numpy_do.py 3.92 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

19
20
21
# Data object module for NIFTy that uses simple numpy ndarrays.

import numpy as np
Philipp Arras's avatar
Philipp Arras committed
22
from numpy import empty, empty_like, exp, full, log
23
from numpy import ndarray as data_object
Philipp Arras's avatar
Philipp Arras committed
24
from numpy import ones, sqrt, tanh, vdot, zeros
25
from numpy import sin, cos, tan, sinh, cosh, sinc
Martin Reinecke's avatar
Martin Reinecke committed
26
from numpy import absolute, sign, clip
Martin Reinecke's avatar
Martin Reinecke committed
27
28
from .random import Random

Martin Reinecke's avatar
Martin Reinecke committed
29
30
31
32
33
34
__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
           "empty", "zeros", "ones", "empty_like", "vdot", "exp",
           "log", "tanh", "sqrt", "from_object", "from_random",
           "local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
           "np_allreduce_min", "np_allreduce_max",
           "distaxis", "from_local_data", "from_global_data", "to_global_data",
Martin Reinecke's avatar
Martin Reinecke committed
35
           "redistribute", "default_distaxis", "is_numpy", "absmax", "norm",
Martin Reinecke's avatar
Martin Reinecke committed
36
           "lock", "locked", "uniform_full", "to_global_data_rw",
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
37
           "ensure_not_distributed", "ensure_default_distributed",
Martin Reinecke's avatar
Martin Reinecke committed
38
           "clipped_exp", "clip", "sin", "cos", "tan", "sinh",
39
           "cosh", "absolute", "sign", "sinc"]
Martin Reinecke's avatar
Martin Reinecke committed
40

Martin Reinecke's avatar
Martin Reinecke committed
41
42
43
44
ntask = 1
rank = 0
master = True

45

Martin Reinecke's avatar
Martin Reinecke committed
46
47
48
49
def is_numpy():
    return True


Martin Reinecke's avatar
Martin Reinecke committed
50
51
52
53
54
55
56
57
58
59
60
61
62
63
def from_object(object, dtype, copy, set_locked):
    if dtype is None:
        dtype = object.dtype
    dtypes_equal = dtype == object.dtype
    if set_locked and dtypes_equal and locked(object):
        return object
    if not dtypes_equal and not copy:
        raise ValueError("cannot change data type without copying")
    if set_locked and not copy:
        raise ValueError("cannot lock object without copying")
    res = np.array(object, dtype=dtype, copy=copy)
    if set_locked:
        lock(res)
    return res
Martin Reinecke's avatar
Martin Reinecke committed
64
65
66
67
68


def from_random(random_type, shape, dtype=np.float64, **kwargs):
    generator_function = getattr(Random, random_type)
    return generator_function(dtype=dtype, shape=shape, **kwargs)
Martin Reinecke's avatar
Martin Reinecke committed
69

Martin Reinecke's avatar
Martin Reinecke committed
70

Martin Reinecke's avatar
Martin Reinecke committed
71
72
73
74
def local_data(arr):
    return arr


75
76
77
78
def ibegin_from_shape(glob_shape, distaxis=-1):
    return (0,)*len(glob_shape)


Martin Reinecke's avatar
Martin Reinecke committed
79
80
81
82
83
84
85
86
def ibegin(arr):
    return (0,)*arr.ndim


def np_allreduce_sum(arr):
    return arr


87
88
89
90
def np_allreduce_min(arr):
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
91
92
93
94
def np_allreduce_max(arr):
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
95
def distaxis(arr):
Martin Reinecke's avatar
Martin Reinecke committed
96
    return -1
Martin Reinecke's avatar
Martin Reinecke committed
97
98


Martin Reinecke's avatar
Martin Reinecke committed
99
def from_local_data(shape, arr, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
100
    if tuple(shape) != arr.shape:
Martin Reinecke's avatar
Martin Reinecke committed
101
102
103
104
        raise ValueError
    return arr


105
def from_global_data(arr, sum_up=False, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
106
107
108
    return arr


Martin Reinecke's avatar
Martin Reinecke committed
109
def to_global_data(arr):
Martin Reinecke's avatar
Martin Reinecke committed
110
111
112
    return arr


113
114
115
116
def to_global_data_rw(arr):
    return arr.copy()


Martin Reinecke's avatar
Martin Reinecke committed
117
def redistribute(arr, dist=None, nodist=None):
Martin Reinecke's avatar
Martin Reinecke committed
118
119
120
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
121
def default_distaxis():
Martin Reinecke's avatar
Martin Reinecke committed
122
123
124
    return -1


125
def local_shape(glob_shape, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
126
    return glob_shape
127
128
129
130
131
132
133
134


def lock(arr):
    arr.flags.writeable = False


def locked(arr):
    return not arr.flags.writeable
Martin Reinecke's avatar
Martin Reinecke committed
135
136
137
138


def uniform_full(shape, fill_value, dtype=None, distaxis=-1):
    return np.broadcast_to(fill_value, shape)
Martin Reinecke's avatar
Martin Reinecke committed
139
140
141
142
143
144
145
146


def ensure_not_distributed(arr, axes):
    return arr, arr


def ensure_default_distributed(arr):
    return arr
Martin Reinecke's avatar
Martin Reinecke committed
147
148
149


def absmax(arr):
150
    return np.linalg.norm(arr.rehape(-1), ord=np.inf)
Martin Reinecke's avatar
Martin Reinecke committed
151
152
153


def norm(arr, ord=2):
154
    return np.linalg.norm(arr.reshape(-1), ord=ord)
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
155
156
157
158


def clipped_exp(arr):
    return np.exp(np.clip(arr, -300, 300))