zhreshold
commented
7 years ago git submodule update --recursive --init
Closed wuleibupt closed 7 years ago
zhreshold
commented
7 years ago git submodule update --recursive --init
Heidisnaps
commented
7 years ago @zhreshold
Hi. I have same issue.
I tried your solution.
git submodule update --recursive --init
but still same error.
conv5_6 = mx.sym.stack_neighbor(data=conv5_5, kernel=(2, 2), name='stack_downsample') AttributeError: module 'mxnet.symbol' has no attribute 'stack_neighbor'
zhreshold
commented
7 years ago Make sure you don't import default mxnet. checkout tools/find_mxnet.py
Heidisnaps
commented
7 years ago @zhreshold
There was another error before the above mentioned error (stack_neighbor).
File "~/mxnet-yolo/dataset/testdb.py", line 2, in
from imdb import Imdb ImportError: No module named 'imdb'
So I tried change '~/mxnet-yolo/dataset/testdb.py' code:
original code:
import os
from imdb import Imdb
~~
changed code:
import os
from .imdb import Imdb
~~
The above error (ImportError: No module named 'imdb') has disappeared. However, an error (stack_neighbor) occurred.
/anaconda/envs/ten_cv/bin/python /Users/heidi/PycharmProjects/mxnet-yolo/demo.py Traceback (most recent call last): File "/Users/heidi/PycharmProjects/mxnet-yolo/demo.py", line 7, in
from detect.detector import Detector File "/Users/heidi/PycharmProjects/mxnet-yolo/detect/detector.py", line 5, in from dataset.testdb import TestDB File "/Users/heidi/PycharmProjects/mxnet-yolo/dataset/testdb.py", line 2, in from imdb import Imdb ImportError: No module named 'imdb'
Could you explain in more detail?
zhreshold
commented
7 years ago If you are using python3, this is going to be corrected
from dataset.imdb import ImdbAgain, the custom layers are not merged to mxnet yet, so you have to build the submodule mxnet, making sure import mxnet is loading this forked mxnet.
Heidisnaps
commented
7 years ago @zhreshold thanks.
Previously, I proceeded without the mxnet build process. The build is now complete. For reference, the following error occurred in mac 10.12.5 version.
~~ Ld: symbol (s) not found for architecture x86_64 Collect2: error: ld returned 1 exit status Make: *** [bin / im2rec] Error 1
so, I tried it in ubuntu environment of AWS server and build it succeeded. follow as :http://newdocs.readthedocs.io/en/latest/build.html
but, still error like as:
$ python demo.py --cpu
Traceback (most recent call last):
File "demo.py", line 100, in <module>
ctx, args.nms_thresh, args.force_nms)
File "demo.py", line 41, in get_detector
.get_symbol(len(CLASSES), nms_thresh, force_nms)
File "~/mxnet-yolo/symbol/symbol_darknet19_yolo.py", line 25, in get_symbol
conv5_6 = mx.sym.stack_neighbor(data=conv5_5, kernel=(2, 2), name='stack_downsample')
AttributeError: module 'mxnet.symbol' has no attribute 'stack_neighbor'
and this is my symbol.py
# coding: utf-8
# pylint: disable=invalid-name, protected-access, too-many-arguments, too-many-lines
# pylint: disable=import-error, no-name-in-module
"""Symbolic configuration API of MXNet."""
from __future__ import absolute_import as _abs
import ctypes
import warnings
from numbers import Number
import os as _os
import sys as _sys
import numpy as _numpy
from .base import _LIB, numeric_types
from .base import c_array, c_str, mx_uint, py_str, string_types
from .base import NDArrayHandle, ExecutorHandle, SymbolHandle, OpHandle
from .base import check_call, MXNetError, _Null # pylint: disable=unused-import
from .context import Context, cpu
from .ndarray import NDArray, _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
from .name import NameManager # pylint: disable=unused-import
from .executor import Executor
from . import _symbol_internal as _internal
from .attribute import AttrScope
from .symbol_doc import _build_doc
# Use different version of SymbolBase
# When possible, use cython to speedup part of computation.
try:
if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0:
from ._ctypes.symbol import SymbolBase, _set_symbol_class
from ._ctypes.symbol import _symbol_creator # pylint: disable=unused-import
elif _sys.version_info >= (3, 0):
from ._cy3.symbol import SymbolBase, _set_symbol_class
from ._cy3.symbol import _symbol_creator # pylint: disable=unused-import
else:
from ._cy2.symbol import SymbolBase, _set_symbol_class
from ._cy2.symbol import _symbol_creator # pylint: disable=unused-import
except ImportError:
if int(_os.environ.get("MXNET_ENFORCE_CYTHON", False)) != 0:
raise ImportError("Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1")
from ._ctypes.symbol import SymbolBase, _set_symbol_class
from ._ctypes.symbol import _symbol_creator # pylint: disable=unused-import
_GRAD_REQ_MAP = {'null': 0, 'write': 1, 'add': 3}
class Symbol(SymbolBase):
"""Symbol is symbolic graph of the mxnet."""
# disable dictionary storage, also do not have parent type.
# pylint: disable=no-member
__slots__ = []
def __repr__(self):
"""Gets a string representation of the symbol."""
name = self.name
if name is None:
name = ', '.join([i.name for i in self])
return '<%s group [%s]>' % (self.__class__.__name__, name)
else:
return '<%s %s>' % (self.__class__.__name__, name)
def __iter__(self):
"""Returns a generator object of symbol.
One can loop through the returned object list to get outputs.
Example usage:
----------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a+b
>>> d = mx.sym.Variable('d')
>>> e = d+c
>>> out = e.get_children()
>>> out
<Symbol Grouped>
>>> for i in out:
... i
...
<Symbol d>
<Symbol _plus0>
"""
return (self[i] for i in self.list_outputs())
def __add__(self, other):
"""x.__add__(y) <=> x+y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_add` instead. """
if isinstance(other, Symbol):
return _internal._Plus(self, other)
if isinstance(other, Number):
return _internal._PlusScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __radd__(self, other):
return self.__add__(other)
def __sub__(self, other):
"""x.__sub__(y) <=> x-y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_sub` instead. """
if isinstance(other, Symbol):
return _internal._Minus(self, other)
if isinstance(other, Number):
return _internal._MinusScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __rsub__(self, other):
"""x.__rsub__(y) <=> y-x
Only `NDArray` is supported for now.
Example usage:
----------
>>> x = mx.nd.ones((2,3))*3
>>> y = mx.nd.ones((2,3))
>>> x.__rsub__(y).asnumpy()
array([[-2., -2., -2.],
[-2., -2., -2.]], dtype=float32)
"""
if isinstance(other, Number):
return _internal._RMinusScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __mul__(self, other):
"""x.__mul__(y) <=> x*y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_mul` instead. """
if isinstance(other, Symbol):
return _internal._Mul(self, other)
if isinstance(other, Number):
return _internal._MulScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __rmul__(self, other):
return self.__mul__(other)
def __div__(self, other):
"""x.__div__(y) <=> x/y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_div` instead. """
if isinstance(other, Symbol):
return _internal._Div(self, other)
if isinstance(other, Number):
return _internal._DivScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __rdiv__(self, other):
"""x.__rdiv__(y) <=> y/x
Only `NDArray` is supported for now.
Example usage:
----------
>>> x = mx.nd.ones((2,3))*3
>>> y = mx.nd.ones((2,3))
>>> x.__rdiv__(y).asnumpy()
array([[ 0.33333334, 0.33333334, 0.33333334],
[ 0.33333334, 0.33333334, 0.33333334]], dtype=float32)
"""
if isinstance(other, Number):
return _internal._RDivScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __mod__(self, other):
"""x.__mod__(y) <=> x%y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_mod` instead. """
if isinstance(other, Symbol):
return _internal._Mod(self, other)
if isinstance(other, Number):
return _internal._ModScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __rmod__(self, other):
"""x.__rmod__(y) <=> y%x
Only `NDArray` is supported for now.
Example usage:
----------
>>> x = mx.nd.ones((2,3))*3
>>> y = mx.nd.ones((2,3))
>>> x.__rmod__(y).asnumpy()
array([[ 1., 1., 1.,
[ 1., 1., 1., dtype=float32)
"""
if isinstance(other, Number):
return _internal._RModScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __truediv__(self, other):
return self.__div__(other)
def __rtruediv__(self, other):
return self.__rdiv__(other)
def __pow__(self, other):
"""x.__pow__(y) <=> x**y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_pow` instead. """
if isinstance(other, Symbol):
return _internal._Power(self, other)
if isinstance(other, Number):
return _internal._PowerScalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __neg__(self):
"""x.__neg__() <=> -x
Numerical negative, element-wise.
Example usage:
----------
>>> a = mx.sym.Variable('a')
>>> a
<Symbol a>
>>> -a
<Symbol _mulscalar0>
>>> a_neg = a.__neg__()
>>> c = a_neg*b
>>> ex = c.eval(ctx=mx.cpu(), a=mx.nd.ones([2,3]), b=mx.nd.ones([2,3]))
>>> ex[0].asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
"""
return self.__mul__(-1.0)
def __copy__(self):
return self.__deepcopy__(None)
def __deepcopy__(self, _):
"""Returns a deep copy of the input object.
This function returns a deep copy of the input object including the current state
of all its parameters such as weights, biases, etc.
Any changes made to the deep copy do not reflect in the original object.
Example usage:
----------
>>> import copy
>>> data = mx.sym.Variable('data')
>>> data_1 = copy.deepcopy(data)
>>> data_1 = 2*data
>>> data_1.tojson()
>>> data_1 is data # Data got modified
False
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolCopy(self.handle,
ctypes.byref(handle)))
return Symbol(handle)
def __eq__(self, other):
"""x.__eq__(y) <=> x==y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_equal` instead. """
if isinstance(other, Symbol):
return _internal._equal(self, other)
if isinstance(other, numeric_types):
return _internal._equal_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __ne__(self, other):
"""x.__ne__(y) <=> x!=y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_not_equal` instead. """
if isinstance(other, Symbol):
return _internal._not_equal(self, other)
if isinstance(other, numeric_types):
return _internal._not_equal_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __gt__(self, other):
"""x.__gt__(y) <=> x>y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_greater` instead. """
if isinstance(other, Symbol):
return _internal._greater(self, other)
if isinstance(other, numeric_types):
return _internal._greater_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __ge__(self, other):
"""x.__ge__(y) <=> x>=y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_greater_equal` instead. """
if isinstance(other, Symbol):
return _internal._greater_equal(self, other)
if isinstance(other, numeric_types):
return _internal._greater_equal_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __lt__(self, other):
"""x.__lt__(y) <=> x<y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_lesser` instead. """
if isinstance(other, Symbol):
return _internal._lesser(self, other)
if isinstance(other, numeric_types):
return _internal._lesser_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __le__(self, other):
"""x.__le__(y) <=> x<=y
Scalar input is supported.
Broadcasting is not supported. Use `broadcast_lesser_equal` instead. """
if isinstance(other, Symbol):
return _internal._lesser_equal(self, other)
if isinstance(other, numeric_types):
return _internal._lesser_equal_scalar(self, scalar=other)
else:
raise TypeError('type %s not supported' % str(type(other)))
def __getstate__(self):
handle = self.handle
if handle is not None:
return {'handle': self.tojson()}
else:
return {'handle': None}
def __setstate__(self, state):
# pylint: disable=assigning-non-slot
handle = state['handle']
if handle is not None:
json_str = handle
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
self.handle = handle
else:
self.handle = None
def __call__(self, *args, **kwargs):
"""Composes symbol using inputs.
x.__call__(y, z) <=> x(y,z)
This function internally calls `_compose` to compose the symbol and
returns the composed symbol.
Example usage:
----------
>>> data = mx.symbol.Variable('data')
>>> net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
>>> net2 = mx.symbol.FullyConnected(name='fc3', num_hidden=10)
>>> composed = net2(fc3_data=net1, name='composed')
>>> composed
<Symbol composed>
>>> called = net2.__call__(fc3_data=net1, name='composed')
>>> called
<Symbol composed>
Parameters
----------
args:
Positional arguments.
kwargs:
Keyword arguments.
Returns
-------
The resulting symbol.
"""
s = self.__copy__()
s._compose(*args, **kwargs)
return s
def _compose(self, *args, **kwargs):
"""Composes symbol using inputs.
x._compose(y, z) <=> x(y,z)
This function mutates the current symbol.
Example usage:
----------
>>> data = mx.symbol.Variable('data')
>>> net1 = mx.symbol.FullyConnected(data=data, name='fc1', num_hidden=10)
>>> net2 = mx.symbol.FullyConnected(name='fc3', num_hidden=10)
>>> net2
<Symbol fc3>
>>> net2._compose(fc3_data=net1, name='composed')
>>> net2
<Symbol composed>
Parameters
----------
args:
Positional arguments.
kwargs:
Keyword arguments.
Returns
-------
The resulting symbol.
"""
name = kwargs.pop('name', None)
if name:
name = c_str(name)
if len(args) != 0 and len(kwargs) != 0:
raise TypeError('compose only accept input Symbols \
either as positional or keyword arguments, not both')
for arg in args:
if not isinstance(arg, Symbol):
raise TypeError('Compose expect `Symbol` as arguments')
for val in kwargs.values():
if not isinstance(val, Symbol):
raise TypeError('Compose expect `Symbol` as arguments')
num_args = len(args) + len(kwargs)
if len(kwargs) != 0:
keys = c_array(ctypes.c_char_p, [c_str(key) for key in kwargs])
args = c_array(SymbolHandle, [s.handle for s in kwargs.values()])
else:
keys = None
args = c_array(SymbolHandle, [s.handle for s in args])
check_call(_LIB.MXSymbolCompose(
self.handle, name, num_args, keys, args))
def __getitem__(self, index):
"""x.__getitem__(i) <=> x[i]
Returns a sliced view of the input symbol.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> a.__getitem__(0)
<Symbol a>
>>> a[0]
<Symbol a>
Parameters
----------
index : int or str
Indexing key
"""
if isinstance(index, string_types):
idx = None
for i, name in enumerate(self.list_outputs()):
if name == index:
if idx is not None:
raise ValueError('There are multiple outputs with name \"%s\"' % index)
idx = i
if idx is None:
raise ValueError('Cannot find output that matches name \"%s\"' % index)
index = idx
if not isinstance(index, int):
raise TypeError('Symbol only support integer index to fetch i-th output')
if index >= (len(self.list_outputs())):
# Important, python determines the end by this exception
raise IndexError
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetOutput(
self.handle, mx_uint(index), ctypes.byref(handle)))
return Symbol(handle=handle)
@property
def name(self):
"""Gets name string from the symbol, this function only works for non-grouped symbol.
Returns
-------
value : str
The name of this symbol, returns ``None`` for grouped symbol.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetName(
self.handle, ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None
def attr(self, key):
"""Returns the attribute string for corresponding input key from the symbol.
This function only works for non-grouped symbols.
Example usage:
----------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.attr('mood')
'angry'
Parameters
----------
key : str
The key corresponding to the desired attribute.
Returns
-------
value : str
The desired attribute value, returns ``None`` if the attribute does not exist.
"""
ret = ctypes.c_char_p()
success = ctypes.c_int()
check_call(_LIB.MXSymbolGetAttr(
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
else:
return None
def list_attr(self, recursive=False):
"""Gets all attributes from the symbol.
Example usage:
----------
>>> data = mx.sym.Variable('data', attr={'mood': 'angry'})
>>> data.list_attr()
{'mood': 'angry'}
Returns
-------
ret : Dict of str to str
A dictionary mapping attribute keys to values.
"""
if recursive:
raise DeprecationWarning("Symbol.list_attr with recursive=True has been deprecated. "
"Please use attr_dict instead.")
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttrShallow
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
return {py_str(pairs[i * 2]): py_str(pairs[i * 2 + 1]) for i in range(size.value)}
def attr_dict(self):
"""Recursively gets all attributes from the symbol and its children.
Example usage:
----------
>>> a = mx.sym.Variable('a', attr={'a1':'a2'})
>>> b = mx.sym.Variable('b', attr={'b1':'b2'})
>>> c = a+b
>>> c.attr_dict()
{'a': {'a1': 'a2'}, 'b': {'b1': 'b2'}}
Returns
-------
ret : Dict of str to dict
There is a key in the returned dict for every child with non-empty attribute set.
For each symbol, the name of the symbol is its key in the dict
and the correspond value is that symbol's attribute list (itself a dictionary).
"""
size = mx_uint()
pairs = ctypes.POINTER(ctypes.c_char_p)()
f_handle = _LIB.MXSymbolListAttr
check_call(f_handle(self.handle, ctypes.byref(size), ctypes.byref(pairs)))
ret = {}
for i in range(size.value):
name, key = py_str(pairs[i * 2]).split('$')
val = py_str(pairs[i * 2 + 1])
if name not in ret:
ret[name] = {}
ret[name][key] = val
return ret
def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set
"""
for key, value in kwargs.items():
if not isinstance(value, string_types):
raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value))))
def get_internals(self):
"""Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of
outputs of all of the internal nodes.
Consider the following code:
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> d = c.get_internals()
>>> d
<Symbol Grouped>
>>> d.list_outputs()
['a', 'b', '_plus4_output']
Returns
-------
sgroup : Symbol
A symbol group containing all internal and leaf nodes of the computation graph
used to compute the symbol.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetInternals(
self.handle, ctypes.byref(handle)))
return Symbol(handle=handle)
def get_children(self):
"""Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol.
Example usage:
----------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.Variable('z')
>>> a = y+z
>>> b = x+a
>>> b.get_children()
<Symbol Grouped>
>>> b.get_children().list_outputs()
['x', '_plus10_output']
>>> b.get_children().get_children().list_outputs()
['y', 'z']
Returns
-------
sgroup : Symbol or None
The children of the head node. If the symbol has no
inputs then ``None`` will be returned.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetChildren(
self.handle, ctypes.byref(handle)))
ret = Symbol(handle=handle)
if len(ret.list_outputs()) == 0:
return None
return ret
def list_arguments(self):
"""Lists all the arguments in the symbol.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_arguments
['a', 'b']
Returns
-------
args : list of string
List containing the names of all the arguments required to compute the symbol.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListArguments(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)]
def list_outputs(self):
"""Lists all the outputs in the symbol.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_outputs()
['_plus12_output']
Returns
-------
list of str
List of all the outputs.
For most symbols, this list contains only the name of this symbol.
For symbol groups, this is a list with the names of all symbols
in the group.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListOutputs(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)]
def list_auxiliary_states(self):
"""Lists all the auxiliary states in the symbol.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> c.list_auxiliary_states()
[]
Example of auxiliary states in `BatchNorm`.
>>> data = mx.symbol.Variable('data')
>>> weight = mx.sym.Variable(name='fc1_weight')
>>> fc1 = mx.symbol.FullyConnected(data = data, weight=weight, name='fc1', num_hidden=128)
>>> fc2 = mx.symbol.BatchNorm(fc1, name='batchnorm0')
>>> fc2.list_auxiliary_states()
['batchnorm0_moving_mean', 'batchnorm0_moving_var']
Returns
-------
aux_states : list of str
List of the auxiliary states in input symbol.
Notes
-----
Auxiliary states are special states of symbols that do not correspond to an argument,
and are not updated by gradient descent. Common examples of auxiliary states
include the `moving_mean` and `moving_variance` in `BatchNorm`.
Most operators do not have auxiliary states.
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.MXSymbolListAuxiliaryStates(
self.handle, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)]
def list_inputs(self):
"""Lists all arguments and auxiliary states of this Symbol.
Returns
-------
inputs : list of str
List of all inputs.
Examples
--------
>>> bn = mx.sym.BatchNorm(name='bn')
>>> bn.list_arguments()
['bn_data', 'bn_gamma', 'bn_beta']
>>> bn.list_auxiliary_states()
['bn_moving_mean', 'bn_moving_var']
>>> bn.list_inputs()
['bn_data', 'bn_gamma', 'bn_beta', 'bn_moving_mean', 'bn_moving_var']
"""
size = ctypes.c_uint()
sarr = ctypes.POINTER(ctypes.c_char_p)()
check_call(_LIB.NNSymbolListInputNames(
self.handle, 0, ctypes.byref(size), ctypes.byref(sarr)))
return [py_str(sarr[i]) for i in range(size.value)]
def infer_type(self, *args, **kwargs):
"""Infers the type of all arguments and all outputs, given the known types
for some arguments.
This function takes the known types of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing types.
Inconsistencies in the known types will cause an error to be raised.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_types, out_types, aux_types = c.infer_type(a='float32')
>>> arg_types
[<type 'numpy.float32'>, <type 'numpy.float32'>]
>>> out_types
[<type 'numpy.float32'>]
>>> aux_types
[]
Parameters
----------
*args :
Type of known arguments in a positional way.
Unknown type can be marked as None.
**kwargs :
Keyword arguments of known types.
Returns
-------
arg_types : list of numpy.dtype or None
List of argument types.
The order is same as the order of list_arguments().
out_types : list of numpy.dtype or None
List of output types.
The order is same as the order of list_outputs().
aux_types : list of numpy.dtype or None
List of auxiliary state types.
The order is same as the order of list_auxiliary_states().
"""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
types either by positional or kwargs way.')
sdata = []
if len(args) != 0:
keys = None
for s in args:
if s is not None:
s = _numpy.dtype(s).type
if s not in _DTYPE_NP_TO_MX:
raise TypeError('Argument need to be one of ' + str(_DTYPE_NP_TO_MX))
sdata.append(_DTYPE_NP_TO_MX[s])
else:
sdata.append(-1)
else:
keys = []
for k, v in kwargs.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
keys.append(c_str(k))
sdata.append(_DTYPE_NP_TO_MX[v])
arg_type_size = mx_uint()
arg_type_data = ctypes.POINTER(ctypes.c_int)()
out_type_size = mx_uint()
out_type_data = ctypes.POINTER(ctypes.c_int)()
aux_type_size = mx_uint()
aux_type_data = ctypes.POINTER(ctypes.c_int)()
complete = ctypes.c_int()
check_call(_LIB.MXSymbolInferType(
self.handle,
mx_uint(len(sdata)),
c_array(ctypes.c_char_p, keys),
c_array(ctypes.c_int, sdata),
ctypes.byref(arg_type_size),
ctypes.byref(arg_type_data),
ctypes.byref(out_type_size),
ctypes.byref(out_type_data),
ctypes.byref(aux_type_size),
ctypes.byref(aux_type_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_types = [
_DTYPE_MX_TO_NP[arg_type_data[i]] for i in range(arg_type_size.value)]
out_types = [
_DTYPE_MX_TO_NP[out_type_data[i]] for i in range(out_type_size.value)]
aux_types = [
_DTYPE_MX_TO_NP[aux_type_data[i]] for i in range(aux_type_size.value)]
return (arg_types, out_types, aux_types)
else:
return (None, None, None)
# pylint: enable=too-many-locals
def infer_shape(self, *args, **kwargs):
"""Infers the shapes of all arguments and all outputs given the known shapes of
some arguments.
This function takes the known shapes of some arguments in either positional way
or keyword argument way as input. It returns a tuple of `None` values
if there is not enough information to deduce the missing shapes.
Example usage:
----------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_shapes, out_shapes, aux_shapes = c.infer_shape(a=(3,3))
>>> arg_shapes
[(3L, 3L), (3L, 3L)]
>>> out_shapes
[(3L, 3L)]
>>> aux_shapes
[]
>>> c.infer_shape(a=(0,3)) # 0s in shape means unknown dimensions. So, returns None.
(None, None, None)
Inconsistencies in the known shapes will cause an error to be raised.
See the following example:
>>> data = mx.sym.Variable('data')
>>> out = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=1000)
>>> out = mx.sym.Activation(data=out, act_type='relu')
>>> out = mx.sym.FullyConnected(data=out, name='fc2', num_hidden=10)
>>> weight_shape= (1, 100)
>>> data_shape = (100, 100)
>>> out.infer_shape(data=data_shape, fc1_weight=weight_shape)
Error in operator fc1: Shape inconsistent, Provided=(1,100), inferred shape=(1000,100)
Parameters
----------
*args :
Shape of arguments in a positional way.
Unknown shape can be marked as None.
**kwargs :
Keyword arguments of the known shapes.
Returns
-------
arg_shapes : list of tuple or None
List of argument shapes.
The order is same as the order of list_arguments().
out_shapes : list of tuple or None
List of output shapes.
The order is same as the order of list_outputs().
aux_shapes : list of tuple or None
List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().
"""
try:
res = self._infer_shape_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_shape_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, shape in zip(arg_names, arg_shapes):
if not shape or not _numpy.prod(shape):
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append('%s: %s' % (name, str(shape)))
warnings.warn(
"Cannot decide shape for the following arguments " +
"(0s in shape means unknown dimensions). " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_shape error. Arguments:")
for i, arg in enumerate(args):
print(" #%d: %s" % (i, arg))
for k, v in kwargs.items():
print(" %s: %s" % (k, v))
raise
def infer_shape_partial(self, *args, **kwargs):
"""Infers the shape partially.
This functions works the same way as `infer_shape`,
except that this function can return partial results.
In the following example, information about fc2 is not available. So, `infer_shape`
will return a tuple of `None` values but `infer_shape_partial` will return partial values.
Example usage:
----------
>>> data = mx.sym.Variable('data')
>>> prev = mx.sym.Variable('prev')
>>> fc1 = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=128)
>>> fc2 = mx.sym.FullyConnected(data=prev, name='fc2', num_hidden=128)
>>> out = mx.sym.Activation(data=mx.sym.elemwise_add(fc1, fc2), act_type='relu')
>>> out.list_arguments()
['data', 'fc1_weight', 'fc1_bias', 'prev', 'fc2_weight', 'fc2_bias']
>>> out.infer_shape(data=(10,64))
(None, None, None)
>>> out.infer_shape_partial(data=(10,64))
([(10L, 64L), (128L, 64L), (128L,), (), (), ()], [(10L, 128L)], [])
>>> # infers shape if you give information about fc2
>>> out.infer_shape(data=(10,64), prev=(10,128))
([(10L, 64L), (128L, 64L), (128L,), (10L, 128L), (128L, 128L), (128L,)], [(10L, 128L)], [])
Parameters
----------
*args :
Shape of arguments in a positional way.
Unknown shape can be marked as None
**kwargs :
Keyword arguments of known shapes.
Returns
-------
arg_shapes : list of tuple or None
List of argument shapes.
The order is same as the order of list_arguments().
out_shapes : list of tuple or None
List of output shapes.
The order is same as the order of list_outputs().
aux_shapes : list of tuple or None
List of auxiliary state shapes.
The order is same as the order of list_auxiliary_states().
"""
return self._infer_shape_impl(True, *args, **kwargs)
def _infer_shape_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling shape inference API."""
# pylint: disable=too-many-locals
if len(args) != 0 and len(kwargs) != 0:
raise ValueError('Can only specify known argument \
shapes either by positional or kwargs way.')
sdata = []
indptr = [0]
if len(args) != 0:
keys = None
for s in args:
if s is not None:
if not isinstance(s, tuple):
raise TypeError('Arguments must be shapes (tuple)')
sdata.extend(s)
indptr.append(len(sdata))
else:
keys = []
for k, v in kwargs.items():
if isinstance(v, tuple):
keys.append(c_str(k))
sdata.extend(v)
indptr.append(len(sdata))
arg_shape_size = mx_uint()
arg_shape_ndim = ctypes.POINTER(mx_uint)()
arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_uint))()
out_shape_size = mx_uint()
out_shape_ndim = ctypes.POINTER(mx_uint)()
out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_uint))()
aux_shape_size = mx_uint()
aux_shape_ndim = ctypes.POINTER(mx_uint)()
aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_uint))()
complete = ctypes.c_int()
if partial:
infer_func = _LIB.MXSymbolInferShapePartial
else:
infer_func = _LIB.MXSymbolInferShape
check_call(infer_func(
self.handle,
mx_uint(len(indptr) - 1),
c_array(ctypes.c_char_p, keys),
c_array(mx_uint, indptr),
c_array(mx_uint, sdata),
ctypes.byref(arg_shape_size),
ctypes.byref(arg_shape_ndim),
ctypes.byref(arg_shape_data),
ctypes.byref(out_shape_size),
ctypes.byref(out_shape_ndim),
ctypes.byref(out_shape_data),
ctypes.byref(aux_shape_size),
ctypes.byref(aux_shape_ndim),
ctypes.byref(aux_shape_data),
ctypes.byref(complete)))
if complete.value != 0:
arg_shapes = [
tuple(arg_shape_data[i][:arg_shape_ndim[i]]) for i in range(arg_shape_size.value)]
out_shapes = [
tuple(out_shape_data[i][:out_shape_ndim[i]]) for i in range(out_shape_size.value)]
aux_shapes = [
tuple(aux_shape_data[i][:aux_shape_ndim[i]]) for i in range(aux_shape_size.value)]
return (arg_shapes, out_shapes, aux_shapes)
else:
return (None, None, None)
# pylint: enable=too-many-locals
def debug_str(self):
"""Gets a debug string of symbol.
It contains Symbol output, variables and operators in the computation graph
with their inputs, variables and attributes.
Returns
-------
string
Debug string of the symbol.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
>>> d = mx.sym.FullyConnected(data=c, num_hidden=10)
>>> d.debug_str()
>>> print d.debug_str()
Symbol Outputs:
output[0]=fullyconnected0(0)
Variable:a
--------------------
Op:_mul_scalar, Name=_mulscalar0
Inputs:
arg[0]=a(0) version=0
Attrs:
scalar=2
--------------------
Op:sin, Name=sin0
Inputs:
arg[0]=a(0) version=0
--------------------
Op:elemwise_add, Name=_plus0
Inputs:
arg[0]=_mulscalar0(0)
arg[1]=sin0(0)
Variable:fullyconnected0_weight
Variable:fullyconnected0_bias
--------------------
Op:FullyConnected, Name=fullyconnected0
Inputs:
arg[0]=_plus0(0)
arg[1]=fullyconnected0_weight(0) version=0
arg[2]=fullyconnected0_bias(0) version=0
Attrs:
num_hidden=10
"""
debug_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolPrint(
self.handle, ctypes.byref(debug_str)))
return py_str(debug_str.value)
def save(self, fname):
"""Saves symbol to a file.
You can also use pickle to do the job if you only work on python.
The advantage of `load`/`save` functions is that the file contents are language agnostic.
This means the model saved by one language binding can be loaded by a different
language binding of `MXNet`.
You also get the benefit of being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file.
- "s3://my-bucket/path/my-s3-symbol"
- "hdfs://my-bucket/path/my-hdfs-symbol"
- "/path-to/my-local-symbol"
See Also
--------
symbol.load : Used to load symbol from file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
check_call(_LIB.MXSymbolSaveToFile(self.handle, c_str(fname)))
def tojson(self):
"""Saves symbol to a JSON string.
See Also
--------
symbol.load_json : Used to load symbol from JSON string.
"""
json_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolSaveToJSON(self.handle, ctypes.byref(json_str)))
return py_str(json_str.value)
@staticmethod
def _get_ndarray_inputs(arg_key, args, arg_names, allow_missing):
"""Helper function to get NDArray lists handles from various inputs.
Parameters
----------
arg_key : str
The name of argument, used for error message.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbols.
If type is list of NDArray, the position is in the same order of arg_names.
If type is dict of str to NDArray, then it maps the name of arguments
to the corresponding NDArray,
args_names : list of string
List of argument names.
allow_missing : boolean
Whether missing argument is allowed.
When allowed, the missing handle will be set to None(null)
Returns
-------
handles : list of NDArrayHandle
The positional list of NDArrayHandles generated from input.
"""
# setup args
arg_handles = []
arg_arrays = []
if isinstance(args, list):
if len(args) != len(arg_names):
raise ValueError('Length of %s does not match the number of arguments' % arg_key)
for narr in args:
if not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
arg_handles.append(narr.handle)
arg_arrays = args
elif isinstance(args, dict):
for name in arg_names:
if name in args:
narr = args[name]
if not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
arg_handles.append(narr.handle)
arg_arrays.append(narr)
else:
if allow_missing:
arg_handles.append(None)
arg_arrays.append(None)
else:
raise ValueError('key `%s` is missing in `%s`' % (name, arg_key))
else:
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
return c_array(NDArrayHandle, arg_handles), arg_arrays
def simple_bind(self, ctx, grad_req='write', type_dict=None, group2ctx=None,
shared_arg_names=None, shared_exec=None, shared_buffer=None, **kwargs):
"""Bind current symbol to get an executor, allocate all the arguments needed.
Allows specifying data types.
This function simplifies the binding procedure. You need to specify only input data shapes.
Before binding the executor, the function allocates arguments and auxiliary states
that were not explicitly specified. Allows specifying data types.
Example usage:
----------
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.FullyConnected(x, num_hidden=4)
>>> exe = y.simple_bind(mx.cpu(), x=(5,4), grad_req='null')
>>> exe.forward()
[<NDArray 5x4 @cpu(0)>]
>>> exe.outputs[0].asnumpy()
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
>>> exe.arg_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
>>> exe.grad_arrays
[<NDArray 5x4 @cpu(0)>, <NDArray 4x4 @cpu(0)>, <NDArray 4 @cpu(0)>]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
grad_req: string
{'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means every time gradient is written to specified `args_grad` NDArray.
- 'add' means every time gradient is added to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
type_dict : Dict of str->numpy.dtype
Input type dictionary, name->dtype
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_arg_names : List of string
The argument names whose `NDArray` of shared_exec can be reused for initializing
the current executor.
shared_exec : Executor
The executor whose arg_arrays, arg_arrays, grad_arrays, and aux_arrays can be
reused for initializing the current executor.
shared_buffer : Dict of string to `NDArray`
The dict mapping argument names to the `NDArray` that can be reused for initializing
the current executor. This buffer will be checked for reuse if one argument name
of the current executor is not found in `shared_arg_names`.
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
The generated executor
"""
num_provided_arg_types = 0
provided_arg_type_names = ctypes.POINTER(ctypes.c_char_p)() # provided type argument names
provided_arg_type_data = ctypes.POINTER(mx_uint)() # provided types
if type_dict is not None:
provided_arg_type_names = []
provided_arg_type_data = []
for k, v in type_dict.items():
v = _numpy.dtype(v).type
if v in _DTYPE_NP_TO_MX:
provided_arg_type_names.append(c_str(k))
provided_arg_type_data.append(ctypes.c_int(_DTYPE_NP_TO_MX[v]))
num_provided_arg_types = mx_uint(len(provided_arg_type_names))
provided_arg_type_names = c_array(ctypes.c_char_p, provided_arg_type_names)
provided_arg_type_data = c_array(ctypes.c_int, provided_arg_type_data)
provided_arg_shape_data = [] # shape data
# argument shape index in sdata,
# e.g. [sdata[indptr[0]], sdata[indptr[1]]) is the shape of the first arg
provided_arg_shape_idx = [0]
provided_arg_shape_names = [] # provided argument names
for k, v in kwargs.items():
# if k not in listed_arguments and k not in listed_aux_states:
# raise ValueError('arg name %s is not valid', k)
if isinstance(v, tuple):
provided_arg_shape_names.append(c_str(k))
provided_arg_shape_data.extend(v)
provided_arg_shape_idx.append(len(provided_arg_shape_data))
provided_req_type_list_len = 0
provided_grad_req_types = ctypes.POINTER(ctypes.c_char_p)()
provided_grad_req_names = ctypes.POINTER(ctypes.c_char_p)()
if grad_req is not None:
if isinstance(grad_req, string_types):
# use provided_req_type_list_len = 0 to indicate this situation
provided_req_type_list_len = 0
provided_grad_req_types = [c_str(grad_req)]
elif isinstance(grad_req, list):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty list')
provided_grad_req_types = [c_str(item) for item in grad_req]
provided_req_type_list_len = len(provided_grad_req_types)
elif isinstance(grad_req, dict):
if len(grad_req) == 0:
raise RuntimeError('grad_req in simple_bind cannot be an empty dict')
provided_grad_req_names = []
provided_grad_req_types = []
for k, v in grad_req.items():
provided_grad_req_names.append(c_str(k))
provided_grad_req_types.append(c_str(v))
provided_grad_req_names = c_array(ctypes.c_char_p, provided_grad_req_names)
provided_req_type_list_len = len(provided_grad_req_types)
provided_grad_req_types = c_array(ctypes.c_char_p, provided_grad_req_types)
num_ctx_map_keys = mx_uint(0)
ctx_map_keys = ctypes.POINTER(ctypes.c_char_p)()
ctx_map_dev_types = ctypes.POINTER(ctypes.c_int)()
ctx_map_dev_ids = ctypes.POINTER(ctypes.c_int)()
if group2ctx is not None:
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
for key, val in group2ctx.items():
ctx_map_keys.append(c_str(key))
ctx_map_dev_types.append(ctypes.c_int(val.device_typeid))
ctx_map_dev_ids.append(ctypes.c_int(val.device_id))
num_ctx_map_keys = mx_uint(len(ctx_map_keys))
ctx_map_keys = c_array(ctypes.c_char_p, ctx_map_keys)
ctx_map_dev_types = c_array(ctypes.c_int, ctx_map_dev_types)
ctx_map_dev_ids = c_array(ctypes.c_int, ctx_map_dev_ids)
# prepare param names
shared_arg_name_list = []
if shared_arg_names is not None:
if not isinstance(shared_arg_names, list):
raise ValueError('shared_arg_names in simple_bind must be a list or None')
shared_arg_name_list = [c_str(name) for name in shared_arg_names]
# prepare shared_buffer
if shared_buffer is None:
shared_buffer_len = ctypes.c_int(-1)
shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
else:
if not isinstance(shared_buffer, dict):
raise ValueError('shared_buffer in simple_bind must be dict or None')
shared_buffer_names = []
shared_buffer_handles = []
for k, v in shared_buffer.items():
shared_buffer_names.append(c_str(k))
shared_buffer_handles.append(v.handle)
shared_buffer_names = c_array(ctypes.c_char_p, shared_buffer_names)
shared_buffer_len = ctypes.c_int(len(shared_buffer_handles))
shared_buffer_handles = c_array(NDArrayHandle, shared_buffer_handles)
updated_shared_buffer_names = ctypes.POINTER(ctypes.c_char_p)()
updated_shared_buffer_handles = ctypes.POINTER(NDArrayHandle)()
# prepare shared_exec_handle
shared_exec_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
# prepare current executor handle
exe_handle = ExecutorHandle()
# prepare current executor's in_args, arg_grads, and aux_states
num_in_args = ctypes.c_uint()
in_arg_handles = ctypes.POINTER(NDArrayHandle)()
arg_grad_handles = ctypes.POINTER(NDArrayHandle)()
num_aux_states = ctypes.c_uint()
aux_state_handles = ctypes.POINTER(NDArrayHandle)()
try:
check_call(_LIB.MXExecutorSimpleBind(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
num_ctx_map_keys,
ctx_map_keys,
ctx_map_dev_types,
ctx_map_dev_ids,
mx_uint(provided_req_type_list_len),
provided_grad_req_names,
provided_grad_req_types,
mx_uint(len(provided_arg_shape_names)),
c_array(ctypes.c_char_p, provided_arg_shape_names),
c_array(mx_uint, provided_arg_shape_data),
c_array(mx_uint, provided_arg_shape_idx),
num_provided_arg_types,
provided_arg_type_names,
provided_arg_type_data,
mx_uint(len(shared_arg_name_list)),
c_array(ctypes.c_char_p, shared_arg_name_list),
ctypes.byref(shared_buffer_len),
shared_buffer_names,
shared_buffer_handles,
ctypes.byref(updated_shared_buffer_names),
ctypes.byref(updated_shared_buffer_handles),
ctypes.byref(num_in_args),
ctypes.byref(in_arg_handles),
ctypes.byref(arg_grad_handles),
ctypes.byref(num_aux_states),
ctypes.byref(aux_state_handles),
shared_exec_handle,
ctypes.byref(exe_handle)))
except MXNetError as e:
error_msg = "simple_bind error. Arguments:\n"
for k, v in kwargs.items():
error_msg += "%s: %s\n" % (k, v)
error_msg += "%s" % e
raise RuntimeError(error_msg)
# update shared_buffer
if shared_buffer is not None:
for i in range(shared_buffer_len.value):
k = py_str(updated_shared_buffer_names[i])
v = NDArray(NDArrayHandle(updated_shared_buffer_handles[i]))
shared_buffer[k] = v
# create in_args, arg_grads, and aux_states for the current executor
arg_arrays = [NDArray(NDArrayHandle(in_arg_handles[i])) for i in range(num_in_args.value)]
grad_arrays = [NDArray(NDArrayHandle(arg_grad_handles[i]))
if arg_grad_handles[i] is not None
else None for i in range(num_in_args.value)]
aux_arrays = [NDArray(NDArrayHandle(aux_state_handles[i]))
for i in range(num_aux_states.value)]
executor = Executor(exe_handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = arg_arrays
executor.grad_arrays = grad_arrays
executor.aux_arrays = aux_arrays
return executor
def bind(self, ctx, args, args_grad=None, grad_req='write',
aux_states=None, group2ctx=None, shared_exec=None):
"""Binds the current symbol to an executor and returns it.
We first declare the computation and then bind to the data to run.
This function returns an executor which provides method `forward()` method for evaluation
and a `outputs()` method to get all the results.
Example usage:
----------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
<Symbol _plus1>
>>> ex = c.bind(ctx=mx.cpu(), args={'a' : mx.nd.ones([2,3]), 'b' : mx.nd.ones([2,3])})
>>> ex.forward()
[<NDArray 2x3 @cpu(0)>]
>>> ex.outputs[0].asnumpy()
[[ 2. 2. 2.]
[ 2. 2. 2.]]
Parameters
----------
ctx : Context
The device context the generated executor to run on.
args : list of NDArray or dict of str to NDArray
Input arguments to the symbol.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding `NDArray`.
- In either case, all the arguments must be provided.
args_grad : list of NDArray or dict of str to `NDArray`, optional
When specified, `args_grad` provides NDArrays to hold
the result of gradient value in backward.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_arguments()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of arguments
to the corresponding NDArray.
- When the type is a dict of str to `NDArray`, one only need to provide the dict
for required argument gradient.
Only the specified argument gradient will be calculated.
grad_req : {'write', 'add', 'null'}, or list of str or dict of str to str, optional
To specify how we should update the gradient to the `args_grad`.
- 'write' means everytime gradient is write to specified `args_grad` `NDArray`.
- 'add' means everytime gradient is add to the specified NDArray.
- 'null' means no action is taken, the gradient may not be calculated.
aux_states : list of `NDArray`, or dict of str to `NDArray`, optional
Input auxiliary states to the symbol, only needed when the output of
`list_auxiliary_states()` is not empty.
- If the input type is a list of `NDArray`, the order should be same as the order
of `list_auxiliary_states()`.
- If the input type is a dict of str to `NDArray`, then it maps the name of
`auxiliary_states` to the corresponding `NDArray`,
- In either case, all the auxiliary states need to be provided.
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
shared_exec : mx.executor.Executor
Executor to share memory with. This is intended for runtime reshaping, variable length
sequences, etc. The returned executor shares state with `shared_exec`, and should not be
used in parallel with it.
Returns
-------
executor : Executor
The generated executor
Notes
-----
Auxiliary states are the special states of symbols that do not correspond
to an argument, and do not have gradient but are still useful
for the specific operations. Common examples of auxiliary states include
the `moving_mean` and `moving_variance` states in `BatchNorm`.
Most operators do not have auxiliary states and in those cases,
this parameter can be safely ignored.
One can give up gradient by using a dict in `args_grad` and only specify
gradient they interested in.
"""
# pylint: disable=too-many-locals, too-many-branches
if not isinstance(ctx, Context):
raise TypeError("Context type error")
listed_arguments = self.list_arguments()
args_handle, args = self._get_ndarray_inputs('args', args, listed_arguments, False)
# setup args gradient
if args_grad is None:
args_grad_handle = c_array(NDArrayHandle, [None] * len(args))
else:
args_grad_handle, args_grad = self._get_ndarray_inputs(
'args_grad', args_grad, listed_arguments, True)
if aux_states is None:
aux_states = []
aux_args_handle, aux_states = self._get_ndarray_inputs(
'aux_states', aux_states, self.list_auxiliary_states(), False)
# setup requirements
if isinstance(grad_req, string_types):
if grad_req not in _GRAD_REQ_MAP:
raise ValueError('grad_req must be in %s' % str(_GRAD_REQ_MAP))
reqs_array = c_array(
mx_uint,
[mx_uint(_GRAD_REQ_MAP[grad_req])] * len(listed_arguments))
elif isinstance(grad_req, list):
reqs_array = c_array(mx_uint, [mx_uint(_GRAD_REQ_MAP[item]) for item in grad_req])
elif isinstance(grad_req, dict):
req_array = []
for name in listed_arguments:
if name in grad_req:
req_array.append(mx_uint(_GRAD_REQ_MAP[grad_req[name]]))
else:
req_array.append(mx_uint(0))
reqs_array = c_array(mx_uint, req_array)
ctx_map_keys = []
ctx_map_dev_types = []
ctx_map_dev_ids = []
if group2ctx:
for key, val in group2ctx.items():
ctx_map_keys.append(c_str(key))
ctx_map_dev_types.append(ctypes.c_int(val.device_typeid))
ctx_map_dev_ids.append(ctypes.c_int(val.device_id))
handle = ExecutorHandle()
shared_handle = shared_exec.handle if shared_exec is not None else ExecutorHandle()
check_call(_LIB.MXExecutorBindEX(self.handle,
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
mx_uint(len(ctx_map_keys)),
c_array(ctypes.c_char_p, ctx_map_keys),
c_array(ctypes.c_int, ctx_map_dev_types),
c_array(ctypes.c_int, ctx_map_dev_ids),
mx_uint(len(args)),
args_handle,
args_grad_handle,
reqs_array,
mx_uint(len(aux_states)),
aux_args_handle,
shared_handle,
ctypes.byref(handle)))
executor = Executor(handle, self, ctx, grad_req, group2ctx)
executor.arg_arrays = args
executor.grad_arrays = args_grad
executor.aux_arrays = aux_states
return executor
def grad(self, wrt):
"""Gets the autodiff of current symbol.
This function can only be used if current symbol is a loss function.
.. note:: This function is currently not implemented.
Parameters
----------
wrt : Array of String
keyword arguments of the symbol that the gradients are taken.
Returns
-------
grad : Symbol
A gradient Symbol with returns to be the corresponding gradients.
"""
handle = SymbolHandle()
c_wrt = c_array(ctypes.c_char_p, [c_str(key) for key in wrt])
check_call(_LIB.MXSymbolGrad(self.handle,
mx_uint(len(wrt)),
c_wrt,
ctypes.byref(handle)))
return Symbol(handle)
# pylint: enable= no-member
def eval(self, ctx=cpu(), **kwargs):
"""Evaluates a symbol given arguments.
The `eval` method combines a call to `bind` (which returns an executor)
with a call to `forward` (executor method).
For the common use case, where you might repeatedly evaluate with same arguments,
eval is slow.
In that case, you should call `bind` once and then repeatedly call forward.
This function allows simpler syntax for less cumbersome introspection.
Example usage:
----------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> c = a + b
>>> ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
>>> ex
[<NDArray 2x3 @cpu(0)>]
>>> ex[0].asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
Parameters
----------
ctx : Context
The device context the generated executor to run on.
kwargs : Keyword arguments of type `NDArray`
Input arguments to the symbol. All the arguments must be provided.
Returns
----------
result : a list of NDArrays corresponding to the values taken by each symbol when
evaluated on given args. When called on a single symbol (not a group),
the result will be a list with one element.
"""
return self.bind(ctx, kwargs).forward()
def reshape(self, shape):
"""Shorthand for mxnet.sym.reshape.
Parameters
----------
shape : tuple of int
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
One shape dimension can be -1. In this case, the value is inferred
from the length of the array and remaining dimensions.
Returns
-------
Symbol
A reshaped symbol.
"""
return reshape(self, shape=shape)
def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None, init=None, **kwargs):
"""Creates a symbolic variable with specified name.
Example usage:
----------
>>> data = mx.sym.Variable('data', attr={'a': 'b'})
>>> data
<Symbol data>
Parameters
----------
name : str
Variable name.
attr : Dict of strings
Additional attributes to set on the variable. Format {string : string}.
shape : tuple
The shape of a variable. If specified, this will be used during the shape inference.
If one has specified a different shape for this variable using
a keyword argument when calling shape inference, this shape information will be ignored.
lr_mult : float
The learning rate multiplier for input variable.
wd_mult : float
Weight decay multiplier for input variable.
dtype : str or numpy.dtype
The dtype for input variable. If not specified, this value will be inferred.
init : initializer (mxnet.init.*)
Initializer for this variable to (optionally) override the default initializer.
kwargs : Additional attribute variables
Additional attributes must start and end with double underscores.
Returns
-------
variable : Symbol
A symbol corresponding to an input to the computation graph.
"""
if not isinstance(name, string_types):
raise TypeError('Expect a string for variable `name`')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateVariable(c_str(name), ctypes.byref(handle)))
ret = Symbol(handle)
attr = AttrScope.current.get(attr)
attr = {} if attr is None else attr
if shape is not None:
attr['__shape__'] = str(shape)
if lr_mult is not None:
attr['__lr_mult__'] = str(lr_mult)
if wd_mult is not None:
attr['__wd_mult__'] = str(wd_mult)
if dtype is not None:
attr['__dtype__'] = str(_DTYPE_NP_TO_MX[_numpy.dtype(dtype).type])
if init is not None:
if not isinstance(init, string_types):
init = init.dumps()
attr['__init__'] = init
for k, v in kwargs.items():
if k.startswith('__') and k.endswith('__'):
attr[k] = str(v)
else:
raise ValueError('Attribute name=%s is not supported.'
' Additional attributes must start and end with double underscores,'
' e.g, __yourattr__' % k)
ret._set_attr(**attr)
return ret
# for back compatibility
Variable = var
def Group(symbols):
"""Creates a symbol that contains a collection of other symbols, grouped together.
Example usage:
----------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.Variable('b')
>>> mx.sym.Group([a,b])
<Symbol Grouped>
Parameters
----------
symbols : list
List of symbols to be grouped.
Returns
-------
sym : Symbol
A group symbol.
"""
ihandles = []
for sym in symbols:
if not isinstance(sym, Symbol):
raise TypeError('Expected a list of symbols as input')
ihandles.append(sym.handle)
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateGroup(
mx_uint(len(ihandles)),
c_array(SymbolHandle, ihandles), ctypes.byref(handle)))
return Symbol(handle)
def load(fname):
"""Loads symbol from a JSON file.
You can also use pickle to do the job if you only work on python.
The advantage of load/save is the file is language agnostic.
This means the file saved using save can be loaded by other language binding of mxnet.
You also get the benefit being able to directly load/save from cloud storage(S3, HDFS).
Parameters
----------
fname : str
The name of the file, examples:
- `s3://my-bucket/path/my-s3-symbol`
- `hdfs://my-bucket/path/my-hdfs-symbol`
- `/path-to/my-local-symbol`
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.save : Used to save symbol into file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromFile(c_str(fname), ctypes.byref(handle)))
return Symbol(handle)
def load_json(json_str):
"""Loads symbol from json string.
Parameters
----------
json_str : str
A JSON string.
Returns
-------
sym : Symbol
The loaded symbol.
See Also
--------
Symbol.tojson : Used to save symbol into json string.
"""
if not isinstance(json_str, string_types):
raise TypeError('fname required to be string')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateFromJSON(c_str(json_str), ctypes.byref(handle)))
return Symbol(handle)
# pylint: disable=no-member
# pylint: disable=redefined-builtin
def pow(base, exp):
"""Returns element-wise result of base element raised to powers from exp element.
Both inputs can be Symbol or scalar number.
Broadcasting is not supported. Use `broadcast_pow` instead.
Parameters
---------
base : Symbol or scalar
The base symbol
exp : Symbol or scalar
The exponent symbol
Returns
-------
Symbol or scalar
The bases in x raised to the exponents in y.
Examples
--------
>>> mx.sym.pow(2, 3)
8
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.pow(x, 2)
>>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
array([ 1., 4.], dtype=float32)
>>> z = mx.sym.pow(3, y)
>>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 27.], dtype=float32)
>>> z = mx.sym.pow(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 64.], dtype=float32)
"""
if isinstance(base, Symbol) and isinstance(exp, Symbol):
return _internal._Power(base, exp)
if isinstance(base, Symbol) and isinstance(exp, Number):
return _internal._PowerScalar(base, scalar=exp)
if isinstance(base, Number) and isinstance(exp, Symbol):
return _internal._RPowerScalar(exp, scalar=base)
if isinstance(base, Number) and isinstance(exp, Number):
return base**exp
else:
raise TypeError('types (%s, %s) not supported' % (str(type(base)), str(type(exp))))
# pylint: disable=no-member
# pylint: disable=redefined-builtin
def maximum(left, right):
"""Returns element-wise maximum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise maximum of the input symbols.
Examples
--------
>>> mx.sym.maximum(2, 3.5)
3.5
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.maximum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 4., 5., 4., 10.], dtype=float32)
>>> z = mx.sym.maximum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10., 4.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Maximum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MaximumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MaximumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left > right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right))))
# pylint: disable=no-member
# pylint: disable=redefined-builtin
def minimum(left, right):
"""Returns element-wise minimum of the input elements.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First symbol to be compared.
right : Symbol or scalar
Second symbol to be compared.
Returns
-------
Symbol or scalar
The element-wise minimum of the input symbols.
Examples
--------
>>> mx.sym.minimum(2, 3.5)
2
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.minimum(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2,10]))[0].asnumpy()
array([ 3., 4., 2., 4.], dtype=float32)
>>> z = mx.sym.minimum(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 3., 2.], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Minimum(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._MinimumScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._MinimumScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return left if left < right else right
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right))))
# pylint: disable=no-member
# pylint: disable=redefined-builtin
def hypot(left, right):
"""Given the "legs" of a right triangle, returns its hypotenuse.
Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise.
Both inputs can be Symbol or scalar number. Broadcasting is not supported.
Parameters
---------
left : Symbol or scalar
First leg of the triangle(s).
right : Symbol or scalar
Second leg of the triangle(s).
Returns
-------
Symbol or scalar
The hypotenuse of the triangle(s)
Examples
--------
>>> mx.sym.hypot(3, 4)
5.0
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.hypot(x, 4)
>>> z.eval(x=mx.nd.array([3,5,2]))[0].asnumpy()
array([ 5., 6.40312433, 4.47213602], dtype=float32)
>>> z = mx.sym.hypot(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
array([ 10.44030666, 4.47213602], dtype=float32)
"""
if isinstance(left, Symbol) and isinstance(right, Symbol):
return _internal._Hypot(left, right)
if isinstance(left, Symbol) and isinstance(right, Number):
return _internal._HypotScalar(left, scalar=right)
if isinstance(left, Number) and isinstance(right, Symbol):
return _internal._HypotScalar(right, scalar=left)
if isinstance(left, Number) and isinstance(right, Number):
return _numpy.hypot(left, right)
else:
raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right))))
def zeros(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol.
"""
if dtype is None:
dtype = _numpy.float32
return _internal._zeros(shape=shape, dtype=dtype, **kwargs)
def ones(shape, dtype=None, **kwargs):
"""Returns a new symbol of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._ones(shape=shape, dtype=dtype, **kwargs)
def arange(start, stop=None, step=1.0, repeat=1, name=None, dtype=None):
"""Returns evenly spaced values within a given interval.
Parameters
----------
start : number
Start of interval. The interval includes this value. The default start value is 0.
stop : number, optional
End of interval. The interval does not include this value.
step : number, optional
Spacing between values.
repeat : int, optional
"The repeating time of all elements.
E.g repeat=3, the element a will be repeated three times --> a, a, a.
dtype : str or numpy.dtype, optional
The value type of the inner value, default to ``np.float32``.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
name=name, dtype=dtype)
def _make_atomic_symbol_function(handle, name):
"""Create an atomic symbol function by handle and funciton name."""
real_name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
key_var_num_args = ctypes.c_char_p()
ret_type = ctypes.c_char_p()
check_call(_LIB.MXSymbolGetAtomicSymbolInfo(
handle, ctypes.byref(real_name), ctypes.byref(desc),
ctypes.byref(num_args),
ctypes.byref(arg_names),
ctypes.byref(arg_types),
ctypes.byref(arg_descs),
ctypes.byref(key_var_num_args),
ctypes.byref(ret_type)))
narg = int(num_args.value)
arg_names = [py_str(arg_names[i]) for i in range(narg)]
arg_types = [py_str(arg_types[i]) for i in range(narg)]
func_name = name
key_var_num_args = py_str(key_var_num_args.value)
ret_type = py_str(ret_type.value) if ret_type.value is not None else ''
doc_str = _build_doc(func_name,
py_str(desc.value),
arg_names,
arg_types,
[py_str(arg_descs[i]) for i in range(narg)],
key_var_num_args,
ret_type)
dtype_name = None
arr_name = None
ndsignature = []
signature = []
ndarg_names = []
kwarg_names = []
for i in range(narg):
name, atype = arg_names[i], arg_types[i]
if name == 'dtype':
dtype_name = name
signature.append('%s=_Null'%name)
elif atype.startswith('NDArray') or atype.startswith('Symbol'):
assert not arr_name, \
"Op can only have one argument with variable " \
"size and it must be the last argument."
if atype.endswith('[]'):
ndsignature.append('*%s'%name)
arr_name = name
else:
ndsignature.append('%s=None'%name)
ndarg_names.append(name)
else:
signature.append('%s=_Null'%name)
kwarg_names.append(name)
#signature.append('is_train=False')
signature.append('name=None')
signature.append('attr=None')
signature.append('out=None')
signature.append('**kwargs')
signature = ndsignature + signature
code = []
if arr_name:
code.append("""
def %s(*%s, **kwargs):"""%(func_name, arr_name))
code.append("""
sym_args = []
for i in {}:
assert isinstance(i, SymbolBase), \\
"Positional arguments must be Symbol instances, " \\
"but got %s"%str(i)
sym_args.append(i)""".format(arr_name))
if dtype_name is not None:
code.append("""
if '%s' in kwargs:
kwargs['%s'] = _numpy.dtype(kwargs['%s']).name"""%(
dtype_name, dtype_name, dtype_name))
code.append("""
attr = kwargs.pop('attr', None)
kwargs.update(AttrScope.current.get(attr))
name = kwargs.pop('name', None)
name = NameManager.current.get(name, '%s')
_ = kwargs.pop('out', None)
keys = []
vals = []
sym_kwargs = dict()
for k, v in kwargs.items():
if isinstance(v, SymbolBase):
sym_kwargs[k] = v
else:
keys.append(k)
vals.append(v)"""%(func_name.lower()))
if key_var_num_args:
code.append("""
if '%s' not in kwargs:
keys.append('%s')
vals.append(len(sym_args) + len(sym_kwargs))"""%(
key_var_num_args, key_var_num_args))
code.append("""
return _symbol_creator(%d, sym_args, sym_kwargs, keys, vals, name)"""%(
handle.value))
else:
code.append("""
def %s(%s):
kwargs.update(AttrScope.current.get(attr))
sym_kwargs = dict()
keys = []
vals = []"""%(func_name, ', '.join(signature)))
code.append("""
for k, v in kwargs.items():
if isinstance(v, SymbolBase):
sym_kwargs[k] = v
else:
keys.append(k)
vals.append(v)""")
# NDArray args
for name in ndarg_names: # pylint: disable=redefined-argument-from-local
code.append("""
if {name} is not None:
assert isinstance({name}, SymbolBase), \\
"Argument {name} must be Symbol instances, but got %s"%str({name})
sym_kwargs['{name}'] = {name}""".format(name=name))
# kwargs
for name in kwarg_names: # pylint: disable=redefined-argument-from-local
code.append("""
if %s is not _Null:
keys.append('%s')
vals.append(%s)"""%(name, name, name))
# dtype
if dtype_name is not None:
code.append("""
if %s is not _Null:
keys.append('%s')
vals.append(_numpy.dtype(%s).name)"""%(dtype_name, dtype_name, dtype_name))
code.append("""
name = NameManager.current.get(name, '%s')
return _symbol_creator(%d, None, sym_kwargs, keys, vals, name)"""%(
func_name.lower(), handle.value))
local = {}
exec(''.join(code), None, local) # pylint: disable=exec-used
symbol_function = local[func_name]
symbol_function.__name__ = func_name
symbol_function.__doc__ = doc_str
symbol_function.__module__ = 'mxnet.symbol'
return symbol_function
def _init_symbol_module(symbol_class, root_namespace):
"""List and add all the atomic symbol functions to current module."""
_set_symbol_class(symbol_class)
plist = ctypes.POINTER(ctypes.c_char_p)()
size = ctypes.c_uint()
check_call(_LIB.MXListAllOpNames(ctypes.byref(size),
ctypes.byref(plist)))
op_names = []
for i in range(size.value):
op_names.append(py_str(plist[i]))
module_obj = _sys.modules["%s.symbol" % root_namespace]
module_internal = _sys.modules["%s._symbol_internal" % root_namespace]
module_contrib = _sys.modules["%s.contrib.symbol" % root_namespace]
for name in op_names:
hdl = OpHandle()
check_call(_LIB.NNGetOpHandle(c_str(name), ctypes.byref(hdl)))
function = _make_atomic_symbol_function(hdl, name)
if function.__name__.startswith('_contrib_'):
function.__name__ = function.__name__[9:]
function.__module__ = 'mxnet.contrib.symbol'
setattr(module_contrib, function.__name__, function)
elif function.__name__.startswith('_'):
setattr(module_internal, function.__name__, function)
else:
setattr(module_obj, function.__name__, function)
# Initialize the atomic symbol in startups
_init_symbol_module(Symbol, "mxnet")
Hi！ I compiled mxnet provide by you.However ,this error still appears. Please tell me how to solve this error