INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Reduce the array along a given axis by mean value | def reduce_mean(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by mean value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'mean', new_attrs, inputs |
Reduce the array along a given axis by minimum value | def reduce_min(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by minimum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'min', new_attrs, inputs |
Reduce the array along a given axis by sum value | def reduce_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'sum', new_attrs, inputs |
Reduce the array along a given axis by product value | def reduce_prod(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by product value"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'prod', new_attrs, inputs |
Reduce the array along a given axis by log sum value | def reduce_log_sum(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
sum_op = symbol.sum(inputs[0], axis=attrs.get('axes'),
keepdims=keep_dims)
log_sym = symbol.log(sum_op)
... |
Reduce the array along a given axis by log sum exp value | def reduce_log_sum_exp(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by log sum exp value"""
keep_dims = True if 'keepdims' not in attrs else attrs.get('keepdims')
exp_op = symbol.exp(inputs[0])
sum_op = symbol.sum(exp_op, axis=attrs.get('axes'),
keepdims=keep... |
Reduce the array along a given axis by sum square value | def reduce_sum_square(attrs, inputs, proto_obj):
"""Reduce the array along a given axis by sum square value"""
square_op = symbol.square(inputs[0])
sum_op = symbol.sum(square_op, axis=attrs.get('axes'),
keepdims=attrs.get('keepdims'))
return sum_op, attrs, inputs |
Reduce input tensor by l1 normalization. | def reduce_l1(attrs, inputs, proto_obj):
"""Reduce input tensor by l1 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
new_attrs = translation_utils._add_extra_attributes(new_attrs,
{'ord' : 1})
return 'n... |
Reduce input tensor by l2 normalization. | def reduce_l2(attrs, inputs, proto_obj):
"""Reduce input tensor by l2 normalization."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'axes':'axis'})
return 'norm', new_attrs, inputs |
Average pooling | def avg_pooling(attrs, inputs, proto_obj):
""" Average pooling"""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
... |
LP Pooling | def lp_pooling(attrs, inputs, proto_obj):
"""LP Pooling"""
p_value = attrs.get('p', 2)
new_attrs = translation_utils._fix_attribute_names(attrs,
{'kernel_shape': 'kernel',
'strides': 'stride',
... |
Max ROI Pooling. | def max_roi_pooling(attrs, inputs, proto_obj):
"""Max ROI Pooling."""
new_attrs = translation_utils._fix_attribute_names(attrs,
{'pooled_shape': 'pooled_size',
'spatial_scale': 'spatial_scale'
... |
Rearranges data from depth into blocks of spatial data. | def depthtospace(attrs, inputs, proto_obj):
"""Rearranges data from depth into blocks of spatial data."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "depth_to_space", new_attrs, inputs |
Rearranges blocks of spatial data into depth. | def spacetodepth(attrs, inputs, proto_obj):
"""Rearranges blocks of spatial data into depth."""
new_attrs = translation_utils._fix_attribute_names(attrs, {'blocksize':'block_size'})
return "space_to_depth", new_attrs, inputs |
Returns batched one - hot vectors. | def hardmax(attrs, inputs, proto_obj):
"""Returns batched one-hot vectors."""
input_tensor_data = proto_obj.model_metadata.get('input_tensor_data')[0]
input_shape = input_tensor_data[1]
axis = int(attrs.get('axis', 1))
axis = axis if axis >= 0 else len(input_shape) + axis
if axis == len(input_... |
ONNX does not have eps attribute so cannot map it to L2normalization in MXNet without that it works as norm operator discussion in PR: https:// github. com/ onnx/ onnx/ pull/ 1330 | def lpnormalization(attrs, inputs, proto_obj):
"""ONNX does not have eps attribute, so cannot map it to L2normalization in MXNet
without that, it works as norm operator discussion in PR:
https://github.com/onnx/onnx/pull/1330"""
new_attrs = translation_utils._fix_attribute_names(attrs, {'p': 'ord'})
... |
download mp4s | def download_mp4(from_idx, to_idx, _params):
"""
download mp4s
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
save_folder = '{src_path}/{nm}'.format(src_path=_params['src_path'], nm=name)
if idx == 0 or os.path.isdir(save_folder):
... |
download aligns | def download_align(from_idx, to_idx, _params):
"""
download aligns
"""
succ = set()
fail = set()
for idx in range(from_idx, to_idx):
name = 's' + str(idx)
if idx == 0:
continue
script = "http://spandh.dcs.shef.ac.uk/gridcorpus/{nm}/align/{nm}.tar".format(nm=na... |
Run unit tests in the emulator and copy the results back to the host through the mounted volume in/ mxnet | def run_ut_py3_qemu():
"""Run unit tests in the emulator and copy the results back to the host through the mounted
volume in /mxnet"""
from vmcontrol import VM
with VM() as vm:
qemu_provision(vm.ssh_port)
logging.info("execute tests")
qemu_ssh(vm.ssh_port, "./runtime_functions.py... |
this runs inside the vm | def run_ut_python3_qemu_internal():
"""this runs inside the vm"""
pkg = glob.glob('mxnet_dist/*.whl')[0]
logging.info("=== NOW Running inside QEMU ===")
logging.info("PIP Installing %s", pkg)
check_call(['sudo', 'pip3', 'install', pkg])
logging.info("PIP Installing mxnet/test_requirements.txt") ... |
Return subword - units presentation given a word/ token. | def _get_subword_units(token, gram):
"""Return subword-units presentation, given a word/token.
"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
return [t[i:i + gram] for i in range(0, len(t) - gram + 1)] |
Train the model using Caffe operator in MXNet | def fit(args, network, data_loader, eval_metrics=None, batch_end_callback=None):
"""Train the model using Caffe operator in MXNet"""
# kvstore
kv = mx.kvstore.create(args.kv_store)
# logging
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
if 'log_file' in args and args.log_file i... |
Preprocess a 210x160x3 uint8 frame into a 6400 ( 80x80 ) ( 1 x input_size ) float vector. | def preprocess(self, img):
"""
Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
float vector.
"""
# Crop, down-sample, erase background and set foreground to 1.
# See https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
img = im... |
Returns a new empty handle. | def _new_empty_handle():
"""Returns a new empty handle.
Empty handle can be used to hold a result.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateNone(ctypes.byref(hdl)))
return hdl |
Return a new handle with specified shape and context. | def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
"""Return a new handle with specified shape and context.
Empty handle is only used to hold results.
Returns
-------
handle
A new empty `NDArray` handle.
"""
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayCreateEx(... |
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
... |
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 absolu... | 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).
... |
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(... |
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)... |
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:
... |
Returns a new array filled with all ones with the given shape and type. | 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.
Def... |
Returns a new array of given shape and type filled with the given value val. | 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
Devi... |
Creates an array from any object exposing the array interface. | 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... |
Moves the source axis into the destination position while leaving the other axes in their original order | 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... |
Returns evenly spaced values within a given interval. | 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
... |
Helper function for element - wise operation. The function will perform numpy - like broadcasting if needed and call different functions. | 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.... |
Returns element - wise sum of the input arrays with broadcasting. | 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,
... |
Returns element - wise difference of the input arrays with broadcasting. | 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 ha... |
Returns element - wise product of the input arrays with broadcasting. | 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 broadca... |
Returns element - wise division of the input arrays with broadcasting. | 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 broadcas... |
Returns element - wise modulo of the input arrays with broadcasting. | 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 broadcasta... |
Returns result of first array elements raised to powers from second array element - wise with broadcasting. | 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 h... |
Returns element - wise maximum of the input arrays with broadcasting. | 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 com... |
Returns element - wise minimum of the input arrays with broadcasting. | 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 com... |
Returns the result of element - wise ** equal to ** ( == ) comparison operation with broadcasting. | def equal(lhs, rhs):
"""Returns the result of element-wise **equal to** (==) comparison operation with
broadcasting.
For each element in input arrays, return 1(true) if corresponding elements are same,
otherwise return 0(false).
Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.... |
Returns the result of element - wise ** not equal to ** ( ! = ) comparison operation with broadcasting. | 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_e... |
Returns the result of element - wise ** greater than ** ( > ) comparison operation with broadcasting. | 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,... |
Returns the result of element - wise ** greater than or equal to ** ( > = ) comparison operation with broadcasting. | 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 `... |
Returns the result of element - wise ** lesser than ** ( < ) comparison operation with broadcasting. | 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)`... |
Returns the result of element - wise ** lesser than or equal to ** ( < = ) comparison operation with broadcasting. | 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... |
Returns the result of element - wise ** logical and ** comparison operation with broadcasting. | 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_logica... |
Returns the result of element - wise ** logical or ** comparison operation with broadcasting. | 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... |
Returns the result of element - wise ** logical xor ** comparison operation with broadcasting. | 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.broadca... |
DEPRECATED use concat instead | 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
T... |
DEPRECATED use mx. img instead | 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
... |
Returns a new array filled with all zeros with the given shape and type. | 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 d... |
Return a 2 - D array with ones on the diagonal and zeros elsewhere. | 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
I... |
Returns a new array of given shape and type without initializing entries. | 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 co... |
Compute the histogram of the input data. | 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
... |
Split an array into multiple sub - arrays. | 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 wi... |
Returns a reference view of NDArray that represents as DLManagedTensor until all previous write operations on the current array are finished. | 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 DLManagedT... |
Returns a reference view of NDArray that represents as DLManagedTensor until all previous read/ write operations on the current array are finished. | 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 DLMa... |
Returns a NDArray backed by a dlpack tensor. | 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))
... |
Returns an MXNet s NDArray backed by Numpy s ndarray. | 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... |
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
... |
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, dtyp... |
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_type... |
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)
_interna... |
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 ... |
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 co... | 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 ... |
Returns a sliced NDArray that shares memory with the current one. This is called through x [ start: stop ]. | 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
Finishin... |
Returns a view of the array sliced at idx in the first dim. This is called through x [ idx ]. | 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
`NDA... |
Returns a ** view ** of this array with a new shape without altering any data. | 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 equa... |
Broadcasts the input array to a new shape. | 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, ... |
Tuple of array dimensions. | 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)... |
Device context of the array. | 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... |
Data - type of the array s elements. | 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='int... |
Whether this array s corresponding gradient array ( registered via autograd. mark_variables ) has been updated by autograd. backward since last reset. | 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
... |
Returns a numpy. ndarray object with value copied from this array. | 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., ... |
Returns a copy of the array after casting to a specified type. | 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
... |
Copies the value of this array to another array. | 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``... |
Returns an array on the target device with the same value as this array. | 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
... |
Attach a gradient buffer to this NDArray so that backward can compute gradient with respect to it. | 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.
- 'wr... |
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. | 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 re... |
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(' ')
... |
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. | 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 ... |
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_... |
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
... |
Clips gradient norm. | 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 ... |
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... |
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("... |
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)
... |
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_v... |
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)) |
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