Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Commit

Permalink
[numpy] add op pad (#17328)
Browse files Browse the repository at this point in the history 
* op pad part

* pad part fix

* style fix

* comment fix

* pylint error fix

* fix pylint error

* pylint error fix

* CI GPU error fix

* fix cast error

* fix macro error

* fix whitespace error

* comment fix

* pylint error fix

* fix stream type error

* fix line length error

* fix whitespace error

* fix kernel switch error

* fix tuple error fix

* tuple error

* fix one dimension error

* fix tuple<tuple<int>> error

* fix scala tuple

* scala error

* scala modified

* line error

* tuple shape error

* tensor error

* tensor error modified

* illegal memory access error

* const type error

* memory access error

* storage pool error

* fix error handling

* fix error handling

* fix dtype error

* comment solved

* blank line added

* fix dlpack version error

* fix sanity error

* position modifed
  • Loading branch information
@ryanwentaoxu
ryanwentaoxu authored Feb 20, 2020
1 parent 4668fbd commit b43ef28
Show file tree
Hide file tree
Showing 10 changed files with 1,359 additions and 4 deletions.
136 changes: 135 additions & 1 deletion python/mxnet/ndarray/numpy/_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@
'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'rot90', 'einsum',
'true_divide', 'nonzero', 'quantile', 'percentile', 'shares_memory', 'may_share_memory',
'diff', 'resize', 'polyval', 'nan_to_num', 'isnan', 'isinf', 'isposinf', 'isneginf', 'isfinite',
'where', 'bincount']
'where', 'bincount', 'pad']


@set_module('mxnet.ndarray.numpy')
Expand Down Expand Up @@ -7356,3 +7356,137 @@ def bincount(x, weights=None, minlength=0):
if weights is None:
return _npi.bincount(x, minlength=minlength, has_weights=False)
return _npi.bincount(x, weights=weights, minlength=minlength, has_weights=True)


@set_module('mxnet.ndarray.numpy')
def pad(x, pad_width, mode='constant', **kwargs): # pylint: disable=too-many-arguments
"""
Pad an array.
Parameters
----------
array : array_like of rank N
The array to pad.
pad_width : {sequence, array_like, int}
Number of values padded to the edges of each axis.
((before_1, after_1), ... (before_N, after_N)) unique pad widths
for each axis.
((before, after),) yields same before and after pad for each axis.
(pad,) or int is a shortcut for before = after = pad width for all
axes.
mode : str or function, optional
One of the following string values or a user supplied function.
'constant' (default)
Pads with a constant value.
'edge'
Pads with the edge values of array.
'linear_ramp'
not supported yet
'maximum'
Pads with the maximum value of all of the
vector along each axis.
'mean'
not supported yet
'median'
not supported yet
'minimum'
Pads with the minimum value of all of the
vector along each axis.
'reflect'
Pads with the reflection of the vector mirrored on
the first and last values of the vector along each
axis.
'symmetric'
Pads with the reflection of the vector mirrored
along the edge of the array.
'wrap'
not supported yet.
'empty'
not supported yet.
<function>
not supported yet.
stat_length : not supported yet
constant_values : scalar, optional
Used in 'constant'. The values to set the padded values for each
axis.
Default is 0.
end_values : not supported yet
reflect_type : {'even', 'odd'}, optional
only support even now
Returns
-------
pad : ndarray
Padded array of rank equal to `array` with shape increased
according to `pad_width`.
"""
# pylint: disable = too-many-return-statements, inconsistent-return-statements
if not _np.asarray(pad_width).dtype.kind == 'i':
raise TypeError('`pad_width` must be of integral type.')
if not isinstance(pad_width, tuple):
raise TypeError("`pad_width` must be tuple.")
if mode == "linear_ramp":
raise ValueError("mode {'linear_ramp'} is not supported.")
if mode == "wrap":
raise ValueError("mode {'wrap'} is not supported.")
if mode == "median":
raise ValueError("mode {'median'} is not supported.")
if mode == "mean":
raise ValueError("mode {'mean'} is not supported.")
if mode == "empty":
raise ValueError("mode {'empty'} is not supported.")
if callable(mode):
raise ValueError("mode {'<function>'} is not supported.")

allowedkwargs = {
'constant': ['constant_values'],
'edge': [],
'linear_ramp': ['end_values'],
'maximum': ['stat_length'],
'mean': ['stat_length'],
'median': ['stat_length'],
'minimum': ['stat_length'],
'reflect': ['reflect_type'],
'symmetric': ['reflect_type'],
'wrap': [],
}

if isinstance(mode, _np.compat.basestring):
# Make sure have allowed kwargs appropriate for mode
for key in kwargs:
if key not in allowedkwargs[mode]:
raise ValueError('%s keyword not in allowed keywords %s' %(key, allowedkwargs[mode]))

unsupported_kwargs = set(kwargs) - set(allowedkwargs[mode])
if unsupported_kwargs:
raise ValueError("unsupported keyword arguments for mode '{}': {}"
.format(mode, unsupported_kwargs))
if mode == "constant":
values = kwargs.get("constant_values", 0)
if isinstance(values, tuple):
raise TypeError("unsupported constant_values type: {'tuple'}.")
_npi.pad(x, pad_width, mode='constant', constant_value=values)
elif mode == "symmetric":
values = kwargs.get("reflect_type", "even")
if values != "even" and values is not None:
raise ValueError("unsupported reflect_type '{}'".format(values))
return _npi.pad(x, pad_width, mode='symmetric', reflect_type="even")
elif mode == "edge":
return _npi.pad(x, pad_width, mode='edge')
elif mode == "reflect":
values = kwargs.get("reflect_type", "even")
if values != "even" and values is not None:
raise ValueError("unsupported reflect_type '{}'".format(values))
return _npi.pad(x, pad_width, mode='reflect', reflect_type="even")
elif mode == "maximum":
values = kwargs.get("stat_length", None)
if values is not None:
raise ValueError("unsupported stat_length '{}'".format(values))
return _npi.pad(x, pad_width, mode='maximum')
elif mode == "minimum":
values = kwargs.get("stat_length", None)
if values is not None:
raise ValueError("unsupported stat_length '{}'".format(values))
return _npi.pad(x, pad_width, mode='minimum')
return _npi.pad(x, pad_width, mode='constant', constant_value=0)
101 changes: 100 additions & 1 deletion python/mxnet/numpy/multiarray.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@
'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal',
'greater', 'less', 'greater_equal', 'less_equal', 'rot90', 'einsum', 'true_divide', 'nonzero',
'quantile', 'percentile', 'shares_memory', 'may_share_memory', 'diff', 'resize', 'matmul',
'nan_to_num', 'isnan', 'isinf', 'isposinf', 'isneginf', 'isfinite', 'polyval', 'where', 'bincount']
'nan_to_num', 'isnan', 'isinf', 'isposinf', 'isneginf', 'isfinite', 'polyval', 'where', 'bincount', 'pad']

__all__ += fallback.__all__

Expand Down Expand Up @@ -9451,3 +9451,102 @@ def bincount(x, weights=None, minlength=0):
array([ 0.3, 0.7, 1.1])
"""
return _mx_nd_np.bincount(x, weights=weights, minlength=minlength)


@set_module('mxnet.numpy')
def pad(x, pad_width=None, mode="constant", **kwargs): # pylint: disable=too-many-arguments
"""
Pad an array.
Parameters
----------
array : array_like of rank N
The array to pad.
pad_width : {sequence, array_like, int}
Number of values padded to the edges of each axis.
((before_1, after_1), ... (before_N, after_N)) unique pad widths
for each axis.
((before, after),) yields same before and after pad for each axis.
(pad,) or int is a shortcut for before = after = pad width for all
axes.
mode : str or function, optional
One of the following string values or a user supplied function.
'constant' (default)
Pads with a constant value.
'edge'
Pads with the edge values of array.
'linear_ramp'
not supported yet
'maximum'
Pads with the maximum value of all of the
vector along each axis.
'mean'
not supported yet
'median'
not supported yet
'minimum'
Pads with the minimum value of all of the
vector along each axis.
'reflect'
Pads with the reflection of the vector mirrored on
the first and last values of the vector along each
axis.
'symmetric'
Pads with the reflection of the vector mirrored
along the edge of the array.
'wrap'
not supported yet.
'empty'
not supported yet.
<function>
not supported yet.
stat_length : not supported yet
constant_values : scalar, optional
Used in 'constant'. The values to set the padded values for each
axis.
Default is 0.
end_values : not supported yet
reflect_type : {'even', 'odd'}, optional
only support even now
Returns
-------
pad : ndarray
Padded array of rank equal to `array` with shape increased
according to `pad_width`.
Examples
--------
>>> a = [1, 2, 3, 4, 5]
>>> np.pad(a, (2, 3), 'edge')
array([1, 1, 1, ..., 5, 5, 5])
>>> np.pad(a, (2, 2), 'maximum')
array([5, 5, 1, 2, 3, 4, 5, 5, 5])
>>> np.pad(a, (2, 2), 'mean')
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> a = [[1, 2], [3, 4]]
>>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
array([[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[3, 3, 3, 4, 3, 3, 3],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1]])
>>> a = [1, 2, 3, 4, 5]
>>> np.pad(a, (2, 3), 'reflect')
array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
>>> np.pad(a, (2, 3), 'symmetric')
array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
>>> a = np.arange(6)
>>> a = a.reshape((2, 3))
>>> np.pad(a, ((2, 2), (2, 2)), pad_with)
array([[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 0, 1, 2, 10, 10],
[10, 10, 3, 4, 5, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10]])
"""
return _mx_nd_np.pad(x, pad_width, mode, **kwargs)
1 change: 1 addition & 0 deletions python/mxnet/numpy_dispatch_protocol.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -182,6 +182,7 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
'isposinf',
'isneginf',
'isinf',
'pad',
]


Expand Down
Loading

0 comments on commit b43ef28

Please sign in to comment.