haojin2 commented on a change in pull request #17328: [numpy] add op pad
URL: https://github.com/apache/incubator-mxnet/pull/17328#discussion_r381000527
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File path: python/mxnet/symbol/numpy/_symbol.py
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@@ -6414,4 +6414,129 @@ def bincount(x, weights=None, minlength=0):
return _npi.bincount(x, weights=weights, minlength=minlength,
has_weights=True)
+@set_module('mxnet.symbol.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 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.")
+ allowed_kwargs = {
+ 'empty': [], 'edge': [], 'wrap': [],
+ 'constant': ['constant_values'],
+ 'linear_ramp': ['end_values'],
+ 'maximum': ['stat_length'],
+ 'mean': ['stat_length'],
+ 'median': ['stat_length'],
+ 'minimum': ['stat_length'],
+ 'reflect': ['reflect_type'],
+ 'symmetric': ['reflect_type'],
+ }
+ try:
+ unsupported_kwargs = set(kwargs) - set(allowed_kwargs[mode])
+ except KeyError:
+ raise ValueError("mode '{}' is not supported".format(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)
+
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