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PaddleDetection
提交 7cd4d0ac 编写于 作者: C chengduoZH
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add Doc fluid.Parameter, program and block

上级 527b22f2
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Showing with 313 addition and 152 deletion
python/paddle/fluid/framework.py
浏览文件 @ 7cd4d0ac
......@@ -43,7 +43,8 @@ ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
def grad_var_name(var_name):
"""
return gradient name for a certain var name
Returns:
str: gradient name for a certain var name
"""
return var_name + GRAD_VAR_SUFFIX
......@@ -51,10 +52,12 @@ def grad_var_name(var_name):
def convert_np_dtype_to_dtype_(np_dtype):
"""
Convert the data type in numpy to the data type in Paddle
Args:
np_dtype(np.dtype): the data type in numpy
np_dtype(np.dtype): the data type in numpy.
Returns(core.VarDesc.VarType): the data type in Paddle
Returns:
core.VarDesc.VarType: the data type in Paddle.
"""
dtype = np.dtype(np_dtype)
......@@ -129,46 +132,44 @@ class Variable(object):
and usages. Please reference the framework.proto for details.
Most of a Variable's member variables can be setted to be None. It mean
it is not avaiable or will be specified later.
it is not available or will be specified later.
Notes: The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(
name="X", shape=[-1, 23, 48], dtype='float32')
Member variables:
Args:
block(Block): The block that the variable belongs to.
type(core.VarDesc.VarType): Variable type. Please reference the
framework.proto for details.
name(str|None): The name of the variable. If setted None, it will be
generated automatically.
Default: None
name(str|None): The name of the variable. If setted None, it will be
generated automatically. Default: None
shape(tuple|list|None): The shape of the variable. -1 means the batch size.
Some kinds of variable do not contain shape, just set it to None.
Default: None
dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
Default: None
lod_level(int|None): The level of lod tensor. 0 means it is not a time
lod_level (int|None): The level of lod tensor. 0 means it is not a time
series data.
Default: None
capacity(int|None): The capacity of Channel variable. Ignored
for other types.
Default: None
persistable(bool|None): True if the variable is persistable. A persistable
variable will not be deleted after an iteration ending.
Defaults: None.
error_clip(BaseErrorClipAttr|None): The error clip attributes of the
corresponding gradient variable.
Default: None
stop_gradient(bool): True if the variable will stop to calculate its
gradients when backward.
Default: False.
is_data(bool): True is the variable is an input data.
Default: False
capacity (int|None): The capacity of Channel variable. Ignored for other
types. Default: None
persistable (bool|None): True if the variable is persistable. A persistable
variable will not be deleted after an iteration ending. Defaults: None.
error_clip (BaseErrorClipAttr|None): The error clip attributes of the
corresponding gradient variable. Default: None
stop_gradient (bool): True if the variable will stop to calculate its
gradients when backward. Default: False.
is_data (bool): True if the variable is an input data. Default: False
Notes:
The constructor of Variable should not be invoked directly. Please
use `Block.create_var` to create a variable.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
"""
def __init__(self,
......@@ -271,13 +272,14 @@ class Variable(object):
Get debug string.
Args:
throw_on_error(bool): True if raise an exception when self is not
intialized.
throw_on_error(bool): True if raise an exception when self is
not initialized.
with_details(bool): more details about variables and parameters
(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
Returns(str): The debug string.
(e.g. trainable, optimize_attr, ...) will be printed when
with_details is True. Default False;
Returns:
str: The debug string.
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
......@@ -294,6 +296,15 @@ class Variable(object):
__repr__ = __str__
def set_desc(self, input):
"""
Set the variable description.
Args:
input(core.VarDesc): The new VarDesc.
Returns:
None
"""
self.desc = input
@property
......@@ -330,6 +341,15 @@ class Variable(object):
return self.desc.type()
def set_error_clip(self, error_clip):
"""
Set the error_clip.
Args:
error_clip(BaseErrorClipAttr) : The new error_clip.
Returns:
None
"""
self.error_clip = error_clip
......@@ -337,8 +357,8 @@ def get_all_op_protos():
"""
Get all registered op proto from PaddlePaddle C++ end.
Returns(list): list of OpProto
Returns:
list: list of OpProto.
"""
protostrs = core.get_all_op_protos()
ret_values = []
......@@ -391,9 +411,45 @@ class OpProtoHolder(object):
class Operator(object):
"""
Python Operator class. The operator represents the build in instructions in a
Block. Users can use the build in instructions to describe their neural
network.
In Fluid, all the operation are represented by Operator, and Operator
is regarded as a build in an instruction of a Block. Users can use the
build in instructions to describe their neural network.
Args:
block(Block): The block has the current operator.
desc(core.OpDesc): The protobuf description of Operator.
type(str): The type of operator.
inputs(dict): The input of this Operator. it is a dictionary, for every
element, key is the input parameter name, and value is a list of
variables. Default None.
outputs(dict): The output of this Operator. it is a dictionary, for
every element, key is the input parameter name, and value is a list
of variables. Default None.
attrs(dict): The attributes of this Operator. it is a dictionary, for
every element, key is attribute name, and value is the attribute value.
The attribute type should be as same as the type registered in C++ side.
Default None.
Returns:
Operator: The initialized Operator.
Raises:
ValueError: If the passed input, output and attrs doesn't match the
initializing Operator's that registered in C++ side.
Notes:
The constructor of operator should not be invoked directly. Use
Block.append_op or Block.prepend_op instead.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
# var1 += var2 + var3
cur_block.append_op(type="sum",
inputs={"X": [var1, var2, var3]},
outputs={"Out": [var1]})
"""
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
......@@ -403,38 +459,9 @@ class Operator(object):
'channel_recv', 'select', 'gen_nccl_id'
}
def __init__(self,
block,
desc,
type=None,
inputs=None,
outputs=None,
def __init__(self, block, desc, type, inputs=None, outputs=None,
attrs=None):
"""
Constructor.
Notes: The constructor of operator should not be invoked directly. Use
Block.append_op or Block.prepend_op instead.
>>> cur_program = Program()
>>> cur_block = cur_program.current_block()
>>> # var1 += var2 + var3
>>> cur_block.append_op(type="sum",
>>> inputs={"X": [var1, var2, var3]},
>>> outputs={"Out": [var1]})
Args:
block(Block): The block has the current operator.
desc(core.OpDesc): The protobuf description.
type(str): The type of operator.
inputs(dict): The input dictionary. Key is the input parameter name.
Value is a list of variables.
outputs(dict): The output dictionary which has the same format with
inputs.
attrs(dict): The attributes dictionary. Key is attribute name. Value
is the attribute value. The attribute type should be as same as
the type registered in C++
"""
self.block = block
self.desc = desc
self.attrs = attrs
......@@ -457,9 +484,7 @@ class Operator(object):
if len(self.desc.type()) != 0:
return
if type is None:
raise ValueError(
"`type` to initilized an Operator can not be None.")
self.desc.set_type(type)
proto = OpProtoHolder.instance().get_op_proto(type)
......@@ -547,12 +572,14 @@ class Operator(object):
def to_string(self, throw_on_error):
"""
To debug string.
Get debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
throw_on_error(bool): Whether to raise exception if self is not
initialized.
Returns(str): The debug string.
Returns:
str: The debug string.
"""
protostr = self.desc.serialize_to_string()
......@@ -570,29 +597,45 @@ class Operator(object):
def input(self, name):
"""
Get input arguments by the input parameter name
Args:
name(str): The input parameter name
Get the input arguments according to the input parameter name.
Returns(list): return the list of argument names associated with the
specific parameter name.
Args:
name(str): The input parameter name.
Returns:
list: return the list of argument names that associated with \
the specific parameter name.
"""
return self.desc.input(name)
def rename_input(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's input.
new_name(str): The new name of the Operator's input.
Returns:
None
"""
self.desc.rename_input(old_name, new_name)
def rename_output(self, old_name, new_name):
"""
Rename the `old_name` to `new_name`.
Args:
old_name(str): The old name of the Operator's output.
new_name(str): The new name of the Operator's output.
Returns:
None
"""
self.desc.rename_output(old_name, new_name)
@property
def input_names(self):
"""
Get all input parameter names
Returns(list): return a list of input parameter names
"""
return self.desc.input_names()
@property
......@@ -605,33 +648,23 @@ class Operator(object):
def output(self, name):
"""
Get output arguments by the output parameter name
Args:
name(str): The output parameter name
Get output arguments by the output parameter name.
Returns(list): return the list of argument names associated with the
specific parameter name.
Args:
name(str): The output parameter name.
Returns:
list: return the list of argument names associated with \
the specific parameter name.
"""
return self.desc.output(name)
@property
def output_names(self):
"""
Get all output parameter names
Returns(list): return a list of output parameter names
"""
return self.desc.output_names()
@property
def idx(self):
"""
Return the array index of current operator.
Returns(int): The array index in block.ops array
Raises:
ValueError: when the operator is not found.
"""
for i, op in enumerate(self.block.ops):
if op == self:
return i
......@@ -640,66 +673,78 @@ class Operator(object):
def has_attr(self, name):
"""
operator has the attribute with name or not.
Whether this Operator has the attribute with name or not.
Args:
name(str): the attribute name
name(str): the attribute name.
Returns(bool): True if has this attribute.
Returns:
bool: True if has this attribute.
"""
return self.desc.has_attr(name)
def attr_type(self, name):
"""
Get the type of attribute by attribute name
Args:
name(str): the attribute name
Get the type of attribute by attribute's name.
Returns(core.AttrType): the attribute type
Args:
name(str): the attribute name.
Returns:
core.AttrType: the attribute type.
"""
return self.desc.attr_type(name)
def set_attr(self, name, val):
"""
Set the value of attribute by attribute's name.
Args:
name(str): the attribute name.
val(bool|int|str|float|list): the value of the attribute.
Raises:
ValueError: If the type of value doesn't match with desc.attr_type(name).
"""
self.attrs[name] = val
self.desc.set_attr(name, val)
@property
def attr_names(self):
"""
Get all attribute names
Returns(list): The list of attribute name
"""
return self.desc.attr_names()
def attr(self, name):
"""
Get attribute by name
Get the attribute by name.
Args:
name(str): the attribute name
name(str): the attribute name.
Returns(bool|int|str|float|list): The attribute value. The return value
Returns:
bool|int|str|float|list: The attribute value. The return value
can be any valid attribute type.
"""
return self.desc.attr(name)
def block_attr(self, name):
"""
Get the block attribute by name
Args:
name(str): the attribute name
Get the block attribute by name.
Returns(int): the block index
Args:
name(str): the attribute name.
Returns:
int: the block index.
"""
return self.desc.block_attr(name)
def all_attrs(self):
"""
Get the attribute dict
Returns(dict): The Operator's attribute dict
Get the attribute dict.
Returns:
dict: The Operator's attribute dict.
"""
attr_names = self.attr_names
attr_map = {}
......@@ -712,6 +757,35 @@ class Operator(object):
class Block(object):
"""
In Fluid, a Program is consistence of multi-Block, and Block stores
VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
One block could have some child blocks, and child block's name scopes
should inherit the parent's so that OpDesc in child block can reference
a VarDesc that is stored in the parent block.
Please reference the framework.proto for details.
Args:
program(Program): The Program that the Block belongs to.
idx(int): The block's id in the Program.
Notes:
The constructor of Block should not be invoked directly. Please
use `Program.create_block()` to create a block.
Examples:
.. code-block:: python
cur_program = Program()
cur_block = cur_program.current_block()
var = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
cur_block.append_op(type="abs",
inputs={"X": [var]},
outputs={"Out": [var]})
"""
def __init__(self, program, idx):
self.desc = program.desc.block(idx)
self.vars = collections.OrderedDict() # var_name --> var
......@@ -724,15 +798,17 @@ class Block(object):
def to_string(self, throw_on_error, with_details=False):
"""
To debug string.
Get debug string.
Args:
throw_on_error(bool): raise exception when self is not initialized
when throw_on_error is True
when throw_on_error is True.
with_details(bool): more details about variables and parameters
(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
Returns(str): The debug string.
(e.g. trainable, optimize_attr, ...) will be printed when
with_details is True. Default False.
Returns:
str: The debug string.
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
......@@ -764,6 +840,15 @@ class Block(object):
return self.desc.get_forward_block_idx()
def set_forward_block_idx(self, idx):
"""
Set the forward block Idx.
Args:
idx(int): the block index.
Returns:
None
"""
self.desc.set_forward_block_idx(idx)
@property
......@@ -771,6 +856,19 @@ class Block(object):
return self.desc.id
def var(self, name):
"""
Get a Variable by name from this block.
Args:
name(str): the Variable's name.
Raises:
ValueError: The If input's type is not str, or this block
doesn't have a Variable with the giving name.
Returns:
Variable: the Variable with the giving name.
"""
if not isinstance(name, basestring):
raise TypeError(
"var require string as parameter, but get %s instead." %
......@@ -781,6 +879,19 @@ class Block(object):
return v
def var_recursive(self, name):
"""
Get a Variable by name from this block recursively.
Args:
name(str): the Variable's name.
Raises:
ValueError: this block and this parent block doesn't
have a Variable with the giving name.
Returns:
Variable: the Variable with the giving name.
"""
frontier = list()
visited = set()
......@@ -827,6 +938,18 @@ class Block(object):
def rename_var(self, name, new_name):
"""
Rename variable in vars and ops' inputs and outputs
Args:
name(str): the name that need to be renamed.
new_name(str): the name that need to rename to.
Raises:
ValueError: If this block doesn't have this the giving name,
or the type of the var with the giving name is not Parameter
or Variable.
Returns:
Variable: the Variable with the giving name.
"""
if not self.has_var(name):
raise ValueError("var %s is not in current block" % name)
......@@ -890,12 +1013,27 @@ class Block(object):
return param
def append_op(self, *args, **kwargs):
"""
Appends a new Operator according to the giving arguments.
Returns:
Operator: the append Operator.
"""
op_desc = self.desc.append_op()
op = Operator(block=self, desc=op_desc, *args, **kwargs)
self.ops.append(op)
return op
def insert_op(self, index, *args, **kwargs):
"""
Insert a Operator according to the giving arguments.
Args:
index(int): the place that the operator to insert.
Returns:
Operator: the insert Operator.
"""
self.sync_with_cpp()
op_desc = self.desc.insert_op(index)
op = Operator(block=self, desc=op_desc, *args, **kwargs)
......@@ -903,11 +1041,30 @@ class Block(object):
return op
def remove_op(self, index):
"""
Remove the specific position operator.
Args:
index(int): the position that the operator to insert.
Returns:
None
"""
self.sync_with_cpp()
self.desc.remove_op(index, index + 1)
del self.ops[index]
def slice_ops(self, start, end):
"""
Return the Operator between start and end.
Args:
start(int): the start position.
end(int): the end position.
Returns:
list: the Operators between start and end.
"""
return self.ops[start:end]
def prepend_op(self, *args, **kwargs):
......@@ -918,9 +1075,8 @@ class Block(object):
def sync_with_cpp(self):
"""
Sync from the desc on the c++ end.
This method is used to synchronize the c++ desc instance generated by backward.
Sync from the desc on the c++ end. This method is used to synchronize
the c++ desc instance generated by backward.
"""
# sync variables from cpp
for var in self.desc.all_vars():
......@@ -985,9 +1141,14 @@ class Block(object):
def copy_param_info_from(self, other):
"""
Copy the information of parameters from the other block
Copy the information of parameters from the other block.
Args:
other(Block): the other block
other(Block): the other block.
Raises:
ValueError: If type of input is not Block, or the `other` and this
block is not in the same topology.
Returns:
None
......@@ -1019,11 +1180,12 @@ class Block(object):
def clone_variable(self, var):
"""
Clone a variable into current block.
Args:
var: the variable to be cloned.
Returns:
The new variable cloned from 'var' in current block.
Variable: the new variable cloned from 'var' in current block.
"""
assert isinstance(var, Variable)
ret_var = None
......@@ -1330,22 +1492,21 @@ class Parameter(Variable):
The training of a neural network is essentially the updating of
its parameters.
Relative to a general Vriable, a Parameter has several its own
Relative to a general Variable, a Parameter has several its own
member variables:
trainable(bool): True if the parameter need to be updated after
iterations.
optimize_attr(map): Parameter attributes related with optimizing.
Currently, it only contains 'learning_rate'.
Default: {'learning_rate': 1.0}
regularizer(WeightDecayRegularizer): The Regularizer which will
be applied on the parameter.
Default: None
gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
which will be applied on the parameter.
Default: None
do_model_average(bool): True if the model average strategy will
be applied on this parameter.
Args:
trainable(bool): True if the parameter need to be updated after
iterations.
optimize_attr(map): Parameter attributes related with optimizing.
Currently, it only contains 'learning_rate'.
Default: {'learning_rate': 1.0}
regularizer(WeightDecayRegularizer): The Regularizer which will
be applied on the parameter. Default: None
gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
which will be applied on the parameter. Default: None
do_model_average(bool): True if the model average strategy will
be applied on this parameter.
"""
def __init__(self, block, shape, dtype, **kwargs):
......
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