1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
|
# Copyright (C) 2020-2021 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Defines the class Shape4D.
from collections import namedtuple
from enum import Enum
from .numeric_util import full_shape
from .numeric_util import round_up
from .numeric_util import round_up_divide
class Shape4D(namedtuple("Shape4D", ["batch", "height", "width", "depth"])):
"""
4D Shape (in NHWC format)
"""
def __new__(cls, n=1, h=1, w=1, c=1):
assert n is not None
if isinstance(n, list):
assert h == 1 and w == 1 and c == 1
tmp = full_shape(4, n, 1)
self = super(Shape4D, cls).__new__(cls, tmp[0], tmp[1], tmp[2], tmp[3])
else:
self = super(Shape4D, cls).__new__(cls, n, h, w, c)
return self
@classmethod
def from_list(cls, shape, base=1):
tmp = full_shape(4, shape, base)
return cls(tmp[0], tmp[1], tmp[2], tmp[3])
@classmethod
def min(cls, lhs, rhs):
return Shape4D(
min(lhs.batch, rhs.batch), min(lhs.height, rhs.height), min(lhs.width, rhs.width), min(lhs.depth, rhs.depth)
)
@classmethod
def max(cls, lhs, rhs):
return Shape4D(
max(lhs.batch, rhs.batch), max(lhs.height, rhs.height), max(lhs.width, rhs.width), max(lhs.depth, rhs.depth)
)
@classmethod
def round_up(cls, lhs, rhs):
return Shape4D(
round_up(lhs.batch, rhs.batch),
round_up(lhs.height, rhs.height),
round_up(lhs.width, rhs.width),
round_up(lhs.depth, rhs.depth),
)
@classmethod
def from_hwc(cls, h, w, c):
return cls(1, h, w, c)
def with_batch(self, new_batch):
return Shape4D(new_batch, self.height, self.width, self.depth)
def with_height(self, new_height):
return Shape4D(self.batch, new_height, self.width, self.depth)
def with_width(self, new_width):
return Shape4D(self.batch, self.height, new_width, self.depth)
def with_hw(self, new_height, new_width):
return Shape4D(self.batch, new_height, new_width, self.depth)
def with_depth(self, new_depth):
return Shape4D(self.batch, self.height, self.width, new_depth)
def with_axis(self, axis, new_val):
shape_as_list = self.as_list()
shape_as_list[axis] = new_val
return Shape4D.from_list(shape_as_list)
@staticmethod
def _clip_len(pos, length, size):
if pos < 0:
length = length + pos
pos = 0
return min(pos + length, size) - pos
def clip(self, offset, sub_shape):
n = Shape4D._clip_len(offset.batch, sub_shape.batch, self.batch)
h = Shape4D._clip_len(offset.height, sub_shape.height, self.height)
w = Shape4D._clip_len(offset.width, sub_shape.width, self.width)
c = Shape4D._clip_len(offset.depth, sub_shape.depth, self.depth)
return Shape4D(n, h, w, c)
def add(self, n, h, w, c):
return Shape4D(self.batch + n, self.height + h, self.width + w, self.depth + c)
def __add__(self, rhs):
return Shape4D(self.batch + rhs.batch, self.height + rhs.height, self.width + rhs.width, self.depth + rhs.depth)
def __sub__(self, rhs):
return Shape4D(self.batch - rhs.batch, self.height - rhs.height, self.width - rhs.width, self.depth - rhs.depth)
def floordiv_const(self, const):
return Shape4D(self.batch // const, self.height // const, self.width // const, self.depth // const)
def __floordiv__(self, rhs):
return Shape4D(
self.batch // rhs.batch, self.height // rhs.height, self.width // rhs.width, self.depth // rhs.depth
)
def __truediv__(self, rhs):
return Shape4D(self.batch / rhs.batch, self.height / rhs.height, self.width / rhs.width, self.depth / rhs.depth)
def __mod__(self, rhs):
return Shape4D(self.batch % rhs.batch, self.height % rhs.height, self.width % rhs.width, self.depth % rhs.depth)
def __str__(self):
return f"<Shape4D {list(self)}>"
def div_round_up(self, rhs):
return Shape4D(
round_up_divide(self.batch, rhs.batch),
round_up_divide(self.height, rhs.height),
round_up_divide(self.width, rhs.width),
round_up_divide(self.depth, rhs.depth),
)
def elements(self):
return self.batch * self.width * self.height * self.depth
def dot_prod(self, rhs):
return self.batch * rhs.batch + self.width * rhs.width + self.height * rhs.height + self.depth * rhs.depth
def elements_wh(self):
return self.width * self.height
def is_empty(self):
return (self.batch + self.width + self.height + self.depth) == 0
def as_list(self):
return list(self)
def get_hw_as_list(self):
return list([self.height, self.width])
class VolumeIterator:
"""
4D Volume iterator. Use to traverse 4D tensor volumes in smaller shapes.
"""
class Direction(Enum):
CWHN = 0
def __init__(
self,
shape: Shape4D,
sub_shape: Shape4D,
start: Shape4D = Shape4D(0, 0, 0, 0),
delta: Shape4D = None,
dir=Direction.CWHN,
):
self.b = start.batch
self.y = start.height
self.x = start.width
self.z = start.depth
self.shape = shape
self.sub_shape = sub_shape
self.delta = sub_shape if delta is None else delta
assert self.delta.elements() > 0, "Iterator will not move"
def __iter__(self):
return self
def __next__(self):
if self.b >= self.shape.batch:
raise StopIteration()
offset = Shape4D(self.b, self.y, self.x, self.z)
# CWHN
self.z += self.delta.depth
if self.z >= self.shape.depth:
self.z = 0
self.x += self.delta.width
if self.x >= self.shape.width:
self.x = 0
self.y += self.delta.height
if self.y >= self.shape.height:
self.y = 0
self.b += self.delta.batch
return offset, self.shape.clip(offset, self.sub_shape)
|