INSTRUCTION
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Returns a dispatch code for calling basic or advanced indexing functions.
|
def _get_indexing_dispatch_code(key):
"""Returns a dispatch code for calling basic or advanced indexing functions."""
if isinstance(key, (NDArray, np.ndarray)):
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, list):
# TODO(junwu): Add support for nested lists besides integer list
for i in key:
if not isinstance(i, integer_types):
raise TypeError('Indexing NDArray only supports a list of integers as index'
' when key is of list type, received element=%s of type=%s'
% (str(i), str(type(i))))
return _NDARRAY_ADVANCED_INDEXING
elif isinstance(key, (integer_types, py_slice)):
return _NDARRAY_BASIC_INDEXING
elif isinstance(key, tuple):
for idx in key:
if isinstance(idx, (NDArray, np.ndarray, list, tuple)):
return _NDARRAY_ADVANCED_INDEXING
elif not isinstance(idx, (py_slice, integer_types)):
raise ValueError("NDArray does not support slicing with key %s of type %s."
% (str(idx), str(type(idx))))
return _NDARRAY_BASIC_INDEXING
else:
return _NDARRAY_UNSUPPORTED_INDEXING
|
Given start, stop, step and array length, return
absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily >= 0, since
absolute stop is -1 in the case of slice(None, None, -1).
|
def _get_index_range(start, stop, length, step=1):
"""Given start, stop, step and array length, return
absolute values of start, stop, and step for generating index range.
The returned values have been compensated by adding length if they
are less than zero for all the cases but slice(None, None, -1).
Note that the returned value of stop is not necessarily >= 0, since
absolute stop is -1 in the case of slice(None, None, -1)."""
if step == 0:
raise ValueError('step size cannot be zero')
if length < 0:
raise ValueError('array length cannot be less than zero')
if step is None:
step = 1
if start is None:
if step > 0:
start = 0
else:
start = length - 1
elif start < 0:
start += length
if start < 0:
raise IndexError('Slicing start %d exceeds limit of %d' % (start-length, length))
elif start >= length:
raise IndexError('Slicing start %d exceeds limit of %d' % (start, length))
if stop is None:
if step > 0:
stop = length
else:
# this supports case such as ::-1
# stop = -1 here refers to the element before index 0,
# instead of the last element in the array
stop = -1
elif stop < 0:
stop += length
if stop < 0:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop-length, length))
elif stop > length:
raise IndexError('Slicing stop %d exceeds limit of %d' % (stop, length))
return start, stop, step
|
Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd.
|
def _get_oshape_of_gather_nd_op(dshape, ishape):
"""Given data and index shapes, get the output `NDArray` shape.
This basically implements the infer shape logic of op gather_nd."""
assert len(dshape) > 0 and len(ishape) > 0
oshape = list(ishape[1:])
if ishape[0] < len(dshape):
oshape.extend(dshape[ishape[0]:])
return tuple(oshape)
|
Given start, stop, and stop, calculate the number of elements
of this slice.
|
def _get_dim_size(start, stop, step):
"""Given start, stop, and stop, calculate the number of elements
of this slice."""
assert step != 0
if step > 0:
assert start < stop
dim_size = (stop - start - 1) // step + 1
else:
assert stop < start
dim_size = (start - stop - 1) // (-step) + 1
return dim_size
|
Given two shapes that are not identical, find the shape
that both input shapes can broadcast to.
|
def _get_broadcast_shape(shape1, shape2):
"""Given two shapes that are not identical, find the shape
that both input shapes can broadcast to."""
if shape1 == shape2:
return shape1
length1 = len(shape1)
length2 = len(shape2)
if length1 > length2:
shape = list(shape1)
else:
shape = list(shape2)
i = max(length1, length2) - 1
for a, b in zip(shape1[::-1], shape2[::-1]):
if a != 1 and b != 1 and a != b:
raise ValueError('shape1=%s is not broadcastable to shape2=%s' % (shape1, shape2))
shape[i] = max(a, b)
i -= 1
return tuple(shape)
|
Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.current_context()``).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A new array of the specified shape filled with all ones.
Examples
--------
>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1., 1.]], dtype=float16)
|
def ones(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all ones, with the given shape and type.
Parameters
----------
shape : int or tuple of int or list of int
The shape of the empty array.
ctx : Context, optional
An optional device context.
Defaults to the current default context (``mxnet.context.current_context()``).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A new array of the specified shape filled with all ones.
Examples
--------
>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1., 1.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._ones(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
|
Returns a new array of given shape and type, filled with the given value `val`.
Parameters
--------
shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
ctx : Context, optional
Device context (default is the current default context).
dtype : `str` or `numpy.dtype`, optional
The data type of the returned `NDArray`. The default datatype is `float32`.
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
`NDArray` filled with `val`, with the given shape, ctx, and dtype.
Examples
--------
>>> mx.nd.full(1, 2.0).asnumpy()
array([ 2.], dtype=float32)
>>> mx.nd.full((1, 2), 2.0, mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()
array([[ 2., 2.]], dtype=float16)
|
def full(shape, val, ctx=None, dtype=mx_real_t, out=None):
"""Returns a new array of given shape and type, filled with the given value `val`.
Parameters
--------
shape : int or tuple of int
The shape of the new array.
val : scalar
Fill value.
ctx : Context, optional
Device context (default is the current default context).
dtype : `str` or `numpy.dtype`, optional
The data type of the returned `NDArray`. The default datatype is `float32`.
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
`NDArray` filled with `val`, with the given shape, ctx, and dtype.
Examples
--------
>>> mx.nd.full(1, 2.0).asnumpy()
array([ 2.], dtype=float32)
>>> mx.nd.full((1, 2), 2.0, mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()
array([[ 2., 2.]], dtype=float16)
"""
out = empty(shape, ctx, dtype) if out is None else out
out[:] = val
return out
|
Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray
An `NDArray` with the same contents as the `source_array`.
|
def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray
An `NDArray` with the same contents as the `source_array`.
"""
if isinstance(source_array, NDArray):
dtype = source_array.dtype if dtype is None else dtype
else:
dtype = mx_real_t if dtype is None else dtype
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=dtype)
except:
raise TypeError('source_array must be array like object')
arr = empty(source_array.shape, ctx, dtype)
arr[:] = source_array
return arr
|
Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but must be unique.
destination : int or sequence of int
Destination position for each of the original axes. Can be negative but must be unique.
Returns
-------
result : mx.nd.array
Array with moved axes.
Examples
--------
>>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])
>>> mx.nd.moveaxis(X, 0, 1).shape
(3L, 2L)
>>> X = mx.nd.zeros((3, 4, 5))
>>> mx.nd.moveaxis(X, [0, 1], [-1, -2]).shape
(5, 4, 3)
|
def moveaxis(tensor, source, destination):
"""Moves the `source` axis into the `destination` position
while leaving the other axes in their original order
Parameters
----------
tensor : mx.nd.array
The array which axes should be reordered
source : int or sequence of int
Original position of the axes to move. Can be negative but must be unique.
destination : int or sequence of int
Destination position for each of the original axes. Can be negative but must be unique.
Returns
-------
result : mx.nd.array
Array with moved axes.
Examples
--------
>>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])
>>> mx.nd.moveaxis(X, 0, 1).shape
(3L, 2L)
>>> X = mx.nd.zeros((3, 4, 5))
>>> mx.nd.moveaxis(X, [0, 1], [-1, -2]).shape
(5, 4, 3)
"""
try:
source = np.core.numeric.normalize_axis_tuple(source, tensor.ndim)
except IndexError:
raise ValueError('Source should verify 0 <= source < tensor.ndim'
'Got %d' % source)
try:
destination = np.core.numeric.normalize_axis_tuple(destination, tensor.ndim)
except IndexError:
raise ValueError('Destination should verify 0 <= destination < tensor.ndim (%d).'
% tensor.ndim, 'Got %d' % destination)
if len(source) != len(destination):
raise ValueError('`source` and `destination` arguments must have '
'the same number of elements')
order = [n for n in range(tensor.ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
order.insert(dest, src)
return op.transpose(tensor, order)
|
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 an `NDArray`.
Parameters
----------
start : number, optional
Start of interval. The default start value is 0.
stop : number
End of interval.
step : number, optional
Spacing between values. The default step size is 1.
repeat : int, optional
Number of times to repeat each element. The default repeat count is 1.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
ctx : Context, optional
Device context. Default context is the current default context.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
NDArray
`NDArray` of evenly spaced values in the specified range.
Examples
--------
>>> mx.nd.arange(3).asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> mx.nd.arange(2, 6).asnumpy()
array([ 2., 3., 4., 5.], dtype=float32)
>>> mx.nd.arange(2, 6, step=2).asnumpy()
array([ 2., 4.], dtype=float32)
>>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()
array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)
>>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()
array([2, 2, 2, 4, 4, 4], dtype=int32)
|
def arange(start, stop=None, step=1.0, repeat=1, infer_range=None, ctx=None, dtype=mx_real_t):
"""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 an `NDArray`.
Parameters
----------
start : number, optional
Start of interval. The default start value is 0.
stop : number
End of interval.
step : number, optional
Spacing between values. The default step size is 1.
repeat : int, optional
Number of times to repeat each element. The default repeat count is 1.
infer_range : boolean, optional
When set to True, infer the stop position from the start, step,
repeat, and output tensor size.
ctx : Context, optional
Device context. Default context is the current default context.
dtype : str or numpy.dtype, optional
The data type of the `NDArray`. The default datatype is `np.float32`.
Returns
-------
NDArray
`NDArray` of evenly spaced values in the specified range.
Examples
--------
>>> mx.nd.arange(3).asnumpy()
array([ 0., 1., 2.], dtype=float32)
>>> mx.nd.arange(2, 6).asnumpy()
array([ 2., 3., 4., 5.], dtype=float32)
>>> mx.nd.arange(2, 6, step=2).asnumpy()
array([ 2., 4.], dtype=float32)
>>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()
array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)
>>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()
array([2, 2, 2, 4, 4, 4], dtype=int32)
"""
if infer_range is not None:
warnings.warn('`infer_range` argument has been deprecated',
DeprecationWarning)
if ctx is None:
ctx = current_context()
return _internal._arange(start=start, stop=stop, step=step, repeat=repeat,
infer_range=False, dtype=dtype, ctx=str(ctx))
|
Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : function
Function to be called if both lhs and rhs are of ``NDArray`` type.
fn_scalar : function
Function to be called if both lhs and rhs are numeric values.
lfn_scalar : function
Function to be called if lhs is ``NDArray`` while rhs is numeric value
rfn_scalar : function
Function to be called if lhs is numeric value while rhs is ``NDArray``;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
--------
NDArray
result array
|
def _ufunc_helper(lhs, rhs, fn_array, fn_scalar, lfn_scalar, rfn_scalar=None):
""" Helper function for element-wise operation.
The function will perform numpy-like broadcasting if needed and call different functions.
Parameters
--------
lhs : NDArray or numeric value
Left-hand side operand.
rhs : NDArray or numeric value
Right-hand operand,
fn_array : function
Function to be called if both lhs and rhs are of ``NDArray`` type.
fn_scalar : function
Function to be called if both lhs and rhs are numeric values.
lfn_scalar : function
Function to be called if lhs is ``NDArray`` while rhs is numeric value
rfn_scalar : function
Function to be called if lhs is numeric value while rhs is ``NDArray``;
if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar
Returns
--------
NDArray
result array
"""
if isinstance(lhs, numeric_types):
if isinstance(rhs, numeric_types):
return fn_scalar(lhs, rhs)
else:
if rfn_scalar is None:
# commutative function
return lfn_scalar(rhs, float(lhs))
else:
return rfn_scalar(rhs, float(lhs))
elif isinstance(rhs, numeric_types):
return lfn_scalar(lhs, float(rhs))
elif isinstance(rhs, NDArray):
return fn_array(lhs, rhs)
else:
raise TypeError('type %s not supported' % str(type(rhs)))
|
Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x+2).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> (x+y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.add(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> (z + y).asnumpy()
array([[ 0., 1.],
[ 1., 2.]], dtype=float32)
|
def add(lhs, rhs):
"""Returns element-wise sum of the input arrays with broadcasting.
Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and
``mx.nd.broadcast_plus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be added.
rhs : scalar or mxnet.ndarray.array
Second array to be added.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise sum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x+2).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> (x+y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.add(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 2., 2., 2.]], dtype=float32)
>>> (z + y).asnumpy()
array([[ 0., 1.],
[ 1., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_add,
operator.add,
_internal._plus_scalar,
None)
|
Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0., 1.],
[-1., 0.]], dtype=float32)
|
def subtract(lhs, rhs):
"""Returns element-wise difference of the input arrays with broadcasting.
Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
``mx.nd.broadcast_minus(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be subtracted.
rhs : scalar or mxnet.ndarray.array
Second array to be subtracted.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise difference of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x-2).asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
>>> (x-y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.subtract(x,y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z-y).asnumpy()
array([[ 0., 1.],
[-1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_sub,
operator.sub,
_internal._minus_scalar,
_internal._rminus_scalar)
|
Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z*y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
|
def multiply(lhs, rhs):
"""Returns element-wise product of the input arrays with broadcasting.
Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be multiplied.
rhs : scalar or mxnet.ndarray.array
Second array to be multiplied.
If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise multiplication of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x*2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x*y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.multiply(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z*y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mul,
operator.mul,
_internal._mul_scalar,
None)
|
Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*2
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 2.],
[ 2.]], dtype=float32)
>>> x/2
<NDArray 2x3 @cpu(0)>
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.divide(x,y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
|
def divide(lhs, rhs):
"""Returns element-wise division of the input arrays with broadcasting.
Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in division.
rhs : scalar or mxnet.ndarray.array
Second array in division.
The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise division of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*2
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 2.],
[ 2.]], dtype=float32)
>>> x/2
<NDArray 2x3 @cpu(0)>
>>> (x/3).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> (x/y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
>>> mx.nd.divide(x,y).asnumpy()
array([[ 3., 3., 3.],
[ 3., 3., 3.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_div,
operator.truediv,
_internal._div_scalar,
_internal._rdiv_scalar)
|
Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise modulo of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*4
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 4.],
[ 4.]], dtype=float32)
>>> x%5
<NDArray 2x3 @cpu(0)>
>>> (x%5).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x%y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.modulo(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
|
def modulo(lhs, rhs):
"""Returns element-wise modulo of the input arrays with broadcasting.
Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array in modulo.
rhs : scalar or mxnet.ndarray.array
Second array in modulo.
The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise modulo of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))*6
>>> y = mx.nd.ones((2,1))*4
>>> x.asnumpy()
array([[ 6., 6., 6.],
[ 6., 6., 6.]], dtype=float32)
>>> y.asnumpy()
array([[ 4.],
[ 4.]], dtype=float32)
>>> x%5
<NDArray 2x3 @cpu(0)>
>>> (x%5).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x%y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.modulo(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_mod,
operator.mod,
_internal._mod_scalar,
_internal._rmod_scalar)
|
Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
base : scalar or NDArray
The base array
exp : scalar or NDArray
The exponent array. If ``base.shape != exp.shape``, they must be
broadcastable to a common shape.
Returns
--------
NDArray
The bases in x raised to the exponents in y.
Examples
--------
>>> x = mx.nd.ones((2,3))*2
>>> y = mx.nd.arange(1,3).reshape((2,1))
>>> z = mx.nd.arange(1,3).reshape((2,1))
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> (x**2).asnumpy()
array([[ 4., 4., 4.],
[ 4., 4., 4.]], dtype=float32)
>>> (x**y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> mx.nd.power(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> (z**y).asnumpy()
array([[ 1.],
[ 4.]], dtype=float32)
|
def power(base, exp):
"""Returns result of first array elements raised to powers from second array, element-wise
with broadcasting.
Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
base : scalar or NDArray
The base array
exp : scalar or NDArray
The exponent array. If ``base.shape != exp.shape``, they must be
broadcastable to a common shape.
Returns
--------
NDArray
The bases in x raised to the exponents in y.
Examples
--------
>>> x = mx.nd.ones((2,3))*2
>>> y = mx.nd.arange(1,3).reshape((2,1))
>>> z = mx.nd.arange(1,3).reshape((2,1))
>>> x.asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> y.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> z.asnumpy()
array([[ 1.],
[ 2.]], dtype=float32)
>>> (x**2).asnumpy()
array([[ 4., 4., 4.],
[ 4., 4., 4.]], dtype=float32)
>>> (x**y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> mx.nd.power(x,y).asnumpy()
array([[ 2., 2., 2.],
[ 4., 4., 4.]], dtype=float32)
>>> (z**y).asnumpy()
array([[ 1.],
[ 4.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
base,
exp,
op.broadcast_power,
operator.pow,
_internal._power_scalar,
_internal._rpower_scalar)
|
Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
|
def maximum(lhs, rhs):
"""Returns element-wise maximum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise maximum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.maximum(x, 2).asnumpy()
array([[ 2., 2., 2.],
[ 2., 2., 2.]], dtype=float32)
>>> mx.nd.maximum(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.maximum(y, z).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_maximum,
lambda x, y: x if x > y else y,
_internal._maximum_scalar,
None)
|
Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise minimum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.minimum(x, 2).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
|
def minimum(lhs, rhs):
"""Returns element-wise minimum of the input arrays with broadcasting.
Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
The element-wise minimum of the input arrays.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.minimum(x, 2).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.minimum(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_minimum,
lambda x, y: x if x < y else y,
_internal._minimum_scalar,
None)
|
Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
>>> (x != 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x != y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.not_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z != y).asnumpy()
array([[ 0., 1.],
[ 1., 0.]], dtype=float32)
|
def not_equal(lhs, rhs):
"""Returns the result of element-wise **not equal to** (!=) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are different,
otherwise return 0(false).
Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1., 0.],
[ 0., 1.]], dtype=float32)
>>> (x != 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x != y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.not_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z != y).asnumpy()
array([[ 0., 1.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_not_equal,
lambda x, y: 1 if x != y else 0,
_internal._not_equal_scalar,
None)
|
Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32)
|
def greater(lhs, rhs):
"""Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater,
lambda x, y: 1 if x > y else 0,
_internal._greater_scalar,
_internal._lesser_scalar)
|
Returns the result of element-wise **greater than or equal to** (>=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,
otherwise return 0(false).
Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x >= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x >= y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.greater_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (z >= y).asnumpy()
array([[ 1., 1.],
[ 0., 1.]], dtype=float32)
|
def greater_equal(lhs, rhs):
"""Returns the result of element-wise **greater than or equal to** (>=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,
otherwise return 0(false).
Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x >= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x >= y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.greater_equal(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (z >= y).asnumpy()
array([[ 1., 1.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater_equal,
lambda x, y: 1 if x >= y else 0,
_internal._greater_equal_scalar,
_internal._lesser_equal_scalar)
|
Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x < 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x < y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.lesser(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (z < y).asnumpy()
array([[ 0., 0.],
[ 1., 0.]], dtype=float32)
|
def lesser(lhs, rhs):
"""Returns the result of element-wise **lesser than** (<) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are less than rhs,
otherwise return 0(false).
Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x < 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x < y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.lesser(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (z < y).asnumpy()
array([[ 0., 0.],
[ 1., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser,
lambda x, y: 1 if x < y else 0,
_internal._lesser_scalar,
_internal._greater_scalar)
|
Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1., 0.],
[ 1., 1.]], dtype=float32)
|
def lesser_equal(lhs, rhs):
"""Returns the result of element-wise **lesser than or equal to** (<=) comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are
lesser than equal to rhs, otherwise return 0(false).
Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1., 0.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_lesser_equal,
lambda x, y: 1 if x <= y else 0,
_internal._lesser_equal_scalar,
_internal._greater_equal_scalar)
|
Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
|
def logical_and(lhs, rhs):
"""Returns the result of element-wise **logical and** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements and rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs and rhs`` and ``mx.nd.broadcast_logical_and(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0., 0., 0.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0., 0.],
[ 0., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_and,
lambda x, y: 1 if x and y else 0,
_internal._logical_and_scalar,
None)
|
Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(z, y).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
|
def logical_or(lhs, rhs):
"""Returns the result of element-wise **logical or** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``lhs or rhs`` and ``mx.nd.broadcast_logical_or(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, 1).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> mx.nd.logical_or(z, y).asnumpy()
array([[ 0., 1.],
[ 1., 1.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_or,
lambda x, y: 1 if x or y else 0,
_internal._logical_or_scalar,
None)
|
Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
|
def logical_xor(lhs, rhs):
"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_xor,
lambda x, y: 1 if bool(x) ^ bool(y) else 0,
_internal._logical_xor_scalar,
None)
|
DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`.
|
def concatenate(arrays, axis=0, always_copy=True):
"""DEPRECATED, use ``concat`` instead
Parameters
----------
arrays : list of `NDArray`
Arrays to be concatenate. They must have identical shape except
the first dimension. They also must have the same data type.
axis : int
The axis along which to concatenate.
always_copy : bool
Default `True`. When not `True`, if the arrays only contain one
`NDArray`, that element will be returned directly, avoid copying.
Returns
-------
NDArray
An `NDArray` that lives on the same context as `arrays[0].context`.
"""
assert isinstance(arrays, list)
assert len(arrays) > 0
assert isinstance(arrays[0], NDArray)
if not always_copy and len(arrays) == 1:
return arrays[0]
shape_axis = arrays[0].shape[axis]
shape_rest1 = arrays[0].shape[0:axis]
shape_rest2 = arrays[0].shape[axis+1:]
dtype = arrays[0].dtype
for arr in arrays[1:]:
shape_axis += arr.shape[axis]
assert shape_rest1 == arr.shape[0:axis]
assert shape_rest2 == arr.shape[axis+1:]
assert dtype == arr.dtype
ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
idx = 0
begin = [0 for _ in ret_shape]
end = list(ret_shape)
for arr in arrays:
if axis == 0:
ret[idx:idx+arr.shape[0]] = arr
else:
begin[axis] = idx
end[axis] = idx+arr.shape[axis]
# pylint: disable=no-member,protected-access
_internal._crop_assign(ret, arr, out=ret,
begin=tuple(begin),
end=tuple(end))
# pylint: enable=no-member,protected-access
idx += arr.shape[axis]
return ret
|
DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing.
|
def imdecode(str_img, clip_rect=(0, 0, 0, 0), out=None, index=0, channels=3, mean=None):
"""DEPRECATED, use mx.img instead
Parameters
----------
str_img : str
Binary image data
clip_rect : iterable of 4 int
Clip decoded image to rectangle (x0, y0, x1, y1).
out : NDArray
Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).
index : int
Output decoded image to i-th slice of 4 dimensional buffer.
channels : int
Number of channels to output. Decode to grey scale when channels = 1.
mean : NDArray
Subtract mean from decode image before outputing.
"""
# pylint: disable= no-member, protected-access, too-many-arguments
if mean is None:
mean = NDArray(_new_empty_handle())
if out is None:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img)
else:
return _internal._imdecode(mean, index,
clip_rect[0],
clip_rect[1],
clip_rect[2],
clip_rect[3],
channels,
len(str_img),
str_img=str_img,
out=out)
|
Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.zeros(1).asnumpy()
array([ 0.], dtype=float32)
>>> mx.nd.zeros((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()
array([[ 0., 0.]], dtype=float16)
|
def zeros(shape, ctx=None, dtype=None, **kwargs):
"""Returns a new array filled with all zeros, with the given shape and type.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
out : NDArray, optional
The output NDArray (default is `None`).
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.zeros(1).asnumpy()
array([ 0.], dtype=float32)
>>> mx.nd.zeros((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()
array([[ 0., 0.]], dtype=float16)
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
|
Return a 2-D array 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.
ctx: Context, optional
An optional device context (default is the current default context)
dtype: str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.eye(2)
[[ 1. 0.]
[ 0. 1.]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.eye(2, 3, 1)
[[ 0. 1. 0.]
[ 0. 0. 1.]]
<NDArray 2x3 @cpu(0)>
|
def eye(N, M=0, k=0, ctx=None, dtype=None, **kwargs):
"""Return a 2-D array 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.
ctx: Context, optional
An optional device context (default is the current default context)
dtype: str or numpy.dtype, optional
An optional value type (default is `float32`)
Returns
-------
NDArray
A created array
Examples
--------
>>> mx.nd.eye(2)
[[ 1. 0.]
[ 0. 1.]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.eye(2, 3, 1)
[[ 0. 1. 0.]
[ 0. 0. 1.]]
<NDArray 2x3 @cpu(0)>
"""
# pylint: disable= unused-argument
if ctx is None:
ctx = current_context()
dtype = mx_real_t if dtype is None else dtype
# pylint: disable= no-member, protected-access
return _internal._eye(N=N, M=M, k=k, ctx=ctx, dtype=dtype, **kwargs)
|
Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
NDArray
A created array.
|
def empty(shape, ctx=None, dtype=None):
"""Returns a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
The shape of the empty array.
ctx : Context, optional
An optional device context (default is the current default context).
dtype : str or numpy.dtype, optional
An optional value type (default is `float32`).
Returns
-------
NDArray
A created array.
"""
if isinstance(shape, int):
shape = (shape, )
if ctx is None:
ctx = current_context()
if dtype is None:
dtype = mx_real_t
return NDArray(handle=_new_alloc_handle(shape, ctx, False, dtype))
|
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), optional
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
-------
NDArray
A created array.
|
def histogram(a, bins=10, range=None):
"""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), optional
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
-------
NDArray
A created array.
"""
# pylint: disable= no-member, protected-access
if isinstance(bins, NDArray):
return _internal._histogram(data=a, bins=bins)
elif isinstance(bins, integer_types):
if range is None:
warnings.warn("range is not specified, using numpy's result "
"to ensure consistency with numpy")
res, bin_bounds = np.histogram(a.asnumpy(), bins=bins)
return array(res), array(bin_bounds)
return _internal._histogram(data=a, bin_cnt=bins, range=range)
raise ValueError("bins argument should be either an integer or an NDArray")
|
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
-------
NDArray
A created array.
|
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
-------
NDArray
A created array.
"""
indices = []
axis_size = ary.shape[axis]
if isinstance(indices_or_sections, int):
sections = indices_or_sections
if axis_size % sections:
raise ValueError('array split does not result in an equal division')
section_size = int(axis_size / sections)
indices = [i * section_size for i in range(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)
|
Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> z
[[1. 1. 1.]
[1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
|
def to_dlpack_for_read(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> z
[[1. 1. 1.]
[1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
"""
data.wait_to_read()
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter)
|
Returns a reference view of NDArray that represents as DLManagedTensor until
all previous read/write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
|
def to_dlpack_for_write(data):
"""Returns a reference view of NDArray that represents as DLManagedTensor until
all previous read/write operations on the current array are finished.
Parameters
----------
data: NDArray
input data.
Returns
-------
PyCapsule (the pointer of DLManagedTensor)
a reference view of NDArray that represents as DLManagedTensor.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
check_call(_LIB.MXNDArrayWaitToWrite(data.handle))
dlpack = DLPackHandle()
check_call(_LIB.MXNDArrayToDLPack(data.handle, ctypes.byref(dlpack)))
return ctypes.pythonapi.PyCapsule_New(dlpack, _c_str_dltensor, _c_dlpack_deleter)
|
Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> type(z)
<class 'mxnet.ndarray.ndarray.NDArray'>
>>> z
[[ 1. 1. 1.]
[ 1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
|
def from_dlpack(dlpack):
"""Returns a NDArray backed by a dlpack tensor.
Parameters
----------
dlpack: PyCapsule (the pointer of DLManagedTensor)
input data
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.to_dlpack_for_read(x)
>>> type(y)
<class 'PyCapsule'>
>>> z = mx.nd.from_dlpack(y)
>>> type(z)
<class 'mxnet.ndarray.ndarray.NDArray'>
>>> z
[[ 1. 1. 1.]
[ 1. 1. 1.]]
<NDArray 2x3 @cpu(0)>
>>> w = mx.nd.to_dlpack_for_write(x)
>>> type(w)
<class 'PyCapsule'>
>>> u = mx.nd.from_dlpack(w)
>>> u += 1
>>> x
[[2. 2. 2.]
[2. 2. 2.]]
<NDArray 2x3 @cpu(0)>
"""
handle = NDArrayHandle()
dlpack = ctypes.py_object(dlpack)
assert ctypes.pythonapi.PyCapsule_IsValid(dlpack, _c_str_dltensor), ValueError(
'Invalid DLPack Tensor. DLTensor capsules can be consumed only once.')
dlpack_handle = ctypes.c_void_p(ctypes.pythonapi.PyCapsule_GetPointer(dlpack, _c_str_dltensor))
check_call(_LIB.MXNDArrayFromDLPack(dlpack_handle, ctypes.byref(handle)))
# Rename PyCapsule (DLPack)
ctypes.pythonapi.PyCapsule_SetName(dlpack, _c_str_used_dltensor)
# delete the deleter of the old dlpack
ctypes.pythonapi.PyCapsule_SetDestructor(dlpack, None)
return NDArray(handle=handle)
|
Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c-contiguous arrays, i.e. array.flags[C_CONTIGUOUS] == True.
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
|
def from_numpy(ndarray, zero_copy=True):
"""Returns an MXNet's NDArray backed by Numpy's ndarray.
Parameters
----------
ndarray: numpy.ndarray
input data
zero_copy: bool
Whether we use DLPack's zero-copy conversion to convert to MXNet's NDArray.
This is only available for c-contiguous arrays, i.e. array.flags[C_CONTIGUOUS] == True.
Returns
-------
NDArray
a NDArray backed by a dlpack tensor
"""
def _make_manager_ctx(obj):
pyobj = ctypes.py_object(obj)
void_p = ctypes.c_void_p.from_buffer(pyobj)
ctypes.pythonapi.Py_IncRef(pyobj)
return void_p
def _make_dl_tensor(array):
if str(array.dtype) not in DLDataType.TYPE_MAP:
raise ValueError(str(array.dtype) + " is not supported.")
dl_tensor = DLTensor()
dl_tensor.data = array.ctypes.data_as(ctypes.c_void_p)
dl_tensor.ctx = DLContext(1, 0)
dl_tensor.ndim = array.ndim
dl_tensor.dtype = DLDataType.TYPE_MAP[str(array.dtype)]
dl_tensor.shape = array.ctypes.shape_as(ctypes.c_int64)
dl_tensor.strides = None
dl_tensor.byte_offset = 0
return dl_tensor
def _make_dl_managed_tensor(array):
c_obj = DLManagedTensor()
c_obj.dl_tensor = _make_dl_tensor(array)
c_obj.manager_ctx = _make_manager_ctx(array)
c_obj.deleter = dl_managed_tensor_deleter
return c_obj
if not zero_copy:
return array(ndarray, dtype=ndarray.dtype)
if not ndarray.flags['C_CONTIGUOUS']:
raise ValueError("Only c-contiguous arrays are supported for zero-copy")
c_obj = _make_dl_managed_tensor(ndarray)
address = ctypes.addressof(c_obj)
address = ctypes.cast(address, ctypes.c_void_p)
handle = NDArrayHandle()
check_call(_LIB.MXNDArrayFromDLPack(address, ctypes.byref(handle)))
return NDArray(handle=handle)
|
Returns an index array for use in scatter_nd and gather_nd.
|
def _get_index_nd(self, key):
"""Returns an index array for use in scatter_nd and gather_nd."""
def _is_advanced_index(index):
"""The definition of advanced index here includes integers as well, while
integers are considered as basic index type when the key contains only
slices and integers."""
return not isinstance(index, py_slice)
if isinstance(key, (NDArray, np.ndarray, list, integer_types, py_slice)):
key = (key,)
assert isinstance(key, tuple),\
'index=%s must be a NDArray, or np.ndarray, or list, or tuple ' \
' type to use advanced indexing, received type=%s' % (str(key), str(type(key)))
assert len(key) > 0, "Cannot slice with empty indices"
shape = self.shape
assert len(shape) >= len(key),\
"Slicing dimensions exceeds array dimensions, %d vs %d" % (len(key), len(shape))
indices = []
dtype = 'int32' # index data type passed to gather_nd op
need_broadcast = (len(key) != 1)
advanced_indices = [] # include list, NDArray, np.ndarray, integer
basic_indices = [] # include only slices
advanced_index_bshape = None # final advanced index shape
for i, idx_i in enumerate(key):
is_advanced_index = True
if isinstance(idx_i, (np.ndarray, list, tuple)):
idx_i = array(idx_i, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, py_slice):
start, stop, step = _get_index_range(idx_i.start, idx_i.stop, shape[i], idx_i.step)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
basic_indices.append(i)
is_advanced_index = False
elif isinstance(idx_i, integer_types):
start, stop, step = _get_index_range(idx_i, idx_i+1, shape[i], 1)
idx_i = arange(start, stop, step, ctx=self.context, dtype=dtype)
advanced_indices.append(i)
elif isinstance(idx_i, NDArray):
if dtype != idx_i.dtype:
idx_i = idx_i.astype(dtype)
advanced_indices.append(i)
else:
raise IndexError('Indexing NDArray with index=%s of type=%s is not supported'
% (str(key), str(type(key))))
if is_advanced_index:
if advanced_index_bshape is None:
advanced_index_bshape = idx_i.shape
elif advanced_index_bshape != idx_i.shape:
need_broadcast = True
advanced_index_bshape = _get_broadcast_shape(advanced_index_bshape, idx_i.shape)
indices.append(idx_i)
# Get final index shape for gather_nd. See the following reference
# for determining the output array shape.
# https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing # pylint: disable=line-too-long
if len(advanced_indices) == 0:
raise ValueError('Advanced index tuple must contain at least one of the following types:'
' list, tuple, NDArray, np.ndarray, integer, received index=%s' % key)
# determine the output array's shape by checking whether advanced_indices are all adjacent
# or separated by slices
advanced_indices_adjacent = True
for i in range(0, len(advanced_indices)-1):
if advanced_indices[i] + 1 != advanced_indices[i+1]:
advanced_indices_adjacent = False
break
index_bshape_list = [] # index broadcasted shape
if advanced_indices_adjacent:
for i in range(0, advanced_indices[0]):
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(advanced_index_bshape)
for i in range(advanced_indices[-1]+1, len(indices)):
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape_list.extend(indices[i].shape)
else:
index_bshape_list.extend(advanced_index_bshape)
for i in basic_indices:
index_bshape_list.extend(indices[i].shape)
if not need_broadcast and indices[i].shape != advanced_index_bshape:
need_broadcast = True
index_bshape = tuple(index_bshape_list)
# Need to broadcast all ndarrays in indices to the final shape.
# For example, suppose an array has shape=(5, 6, 7, 8) and
# key=(slice(1, 5), [[1, 2]], slice(2, 5), [1]).
# Since key[1] and key[3] are two advanced indices here and they are
# separated by basic indices key[0] and key[2], the output shape
# is (1, 2, 4, 3), where the first two elements come from the shape
# that key[1] and key[3] should broadcast to, which is (1, 2), and
# the last two elements come from the shape of two basic indices.
# In order to broadcast all basic and advanced indices to the output shape,
# we need to reshape them based on their axis. For example, to broadcast key[0],
# with shape=(4,), we first need to reshape it into (1, 1, 4, 1), and then
# broadcast the reshaped array to (1, 2, 4, 3); to broadcast key[1], we first
# reshape it into (1, 2, 1, 1), then broadcast the reshaped array to (1, 2, 4, 3).
if need_broadcast:
broadcasted_indices = []
idx_rshape = [1] * len(index_bshape)
if advanced_indices_adjacent:
advanced_index_bshape_start = advanced_indices[0] # start index of advanced_index_bshape in index_shape
advanced_index_bshape_stop = advanced_index_bshape_start + len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = advanced_index_bshape_stop
# find the reshaped shape for indices[i]
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
if i < advanced_indices[0]: # slice is on the left side of advanced indices
idx_rshape[i] = indices[i].shape[0]
elif i > advanced_indices[-1]: # slice is on the right side of advanced indices
idx_rshape[i-len(key)] = indices[i].shape[0]
else:
raise ValueError('basic index i=%d cannot be between advanced index i=%d and i=%d'
% (i, advanced_indices[0], advanced_indices[-1]))
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
else:
basic_index_offset = len(advanced_index_bshape)
for i, idx in enumerate(key):
if _is_advanced_index(idx):
k = len(advanced_index_bshape)
for dim_size in indices[i].shape[::-1]:
k -= 1
idx_rshape[k] = dim_size
else:
idx_rshape[basic_index_offset] = indices[i].shape[0]
basic_index_offset += 1
# broadcast current index to the final shape
broadcasted_indices.append(indices[i].reshape(tuple(idx_rshape)).broadcast_to(index_bshape))
# reset idx_rshape to ones
for j, _ in enumerate(idx_rshape):
idx_rshape[j] = 1
indices = broadcasted_indices
return op.stack(*indices)
|
Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape.
|
def _prepare_value_nd(self, value, vshape):
"""Given value and vshape, create an `NDArray` from value with the same
context and dtype as the current one and broadcast it to vshape."""
if isinstance(value, numeric_types):
value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
elif isinstance(value, NDArray):
value_nd = value.as_in_context(self.context)
if value_nd.dtype != self.dtype:
value_nd = value_nd.astype(self.dtype)
else:
try:
value_nd = array(value, ctx=self.context, dtype=self.dtype)
except:
raise TypeError('NDArray does not support assignment with non-array-like'
' object %s of type %s' % (str(value), str(type(value))))
if value_nd.shape != vshape:
value_nd = value_nd.broadcast_to(vshape)
return value_nd
|
This function is called by __setitem__ when key is an advanced index.
|
def _set_nd_advanced_indexing(self, key, value):
"""This function is called by __setitem__ when key is an advanced index."""
indices = self._get_index_nd(key)
vshape = _get_oshape_of_gather_nd_op(self.shape, indices.shape)
value_nd = self._prepare_value_nd(value, vshape)
_internal._scatter_set_nd(lhs=self, rhs=value_nd, indices=indices,
shape=self.shape, out=self)
|
This function is called by __setitem__ when key is a basic index, i.e.
an integer, or a slice, or a tuple of integers and slices. No restrictions
on the values of slices' steps.
|
def _set_nd_basic_indexing(self, key, value):
"""This function is called by __setitem__ when key is a basic index, i.e.
an integer, or a slice, or a tuple of integers and slices. No restrictions
on the values of slices' steps."""
shape = self.shape
if isinstance(key, integer_types):
if key < 0:
key += shape[0]
if key < 0 or key >= shape[0]:
if key < 0:
key -= shape[0]
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (key, shape[0]))
key = py_slice(key, key+1) # key must be >= 0 here
if isinstance(key, py_slice):
assign_to_self = key.step is None or key.step == 1
assign_to_self &= key.start is None or key.start == 0
assign_to_self &= key.stop is None or key.stop == shape[0]
if assign_to_self: # trivial case, assign value to self
if isinstance(value, NDArray):
if value.handle is not self.handle:
if value.shape != shape:
value = value.broadcast_to(shape)
value.copyto(self)
elif isinstance(value, numeric_types):
_internal._full(shape=shape, ctx=self.context,
dtype=self.dtype, value=float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
if isinstance(value, np.generic) or value.shape != shape:
value = np.broadcast_to(value, shape)
self._sync_copyfrom(value)
else: # value might be a list or a tuple
value_nd = self._prepare_value_nd(value, shape)
value_nd.copyto(self)
return
else: # non-trivial case, use _slice_assign or _slice_assign_scalar
key = (key,)
assert isinstance(key, tuple), "key=%s must be a tuple of slices and integers" % str(key)
assert len(key) <= len(shape), "Indexing dimensions exceed array dimensions, %d vs %d"\
% (len(key), len(shape))
begin = []
end = []
steps = []
oshape = [] # output shape of slice using key
vshape = [] # value shape of data[key]
for i, slice_i in enumerate(key):
dim_size = 1
if isinstance(slice_i, py_slice):
begin.append(slice_i.start)
end.append(slice_i.stop)
steps.append(slice_i.step)
start, stop, step = _get_index_range(slice_i.start, slice_i.stop,
shape[i], slice_i.step)
dim_size = _get_dim_size(start, stop, step)
vshape.append(dim_size)
elif isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
steps.append(1)
else:
raise ValueError("basic indexing does not support index=%s of type=%s"
% (str(slice_i), str(type(slice_i))))
oshape.append(dim_size)
oshape.extend(shape[len(key):])
vshape.extend(shape[len(key):])
# if key contains all integers, vshape should be (1,)
if len(vshape) == 0:
vshape.append(1)
oshape = tuple(oshape)
vshape = tuple(vshape)
if isinstance(value, numeric_types):
_internal._slice_assign_scalar(self, out=self, begin=begin, end=end,
step=steps, scalar=float(value))
else:
value_nd = self._prepare_value_nd(value, vshape)
if vshape != oshape:
value_nd = value_nd.reshape(oshape)
_internal._slice_assign(self, value_nd, begin, end, steps, out=self)
|
This function is called when key is a slice, or an integer,
or a tuple of slices or integers
|
def _get_nd_basic_indexing(self, key):
"""This function is called when key is a slice, or an integer,
or a tuple of slices or integers"""
shape = self.shape
if isinstance(key, integer_types):
if key > shape[0] - 1:
raise IndexError(
'index {} is out of bounds for axis 0 with size {}'.format(
key, shape[0]))
return self._at(key)
elif isinstance(key, py_slice):
if key.step is not None and key.step != 1:
if key.step == 0:
raise ValueError("slice step cannot be zero")
return op.slice(self, begin=(key.start,), end=(key.stop,), step=(key.step,))
elif key.start is not None or key.stop is not None:
return self._slice(key.start, key.stop)
else:
return self
if not isinstance(key, tuple):
raise ValueError('index=%s must be a slice, or an ineger, or a tuple'
' of slices and integers to use basic indexing, received type=%s'
% (str(key), str(type(key))))
assert len(key) != 0, 'basic index cannot be an empty tuple'
begin = []
end = []
step = []
kept_axes = [] # axes where slice_i is a slice
i = -1
for i, slice_i in enumerate(key):
if isinstance(slice_i, integer_types):
begin.append(slice_i)
end.append(slice_i+1 if slice_i != -1 else self.shape[i])
step.append(1)
elif isinstance(slice_i, py_slice):
if slice_i.step == 0:
raise ValueError('basic index=%s cannot have slice=%s with step = 0'
% (str(key), str(slice_i)))
begin.append(slice_i.start)
end.append(slice_i.stop)
step.append(slice_i.step)
kept_axes.append(i)
else:
raise ValueError('basic_indexing does not support slicing with '
'index=%s of type=%s.' % (str(slice_i), str(type(slice_i))))
kept_axes.extend(range(i+1, len(shape)))
sliced_nd = op.slice(self, begin, end, step)
if len(kept_axes) == len(shape):
return sliced_nd
# squeeze sliced_shape to remove the axes indexed by integers
oshape = []
sliced_shape = sliced_nd.shape
for axis in kept_axes:
oshape.append(sliced_shape[axis])
# if key is a tuple of integers, still need to keep 1 dim
# while in Numpy, the output will become an value instead of an ndarray
if len(oshape) == 0:
oshape.append(1)
oshape = tuple(oshape)
assert np.prod(oshape) == np.prod(sliced_shape), 'oshape=%s has different size'\
' than sliced_shape=%s'\
% (oshape, sliced_shape)
return sliced_nd.reshape(oshape)
|
Performs a synchronized copy from the `source_array` to the current array.
This is called through ``x[:] = source_array``, where the `source_array`
is a `numpy.ndarray` or array-like object.
This function blocks until all the pending read/write operations with respect
to the current `NDArray` are finished and carry out the copy operation to the
current NDArray.
Parameters
----------
source_array : array_like
The data source we would like to copy from.
Example
-------
>>> a = mx.nd.array([1, 2])
>>> a.asnumpy()
array([ 1., 2.], dtype=float32)
>>> a[:] = np.array([3, 4])
>> a.asnumpy()
array([ 3., 4.], dtype=float32)
|
def _sync_copyfrom(self, source_array):
"""Performs a synchronized copy from the `source_array` to the current array.
This is called through ``x[:] = source_array``, where the `source_array`
is a `numpy.ndarray` or array-like object.
This function blocks until all the pending read/write operations with respect
to the current `NDArray` are finished and carry out the copy operation to the
current NDArray.
Parameters
----------
source_array : array_like
The data source we would like to copy from.
Example
-------
>>> a = mx.nd.array([1, 2])
>>> a.asnumpy()
array([ 1., 2.], dtype=float32)
>>> a[:] = np.array([3, 4])
>> a.asnumpy()
array([ 3., 4.], dtype=float32)
"""
if not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=self.dtype)
except:
raise TypeError('array must consist of array-like data,' +
'type %s is not supported' % str(type(array)))
source_array = np.asarray(source_array, dtype=self.dtype, order='C')
if source_array.shape != self.shape:
raise ValueError('Shape inconsistent: expected %s vs got %s'%(
str(source_array.shape), str(self.shape)))
check_call(_LIB.MXNDArraySyncCopyFromCPU(
self.handle,
source_array.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(source_array.size)))
|
Returns a sliced NDArray that shares memory with the current one.
This is called through ``x[start:stop]``.
Parameters
----------
start : int
Starting inclusive index of slice in the first dim.
stop : int
Finishing exclusive index of slice in the first dim.
Returns
-------
`NDArray` sharing the memory with the current one sliced from
start to stop in the first dim.
Examples:
>>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])
>>> a[1:2].asnumpy()
array([[ 3., 4.]], dtype=float32)
>>> a[1:1].asnumpy()
array([], shape=(0, 2), dtype=float32)
|
def _slice(self, start, stop):
"""Returns a sliced NDArray that shares memory with the current one.
This is called through ``x[start:stop]``.
Parameters
----------
start : int
Starting inclusive index of slice in the first dim.
stop : int
Finishing exclusive index of slice in the first dim.
Returns
-------
`NDArray` sharing the memory with the current one sliced from
start to stop in the first dim.
Examples:
>>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])
>>> a[1:2].asnumpy()
array([[ 3., 4.]], dtype=float32)
>>> a[1:1].asnumpy()
array([], shape=(0, 2), dtype=float32)
"""
handle = NDArrayHandle()
start, stop, _ = _get_index_range(start, stop, self.shape[0])
check_call(_LIB.MXNDArraySlice(
self.handle, mx_uint(start), mx_uint(stop), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable)
|
Returns a view of the array sliced at `idx` in the first dim.
This is called through ``x[idx]``.
Parameters
----------
idx : int
index for slicing the `NDArray` in the first dim.
Returns
-------
NDArray
`NDArray` sharing the memory with the current one sliced at `idx` in the first dim.
Examples
--------
>>> a = mx.nd.array([[1,2], [3, 4]])
>>> a[1].asnumpy()
array([ 3., 4.], dtype=float32)
>>> b = mx.nd.array([1, 2, 3, 4])
>>> b[0].asnumpy()
array([ 1.], dtype=float32)
|
def _at(self, idx):
"""Returns a view of the array sliced at `idx` in the first dim.
This is called through ``x[idx]``.
Parameters
----------
idx : int
index for slicing the `NDArray` in the first dim.
Returns
-------
NDArray
`NDArray` sharing the memory with the current one sliced at `idx` in the first dim.
Examples
--------
>>> a = mx.nd.array([[1,2], [3, 4]])
>>> a[1].asnumpy()
array([ 3., 4.], dtype=float32)
>>> b = mx.nd.array([1, 2, 3, 4])
>>> b[0].asnumpy()
array([ 1.], dtype=float32)
"""
handle = NDArrayHandle()
if idx < 0:
length = self.shape[0]
idx += length
if idx < 0:
raise IndexError('index %d is out of bounds for axis 0 with size %d'
% (idx-length, length))
check_call(_LIB.MXNDArrayAt(
self.handle, mx_uint(idx), ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable)
|
Returns a **view** of this array with a new shape without altering any data.
Parameters
----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the
input dimensions keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
- input shape = (2,3,4), shape=(-1,), output shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape as the
output dimension.
Example::
- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent to
-4 in shape (can contain -1).
Example::
- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
- If the argument `reverse` is set to 1, then the special values are inferred from right
to left.
Example::
- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
(40,5).
- with reverse=1, output shape will be (50,4).
reverse : bool, default False
If true then the special values are inferred from right to left. Only supported as
keyword argument.
Returns
-------
NDArray
An array with desired shape that shares data with this array.
Examples
--------
>>> x = mx.nd.arange(0,6).reshape(2,3)
>>> x.asnumpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,-1)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(-3)
>>> y.asnumpy()
array([ 0. 1. 2. 3. 4. 5.], dtype=float32)
>>> y[:] = -1
>>> x.asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
|
def reshape(self, *shape, **kwargs):
"""Returns a **view** of this array with a new shape without altering any data.
Parameters
----------
shape : tuple of int, or n ints
The new shape should not change the array size, namely
``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
The significance of each is explained below:
- ``0`` copy this dimension from the input to the output shape.
Example::
- input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
- input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)
- ``-1`` infers the dimension of the output shape by using the remainder of the
input dimensions keeping the size of the new array same as that of the input array.
At most one dimension of shape can be -1.
Example::
- input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
- input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
- input shape = (2,3,4), shape=(-1,), output shape = (24,)
- ``-2`` copy all/remainder of the input dimensions to the output shape.
Example::
- input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
- input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
- input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)
- ``-3`` use the product of two consecutive dimensions of the input shape as the
output dimension.
Example::
- input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
- input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
- input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
- input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)
- ``-4`` split one dimension of the input into two dimensions passed subsequent to
-4 in shape (can contain -1).
Example::
- input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
- input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)
- If the argument `reverse` is set to 1, then the special values are inferred from right
to left.
Example::
- without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
(40,5).
- with reverse=1, output shape will be (50,4).
reverse : bool, default False
If true then the special values are inferred from right to left. Only supported as
keyword argument.
Returns
-------
NDArray
An array with desired shape that shares data with this array.
Examples
--------
>>> x = mx.nd.arange(0,6).reshape(2,3)
>>> x.asnumpy()
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,-1)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(3,2)
>>> y.asnumpy()
array([[ 0., 1.],
[ 2., 3.],
[ 4., 5.]], dtype=float32)
>>> y = x.reshape(-3)
>>> y.asnumpy()
array([ 0. 1. 2. 3. 4. 5.], dtype=float32)
>>> y[:] = -1
>>> x.asnumpy()
array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
"""
if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
shape = shape[0]
elif not shape:
shape = kwargs.get('shape')
assert shape, "Shape must be provided."
if not all(k in ['shape', 'reverse'] for k in kwargs):
raise TypeError(
"Got unknown keywords in reshape: {}. " \
"Accepted keyword arguments are 'shape' and 'reverse'.".format(
', '.join([k for k in kwargs if k not in ['shape', 'reverse']])))
reverse = kwargs.get('reverse', False)
handle = NDArrayHandle()
# Actual reshape
check_call(_LIB.MXNDArrayReshape64(self.handle,
len(shape),
c_array(ctypes.c_int64, shape),
reverse,
ctypes.byref(handle)))
return NDArray(handle=handle, writable=self.writable)
|
Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).
Parameters
----------
shape : tuple of int
The shape of the desired array.
Returns
-------
NDArray
A NDArray with the desired shape that is not sharing data with this
array, even if the new shape is the same as ``self.shape``.
Examples
--------
>>> x = mx.nd.arange(0,3).reshape((1,3,1))
>>> x.asnumpy()
array([[[ 0.],
[ 1.],
[ 2.]]], dtype=float32)
>>> y = x.broadcast_to((2,3,3))
>>> y.asnumpy()
array([[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]]], dtype=float32)
|
def broadcast_to(self, shape):
"""Broadcasts the input array to a new shape.
Broadcasting is only allowed on axes with size 1. The new shape cannot change
the number of dimensions.
For example, you could broadcast from shape (2, 1) to (2, 3), but not from
shape (2, 3) to (2, 3, 3).
Parameters
----------
shape : tuple of int
The shape of the desired array.
Returns
-------
NDArray
A NDArray with the desired shape that is not sharing data with this
array, even if the new shape is the same as ``self.shape``.
Examples
--------
>>> x = mx.nd.arange(0,3).reshape((1,3,1))
>>> x.asnumpy()
array([[[ 0.],
[ 1.],
[ 2.]]], dtype=float32)
>>> y = x.broadcast_to((2,3,3))
>>> y.asnumpy()
array([[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 1., 1.],
[ 2., 2., 2.]]], dtype=float32)
"""
cur_shape = self.shape
err_str = 'operands could not be broadcast together with remapped shapes' \
'[original->remapped]: {} and requested shape {}'.format(cur_shape, shape)
if len(shape) < len(cur_shape):
raise ValueError(err_str)
cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape
cur_shape_arr = np.array(cur_shape)
broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape))
if (cur_shape_arr[broadcasting_axes] != 1).any():
raise ValueError(err_str)
if cur_shape != self.shape:
return op.broadcast_to(self.reshape(cur_shape), shape=shape)
else:
return op.broadcast_to(self, shape=tuple(shape))
|
Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
|
def shape(self):
"""Tuple of array dimensions.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.shape
(4L,)
>>> y = mx.nd.zeros((2, 3, 4))
>>> y.shape
(2L, 3L, 4L)
"""
ndim = mx_int()
pdata = ctypes.POINTER(mx_int)()
check_call(_LIB.MXNDArrayGetShapeEx(
self.handle, ctypes.byref(ndim), ctypes.byref(pdata)))
if ndim.value == -1:
return None
else:
return tuple(pdata[:ndim.value])
|
Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0)
|
def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0)
"""
dev_typeid = ctypes.c_int()
dev_id = ctypes.c_int()
check_call(_LIB.MXNDArrayGetContext(
self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
return Context(Context.devtype2str[dev_typeid.value], dev_id.value)
|
Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
<type 'numpy.int32'>
|
def dtype(self):
"""Data-type of the array's elements.
Returns
-------
numpy.dtype
This NDArray's data type.
Examples
--------
>>> x = mx.nd.zeros((2,3))
>>> x.dtype
<type 'numpy.float32'>
>>> y = mx.nd.zeros((2,3), dtype='int32')
>>> y.dtype
<type 'numpy.int32'>
"""
mx_dtype = ctypes.c_int()
check_call(_LIB.MXNDArrayGetDType(
self.handle, ctypes.byref(mx_dtype)))
return _DTYPE_MX_TO_NP[mx_dtype.value]
|
Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
|
def _fresh_grad(self):
"""Whether this array's corresponding gradient array
(registered via `autograd.mark_variables`) has been
updated by `autograd.backward` since last reset.
`_fresh_grad` need to be manually set to False
after consuming gradient (usually after updating this
array).
"""
out = ctypes.c_int()
check_call(_LIB.MXNDArrayGetGradState(self.handle, ctypes.byref(out)))
return out.value
|
Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> z = mx.nd.ones((2,3), dtype='int32')
>>> z.asnumpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
|
def asnumpy(self):
"""Returns a ``numpy.ndarray`` object with value copied from this array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.asnumpy()
>>> type(y)
<type 'numpy.ndarray'>
>>> y
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> z = mx.nd.ones((2,3), dtype='int32')
>>> z.asnumpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
"""
data = np.empty(self.shape, dtype=self.dtype)
check_call(_LIB.MXNDArraySyncCopyToCPU(
self.handle,
data.ctypes.data_as(ctypes.c_void_p),
ctypes.c_size_t(data.size)))
return data
|
Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array after casting to the specified type, or
the same array if copy=False and dtype is the same as the input
array.
Examples
--------
>>> x = mx.nd.zeros((2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
|
def astype(self, dtype, copy=True):
"""Returns a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array after casting to the specified type, or
the same array if copy=False and dtype is the same as the input
array.
Examples
--------
>>> x = mx.nd.zeros((2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = empty(self.shape, ctx=self.context, dtype=dtype)
self.copyto(res)
return res
|
Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or Context
The destination array or context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray``, then the return value
and ``other`` will point to the same ``NDArray``.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> z = x.copyto(y)
>>> z is y
True
>>> y.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.copyto(mx.gpu(0))
<NDArray 2x3 @gpu(0)>
|
def copyto(self, other):
"""Copies the value of this array to another array.
If ``other`` is a ``NDArray`` object, then ``other.shape`` and
``self.shape`` should be the same. This function copies the value from
``self`` to ``other``.
If ``other`` is a context, a new ``NDArray`` will be first created on
the target context, and the value of ``self`` is copied.
Parameters
----------
other : NDArray or Context
The destination array or context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The copied array. If ``other`` is an ``NDArray``, then the return value
and ``other`` will point to the same ``NDArray``.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> z = x.copyto(y)
>>> z is y
True
>>> y.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.copyto(mx.gpu(0))
<NDArray 2x3 @gpu(0)>
"""
if isinstance(other, NDArray):
if other.handle is self.handle:
warnings.warn('You are attempting to copy an array to itself', RuntimeWarning)
return False
return _internal._copyto(self, out=other)
elif isinstance(other, Context):
hret = NDArray(_new_alloc_handle(self.shape, other, True, self.dtype))
return _internal._copyto(self, out=hret)
else:
raise TypeError('copyto does not support type ' + str(type(other)))
|
Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
The target context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The target array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.as_in_context(mx.cpu())
>>> y is x
True
>>> z = x.as_in_context(mx.gpu(0))
>>> z is x
False
|
def as_in_context(self, context):
"""Returns an array on the target device with the same value as this array.
If the target context is the same as ``self.context``, then ``self`` is
returned. Otherwise, a copy is made.
Parameters
----------
context : Context
The target context.
Returns
-------
NDArray, CSRNDArray or RowSparseNDArray
The target array.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = x.as_in_context(mx.cpu())
>>> y is x
True
>>> z = x.as_in_context(mx.gpu(0))
>>> z is x
False
"""
if self.context == context:
return self
return self.copyto(context)
|
Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
- 'add': gradient will be added to existing value on every backward.
- 'null': do not compute gradient for this NDArray.
stype : str, optional
The storage type of the gradient array. Defaults to the same stype of this NDArray.
|
def attach_grad(self, grad_req='write', stype=None):
"""Attach a gradient buffer to this NDArray, so that `backward`
can compute gradient with respect to it.
Parameters
----------
grad_req : {'write', 'add', 'null'}
How gradient will be accumulated.
- 'write': gradient will be overwritten on every backward.
- 'add': gradient will be added to existing value on every backward.
- 'null': do not compute gradient for this NDArray.
stype : str, optional
The storage type of the gradient array. Defaults to the same stype of this NDArray.
"""
from . import zeros as _zeros
if stype is not None:
grad = _zeros(self.shape, stype=stype)
else:
grad = op.zeros_like(self) # pylint: disable=undefined-variable
grad_req = _GRAD_REQ_MAP[grad_req]
check_call(_LIB.MXAutogradMarkVariables(
1, ctypes.pointer(self.handle),
ctypes.pointer(mx_uint(grad_req)),
ctypes.pointer(grad.handle)))
|
Returns gradient buffer attached to this NDArray.
|
def grad(self):
"""Returns gradient buffer attached to this NDArray."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl)))
if hdl.value is None:
return None
return _ndarray_cls(hdl)
|
Returns a new NDArray, detached from the current graph.
|
def detach(self):
"""Returns a new NDArray, detached from the current graph."""
from . import _ndarray_cls
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
return _ndarray_cls(hdl)
|
Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to retain the computaion graph for another backward
pass on the same graph. By default the computaion history
is cleared.
train_mode : bool, optional
Whether to compute gradient for training or inference.
|
def backward(self, out_grad=None, retain_graph=False, train_mode=True):
"""Compute the gradients of this NDArray w.r.t variables.
Parameters
----------
out_grad : NDArray, optional
Gradient with respect to head.
retain_graph : bool, optional
Whether to retain the computaion graph for another backward
pass on the same graph. By default the computaion history
is cleared.
train_mode : bool, optional
Whether to compute gradient for training or inference.
"""
if out_grad is None:
ograd_handles = [NDArrayHandle(0)]
else:
ograd_handles = [out_grad.handle]
check_call(_LIB.MXAutogradBackwardEx(
1, c_handle_array([self]),
c_array(NDArrayHandle, ograd_handles),
0,
ctypes.c_void_p(0),
ctypes.c_int(retain_graph),
ctypes.c_int(0),
ctypes.c_int(train_mode),
ctypes.c_void_p(0),
ctypes.c_void_p(0)))
|
Build the align array
|
def build(self, align_path):
"""
Build the align array
"""
file = open(align_path, 'r')
lines = file.readlines()
file.close()
# words: list([op, ed, word])
words = []
for line in lines:
_op, _ed, word = line.strip().split(' ')
if word not in Align.skip_list:
words.append((int(_op), int(_ed), word))
self.words = words
self.n_words = len(words)
self.sentence_str = " ".join([w[2] for w in self.words])
self.sentence_length = len(self.sentence_str)
|
Get sentence
|
def sentence(self, padding=75):
"""
Get sentence
"""
vec = word_to_vector(self.sentence_str)
vec += [-1] * (padding - self.sentence_length)
return np.array(vec, dtype=np.int32)
|
Get words
|
def word(self, _id, padding=75):
"""
Get words
"""
word = self.words[_id][2]
vec = word_to_vector(word)
vec += [-1] * (padding - len(vec))
return np.array(vec, dtype=np.int32)
|
Get the position of words
|
def word_frame_pos(self, _id):
"""
Get the position of words
"""
left = int(self.words[_id][0]/1000)
right = max(left+1, int(self.words[_id][1]/1000))
return (left, right)
|
Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
|
def prepare_sparse_params(self, param_rowids):
'''Prepares the module for processing a data batch by pulling row_sparse
parameters from kvstore to all devices based on rowids.
Parameters
----------
param_rowids : dict of str to NDArray of list of NDArrays
'''
if not self._kvstore:
return
assert(isinstance(param_rowids, dict))
for param_name, rowids in param_rowids.items():
if isinstance(rowids, (tuple, list)):
rowids_1d = []
for r in rowids:
rowids_1d.append(r.reshape((-1,)).astype(np.int64))
rowid = mx.nd.concat(*rowids_1d, dim=0)
else:
rowid = rowids
param_idx = self._exec_group.param_names.index(param_name)
param_val = self._exec_group.param_arrays[param_idx]
self._kvstore.row_sparse_pull(param_name, param_val, row_ids=rowid,
priority=-param_idx)
|
Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
|
def save_params(self, fname):
"""Saves model parameters to file.
Parameters
----------
fname : str
Path to output param file.
Examples
--------
>>> # An example of saving module parameters.
>>> mod.save_params('myfile')
"""
arg_params, aux_params = self.get_params_from_kv(self._arg_params, self._aux_params)
save_dict = {('arg:%s' % k) : v.as_in_context(mx.cpu()) for k, v in arg_params.items()}
save_dict.update({('aux:%s' % k) : v.as_in_context(mx.cpu()) for k, v in aux_params.items()})
mx.nd.save(fname, save_dict)
|
Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArrays in arg_params and aux_params.
|
def get_params_from_kv(self, arg_params, aux_params):
""" Copy data from kvstore to `arg_params` and `aux_params`.
Parameters
----------
arg_params : list of NDArray
Target parameter arrays.
aux_params : list of NDArray
Target aux arrays.
Notes
-----
- This function will inplace update the NDArrays in arg_params and aux_params.
"""
assert(self._kvstore is not None)
for name, block in zip(self._exec_group.param_names, self._exec_group.param_arrays):
assert(isinstance(block, list))
if block[0].stype == 'row_sparse':
row_ids = mx.nd.arange(start=0, stop=block[0].shape[0], dtype='int64')
self._kvstore.row_sparse_pull(name, arg_params[name], row_ids=row_ids)
else:
assert(block[0].stype == 'default')
self._kvstore.pull(name, out=arg_params[name])
if len(aux_params) > 0:
raise NotImplementedError()
return arg_params, aux_params
|
Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients are concatenated per context in this implementation.
Examples
--------
An example of using clip_grad_norm to clip the gradient before updating the parameters::
>>> #Get the gradient via back-propagation
>>> net.forward_backward(data_batch=data_batch)
>>> norm_val = net.clip_by_global_norm(max_norm=2.0, param_names='w0')
>>> net.update()
|
def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None):
"""Clips gradient norm.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
The method is first used in
`[ICML2013] On the difficulty of training recurrent neural networks`
Note that the gradients are concatenated per context in this implementation.
Examples
--------
An example of using clip_grad_norm to clip the gradient before updating the parameters::
>>> #Get the gradient via back-propagation
>>> net.forward_backward(data_batch=data_batch)
>>> norm_val = net.clip_by_global_norm(max_norm=2.0, param_names='w0')
>>> net.update()
"""
assert self.binded and self.params_initialized and self.optimizer_initialized
num_ctx = len(self._exec_group.grad_arrays[0])
grad_array_per_ctx = [[] for i in range(num_ctx)]
assert(param_names is not None)
for param_name in param_names:
param_idx = self._exec_group.param_names.index(param_name)
grad_val = self._exec_group.grad_arrays[param_idx]
assert(len(grad_val) == num_ctx)
for i in range(num_ctx):
grad_array_per_ctx[i].append(grad_val[i])
norm_vals = []
for i in range(num_ctx):
mx.gluon.utils.clip_global_norm(grad_array_per_ctx[i], max_norm)
|
Rescale the gradient of provided parameters by a certain scale
|
def rescale_grad(self, scale=None, param_name=None):
""" Rescale the gradient of provided parameters by a certain scale """
if scale is None or param_name is None:
return
param_idx = self._exec_group.param_names.index(param_name)
grad_vals = self._exec_group.grad_arrays[param_idx]
for grad in grad_vals:
grad[:] *= scale
|
builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
|
def factorization_machine_model(factor_size, num_features,
lr_mult_config, wd_mult_config, init_config):
""" builds factorization machine network with proper formulation:
y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
"""
x = mx.symbol.Variable("data", stype='csr')
# factor, linear and bias terms
v = mx.symbol.Variable("v", shape=(num_features, factor_size), stype='row_sparse',
init=init_config['v'], lr_mult=lr_mult_config['v'],
wd_mult=wd_mult_config['v'])
w = mx.symbol.Variable('w', shape=(num_features, 1), stype='row_sparse',
init=init_config['w'], lr_mult=lr_mult_config['w'],
wd_mult=wd_mult_config['w'])
w0 = mx.symbol.Variable('w0', shape=(1,), init=init_config['w0'],
lr_mult=lr_mult_config['w0'], wd_mult=wd_mult_config['w0'])
w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w), w0)
# squared terms for subtracting self interactions
v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
x_s = x.square()
bd_sum = mx.sym.dot(x_s, v_s)
# interactions
w2 = mx.symbol.dot(x, v)
w2_squared = 0.5 * mx.symbol.square(data=w2)
# putting everything together
w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
sum1 = w_all.sum(axis=1, keepdims=True)
sum2 = -0.5 * bd_sum
model = sum1 + sum2
y = mx.symbol.Variable("softmax_label")
model = mx.symbol.LogisticRegressionOutput(data=model, label=y)
return model
|
Reshape data into (num_example, batch_size)
|
def batchify(data, batch_size):
"""Reshape data into (num_example, batch_size)"""
nbatch = data.shape[0] // batch_size
data = data[:nbatch * batch_size]
data = data.reshape((batch_size, nbatch)).T
return data
|
Tokenizes a text file.
|
def tokenize(self, path):
"""Tokenizes a text file."""
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
for line in f:
words = line.split() + ['<eos>']
tokens += len(words)
for word in words:
self.dictionary.add_word(word)
# Tokenize file content
with open(path, 'r') as f:
ids = np.zeros((tokens,), dtype='int32')
token = 0
for line in f:
words = line.split() + ['<eos>']
for word in words:
ids[token] = self.dictionary.word2idx[word]
token += 1
return mx.nd.array(ids, dtype='int32')
|
Build docstring for symbolic functions.
|
def _build_doc(func_name,
desc,
arg_names,
arg_types,
arg_desc,
key_var_num_args=None,
ret_type=None):
"""Build docstring for symbolic functions."""
param_str = _build_param_doc(arg_names, arg_types, arg_desc)
if key_var_num_args:
desc += '\nThis function support variable length of positional input.'
doc_str = ('%s\n\n' +
'%s\n' +
'name : string, optional.\n' +
' Name of the resulting symbol.\n\n' +
'Returns\n' +
'-------\n' +
'Symbol\n' +
' The result symbol.')
doc_str = doc_str % (desc, param_str)
extra_doc = "\n" + '\n'.join([x.__doc__ for x in type.__subclasses__(SymbolDoc)
if x.__name__ == '%sDoc' % func_name])
doc_str += _re.sub(_re.compile(" "), "", extra_doc)
doc_str = _re.sub('NDArray-or-Symbol', 'Symbol', doc_str)
return doc_str
|
Get user friendly information of the output shapes.
|
def get_output_shape(sym, **input_shapes):
"""Get user friendly information of the output shapes."""
_, s_outputs, _ = sym.infer_shape(**input_shapes)
return dict(zip(sym.list_outputs(), s_outputs))
|
Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
|
def num_gpus():
"""Query CUDA for the number of GPUs present.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
count : int
The number of GPUs.
"""
count = ctypes.c_int()
check_call(_LIB.MXGetGPUCount(ctypes.byref(count)))
return count.value
|
Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
(free, total) : (int, int)
The number of GPUs.
|
def gpu_memory_info(device_id=0):
"""Query CUDA for the free and total bytes of GPU global memory.
Parameters
----------
device_id : int, optional
The device id of the GPU device.
Raises
------
Will raise an exception on any CUDA error.
Returns
-------
(free, total) : (int, int)
The number of GPUs.
"""
free = ctypes.c_uint64()
total = ctypes.c_uint64()
dev_id = ctypes.c_int(device_id)
check_call(_LIB.MXGetGPUMemoryInformation64(dev_id, ctypes.byref(free), ctypes.byref(total)))
return (free.value, total.value)
|
Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
Examples
-------
>>> mx.current_context()
cpu(0)
>>> with mx.Context('gpu', 1): # Context changed in `with` block.
... mx.current_context() # Computation done here will be on gpu(1).
...
gpu(1)
>>> mx.current_context() # Back to default context.
cpu(0)
Returns
-------
default_ctx : Context
|
def current_context():
"""Returns the current context.
By default, `mx.cpu()` is used for all the computations
and it can be overridden by using `with mx.Context(x)` statement where
x can be cpu(device_id) or gpu(device_id).
Examples
-------
>>> mx.current_context()
cpu(0)
>>> with mx.Context('gpu', 1): # Context changed in `with` block.
... mx.current_context() # Computation done here will be on gpu(1).
...
gpu(1)
>>> mx.current_context() # Back to default context.
cpu(0)
Returns
-------
default_ctx : Context
"""
if not hasattr(Context._default_ctx, "value"):
Context._default_ctx.value = Context('cpu', 0)
return Context._default_ctx.value
|
Populates synsets - a map of index to label for the data items.
Populates the data in the dataset, making tuples of (data, label)
|
def _list_audio_files(self, root, skip_rows=0):
"""Populates synsets - a map of index to label for the data items.
Populates the data in the dataset, making tuples of (data, label)
"""
self.synsets = []
self.items = []
if not self._train_csv:
# The audio files are organized in folder structure with
# directory name as label and audios in them
self._folder_structure(root)
else:
# train_csv contains mapping between filename and label
self._csv_labelled_dataset(root, skip_rows=skip_rows)
# Generating the synset.txt file now
if not os.path.exists("./synset.txt"):
with open("./synset.txt", "w") as synsets_file:
for item in self.synsets:
synsets_file.write(item+os.linesep)
print("Synsets is generated as synset.txt")
else:
warnings.warn("Synset file already exists in the current directory! Not generating synset.txt.")
|
Returns a new dataset with the first element of each sample
transformed by the transformer function `fn`.
This is useful, for example, when you only want to transform data
while keeping label as is.
lazy=False is passed to transform_first for dataset so that all tramsforms could be performed in
one shot and not during training. This is a performance consideration.
Parameters
----------
fn : callable
A transformer function that takes the first element of a sample
as input and returns the transformed element.
lazy : bool, default False
If False, transforms all samples at once. Otherwise,
transforms each sample on demand. Note that if `fn`
is stochastic, you must set lazy to True or you will
get the same result on all epochs.
Returns
-------
Dataset
The transformed dataset.
|
def transform_first(self, fn, lazy=False):
"""Returns a new dataset with the first element of each sample
transformed by the transformer function `fn`.
This is useful, for example, when you only want to transform data
while keeping label as is.
lazy=False is passed to transform_first for dataset so that all tramsforms could be performed in
one shot and not during training. This is a performance consideration.
Parameters
----------
fn : callable
A transformer function that takes the first element of a sample
as input and returns the transformed element.
lazy : bool, default False
If False, transforms all samples at once. Otherwise,
transforms each sample on demand. Note that if `fn`
is stochastic, you must set lazy to True or you will
get the same result on all epochs.
Returns
-------
Dataset
The transformed dataset.
"""
return super(AudioFolderDataset, self).transform_first(fn, lazy=lazy)
|
Try to configure cython and return cython configuration
|
def config_cython():
"""Try to configure cython and return cython configuration"""
if not with_cython:
return []
# pylint: disable=unreachable
if os.name == 'nt':
print("WARNING: Cython is not supported on Windows, will compile without cython module")
return []
try:
from Cython.Build import cythonize
# from setuptools.extension import Extension
if sys.version_info >= (3, 0):
subdir = "_cy3"
else:
subdir = "_cy2"
ret = []
path = "mxnet/cython"
if os.name == 'nt':
library_dirs = ['mxnet', '../build/Release', '../build']
libraries = ['libmxnet']
else:
library_dirs = None
libraries = None
for fn in os.listdir(path):
if not fn.endswith(".pyx"):
continue
ret.append(Extension(
"mxnet/%s/.%s" % (subdir, fn[:-4]),
["mxnet/cython/%s" % fn],
include_dirs=["../include/", "../3rdparty/tvm/nnvm/include"],
library_dirs=library_dirs,
libraries=libraries,
language="c++"))
return cythonize(ret)
except ImportError:
print("WARNING: Cython is not installed, will compile without cython module")
return []
|
Compose symbol on inputs.
This call mutates the current symbol.
Parameters
----------
args:
provide positional arguments
kwargs:
provide keyword arguments
Returns
-------
the resulting symbol
|
def _compose(self, *args, **kwargs):
"""Compose symbol on inputs.
This call mutates the current symbol.
Parameters
----------
args:
provide positional arguments
kwargs:
provide 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, SymbolBase):
raise TypeError('Compose expect `Symbol` as arguments')
for val in kwargs.values():
if not isinstance(val, SymbolBase):
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(kwargs.values())
check_call(_LIB.NNSymbolCompose(
self.handle, name, num_args, keys, args))
|
Set the attribute of the symbol.
Parameters
----------
**kwargs
The attributes to set
|
def _set_attr(self, **kwargs):
"""Set the attribute of the symbol.
Parameters
----------
**kwargs
The attributes to set
"""
keys = c_str_array(kwargs.keys())
vals = c_str_array([str(s) for s in kwargs.values()])
num_args = mx_uint(len(kwargs))
check_call(_LIB.MXSymbolSetAttrs(
self.handle, num_args, keys, vals))
|
Configuration factory for various networks
Parameters
----------
network : str
base network name, such as vgg_reduced, inceptionv3, resnet...
data_shape : int
input data dimension
kwargs : dict
extra arguments
|
def get_config(network, data_shape, **kwargs):
"""Configuration factory for various networks
Parameters
----------
network : str
base network name, such as vgg_reduced, inceptionv3, resnet...
data_shape : int
input data dimension
kwargs : dict
extra arguments
"""
if network == 'vgg16_reduced':
if data_shape >= 448:
from_layers = ['relu4_3', 'relu7', '', '', '', '', '']
num_filters = [512, -1, 512, 256, 256, 256, 256]
strides = [-1, -1, 2, 2, 2, 2, 1]
pads = [-1, -1, 1, 1, 1, 1, 1]
sizes = [[.07, .1025], [.15,.2121], [.3, .3674], [.45, .5196], [.6, .6708], \
[.75, .8216], [.9, .9721]]
ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
[1,2,.5,3,1./3], [1,2,.5], [1,2,.5]]
normalizations = [20, -1, -1, -1, -1, -1, -1]
steps = [] if data_shape != 512 else [x / 512.0 for x in
[8, 16, 32, 64, 128, 256, 512]]
else:
from_layers = ['relu4_3', 'relu7', '', '', '', '']
num_filters = [512, -1, 512, 256, 256, 256]
strides = [-1, -1, 2, 2, 1, 1]
pads = [-1, -1, 1, 1, 0, 0]
sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
[1,2,.5], [1,2,.5]]
normalizations = [20, -1, -1, -1, -1, -1]
steps = [] if data_shape != 300 else [x / 300.0 for x in [8, 16, 32, 64, 100, 300]]
if not (data_shape == 300 or data_shape == 512):
logging.warn('data_shape %d was not tested, use with caucious.' % data_shape)
return locals()
elif network == 'inceptionv3':
from_layers = ['ch_concat_mixed_7_chconcat', 'ch_concat_mixed_10_chconcat', '', '', '', '']
num_filters = [-1, -1, 512, 256, 256, 128]
strides = [-1, -1, 2, 2, 2, 2]
pads = [-1, -1, 1, 1, 1, 1]
sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
[1,2,.5], [1,2,.5]]
normalizations = -1
steps = []
return locals()
elif network == 'resnet50':
num_layers = 50
image_shape = '3,224,224' # resnet require it as shape check
network = 'resnet'
from_layers = ['_plus12', '_plus15', '', '', '', '']
num_filters = [-1, -1, 512, 256, 256, 128]
strides = [-1, -1, 2, 2, 2, 2]
pads = [-1, -1, 1, 1, 1, 1]
sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
[1,2,.5], [1,2,.5]]
normalizations = -1
steps = []
return locals()
elif network == 'resnet101':
num_layers = 101
image_shape = '3,224,224'
network = 'resnet'
from_layers = ['_plus29', '_plus32', '', '', '', '']
num_filters = [-1, -1, 512, 256, 256, 128]
strides = [-1, -1, 2, 2, 2, 2]
pads = [-1, -1, 1, 1, 1, 1]
sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
[1,2,.5], [1,2,.5]]
normalizations = -1
steps = []
return locals()
else:
msg = 'No configuration found for %s with data_shape %d' % (network, data_shape)
raise NotImplementedError(msg)
|
Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
|
def get_symbol_train(network, data_shape, **kwargs):
"""Wrapper for get symbol for train
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol_train for more details
"""
if network.startswith('legacy'):
logging.warn('Using legacy model.')
return symbol_builder.import_module(network).get_symbol_train(**kwargs)
config = get_config(network, data_shape, **kwargs).copy()
config.update(kwargs)
return symbol_builder.get_symbol_train(**config)
|
Set the trainer this parameter is associated with.
|
def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set the trainer for Parameter '%s' because it was already set. " \
"More than one trainers for a %s Parameter is not supported." \
%(self.name, self._stype))
self._trainer = trainer
|
Get row_sparse data from row_sparse parameters based on row_id.
|
def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id)))
if not self._trainer:
raise RuntimeError("Cannot get row_sparse data for Parameter '%s' when no " \
"Trainer is created with it."%self.name)
results = self._check_and_get(arr_list, ctx)
# fetch row sparse params from the trainer
self._trainer._row_sparse_pull(self, results, row_id)
return results
|
(Re)initializes by loading from data.
|
def _load_init(self, data, ctx):
"""(Re)initializes by loading from data."""
if self.shape:
for self_dim, data_dim in zip(self.shape, data.shape):
assert self_dim in (0, data_dim), \
"Failed loading Parameter '%s' from saved params: " \
"shape incompatible expected %s vs saved %s"%(
self.name, str(self.shape), str(data.shape))
self.shape = tuple(i if i != 0 else j for i, j in zip(self.shape, data.shape))
if self.dtype:
assert np.dtype(self.dtype).type == data.dtype, \
"Failed loading Parameter '%s' from saved params: " \
"dtype incompatible expected %s vs saved %s"%(
self.name, str(self.dtype), str(data.dtype))
if self._stype != data.stype:
data = data.tostype(self._stype)
if isinstance(ctx, Context):
ctx = [ctx]
if self._data is None:
if self._deferred_init:
assert ctx is None or set(ctx) == set(self._deferred_init[1]), \
"Failed to load Parameter '%s' on %s because it was " \
"previous initialized on %s."%(
self.name, str(ctx), str(self.list_ctx()))
ctx = self._deferred_init[1]
elif ctx is None:
ctx = [cpu()]
self._init_impl(data, ctx)
else:
assert ctx is None or set(ctx) == set(self.list_ctx()), \
"Failed to load Parameter '%s' on %s because it was " \
"previous initialized on %s."%(
self.name, str(ctx), str(self.list_ctx()))
self.set_data(data)
self._deferred_init = ()
|
Finishes deferred initialization.
|
def _finish_deferred_init(self):
"""Finishes deferred initialization."""
if not self._deferred_init:
return
init, ctx, default_init, data = self._deferred_init
self._deferred_init = ()
assert self.shape is not None and np.prod(self.shape) > 0, \
"Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s. Please specify in_units, " \
"in_channels, etc for `Block`s."%(
self.name, str(self.shape))
with autograd.pause():
if data is None:
data = ndarray.zeros(shape=self.shape, dtype=self.dtype,
ctx=context.cpu(), stype=self._stype)
initializer.create(default_init)(
initializer.InitDesc(self.name, {'__init__': init}), data)
self._init_impl(data, ctx)
|
Sets data and grad.
|
def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
while len(dev_list) <= ctx.device_id:
dev_list.append(None)
dev_list[ctx.device_id] = i
self._data = [data.copyto(ctx) for ctx in self._ctx_list]
self._init_grad()
|
Initialize grad buffers.
|
def _init_grad(self):
"""Initialize grad buffers."""
if self.grad_req == 'null':
self._grad = None
return
self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
stype=self._grad_stype) for i in self._data]
autograd.mark_variables(self._check_and_get(self._data, list),
self._grad, self.grad_req)
|
Reduce data from multiple context to cpu.
|
def _reduce(self):
"""Reduce data from multiple context to cpu."""
ctx = context.cpu()
if self._stype == 'default':
block = self.list_data()
data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
else:
# fetch all rows for 'row_sparse' param
all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64', ctx=ctx)
data = ndarray.zeros(self.shape, stype='row_sparse', ctx=ctx)
self._trainer._row_sparse_pull(self, data, all_row_ids, full_idx=True)
return data
|
Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
Parameters
----------
init : Initializer
The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.
ctx : Context or list of Context, defaults to :py:meth:`context.current_context()`.
Initialize Parameter on given context. If ctx is a list of Context, a
copy will be made for each context.
.. note::
Copies are independent arrays. User is responsible for keeping
their values consistent when updating.
Normally :py:class:`gluon.Trainer` does this for you.
default_init : Initializer
Default initializer is used when both :py:func:`init`
and :py:meth:`Parameter.init` are ``None``.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(ctx=mx.cpu(0))
>>> weight.data()
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> weight.grad()
[[ 0. 0.]
[ 0. 0.]]
<NDArray 2x2 @cpu(0)>
>>> weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
|
def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(),
force_reinit=False):
"""Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
Parameters
----------
init : Initializer
The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.
ctx : Context or list of Context, defaults to :py:meth:`context.current_context()`.
Initialize Parameter on given context. If ctx is a list of Context, a
copy will be made for each context.
.. note::
Copies are independent arrays. User is responsible for keeping
their values consistent when updating.
Normally :py:class:`gluon.Trainer` does this for you.
default_init : Initializer
Default initializer is used when both :py:func:`init`
and :py:meth:`Parameter.init` are ``None``.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(ctx=mx.cpu(0))
>>> weight.data()
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> weight.grad()
[[ 0. 0.]
[ 0. 0.]]
<NDArray 2x2 @cpu(0)>
>>> weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
"""
if self._data is not None and not force_reinit:
warnings.warn("Parameter '%s' is already initialized, ignoring. " \
"Set force_reinit=True to re-initialize."%self.name,
stacklevel=2)
return
self._data = self._grad = None
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if init is None:
init = default_init if self.init is None else self.init
if not self.shape or np.prod(self.shape) <= 0:
if self._allow_deferred_init:
self._deferred_init = (init, ctx, default_init, None)
return
raise ValueError("Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s."%(self.name, str(self.shape)))
self._deferred_init = (init, ctx, default_init, None)
self._finish_deferred_init()
|
Re-assign Parameter to other contexts.
Parameters
----------
ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
|
def reset_ctx(self, ctx):
"""Re-assign Parameter to other contexts.
Parameters
----------
ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
"""
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if self._data:
data = self._reduce()
with autograd.pause():
self._init_impl(data, ctx)
elif self._deferred_init:
init, _, default_init, data = self._deferred_init
self._deferred_init = (init, ctx, default_init, data)
else:
raise ValueError("Cannot reset context for Parameter '%s' because it "
"has not been initialized."%self.name)
|
Sets this parameter's value on all contexts.
|
def set_data(self, data):
"""Sets this parameter's value on all contexts."""
self.shape = data.shape
if self._data is None:
assert self._deferred_init, \
"Parameter '%s' has not been initialized"%self.name
self._deferred_init = self._deferred_init[:3] + (data,)
return
# if update_on_kvstore, we need to make sure the copy stored in kvstore is in sync
if self._trainer and self._trainer._kv_initialized and self._trainer._update_on_kvstore:
if self not in self._trainer._params_to_init:
self._trainer._reset_kvstore()
for arr in self._check_and_get(self._data, list):
arr[:] = data
|
Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
NDArray on row_id's context
|
def row_sparse_data(self, row_id):
"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
NDArray on row_id's context
"""
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
"because its storage type is %s. Please use data() instead." \
%(self.name, self._stype))
return self._get_row_sparse(self._data, row_id.context, row_id)
|
Returns copies of the 'row_sparse' parameter on all contexts, in the same order
as creation. The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
list of NDArrays
|
def list_row_sparse_data(self, row_id):
"""Returns copies of the 'row_sparse' parameter on all contexts, in the same order
as creation. The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
list of NDArrays
"""
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_row_sparse_data() because its storage type is %s. Please " \
"use data() instead." % (self.name, self._stype))
return self._get_row_sparse(self._data, list, row_id)
|
Returns a copy of this parameter on one context. Must have been
initialized on this context before. For sparse parameters, use
:py:meth:`Parameter.row_sparse_data` instead.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
|
def data(self, ctx=None):
"""Returns a copy of this parameter on one context. Must have been
initialized on this context before. For sparse parameters, use
:py:meth:`Parameter.row_sparse_data` instead.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
"""
if self._stype != 'default':
raise RuntimeError("Cannot return a copy of Parameter '%s' on ctx %s via data() " \
"because its storage type is %s. Please use row_sparse_data() " \
"instead." % (self.name, str(ctx), self._stype))
return self._check_and_get(self._data, ctx)
|
Returns copies of this parameter on all contexts, in the same order
as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`
instead.
Returns
-------
list of NDArrays
|
def list_data(self):
"""Returns copies of this parameter on all contexts, in the same order
as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`
instead.
Returns
-------
list of NDArrays
"""
if self._stype != 'default':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_data() because its storage type is %s. Please use " \
"row_sparse_data() instead." % (self.name, self._stype))
return self._check_and_get(self._data, list)
|
Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
|
def grad(self, ctx=None):
"""Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
"""
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get(self._grad, ctx)
|
Returns gradient buffers on all contexts, in the same order
as :py:meth:`values`.
|
def list_grad(self):
"""Returns gradient buffers on all contexts, in the same order
as :py:meth:`values`."""
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get(self._grad, list)
|
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