dtype, mstype.number_type + (mstype.bool_,), "Tensor")
if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']):
input_data = np.ascontiguousarray(input_data)
if dtype is None:
Tensor_.__init__(self, input_data)
if init is None:
validator.check_value_type('input_data', input_data, (Tensor_, np.ndarray, list, tuple, float, int, bool),
'Tensor')
valid_dtypes = (np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64,
np.float16, np.float32, np.float64, np.bool_)
if isinstance(input_data, np.ndarray) and input_data.dtype not in valid_dtypes:
raise TypeError(f"For Tensor, the input_data is a numpy array, "
f"but it's data type is not in supported list:\
{list(i.__name__ for i in valid_dtypes)}.")
if isinstance(input_data, (tuple, list)):
if np.array(input_data).dtype not in valid_dtypes:
raise TypeError(f"For Tensor, the input_data is {input_data} that contain unsupported element.")
if dtype is not None:
validator.check_type_name('dtype', dtype, mstype.number_type + (mstype.bool_,), "Tensor")
if isinstance(input_data, np.ndarray) and (not input_data.flags['FORC']):
input_data = np.ascontiguousarray(input_data)
if dtype is None:
Tensor_.__init__(self, input_data)
else:
Tensor_.__init__(self, input_data, dtype)
else:
Tensor_.__init__(self, input_data, dtype)
Tensor_.__init__(self, dtype, shape)
self._virtual_flag = False
self.init = init
def __deepcopy__(self, memodict):
new_obj = Tensor(self)
new_obj.init = self.init
new_obj._virtual_flag = self._virtual_flag # pylint:disable=w0212
return new_obj
def __repr__(self):
Tensor_.data_sync(self, False)
@ -248,6 +262,11 @@ class Tensor(Tensor_):
"""The ndim of tensor is an integer."""
return len(self._shape)
@property
def has_init(self):
"""tensor is inited."""
return self.init is not None
@property
def virtual_flag(self):
"""Mark tensor is virtual."""
@ -366,6 +385,69 @@ class Tensor(Tensor_):
return tensor_operator_registry.get('mean')(keep_dims)(self, axis)
def init_data(self, slice_index=None, shape=None, opt_shard_group=None):
"""
Get the tensor format data of this Tensor.
Args:
slice_index (int): Slice index of a parameter's slices.
It is used when initialize a slice of a parameter, it guarantees that devices
using the same slice can generate the same tensor.
shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
opt_shard_group(str): Optimizer shard group which is used in auto or semi auto parallel mode
to get one shard of a parameter's slice.
"""
if self.init is None:
raise TypeError("init_data must be set Tensor.init, init can't be None")
if shape is None:
shape = self.shape
try:
arr = np.ndarray(shape, dtype=mstype.dtype_to_nptype(self.dtype))
except ValueError:
msg = "Error shape={}".format(shape)
logger.error(msg)
raise ValueError(msg)
class seed_context:
'''set and restore seed'''
def __init__(self, init):
self.init = init
from .seed import get_seed
global_seed = get_seed()
self._np_seed = np.random.get_state()[1][0]
self.need_set_seed = ((slice_index is not None) and (global_seed is None))
def __enter__(self):
if self.need_set_seed:
self.seed = self.init.seed
np.random.seed(slice_index)
self.init.seed = slice_index
def __exit__(self, ptype, value, trace):
if self.need_set_seed:
np.random.seed(self._np_seed)
self.init.seed, _ = self.seed
with seed_context(self.init):
self.init(arr)
data = np.array(arr)
if opt_shard_group:
rank = get_rank(opt_shard_group)
size = get_group_size(opt_shard_group)
data = np.split(data, size)[rank]
return Tensor(data, dtype=self.dtype)
def to_tensor(self, slice_index=None, shape=None, opt_shard_group=None):
"""Return init_data()."""
logger.warning("WARN_DEPRECATED: The usage of to_tensor is deprecated."
" Please use init_data")
return self.init_data(slice_index, shape, opt_shard_group)
class RowTensor:
"""
A sparse representation of a set of tensor slices at given indices.
@ -498,76 +580,6 @@ class SparseTensor:
return self.__dense_shape
class MetaTensor(MetaTensor_):
"""
The base class of the MetaTensor.
Initialization of tensor basic attributes and model weight values.
Returns:
Array, an array after being initialized.
"""
def __init__(self, dtype, shape, init=None):
# check param
self.init = init
MetaTensor_.__init__(self, dtype, shape)
def to_tensor(self, slice_index=None, shape=None, opt_shard_group=None):
"""
Get the tensor format data of this MetaTensor.
Args:
slice_index (int): Slice index of a parameter's slices.
It is used when initialize a slice of a parameter, it guarantees that devices
using the same slice can generate the same tensor.
shape (list[int]): Shape of the slice, it is used when initialize a slice of the parameter.
opt_shard_group(str): Optimizer shard group which is used in auto or semi auto parallel mode
to get one shard of a parameter's slice.
"""
if self.init is None:
raise TypeError("to_dense must be set MetaTensor.init, init can't be None")
if shape is None:
shape = self.shape
try:
arr = np.ndarray(shape, dtype=mstype.dtype_to_nptype(self.dtype))
except ValueError:
msg = "Error shape={}".format(shape)
logger.error(msg)
raise ValueError(msg)
class seed_context:
'''set and restore seed'''
def __init__(self, init):
self.init = init
from .seed import get_seed
global_seed = get_seed()
self._np_seed = np.random.get_state()[1][0]
self.need_set_seed = ((slice_index is not None) and (global_seed is None))
def __enter__(self):
if self.need_set_seed:
self.seed = self.init.seed
np.random.seed(slice_index)
self.init.seed = slice_index
def __exit__(self, ptype, value, trace):
if self.need_set_seed:
np.random.seed(self._np_seed)
self.init.seed, _ = self.seed
with seed_context(self.init):
self.init(arr)
data = np.array(arr)
if opt_shard_group:
rank = get_rank(opt_shard_group)
size = get_group_size(opt_shard_group)
data = np.split(data, size)[rank]
return Tensor(data, dtype=self.dtype)
def _vm_compare(*args):
"""Implement `vm_compare` for tensor."""
obj_str = args[-1]

4
mindspore/nn/cell.py
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@ -29,7 +29,7 @@ from .._checkparam import Validator
from ..common import dtype as mstype
from ..common.api import _executor, _pynative_exec
from ..common.parameter import Parameter, ParameterTuple
from ..common.tensor import Tensor, MetaTensor
from ..common.tensor import Tensor
from ..ops.functional import cast
from ..ops.operations import HookBackward
from ..ops.primitive import Primitive
@ -589,7 +589,7 @@ class Cell(Cell_):
new_inputs = []
for i in inputs:
if isinstance(i, (Tensor, MetaTensor)):
if isinstance(i, Tensor):
new_inputs.append(i)
if self._auto_parallel_mode:

6
mindspore/nn/layer/embedding.py
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@ -15,7 +15,7 @@
"""embedding"""
import mindspore.common.dtype as mstype
from mindspore import log as logger
from mindspore.common.tensor import Tensor, MetaTensor
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
@ -101,8 +101,8 @@ class Embedding(Cell):
if padding_idx is not None:
self.padding_idx = validator.check_int_range(padding_idx, 0, vocab_size, Rel.INC_BOTH,
"padding_idx", self.cls_name)
if isinstance(self.init_tensor, MetaTensor):
self.init_tensor = self.init_tensor.to_tensor()
if isinstance(self.init_tensor, Tensor) and self.init_tensor.init is not None:
self.init_tensor = self.init_tensor.init_data()
self.init_tensor = self.init_tensor.asnumpy()
self.init_tensor[self.padding_idx] = 0
self.init_tensor = Tensor(self.init_tensor)

10
mindspore/ops/operations/math_ops.py
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@ -23,7 +23,7 @@ from .. import signature as sig
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ...common.tensor import Tensor, MetaTensor
from ...common.tensor import Tensor
from .._utils import get_broadcast_shape
from ..primitive import PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register, _run_op
@ -2542,10 +2542,10 @@ class Equal(_LogicBinaryOp):
def infer_value(self, x, y):
if x is None or y is None:
return None
if isinstance(x, MetaTensor):
x = x.to_tensor()
if isinstance(y, MetaTensor):
y = y.to_tensor()
if isinstance(x, Tensor) and x.has_init:
x = x.init_data()
if isinstance(y, Tensor) and y.has_init:
y = y.init_data()
return Tensor(x.asnumpy() == y.asnumpy())

20
model_zoo/official/cv/centerface/src/var_init.py
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@ -253,33 +253,33 @@ def default_recurisive_init(custom_cell):
if 'hm' in name or 'wh' in name or 'off' in name or 'kps' in name:
if isinstance(cell, (nn.Conv2d)):
cell.weight.set_data(init.initializer(RandomNormal(), cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.data.shape,
cell.bias.data.dtype).to_tensor())
cell.bias.data.dtype))
continue
if isinstance(cell, (nn.Conv2d)):
cell.weight.set_data(init.initializer(KaimingNormal(mode='fan_out'),
cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.data.shape,
cell.bias.data.dtype).to_tensor())
cell.bias.data.dtype))
elif isinstance(cell, nn.Dense):
cell.weight.set_data(init.initializer(KaimingNormal(mode='fan_out'),
cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.data.shape,
cell.bias.data.dtype).to_tensor())
cell.bias.data.dtype))
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
cell.gamma.set_data(init.initializer('ones', cell.gamma.data.shape).to_tensor())
cell.beta.set_data(init.initializer('zeros', cell.beta.data.shape).to_tensor())
cell.gamma.set_data(init.initializer('ones', cell.gamma.data.shape))
cell.beta.set_data(init.initializer('zeros', cell.beta.data.shape))
elif isinstance(cell, nn.Conv2dTranspose):
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5), mode='fan_out'),
cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
cell.bias.set_data(init.initializer('zeros', cell.bias.data.shape,
cell.bias.data.dtype).to_tensor())
cell.bias.data.dtype))

2
model_zoo/official/cv/faster_rcnn/src/FasterRcnn/fpn_neck.py
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@ -31,7 +31,7 @@ def bias_init_zeros(shape):
def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
"""Conv2D wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
biass = bias_init_zeros(shape_bias)
return nn.Conv2d(in_channels, out_channels,

10
model_zoo/official/cv/faster_rcnn/src/FasterRcnn/rcnn.py
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@ -29,7 +29,7 @@ class DenseNoTranpose(nn.Cell):
super(DenseNoTranpose, self).__init__()
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16))
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor())
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16))
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
@ -79,16 +79,16 @@ class Rcnn(nn.Cell):
self.test_batch_size = cfg.test_batch_size
shape_0 = (self.rcnn_fc_out_channels, representation_size)
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16)
shape_1 = (self.rcnn_fc_out_channels, self.rcnn_fc_out_channels)
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16)
self.shared_fc_0 = DenseNoTranpose(representation_size, self.rcnn_fc_out_channels, weights_0)
self.shared_fc_1 = DenseNoTranpose(self.rcnn_fc_out_channels, self.rcnn_fc_out_channels, weights_1)
cls_weight = initializer('Normal', shape=[num_classes, self.rcnn_fc_out_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
reg_weight = initializer('Normal', shape=[num_classes * 4, self.rcnn_fc_out_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
self.cls_scores = DenseNoTranpose(self.rcnn_fc_out_channels, num_classes, cls_weight)
self.reg_scores = DenseNoTranpose(self.rcnn_fc_out_channels, num_classes * 4, reg_weight)

12
model_zoo/official/cv/faster_rcnn/src/FasterRcnn/rpn.py
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@ -164,18 +164,18 @@ class RPN(nn.Cell):
shp_weight_conv = (feat_channels, in_channels, 3, 3)
shp_bias_conv = (feat_channels,)
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16)
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16)
shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
shp_bias_cls = (num_anchors * cls_out_channels,)
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16).to_tensor()
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16).to_tensor()
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16)
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16)
shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
shp_bias_reg = (num_anchors * 4,)
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16).to_tensor()
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16).to_tensor()
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16)
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16)
for i in range(num_layers):
rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \

2
model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py
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@ -29,7 +29,7 @@ def bias_init_zeros(shape):
def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
"""Conv2D wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
biass = bias_init_zeros(shape_bias)
return nn.Conv2d(in_channels, out_channels,

10
model_zoo/official/cv/maskrcnn/src/maskrcnn/rcnn_cls.py
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@ -27,7 +27,7 @@ class DenseNoTranpose(nn.Cell):
def __init__(self, input_channels, output_channels, weight_init):
super(DenseNoTranpose, self).__init__()
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16))
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor())
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16))
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
@ -41,16 +41,16 @@ class FpnCls(nn.Cell):
super(FpnCls, self).__init__()
representation_size = input_channels * pool_size * pool_size
shape_0 = (output_channels, representation_size)
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16)
shape_1 = (output_channels, output_channels)
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16)
self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)

4
model_zoo/official/cv/maskrcnn/src/maskrcnn/rcnn_mask.py
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@ -24,7 +24,7 @@ from mindspore.common.initializer import initializer
def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
"""Conv2D wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
return nn.Conv2d(in_channels, out_channels,
@ -34,7 +34,7 @@ def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mod
def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
"""ConvTranspose wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
return nn.Conv2dTranspose(in_channels, out_channels,

12
model_zoo/official/cv/maskrcnn/src/maskrcnn/rpn.py
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@ -164,18 +164,18 @@ class RPN(nn.Cell):
shp_weight_conv = (feat_channels, in_channels, 3, 3)
shp_bias_conv = (feat_channels,)
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16)
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16)
shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
shp_bias_cls = (num_anchors * cls_out_channels,)
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16).to_tensor()
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16).to_tensor()
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16)
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16)
shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
shp_bias_reg = (num_anchors * 4,)
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16).to_tensor()
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16).to_tensor()
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16)
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16)
for i in range(num_layers):
rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \

10
model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_cls.py
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@ -28,7 +28,7 @@ class DenseNoTranpose(nn.Cell):
super(DenseNoTranpose, self).__init__()
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16),
name="weight")
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16).to_tensor(), name="bias")
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16), name="bias")
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
@ -42,16 +42,16 @@ class FpnCls(nn.Cell):
super(FpnCls, self).__init__()
representation_size = input_channels * pool_size * pool_size
shape_0 = (output_channels, representation_size)
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16).to_tensor()
weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16)
shape_1 = (output_channels, output_channels)
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16).to_tensor()
weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16)
self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
dtype=mstype.float16).to_tensor()
dtype=mstype.float16)
self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)

4
model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_mask.py
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@ -24,7 +24,7 @@ from mindspore.common.initializer import initializer
def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
"""Conv2D wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
return nn.Conv2d(in_channels, out_channels,
@ -34,7 +34,7 @@ def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mod
def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
"""ConvTranspose wrapper."""
shape = (out_channels, in_channels, kernel_size, kernel_size)
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
shape_bias = (out_channels,)
bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
return nn.Conv2dTranspose(in_channels, out_channels,

12
model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rpn.py
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@ -164,18 +164,18 @@ class RPN(nn.Cell):
shp_weight_conv = (feat_channels, in_channels, 3, 3)
shp_bias_conv = (feat_channels,)
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16).to_tensor()
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16).to_tensor()
weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16)
bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16)
shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
shp_bias_cls = (num_anchors * cls_out_channels,)
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16).to_tensor()
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16).to_tensor()
weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16)
bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16)
shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
shp_bias_reg = (num_anchors * 4,)
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16).to_tensor()
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16).to_tensor()
weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16)
bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16)
for i in range(num_layers):
rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \

2
model_zoo/official/cv/vgg16/src/vgg.py
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@ -34,7 +34,7 @@ def _make_layer(base, args, batch_norm):
weight = 'ones'
if args.initialize_mode == "XavierUniform":
weight_shape = (v, in_channels, 3, 3)
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
conv2d = nn.Conv2d(in_channels=in_channels,
out_channels=v,

4
model_zoo/official/cv/yolov3_darknet53_quant/src/initializer.py
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@ -158,7 +158,7 @@ def default_recurisive_init(custom_cell):
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.data.asnumpy())
bound = 1 / math.sqrt(fan_in)
@ -167,7 +167,7 @@ def default_recurisive_init(custom_cell):
elif isinstance(cell, nn.Dense):
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
cell.weight.data.shape,
cell.weight.data.dtype).to_tensor())
cell.weight.data.dtype))
if cell.bias is not None:
fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.data.asnumpy())
bound = 1 / math.sqrt(fan_in)

6
model_zoo/official/nlp/bert_thor/src/bert_model.py
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@ -879,13 +879,13 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
if not self.input_mask_from_dataset:
self.input_mask = initializer(
"ones", [config.batch_size, config.seq_length], mstype.int32).to_tensor()
"ones", [config.batch_size, config.seq_length], mstype.int32).init_data()
self.cast = P.Cast()
self.reshape = P.Reshape()
self.shape = (config.batch_size, 1, config.seq_length)
self.broadcast_ones = initializer(
"ones", [config.batch_size, config.seq_length, 1], mstype.float32).to_tensor()
"ones", [config.batch_size, config.seq_length, 1], mstype.float32).init_data()
self.batch_matmul = P.BatchMatMul()
def construct(self, input_mask):
@ -932,7 +932,7 @@ class BertModel(nn.Cell):
if not self.token_type_ids_from_dataset:
self.token_type_ids = initializer(
"zeros", [self.batch_size, self.seq_length], mstype.int32).to_tensor()
"zeros", [self.batch_size, self.seq_length], mstype.int32).init_data()
self.bert_embedding_lookup = Embedding_Thor(
vocab_size=config.vocab_size,

2
tests/st/networks/models/bert/bert_performance/test_bert_tdt_lossscale.py
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@ -209,7 +209,7 @@ def test_bert_performance():
for param in params:
value = param.data
name = param.name
if isinstance(value, Tensor):
if isinstance(value, Tensor) and not value.has_init:
if name.split('.')[-1] in ['weight']:
if name.split('.')[-3] in ['cls2']:
logger.info("***************** BERT param name is 1 {}".format(name))

6
tests/st/networks/models/bert/src/bert_model.py
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@ -817,13 +817,13 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
if not self.input_mask_from_dataset:
self.input_mask = initializer(
"ones", [config.batch_size, config.seq_length], mstype.int32).to_tensor()
"ones", [config.batch_size, config.seq_length], mstype.int32).init_data()
self.cast = P.Cast()
self.reshape = P.Reshape()
self.shape = (config.batch_size, 1, config.seq_length)
self.broadcast_ones = initializer(
"ones", [config.batch_size, config.seq_length, 1], mstype.float32).to_tensor()
"ones", [config.batch_size, config.seq_length, 1], mstype.float32).init_data()
self.batch_matmul = P.BatchMatMul()
def construct(self, input_mask):
@ -869,7 +869,7 @@ class BertModel(nn.Cell):
if not self.token_type_ids_from_dataset:
self.token_type_ids = initializer(
"zeros", [self.batch_size, self.seq_length], mstype.int32).to_tensor()
"zeros", [self.batch_size, self.seq_length], mstype.int32).init_data()
self.bert_embedding_lookup = EmbeddingLookup(
vocab_size=config.vocab_size,

4
tests/ut/python/nn/test_parameter.py
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@ -195,14 +195,14 @@ def test_parameter_lazy_init():
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8)
# Call init_data() without set set_data.
para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test1')
assert not isinstance(para.data, Tensor)
assert isinstance(para.data, Tensor)
para = para.init_data()
assert isinstance(para.data, Tensor)
assert np.array_equal(para.data.asnumpy(), np.ones((1, 2, 3)))
# Call init_data() after set_data is set.
para = Parameter(initializer('ones', [1, 2, 3], mstype.float32), 'test2')
assert not isinstance(para.data, Tensor)
assert isinstance(para.data, Tensor)
# expect type error when not init
with pytest.raises(TypeError):
para.set_data(Tensor(np.zeros((1, 2, 3))))

6
tests/ut/python/pynative_mode/test_staging.py
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@ -20,7 +20,7 @@ import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore._c_expression import MetaTensor as MetaTensor_
from mindspore._c_expression import MetaTensor
from mindspore.common import dtype
from mindspore.common.api import ms_function
from mindspore.ops import functional as F
@ -70,8 +70,8 @@ def scalar_mul_while(x):
return rv
@ms_function(input_signature=(MetaTensor_(dtype.float32, (1, 1, 3, 3)),
MetaTensor_(dtype.float32, (1, 1, 3, 3))))
@ms_function(input_signature=(MetaTensor(dtype.float32, (1, 1, 3, 3)),
MetaTensor(dtype.float32, (1, 1, 3, 3))))
def tensor_add_test(x, y):
""" tensor_add_test """
z = F.tensor_add(x, y)

54
tests/ut/python/utils/test_initializer.py
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@ -24,7 +24,7 @@ import mindspore.common.initializer as init
import mindspore.nn as nn
from mindspore import context
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor, MetaTensor
from mindspore.common.tensor import Tensor
from mindspore.nn import Conv2d
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
@ -58,8 +58,8 @@ def _check_uniform(tensor, boundary_a, boundary_b):
def test_init_Initializer():
tensor = init.initializer(InitTwo(), [2, 2], ms.int32)
assert tensor.shape == [2, 2]
_check_value(tensor.to_tensor(), 2, 2)
assert tensor.shape == (2, 2)
_check_value(tensor.init_data(), 2, 2)
def test_init_tensor():
@ -71,70 +71,70 @@ def test_init_tensor():
def test_init_zero_default_dtype():
tensor = init.initializer(init.Zero(), [2, 2])
assert tensor.dtype == ms.float32
_check_value(tensor.to_tensor(), 0, 0)
_check_value(tensor.init_data(), 0, 0)
def test_init_zero():
tensor = init.initializer(init.Zero(), [2, 2], ms.float32)
_check_value(tensor.to_tensor(), 0, 0)
_check_value(tensor.init_data(), 0, 0)
def test_init_zero_alias_default_dtype():
tensor = init.initializer('zeros', [1, 2])
assert tensor.dtype == ms.float32
_check_value(tensor.to_tensor(), 0, 0)
_check_value(tensor.init_data(), 0, 0)
def test_init_zero_alias():
tensor = init.initializer('zeros', [1, 2], ms.float32)
_check_value(tensor.to_tensor(), 0, 0)
_check_value(tensor.init_data(), 0, 0)
def test_init_one():
tensor = init.initializer(init.One(), [2, 2], ms.float32)
_check_value(tensor.to_tensor(), 1, 1)
_check_value(tensor.init_data(), 1, 1)
def test_init_one_alias():
tensor = init.initializer('ones', [1, 2], ms.float32)
_check_value(tensor.to_tensor(), 1, 1)
_check_value(tensor.init_data(), 1, 1)
def test_init_constant():
tensor = init.initializer(init.Constant(1), [2, 2], ms.float32)
_check_value(tensor.to_tensor(), 1, 1)
_check_value(tensor.init_data(), 1, 1)
def test_init_uniform():
scale = 10
tensor = init.initializer(init.Uniform(scale=scale), [5, 4], ms.float32)
_check_value(tensor.to_tensor(), -scale, scale)
_check_value(tensor.init_data(), -scale, scale)
def test_init_uniform_alias():
scale = 100
tensor = init.initializer('uniform', [5, 4], ms.float32)
_check_value(tensor.to_tensor(), -scale, scale)
_check_value(tensor.init_data(), -scale, scale)
def test_init_normal():
tensor = init.initializer(init.Normal(), [5, 4], ms.float32)
assert isinstance(tensor, MetaTensor), 'Normal init failed!'
assert isinstance(tensor, Tensor), 'Normal init failed!'
def test_init_truncated_normal():
tensor = init.initializer(init.TruncatedNormal(), [5, 4], ms.float32)
assert isinstance(tensor, MetaTensor), 'TruncatedNormal init failed!'
assert isinstance(tensor, Tensor), 'TruncatedNormal init failed!'
def test_init_normal_alias():
tensor = init.initializer('normal', [5, 4], ms.float32)
assert isinstance(tensor, MetaTensor), 'Normal init failed!'
assert isinstance(tensor, Tensor), 'Normal init failed!'
def test_init_truncatednormal_alias():
tensor = init.initializer('truncatednormal', [5, 4], ms.float32)
assert isinstance(tensor, MetaTensor), 'TruncatedNormal init failed!'
assert isinstance(tensor, Tensor), 'TruncatedNormal init failed!'
def test_init_abnormal():
@ -144,18 +144,18 @@ def test_init_abnormal():
def test_initializer_reinit():
weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
assert isinstance(weights, MetaTensor), 'XavierUniform init failed!'
assert isinstance(weights, Tensor), 'XavierUniform init failed!'
def test_init_xavier_uniform():
""" test_init_xavier_uniform """
gain = 1.2
tensor1 = init.initializer(init.XavierUniform(gain=gain), [20, 22], ms.float32).to_tensor()
tensor2 = init.initializer(init.XavierUniform(), [20, 22], ms.float32).to_tensor()
tensor3 = init.initializer(init.XavierUniform(gain=gain), [20, 22, 5, 5], ms.float32).to_tensor()
tensor4 = init.initializer(init.XavierUniform(), [20, 22, 5, 5], ms.float32).to_tensor()
tensor5 = init.initializer('xavier_uniform', [20, 22, 5, 5], ms.float32).to_tensor()
tensor6 = init.initializer('xavier_uniform', [20, 22], ms.float32).to_tensor()
tensor1 = init.initializer(init.XavierUniform(gain=gain), [20, 22], ms.float32).init_data()
tensor2 = init.initializer(init.XavierUniform(), [20, 22], ms.float32).init_data()
tensor3 = init.initializer(init.XavierUniform(gain=gain), [20, 22, 5, 5], ms.float32).init_data()
tensor4 = init.initializer(init.XavierUniform(), [20, 22, 5, 5], ms.float32).init_data()
tensor5 = init.initializer('xavier_uniform', [20, 22, 5, 5], ms.float32).init_data()
tensor6 = init.initializer('xavier_uniform', [20, 22], ms.float32).init_data()
tensor_dict = {tensor1: gain, tensor2: None, tensor3: gain, tensor4: None, tensor5: None, tensor6: None}
for tensor, gain_value in tensor_dict.items():
@ -175,7 +175,7 @@ def test_init_xavier_uniform():
def test_init_xavier_uniform_error():
with py.raises(ValueError):
init.initializer(init.XavierUniform(), [6], ms.float32).to_tensor()
init.initializer(init.XavierUniform(), [6], ms.float32).init_data()
def test_init_he_uniform():
@ -184,7 +184,7 @@ def test_init_he_uniform():
tensor2 = init.initializer(init.HeUniform(), [20, 22, 5, 5], ms.float32)
tensor3 = init.initializer('he_uniform', [20, 22, 5, 5], ms.float32)
tensor4 = init.initializer('he_uniform', [20, 22], ms.float32)
tensors = [tensor1.to_tensor(), tensor2.to_tensor(), tensor3.to_tensor(), tensor4.to_tensor()]
tensors = [tensor1.init_data(), tensor2.init_data(), tensor3.init_data(), tensor4.init_data()]
for tensor in tensors:
shape = tensor.asnumpy().shape
@ -200,7 +200,7 @@ def test_init_he_uniform():
def test_init_he_uniform_error():
with py.raises(ValueError):
init.initializer(init.HeUniform(), [6], ms.float32).to_tensor()
init.initializer(init.HeUniform(), [6], ms.float32).init_data()
def test_conv2d_abnormal_kernel_negative():
@ -224,7 +224,7 @@ def test_conv2d_abnormal_kernel_normal():
@non_graph_engine
def test_conv2d_abnormal_kernel_truncated_normal():
input_data = init.initializer(init.TruncatedNormal(), [64, 3, 7, 7], ms.float32).to_tensor()
input_data = init.initializer(init.TruncatedNormal(), [64, 3, 7, 7], ms.float32).init_data()
context.set_context(mode=context.GRAPH_MODE)
model = ms.Model(
Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=3,

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