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apache / mxnet / c8922fedff48cf0c4f15bf0e5fe805be8be34512 / . / python / mxnet / symbol / symbol.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# 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."""
try:
from __builtin__ import slice as py_slice
except ImportError:
from builtins import slice as py_slice
from array import array
import ctypes
import warnings
from numbers import Number
import numpy as _numpy # pylint: disable=relative-import
from .. import attribute
from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str, c_str_array, c_handle_array
from ..base import mx_uint, py_str, string_types, integer_types, mx_int, mx_int64
from ..base import NDArrayHandle, SymbolHandle
from ..base import check_call, MXNetError, NotImplementedForSymbol
from ..device import Device, current_device
from ..ndarray import NDArray, dtype_np_to_mx, dtype_mx_to_np, is_mx_dtype
from ..ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID, _int64_enabled, _SIGNED_INT32_UPPER_LIMIT
from ..executor import Executor
from . import _internal
from . import op
from ._internal import SymbolBase, _set_symbol_class
from ..util import is_np_shape
from ..profiler import scope as _profiler_scope
from ..profiler import _current_scope as _current_profiler_scope
__all__ = ["Symbol", "var", "Variable", "Group", "load", "fromjson",
"pow", "power", "maximum", "minimum", "hypot", "eye", "zeros",
"ones", "full", "arange", "linspace", "histogram", "split_v2"]
class Symbol(SymbolBase):
"""Symbol is symbolic graph of the mxnet."""
# disable dictionary storage, also do not have parent type.
# pylint: disable=no-member
__slots__ = []
# Make numpy functions return Symbol instead of numpy object array
__array_priority__ = 1000.0
def as_np_ndarray(self):
"""Convert mx.sym.Symbol to mx.sym.np._Symbol."""
from .numpy import _Symbol
hdl = SymbolHandle()
check_call(_LIB.MXShallowCopySymbol(self.handle, ctypes.byref(hdl)))
return _Symbol(hdl)
def as_nd_ndarray(self):
"""Returns self. For the convenience of conversion between legacy and np symbols."""
return self
def __repr__(self):
"""Gets a string representation of the symbol."""
if self._alive:
name = self.name
if name is None:
name = ', '.join([i.name for i in self])
return f'<{self.__class__.__name__} group [{name}]>'
else:
return f'<{self.__class__.__name__} {name}>'
else:
return '<FREED {}>'.format(self.__class__.__name__)
def __iter__(self):
"""Returns a generator object of symbol.
One can loop through the returned object list to get outputs.
Example
-------
>>> 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:
... print(i)
...
<Symbol d>
<Symbol _plus0>
"""
return (self[i] for i in range(len(self)))
def __abs__(self):
"""x.__abs__() <=> abs(x) <=> x.abs() <=> mx.symbol.abs(x, y)"""
return self.abs()
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(f'type {str(type(other))} not supported')
def __bool__(self):
raise NotImplementedForSymbol(self.__bool__, 'bool')
__nonzero__ = __bool__
def __iadd__(self, other):
raise NotImplementedForSymbol(self.__iadd__, '+=', other, 1)
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(f'type {str(type(other))} not supported')
def __isub__(self, other):
raise NotImplementedForSymbol(self.__isub__, '-=', other)
def __rsub__(self, other):
"""x.__rsub__(y) <=> y-x
Only `NDArray` is supported for now.
Example
-------
>>> 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, Symbol):
return other.__sub__(self)
if isinstance(other, Number):
return _internal._RMinusScalar(self, scalar=other)
else:
raise TypeError(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
def __imul__(self, other):
raise NotImplementedForSymbol(self.__imul__, '*=', 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(f'type {str(type(other))} not supported')
def __rdiv__(self, other):
"""x.__rdiv__(y) <=> y/x
Only `NDArray` is supported for now.
Example
-------
>>> 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, Symbol):
return other.__truediv__(self)
if isinstance(other, Number):
return _internal._RDivScalar(self, scalar=other)
else:
raise TypeError(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
def __rmod__(self, other):
"""x.__rmod__(y) <=> y%x
Only `NDArray` is supported for now.
Example
-------
>>> 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, Symbol):
return other.__mod__(self)
if isinstance(other, Number):
return _internal._RModScalar(self, scalar=other)
else:
raise TypeError(f'type {str(type(other))} not supported')
def __idiv__(self, other):
raise NotImplementedForSymbol(self.__idiv__, '/=', other)
def __truediv__(self, other):
return self.__div__(other)
def __rtruediv__(self, other):
return self.__rdiv__(other)
def __itruediv__(self, other):
raise NotImplementedForSymbol(self.__itruediv__, '/=', 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(f'type {str(type(other))} not supported')
def __rpow__(self, other):
"""x.__rpow__(y) <=> y ** x"""
if isinstance(other, Symbol):
return other.__pow__(self)
elif isinstance(other, Number):
return _internal._rpower_scalar(self, scalar=other)
else:
raise TypeError(f'type {str(type(other))} not supported')
def __neg__(self):
"""x.__neg__() <=> -x
Numerical negative, element-wise.
Example
-------
>>> 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
-------
>>> 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(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
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(f'type {str(type(other))} not supported')
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
-------
>>> 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
-------
>>> 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_str_array(kwargs.keys())
args = c_handle_array(kwargs.values())
else:
keys = None
args = c_handle_array(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
-------
>>> a = mx.sym.var('a')
>>> a.__getitem__(0)
<Symbol a>
>>> a[0]
<Symbol a>
Parameters
----------
index : int or str
Indexing key
"""
output_count = len(self)
if isinstance(index, py_slice):
start = 0 if index.start is None else index.start
stop = output_count if index.stop is None else index.stop
step = 1 if index.step is None else index.step
return Group([self[i] for i in range(start, stop, step)])
if isinstance(index, string_types):
# Returning this list of names is expensive. Some symbols may have hundreds of outputs
output_names = self.list_outputs()
idx = None
for i, name in enumerate(output_names):
if name == index:
if idx is not None:
raise ValueError(f'There are multiple outputs with name \"{index}\"')
idx = i
if idx is None:
raise ValueError(f'Cannot find output that matches name \"{index}\"')
index = idx
if not isinstance(index, int):
raise TypeError('Symbol only support integer index to fetch i-th output')
if index >= output_count:
# 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
-------
>>> 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
-------
>>> 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
-------
>>> 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_inputs(self):
"""Gets a new grouped symbol `sgroup`. The output of `sgroup` is a list of inputs to this symbol.
Consider the following code:
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> d = c.get_inputs()
>>> d
<Symbol Grouped>
>>> d.list_outputs()
['a', 'b']
Returns
-------
sgroup : Symbol
A symbol group containing all input nodes of the computation graph
used to compute the symbol.
"""
handle = SymbolHandle()
check_call(_LIB.MXSymbolGetInputs(
self.handle, ctypes.byref(handle)))
return Symbol(handle=handle)
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
-------
>>> 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
-------
>>> 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
-------
>>> 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
-------
>>> 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)]
# pylint: disable=invalid-length-returned
def __len__(self):
"""Get number of outputs for the symbol.
Example
-------
>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> len(c)
Returns
-------
len(self): Number of outputs
Number of outputs
"""
output_count = mx_uint()
check_call(_LIB.MXSymbolGetNumOutputs(self.handle, ctypes.byref(output_count)))
return output_count.value
def list_auxiliary_states(self):
"""Lists all the auxiliary states in the symbol.
Example
-------
>>> 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
-------
>>> 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().
"""
try:
res = self._infer_type_impl(False, *args, **kwargs)
if res[1] is None:
arg_shapes, _, _ = self._infer_type_impl(True, *args, **kwargs)
arg_names = self.list_arguments()
unknowns = []
for name, dtype in zip(arg_names, arg_shapes):
if not dtype:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append(f'{name}: {str(dtype)}')
warnings.warn(
"Cannot decide type for the following arguments. " +
"Consider providing them as input:\n\t" +
"\n\t".join(unknowns), stacklevel=2)
return res
except MXNetError:
print("infer_type error. Arguments:")
for i, arg in enumerate(args):
print(f" #{i}: {arg}")
for k, v in kwargs.items():
print(f" {k}: {v}")
raise
def infer_type_partial(self, *args, **kwargs):
"""Infers the type partially.
This functions works the same way as `infer_type`,
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
-------
>>> data = mx.sym.Variable('data')
>>> prev = mx.sym.Variable('prev')
>>> casted_prev = mx.sym.cast(prev, dtype='float32')
>>> out = mx.sym.Activation(data=mx.sym.elemwise_add(data, casted_prev), act_type='relu')
>>> out.list_arguments()
['data', 'prev']
>>> out.infer_type(data='float32')
(None, None, None)
>>> out.infer_type_partial(data='float32')
([numpy.float32, None], [numpy.float32], [])
>>> # infers type if you give information about prev
>>> out.infer_type(data='float32', prev='float16')
([numpy.float32, numpy.float16], [numpy.float32], [])
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().
"""
return self._infer_type_impl(True, *args, **kwargs)
def _infer_type_impl(self, partial, *args, **kwargs):
"""The actual implementation for calling type inference API."""
# 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 = c_array(ctypes.c_char_p, [])
for s in args:
if s is not None:
sdata.append(dtype_np_to_mx(s))
else:
sdata.append(-1)
else:
str_keys = []
for k, v in kwargs.items():
if is_mx_dtype(v):
v = dtype_np_to_mx(v)
str_keys.append(k)
sdata.append(v)
keys = c_str_array(str_keys)
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()
if partial:
infer_func = _LIB.MXSymbolInferTypePartial
else:
infer_func = _LIB.MXSymbolInferType
check_call(infer_func(
self.handle,
mx_uint(len(sdata)),
keys,
c_array_buf(ctypes.c_int, array('i', 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)
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
-------
>>> 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().
"""
# pylint: disable=too-many-locals
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 is_np_shape():
shape_is_none = not shape or -1 in shape
else:
shape_is_none = not shape or 0 in shape
if shape_is_none:
if len(unknowns) >= 10:
unknowns.append('...')
break
unknowns.append(f'{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(f" #{i}: {arg}")
for k, v in kwargs.items():
print(f" {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
-------
>>> 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 = c_array(ctypes.c_char_p, [])
for i, s in enumerate(args):
if s is not None:
if not isinstance(s, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
f"but argument {i} is {type(s)}.")
sdata.extend(s)
indptr.append(len(sdata))
else:
str_keys = []
for k, v in kwargs.items():
if not isinstance(v, tuple):
raise TypeError("Arguments need to be shapes (tuple), "
f"but '{k}' is {type(v)}.")
str_keys.append(k)
sdata.extend(v)
indptr.append(len(sdata))
keys = c_str_array(str_keys)
arg_shape_size = mx_uint()
arg_shape_ndim = ctypes.POINTER(mx_int)()
out_shape_size = mx_uint()
out_shape_ndim = ctypes.POINTER(mx_int)()
aux_shape_size = mx_uint()
aux_shape_ndim = ctypes.POINTER(mx_int)()
complete = ctypes.c_int()
if _int64_enabled():
arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int64))()
if partial:
infer_func = _LIB.MXSymbolInferShapePartial64
else:
infer_func = _LIB.MXSymbolInferShape64
check_call(infer_func(
self.handle,
mx_uint(len(indptr) - 1),
keys,
c_array_buf(mx_int64, array('q', indptr)),
c_array_buf(mx_int64, array('q', 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)))
else:
for size in sdata:
if size > _SIGNED_INT32_UPPER_LIMIT:
raise Exception("[_infer_shape_impl] Size of tensor you are trying to " +
"allocate is larger than 2^31 elements. Please build " +
"with flag USE_INT64_TENSOR_SIZE=1")
arg_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
out_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
aux_shape_data = ctypes.POINTER(ctypes.POINTER(mx_int))()
if partial:
infer_func = _LIB.MXSymbolInferShapePartial
else:
infer_func = _LIB.MXSymbolInferShape
check_call(infer_func(
self.handle,
mx_uint(len(indptr) - 1),
keys,
c_array_buf(mx_uint, array('I', indptr)),
c_array_buf(mx_int, array('i', 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]])
if arg_shape_ndim[i] >= 0 else None
for i in range(arg_shape_size.value)]
out_shapes = [tuple(out_shape_data[i][:out_shape_ndim[i]])
if out_shape_ndim[i] >= 0 else None
for i in range(out_shape_size.value)]
aux_shapes = [tuple(aux_shape_data[i][:aux_shape_ndim[i]])
if aux_shape_ndim[i] >= 0 else None
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, remove_amp_cast=True):
"""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"
remove_amp_cast : bool, optional
Whether to remove the amp_cast and amp_multicast operators, before saving the model.
See Also
--------
symbol.load : Used to load symbol from file.
"""
if not isinstance(fname, string_types):
raise TypeError('fname need to be string')
if remove_amp_cast:
handle = SymbolHandle()
check_call(_LIB.MXSymbolRemoveAmpCast(self.handle, ctypes.byref(handle)))
check_call(_LIB.MXSymbolSaveToFile(handle, c_str(fname)))
else:
check_call(_LIB.MXSymbolSaveToFile(self.handle, c_str(fname)))
def tojson(self, remove_amp_cast=True):
"""Saves symbol to a JSON string.
See Also
--------
symbol.fromjson : Used to load symbol from JSON string.
"""
json_str = ctypes.c_char_p()
if remove_amp_cast:
handle = SymbolHandle()
check_call(_LIB.MXSymbolRemoveAmpCast(self.handle, ctypes.byref(handle)))
check_call(_LIB.MXSymbolSaveToJSON(handle, ctypes.byref(json_str)))
else:
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(f'Length of {arg_key} does not match the number of arguments')
for narr in args:
if narr is None and allow_missing:
arg_handles.append(None)
elif not isinstance(narr, NDArray):
raise TypeError('Only accept list of NDArrays or dict of str to NDArray')
else:
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(f'key `{name}` is missing in `{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 _gen_atomic_symbol(self):
handle = SymbolHandle()
check_call(_LIB.MXGenAtomicSymbolFromSymbol(self.handle, ctypes.byref(handle)))
return Symbol(handle)
# pylint: disable=too-many-locals
def optimize_for(self, backend, args=None, aux=None, ctx=None,
shape_dict=None, type_dict=None, stype_dict=None, skip_infer=False, **kwargs):
r"""Partitions current symbol and optimizes it for a given backend.
The backend must have registered the partitioning graph pass in
``SubgraphBackendRegistry``.
Parameters
----------
backend : str
The name of backend, as registered in ``SubgraphBackendRegistry``
args : dict of str to NDArray, optional
Input arguments to the symbol, required to infer shapes/types before partitioning
If type is a dict of str to NDArray, then it maps the names of arguments
to the corresponding NDArray. Undefined arguments' NDArrays
don't have to be specified in the dict.
aux : dict of str to NDArray, optional
Input auxiliary arguments to the symbol
If type is a dict of str to :class:`NDArray`, then it maps the name of arguments
to the corresponding :class:`NDArray`.
ctx : Context, optional
Device context, used to infer stypes
shape_dict : Dict of str->tuple, optional
Input shape dictionary.
Used iff input :class:`NDArray` is not in ``args``.
type_dict : Dict of str->numpy.dtype, optional
Input type dictionary.
Used iff input :class:`NDArray` is not in ``args``.
stype_dict : Dict of str->str, optional
Input storage type dictionary.
Used iff input :class:`NDArray` is not in ``args``.
skip_infer : bool, optional
If True, the optimization skips the shape, type and storage type inference pass.
kwargs : optional arguments
Passed on to ``PrePartition`` and ``PostPartition`` functions of ``SubgraphProperty``
Returns
-------
out : SymbolHandle
A symbol with the partitioned graph for target backend.
"""
out = SymbolHandle()
assert isinstance(backend, str)
assert isinstance(args, dict) or args is None
assert isinstance(aux, dict) or aux is None
if args is None or len(args) == 0:
args_ = []
args_handle = c_array(NDArrayHandle, [])
else:
args_handle, args_ = self._get_ndarray_inputs('args', args,
self.list_arguments(), True)
if aux is None or len(aux) == 0:
aux_ = []
aux_handle = c_array(NDArrayHandle, [])
else:
aux_handle, aux_ = self._get_ndarray_inputs('aux_states', aux,
self.list_auxiliary_states(), True)
if ctx is None:
ctx = current_device()
assert isinstance(ctx, Device)
# parse input data shape dict
num_input_shapes = 0
input_shape_names = ctypes.POINTER(ctypes.c_char_p)()
input_shape_data = ctypes.POINTER(mx_int64)()
input_shape_idx = ctypes.POINTER(mx_uint)()
if shape_dict is not None:
input_shape_names = []
input_shape_data = []
input_shape_idx = [0]
for k, v in shape_dict.items():
if isinstance(v, (tuple, list)):
input_shape_names.append(k)
input_shape_data.extend(v)
input_shape_idx.append(len(input_shape_data))
else:
raise ValueError(str(v) + " has to be a tuple or list.")
num_input_shapes = mx_uint(len(input_shape_names))
input_shape_names = c_str_array(input_shape_names)
input_shape_data = c_array_buf(mx_int64, array('q', input_shape_data))
input_shape_idx = c_array_buf(mx_uint, array('i', input_shape_idx))
# parse input data types dict
num_input_types = 0
input_type_names = ctypes.POINTER(ctypes.c_char_p)() # provided type argument names
input_type_data = ctypes.POINTER(mx_uint)() # provided types
if type_dict is not None:
input_type_names = []
input_type_data = []
for k, v in type_dict.items():
v = dtype_np_to_mx(v)
input_type_names.append(k)
input_type_data.append(v)
num_input_types = mx_uint(len(input_type_names))
input_type_names = c_str_array(input_type_names)
input_type_data = c_array_buf(ctypes.c_int, array('i', input_type_data))
# parse input data storage types dict
num_input_stypes = 0
# provided storage type argument names
input_stype_names = ctypes.POINTER(ctypes.c_char_p)()
input_stype_data = ctypes.POINTER(mx_uint)() # provided storage types
if stype_dict is not None:
input_stype_names = []
input_stype_data = []
for k, v in stype_dict.items():
if v in _STORAGE_TYPE_STR_TO_ID:
input_stype_names.append(k)
input_stype_data.append(_STORAGE_TYPE_STR_TO_ID[v])
else:
raise ValueError(str(v) + " is not a MXNet storage type.")
num_input_stypes = mx_uint(len(input_stype_names))
input_stype_names = c_str_array(input_stype_names)
input_stype_data = c_array_buf(ctypes.c_int, array('i', input_stype_data))
new_args_size = ctypes.c_uint()
new_arg_names = ctypes.POINTER(ctypes.c_char_p)()
new_args_handle = ctypes.POINTER(NDArrayHandle)()
new_aux_size = ctypes.c_uint()
new_aux_names = ctypes.POINTER(ctypes.c_char_p)()
new_aux_handle = ctypes.POINTER(NDArrayHandle)()
key_list = []
val_list = []
for key, val in kwargs.items():
key_list.append(key)
val_list.append(str(val))
check_call(_LIB.MXOptimizeForBackend(self.handle,
c_str(backend),
ctypes.c_int(ctx.device_typeid),
ctypes.byref(out),
mx_uint(len(args_)),
args_handle,
mx_uint(len(aux_)),
aux_handle,
mx_uint(len(key_list)),
c_str_array(key_list),
c_str_array(val_list),
num_input_shapes,
input_shape_names,
input_shape_data,
input_shape_idx,
num_input_types,
input_type_names,
input_type_data,
num_input_stypes,
input_stype_names,
input_stype_data,
ctypes.c_bool(skip_infer),
ctypes.byref(new_args_size),
ctypes.byref(new_args_handle),
ctypes.byref(new_arg_names),
ctypes.byref(new_aux_size),
ctypes.byref(new_aux_handle),
ctypes.byref(new_aux_names)))
# add new args/aux
if not args is None:
for i in range(new_args_size.value):
args[py_str(new_arg_names[i])] = NDArray(NDArrayHandle(new_args_handle[i]))
elif new_args_size.value > 0:
raise RuntimeError('Cannot add new args in optimize_for since args is None\n' +
'Provide a dictionary to the args argument to optimize_for')
if not aux is None:
for i in range(new_aux_size.value):
aux[py_str(new_aux_names[i])] = NDArray(NDArrayHandle(new_aux_handle[i]))
elif new_aux_size.value > 0:
raise RuntimeError('Cannot add new aux in optimize_for since aux is None\n' +
'Provide a dictionary to the aux argument to optimize_for')
new_sym = Symbol(out)
arg_names = self.list_arguments()
new_arg_names = new_sym.list_arguments()
deleted_arg_names = set([item for item in arg_names
if item not in set(new_arg_names)])
if len(deleted_arg_names) > 0:
if args is not None:
for a_n in deleted_arg_names:
if a_n in args:
args.pop(a_n)
else:
warnings.warn('A param was deleted during optimization, but no args dictionary was provided.\n' +
'Please ensure that your model weights match the newly optimized model.')
aux_names = self.list_auxiliary_states()
new_aux_names = new_sym.list_auxiliary_states()
deleted_aux_names = set([item for item in aux_names
if item not in set(new_aux_names)])
if len(deleted_aux_names) > 0:
if aux is not None:
for a_n in deleted_aux_names:
if a_n in aux:
aux.pop(a_n)
else:
warnings.warn('A param was deleted during optimization, but no args dictionary was provided.\n' +
'Please ensure that your model weights match the newly optimized model.')
return new_sym
# pylint: disable=too-many-locals
def _simple_bind(self, ctx, grad_req='write', type_dict=None, stype_dict=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
-------
>>> 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
stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
The generated executor
"""
assert isinstance(grad_req, (str, dict))
# infer shape
arg_shapes, _, aux_shapes = self.infer_shape(**kwargs)
type_dict = {} if type_dict is None else type_dict
arg_dtypes, _, _ = None, None, None
try:
arg_dtypes, _, aux_dtypes = self.infer_type(**type_dict)
except Exception: # pylint: disable=broad-except
pass
args = [None] * len(arg_shapes) if arg_shapes else []
aux_states = [None] * len(aux_shapes) if aux_shapes else []
arg_names = self.list_arguments()
aux_names = self.list_auxiliary_states()
from ..ndarray import zeros as nd_zeros
if arg_shapes:
for i, shape in enumerate(arg_shapes):
if arg_dtypes:
args[i] = nd_zeros(shape, dtype=arg_dtypes[i])
else:
args[i] = nd_zeros(shape)
if aux_shapes:
for i, shape in enumerate(aux_shapes):
if aux_dtypes:
aux_states[i] = nd_zeros(shape, dtype=aux_dtypes[i])
else:
aux_states[i] = nd_zeros(shape)
if stype_dict:
for name, stype in stype_dict.items():
if name in arg_names:
index = arg_names.index(name)
args[index] = args[index].tostype(stype)
else:
assert name in aux_names
index = aux_names.index(name)
aux_states[index] = aux_states[index].totype(stype)
with _profiler_scope("symbol:arg_grad:"):
if grad_req == 'null':
args_grad = None
elif isinstance(grad_req, dict):
args_grad = {}
for i, name in enumerate(arg_names):
if grad_req[name] != 'null':
args_grad[name] = args[i].copy()
else:
args_grad = [x.copy() for x in args]
return Executor(self, ctx, args, args_grad, grad_req, aux_states)
def _bind(self, ctx, args, args_grad=None, grad_req='write',
aux_states=None, static_alloc=False):
"""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
-------
>>> 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.
static_alloc : bool, default False
Statically allocate memory to improve speed. Memory usage may increase.
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.
"""
assert isinstance(grad_req, (str, dict))
return Executor(self, ctx, args, args_grad, grad_req, aux_states, static_alloc)
def gradient(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_str_array(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=None, **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
-------
>>> 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.
"""
if ctx is None:
ctx = current_device()
return self._bind(ctx, kwargs).forward()
def reshape(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`reshape`.
The arguments are the same as for :py:func:`reshape`, with
this array as data.
"""
return op.reshape(self, *args, **kwargs)
def reshape_like(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`reshape_like`.
The arguments are the same as for :py:func:`reshape_like`, with
this array as data.
"""
return op.reshape_like(self, *args, **kwargs)
def astype(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`cast`.
The arguments are the same as for :py:func:`cast`, with
this array as data.
"""
return op.cast(self, *args, **kwargs)
def zeros_like(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`zeros_like`.
The arguments are the same as for :py:func:`zeros_like`, with
this array as data.
"""
return op.zeros_like(self, *args, **kwargs)
def ones_like(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`ones_like`.
The arguments are the same as for :py:func:`ones_like`, with
this array as data.
"""
return op.ones_like(self, *args, **kwargs)
def broadcast_axes(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`broadcast_axes`.
The arguments are the same as for :py:func:`broadcast_axes`, with
this array as data.
"""
return op.broadcast_axes(self, *args, **kwargs)
def repeat(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`repeat`.
The arguments are the same as for :py:func:`repeat`, with
this array as data.
"""
return op.repeat(self, *args, **kwargs)
def pad(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`pad`.
The arguments are the same as for :py:func:`pad`, with
this array as data.
"""
return op.pad(self, *args, **kwargs)
def swapaxes(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`swapaxes`.
The arguments are the same as for :py:func:`swapaxes`, with
this array as data.
"""
return op.swapaxes(self, *args, **kwargs)
def split(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`split`.
The arguments are the same as for :py:func:`split`, with
this array as data.
"""
return op.split(self, *args, **kwargs)
def split_v2(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`split_v2`.
The arguments are the same as for :py:func:`split_v2`, with
this array as data.
"""
return split_v2(self, *args, **kwargs)
def slice(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`slice`.
The arguments are the same as for :py:func:`slice`, with
this array as data.
"""
return op.slice(self, *args, **kwargs)
def slice_axis(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`slice_axis`.
The arguments are the same as for :py:func:`slice_axis`, with
this array as data.
"""
return op.slice_axis(self, *args, **kwargs)
def slice_like(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`slice_like`.
The arguments are the same as for :py:func:`slice_like`, with
this array as data.
"""
return op.slice_like(self, *args, **kwargs)
def take(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`take`.
The arguments are the same as for :py:func:`take`, with
this array as data.
"""
return op.take(self, *args, **kwargs)
def one_hot(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`one_hot`.
The arguments are the same as for :py:func:`one_hot`, with
this array as data.
"""
return op.one_hot(self, *args, **kwargs)
def pick(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`pick`.
The arguments are the same as for :py:func:`pick`, with
this array as data.
"""
return op.pick(self, *args, **kwargs)
def sort(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sort`.
The arguments are the same as for :py:func:`sort`, with
this array as data.
"""
return op.sort(self, *args, **kwargs)
def topk(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`topk`.
The arguments are the same as for :py:func:`topk`, with
this array as data.
"""
return op.topk(self, *args, **kwargs)
def argsort(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`argsort`.
The arguments are the same as for :py:func:`argsort`, with
this array as data.
"""
return op.argsort(self, *args, **kwargs)
def argmax(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`argmax`.
The arguments are the same as for :py:func:`argmax`, with
this array as data.
"""
return op.argmax(self, *args, **kwargs)
def argmax_channel(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`argmax_channel`.
The arguments are the same as for :py:func:`argmax_channel`, with
this array as data.
"""
return op.argmax_channel(self, *args, **kwargs)
def argmin(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`argmin`.
The arguments are the same as for :py:func:`argmin`, with
this array as data.
"""
return op.argmin(self, *args, **kwargs)
def clip(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`clip`.
The arguments are the same as for :py:func:`clip`, with
this array as data.
"""
return op.clip(self, *args, **kwargs)
def abs(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`abs`.
The arguments are the same as for :py:func:`abs`, with
this array as data.
"""
return op.abs(self, *args, **kwargs)
def sign(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sign`.
The arguments are the same as for :py:func:`sign`, with
this array as data.
"""
return op.sign(self, *args, **kwargs)
def flatten(self, inplace=False, **kwargs): # pylint: disable=unused-argument
"""Convenience fluent method for :py:func:`flatten`.
The arguments are the same as for :py:func:`flatten`, with
this array as data.
"""
return op.flatten(self, **kwargs)
def shape_array(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`shape_array`.
The arguments are the same as for :py:func:`shape_op`, with
this array as data.
"""
return op.shape_array(self, *args, **kwargs)
def size_array(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`size_array`.
The arguments are the same as for :py:func:`size_array`, with
this array as data.
"""
return op.size_array(self, *args, **kwargs)
def expand_dims(self, axis, inplace=False, **kwargs): # pylint: disable=unused-argument
"""Convenience fluent method for :py:func:`expand_dims`.
The arguments are the same as for :py:func:`expand_dims`, with
this array as data.
"""
return op.expand_dims(self, axis=axis, **kwargs)
def broadcast_to(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`broadcast_to`.
The arguments are the same as for :py:func:`broadcast_to`, with
this array as data.
"""
return op.broadcast_to(self, *args, **kwargs)
def broadcast_like(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`broadcast_like`.
The arguments are the same as for :py:func:`broadcast_like`, with
this array as data.
"""
return op.broadcast_like(self, *args, **kwargs)
def tile(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`tile`.
The arguments are the same as for :py:func:`tile`, with
this array as data.
"""
return op.tile(self, *args, **kwargs)
def transpose(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`transpose`.
The arguments are the same as for :py:func:`transpose`, with
this array as data.
"""
return op.transpose(self, *args, **kwargs)
def flip(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`flip`.
The arguments are the same as for :py:func:`flip`, with
this array as data.
"""
return op.flip(self, *args, **kwargs)
def depth_to_space(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`depth_to_space`.
The arguments are the same as for :py:func:`depth_to_space`, with
this array as data.
"""
return op.depth_to_space(self, *args, **kwargs)
def space_to_depth(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`space_to_depth`.
The arguments are the same as for :py:func:`space_to_depth`, with
this array as data.
"""
return op.space_to_depth(self, *args, **kwargs)
def diag(self, k=0, **kwargs):
"""Convenience fluent method for :py:func:`diag`.
The arguments are the same as for :py:func:`diag`, with
this array as data.
"""
return op.diag(self, k, **kwargs)
def sum(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sum`.
The arguments are the same as for :py:func:`sum`, with
this array as data.
"""
return op.sum(self, *args, **kwargs)
def nansum(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`nansum`.
The arguments are the same as for :py:func:`nansum`, with
this array as data.
"""
return op.nansum(self, *args, **kwargs)
def prod(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`prod`.
The arguments are the same as for :py:func:`prod`, with
this array as data.
"""
return op.prod(self, *args, **kwargs)
def nanprod(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`nanprod`.
The arguments are the same as for :py:func:`nanprod`, with
this array as data.
"""
return op.nanprod(self, *args, **kwargs)
def mean(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`mean`.
The arguments are the same as for :py:func:`mean`, with
this array as data.
"""
return op.mean(self, *args, **kwargs)
def max(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`max`.
The arguments are the same as for :py:func:`max`, with
this array as data.
"""
return op.max(self, *args, **kwargs)
def min(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`min`.
The arguments are the same as for :py:func:`min`, with
this array as data.
"""
return op.min(self, *args, **kwargs)
def norm(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`norm`.
The arguments are the same as for :py:func:`norm`, with
this array as data.
"""
return op.norm(self, *args, **kwargs)
def round(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`round`.
The arguments are the same as for :py:func:`round`, with
this array as data.
"""
return op.round(self, *args, **kwargs)
def rint(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`rint`.
The arguments are the same as for :py:func:`rint`, with
this array as data.
"""
return op.rint(self, *args, **kwargs)
def fix(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`fix`.
The arguments are the same as for :py:func:`fix`, with
this array as data.
"""
return op.fix(self, *args, **kwargs)
def floor(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`floor`.
The arguments are the same as for :py:func:`floor`, with
this array as data.
"""
return op.floor(self, *args, **kwargs)
def ceil(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`ceil`.
The arguments are the same as for :py:func:`ceil`, with
this array as data.
"""
return op.ceil(self, *args, **kwargs)
def trunc(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`trunc`.
The arguments are the same as for :py:func:`trunc`, with
this array as data.
"""
return op.trunc(self, *args, **kwargs)
def sin(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sin`.
The arguments are the same as for :py:func:`sin`, with
this array as data.
"""
return op.sin(self, *args, **kwargs)
def cos(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`cos`.
The arguments are the same as for :py:func:`cos`, with
this array as data.
"""
return op.cos(self, *args, **kwargs)
def tan(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`tan`.
The arguments are the same as for :py:func:`tan`, with
this array as data.
"""
return op.tan(self, *args, **kwargs)
def arcsin(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arcsin`.
The arguments are the same as for :py:func:`arcsin`, with
this array as data.
"""
return op.arcsin(self, *args, **kwargs)
def arccos(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arccos`.
The arguments are the same as for :py:func:`arccos`, with
this array as data.
"""
return op.arccos(self, *args, **kwargs)
def arctan(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arctan`.
The arguments are the same as for :py:func:`arctan`, with
this array as data.
"""
return op.arctan(self, *args, **kwargs)
def degrees(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`degrees`.
The arguments are the same as for :py:func:`degrees`, with
this array as data.
"""
return op.degrees(self, *args, **kwargs)
def radians(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`radians`.
The arguments are the same as for :py:func:`radians`, with
this array as data.
"""
return op.radians(self, *args, **kwargs)
def sinh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sinh`.
The arguments are the same as for :py:func:`sinh`, with
this array as data.
"""
return op.sinh(self, *args, **kwargs)
def cosh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`cosh`.
The arguments are the same as for :py:func:`cosh`, with
this array as data.
"""
return op.cosh(self, *args, **kwargs)
def tanh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`tanh`.
The arguments are the same as for :py:func:`tanh`, with
this array as data.
"""
return op.tanh(self, *args, **kwargs)
def arcsinh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arcsinh`.
The arguments are the same as for :py:func:`arcsinh`, with
this array as data.
"""
return op.arcsinh(self, *args, **kwargs)
def arccosh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arccosh`.
The arguments are the same as for :py:func:`arccosh`, with
this array as data.
"""
return op.arccosh(self, *args, **kwargs)
def arctanh(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`arctanh`.
The arguments are the same as for :py:func:`arctanh`, with
this array as data.
"""
return op.arctanh(self, *args, **kwargs)
def exp(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`exp`.
The arguments are the same as for :py:func:`exp`, with
this array as data.
"""
return op.exp(self, *args, **kwargs)
def expm1(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`expm1`.
The arguments are the same as for :py:func:`expm1`, with
this array as data.
"""
return op.expm1(self, *args, **kwargs)
def log(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log`.
The arguments are the same as for :py:func:`log`, with
this array as data.
"""
return op.log(self, *args, **kwargs)
def log10(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log10`.
The arguments are the same as for :py:func:`log10`, with
this array as data.
"""
return op.log10(self, *args, **kwargs)
def log2(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log2`.
The arguments are the same as for :py:func:`log2`, with
this array as data.
"""
return op.log2(self, *args, **kwargs)
def log1p(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log1p`.
The arguments are the same as for :py:func:`log1p`, with
this array as data.
"""
return op.log1p(self, *args, **kwargs)
def log_sigmoid(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log_sigmoid`.
The arguments are the same as for :py:func:`log_sigmoid`, with
this array as data.
"""
return op.log_sigmoid(self, *args, **kwargs)
def mish(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`mish`.
The arguments are the same as for :py:func:`mish`, with
this array as data.
"""
return op.mish(self, *args, **kwargs)
def sqrt(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sqrt`.
The arguments are the same as for :py:func:`sqrt`, with
this array as data.
"""
return op.sqrt(self, *args, **kwargs)
def rsqrt(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`rsqrt`.
The arguments are the same as for :py:func:`rsqrt`, with
this array as data.
"""
return op.rsqrt(self, *args, **kwargs)
def cbrt(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`cbrt`.
The arguments are the same as for :py:func:`cbrt`, with
this array as data.
"""
return op.cbrt(self, *args, **kwargs)
def rcbrt(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`rcbrt`.
The arguments are the same as for :py:func:`rcbrt`, with
this array as data.
"""
return op.rcbrt(self, *args, **kwargs)
def square(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`square`.
The arguments are the same as for :py:func:`square`, with
this array as data.
"""
return op.square(self, *args, **kwargs)
def reciprocal(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`reciprocal`.
The arguments are the same as for :py:func:`reciprocal`, with
this array as data.
"""
return op.reciprocal(self, *args, **kwargs)
def relu(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`relu`.
The arguments are the same as for :py:func:`relu`, with
this array as data.
"""
return op.relu(self, *args, **kwargs)
def sigmoid(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`sigmoid`.
The arguments are the same as for :py:func:`sigmoid`, with
this array as data.
"""
return op.sigmoid(self, *args, **kwargs)
def softmax(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`softmax`.
The arguments are the same as for :py:func:`softmax`, with
this array as data.
"""
return op.softmax(self, *args, **kwargs)
def log_softmax(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`log_softmax`.
The arguments are the same as for :py:func:`log_softmax`, with
this array as data.
"""
return op.log_softmax(self, *args, **kwargs)
def softmin(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`softmin`.
The arguments are the same as for :py:func:`softmin`, with
this array as data.
"""
return op.softmin(self, *args, **kwargs)
def squeeze(self, axis=None, inplace=False, **kwargs): # pylint: disable=unused-argument
"""Convenience fluent method for :py:func:`squeeze`.
The arguments are the same as for :py:func:`squeeze`, with
this array as data.
"""
return op.squeeze(self, axis=axis, **kwargs)
def get_backend_symbol(self, backend):
"""Return symbol for target backend.
Parameters
----------
backend : str
The backend names.
Returns
-------
out : Symbol
The created Symbol for target backend.
"""
out = SymbolHandle()
check_call(_LIB.MXGenBackendSubgraph(self.handle, c_str(backend), ctypes.byref(out)))
return Symbol(out)
def wait_to_read(self):
raise NotImplementedForSymbol(self.wait_to_read, None)
def asnumpy(self):
raise NotImplementedForSymbol(self.asnumpy, None)
def asscalar(self):
raise NotImplementedForSymbol(self.asscalar, None)
def copy(self):
raise NotImplementedForSymbol(self.copy, None)
def as_in_context(self):
raise NotImplementedForSymbol(self.as_in_context, None)
def detach(self):
raise NotImplementedForSymbol(self.detach, None)
def backward(self):
raise NotImplementedForSymbol(self.backward, None)
def has_dynamic_shape_op(self):
"""Check if any dynamic shape op is present in the symbol.
"""
has_dynamic_shape = ctypes.c_bool(False)
check_call(_LIB.MXCheckDynamicShapeOp(self.handle,
ctypes.byref(has_dynamic_shape)))
return has_dynamic_shape.value
def var(name, attr=None, shape=None, lr_mult=None, wd_mult=None, dtype=None,
init=None, stype=None, profiler_scope=None, **kwargs):
"""Creates a symbolic variable with specified name.
Example
-------
>>> data = mx.sym.Variable('data', attr={'a': 'b'})
>>> data
<Symbol data>
>>> csr_data = mx.sym.Variable('csr_data', stype='csr')
>>> csr_data
<Symbol csr_data>
>>> row_sparse_weight = mx.sym.Variable('weight', stype='row_sparse')
>>> row_sparse_weight
<Symbol weight>
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.
stype : str
The storage type of the variable, such as 'row_sparse', 'csr', 'default', etc
profiler_scope : str
The profiler scope for input variable.
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 = attribute.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(dtype))
if init is not None:
if not isinstance(init, string_types):
init = init.dumps()
attr['__init__'] = init
if stype is not None:
attr['__storage_type__'] = str(_STORAGE_TYPE_STR_TO_ID[stype])
if profiler_scope is not None:
attr['__profiler_scope__'] = profiler_scope
else:
attr['__profiler_scope__'] = _current_profiler_scope.get()
for k, v in kwargs.items():
if k.startswith('__') and k.endswith('__'):
attr[k] = str(v)
else:
raise ValueError(f'Attribute name={k} is not supported.'
' Additional attributes must start and end with double underscores,'
' e.g, __yourattr__')
ret._set_attr(**attr)
return ret
# for back compatibility
Variable = var
def Group(symbols, create_fn=Symbol):
"""Creates a symbol that contains a collection of other symbols, grouped together.
A classic symbol (`mx.sym.Symbol`) will be returned if all the symbols in the list
are of that type; a numpy symbol (`mx.sym.np._Symbol`) will be returned if all the
symbols in the list are of that type. A type error will be raised if a list of mixed
classic and numpy symbols are provided.
Example
-------
>>> 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.
create_fn : mx.sym.Symbol or mx.sym.np._Symbol
Symbol class for creating the grouped symbol.
Returns
-------
sym : Symbol
A group symbol.
"""
if not symbols or any(not isinstance(sym, Symbol) for sym in symbols):
raise TypeError('Expected a list of symbols as input')
handle = SymbolHandle()
check_call(_LIB.MXSymbolCreateGroup(
mx_uint(len(symbols)),
c_handle_array(symbols), ctypes.byref(handle)))
return create_fn(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 fromjson(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.
`sym.pow` is being deprecated, please use `sym.power` 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(f'types ({str(type(base))}, {str(type(exp))}) not supported')
def power(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.power(2, 3)
8
>>> x = mx.sym.Variable('x')
>>> y = mx.sym.Variable('y')
>>> z = mx.sym.power(x, 2)
>>> z.eval(x=mx.nd.array([1,2]))[0].asnumpy()
array([ 1., 4.], dtype=float32)
>>> z = mx.sym.power(3, y)
>>> z.eval(y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 27.], dtype=float32)
>>> z = mx.sym.power(x, y)
>>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([2,3]))[0].asnumpy()
array([ 9., 64.], dtype=float32)
"""
return pow(base, 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(f'types ({str(type(left))}, {str(type(right))}) not supported')
# 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(f'types ({str(type(left))}, {str(type(right))}) not supported')
# 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(f'types ({str(type(left))}, {str(type(right))}) not supported')
def eye(N, M=0, k=0, dtype=None, **kwargs):
"""Returns a new symbol of 2-D shpae, filled with ones on the diagonal and zeros elsewhere.
Parameters
----------
N: int
Number of rows in the output.
M: int, optional
Number of columns in the output. If 0, defaults to N.
k: int, optional
Index of the diagonal: 0 (the default) refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
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._eye(N, M, k, dtype=dtype, **kwargs)
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 full(shape, val, dtype=None, **kwargs):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array.
val : scalar
Fill value.
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._full(shape=shape, dtype=dtype, value=float(val), **kwargs)
# pylint: disable=redefined-outer-name
def arange(start, stop=None, step=1.0, repeat=1, infer_range=False, name=None, dtype=None):
"""Returns evenly spaced values within a given interval.
Values are generated within the half-open interval [`start`, `stop`). In other
words, the interval includes `start` but excludes `stop`. The function is
similar to the built-in Python function `range` and to `numpy.arange`,
but returns a `Symbol`.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default start value is 0.
stop : number
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.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
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,
infer_range=infer_range, name=name, dtype=dtype)
def linspace(start, stop, num, endpoint=True, name=None, dtype=None):
"""Return evenly spaced numbers within a specified interval.
Values are generated within the half-open interval [`start`, `stop`) or
closed interval [start, stop] depending on whether `endpoint` is True or
False. The function is similar to `numpy.linspace`, but returns a `Symbol`.
Parameters
----------
start : number
Start of interval.
stop : number
End of interval, unless endpoint is set to False. In that case,
the sequence consists of all but the last of `num + 1` evenly spaced
samples, so that stop is excluded. Note that the step size changes
when endpoint is False.
num : number
Number of samples to generate. Must be non-negative.
endpoint : bool
If True, stop is the last sample. Otherwise, it is not included.
The default is True.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
out : Symbol
The created Symbol
"""
if dtype is None:
dtype = _numpy.float32
return _internal._linspace(start=start, stop=stop, num=num, endpoint=endpoint,
name=name, dtype=dtype)
def histogram(a, bins=10, range=None, **kwargs):
"""Compute the histogram of the input data.
Parameters
----------
a : NDArray
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars
If bins is an int, it defines the number of equal-width bins in the
given range (10, by default). If bins is a sequence, it defines the bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
range : (float, float), required if bins is an integer
The lower and upper range of the bins. If not provided, range is simply (a.min(), a.max()).
Values outside the range are ignored. The first element of the range must be less than or
equal to the second. range affects the automatic bin computation as well, the range will
be equally divided by the number of bins.
Returns
-------
out : Symbol
The created Symbol
"""
if isinstance(bins, Symbol):
return _internal._histogram(data=a, bins=bins, **kwargs)
elif isinstance(bins, integer_types):
if range is None:
raise ValueError("null range is not supported in symbol mode")
return _internal._histogram(data=a, bin_cnt=bins, range=range, **kwargs)
raise ValueError("bins argument should be either an integer or an NDArray")
def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol
"""
indices = []
sections = 0
if isinstance(indices_or_sections, int):
sections = indices_or_sections
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis, sections)
_set_symbol_class(Symbol)