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https://github.com/KrisKennaway/ii-vision.git
synced 2025-02-01 01:34:13 +00:00
Refactor opcode schedulers and implement one based on the ortools TSP
solver to minimize the cycle cost to visit all changes in our estimated list. This is fortunately a tractable (though slow) computation that does give improvements on the previous heuristic at the level of ~6% better throughput. This opcode schedule prefers to group by page and vary over content, so implement a fast heuristic that does that. This scheduler is within 2% of the TSP solution.
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kris
parent
a8688a6a7e
commit
ab4b4f22fd
136
main.py
136
main.py
@ -10,53 +10,105 @@ MAX_OUT = 20 * 1024
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VIDEO_FPS = 30
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APPLE_FPS = 10
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# Old naive XOR algorithm:
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#
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#stores=1894, content changes=15, page changes=365
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#Frame 0, 2654 bytes, similarity = 0.850856
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#stores=1750, content changes=19, page changes=444
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#Frame 3, 2676 bytes, similarity = 0.903088
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#stores=1648, content changes=20, page changes=501
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#Frame 6, 2690 bytes, similarity = 0.922024
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#stores=1677, content changes=18, page changes=486
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#Frame 9, 2685 bytes, similarity = 0.912723
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#stores=1659, content changes=18, page changes=497
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#Frame 12, 2689 bytes, similarity = 0.923438
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#stores=1681, content changes=17, page changes=485
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#Frame 15, 2685 bytes, similarity = 0.922656
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#stores=1686, content changes=17, page changes=482
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#Frame 18, 2684 bytes, similarity = 0.921912
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#stores=1669, content changes=17, page changes=492
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# stores=1894, content changes=15, page changes=365
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# Frame 0, 2654 bytes, similarity = 0.850856
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# stores=1750, content changes=19, page changes=444
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# Frame 3, 2676 bytes, similarity = 0.903088
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# stores=1648, content changes=20, page changes=501
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# Frame 6, 2690 bytes, similarity = 0.922024
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# stores=1677, content changes=18, page changes=486
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# Frame 9, 2685 bytes, similarity = 0.912723
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# stores=1659, content changes=18, page changes=497
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# Frame 12, 2689 bytes, similarity = 0.923438
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# stores=1681, content changes=17, page changes=485
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# Frame 15, 2685 bytes, similarity = 0.922656
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# stores=1686, content changes=17, page changes=482
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# Frame 18, 2684 bytes, similarity = 0.921912
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# stores=1669, content changes=17, page changes=492
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# New
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#stores=2260, content changes=277, page changes=125
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#Frame 0, 3064 bytes, similarity = 0.874740
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#stores=2162, content changes=325, page changes=131
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#Frame 3, 3074 bytes, similarity = 0.925670
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#stores=2241, content changes=313, page changes=102
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#Frame 6, 3071 bytes, similarity = 0.936942
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#stores=2265, content changes=313, page changes=90
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#Frame 9, 3071 bytes, similarity = 0.931882
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#stores=2225, content changes=334, page changes=91
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#Frame 12, 3075 bytes, similarity = 0.929427
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#stores=2216, content changes=342, page changes=89
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#Frame 15, 3078 bytes, similarity = 0.919978
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#stores=2222, content changes=339, page changes=88
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# stores=2260, content changes=277, page changes=125
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# Frame 0, 3064 bytes, similarity = 0.874740
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# stores=2162, content changes=325, page changes=131
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# Frame 3, 3074 bytes, similarity = 0.925670
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# stores=2241, content changes=313, page changes=102
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# Frame 6, 3071 bytes, similarity = 0.936942
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# stores=2265, content changes=313, page changes=90
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# Frame 9, 3071 bytes, similarity = 0.931882
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# stores=2225, content changes=334, page changes=91
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# Frame 12, 3075 bytes, similarity = 0.929427
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# stores=2216, content changes=342, page changes=89
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# Frame 15, 3078 bytes, similarity = 0.919978
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# stores=2222, content changes=339, page changes=88
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# Optimized new
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#stores=1762, content changes=15, page changes=338
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#Frame 0, 2468 bytes, similarity = 0.841034
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#stores=2150, content changes=28, page changes=465
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#Frame 3, 3136 bytes, similarity = 0.921987
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#stores=2067, content changes=30, page changes=573
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#Frame 6, 3273 bytes, similarity = 0.939583
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#stores=1906, content changes=29, page changes=551
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#Frame 9, 3066 bytes, similarity = 0.928237
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#stores=1876, content changes=27, page changes=560
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#Frame 12, 3050 bytes, similarity = 0.933705
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#stores=1856, content changes=30, page changes=575
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#Frame 15, 3066 bytes, similarity = 0.929539
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#stores=1827, content changes=30, page changes=562
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# Fullness = 1.384560, cycles = 90738/104857 budget
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# stores=1872, content changes=15, page changes=352
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# Frame 0, 2606 bytes, similarity = 0.849219
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# Fullness = 1.452588, cycles = 110009/104857 budget
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# stores=2163, content changes=28, page changes=472
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# Frame 3, 3163 bytes, similarity = 0.924256
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# Fullness = 1.577072, cycles = 113843/104857 budget
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# stores=2062, content changes=30, page changes=577
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# Frame 6, 3276 bytes, similarity = 0.939918
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# Fullness = 1.597466, cycles = 106213/104857 budget
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# stores=1899, content changes=29, page changes=550
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# Frame 9, 3057 bytes, similarity = 0.928274
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# Fullness = 1.615001, cycles = 106008/104857 budget
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# stores=1875, content changes=27, page changes=561
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# Frame 12, 3051 bytes, similarity = 0.933854
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# Fullness = 1.639691, cycles = 106460/104857 budget
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# stores=1855, content changes=30, page changes=575
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# Frame 15, 3065 bytes, similarity = 0.929725
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# Fullness = 1.635406, cycles = 104583/104857 budget
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# stores=1827, content changes=30, page changes=562
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# TSP solver
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# Fullness = 1.336189, cycles = 87568/104857 budget
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# stores=1872, content changes=320, page changes=32
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# Frame 0, 2576 bytes, similarity = 0.849219
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# Fullness = 1.386065, cycles = 108771/104857 budget
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# stores=2242, content changes=452, page changes=33
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# Frame 3, 3212 bytes, similarity = 0.927604
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# Fullness = 1.482284, cycles = 112136/104857 budget
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# stores=2161, content changes=552, page changes=33
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# Frame 6, 3331 bytes, similarity = 0.943415
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# Fullness = 1.501014, cycles = 106182/104857 budget
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# stores=2021, content changes=535, page changes=33
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# Frame 9, 3157 bytes, similarity = 0.934263
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# Fullness = 1.523818, cycles = 106450/104857 budget
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# stores=1995, content changes=554, page changes=33
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# Frame 12, 3169 bytes, similarity = 0.939844
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# Fullness = 1.543029, cycles = 106179/104857 budget
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# stores=1966, content changes=566, page changes=33
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# Frame 15, 3164 bytes, similarity = 0.935231
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# Fullness = 1.538659, cycles = 104560/104857 budget
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# stores=1941, content changes=554, page changes=33
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# page first
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# Fullness = 1.366463, cycles = 89552/104857 budget
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# stores=1872, content changes=352, page changes=32
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# Frame 0, 2640 bytes, similarity = 0.849219
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# Fullness = 1.413155, cycles = 108440/104857 budget
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# stores=2192, content changes=476, page changes=32
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# Frame 3, 3208 bytes, similarity = 0.925744
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# Fullness = 1.516888, cycles = 112554/104857 budget
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# stores=2120, content changes=583, page changes=32
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# Frame 6, 3350 bytes, similarity = 0.942187
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# Fullness = 1.535086, cycles = 106115/104857 budget
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# stores=1975, content changes=561, page changes=32
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# Frame 9, 3161 bytes, similarity = 0.932106
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# Fullness = 1.553913, cycles = 106143/104857 budget
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# stores=1951, content changes=575, page changes=32
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# Frame 12, 3165 bytes, similarity = 0.937835
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# Fullness = 1.571548, cycles = 106047/104857 budget
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# stores=1927, content changes=587, page changes=32
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# Frame 15, 3165 bytes, similarity = 0.933259
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# Fullness = 1.572792, cycles = 104940/104857 budget
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# stores=1906, content changes=581, page changes=32
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def main():
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s = screen.Screen()
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@ -117,7 +169,7 @@ def main():
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break
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print("Frame %d, %d bytes, similarity = %f" % (
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idx, len(stream), s.similarity(im,bm)))
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idx, len(stream), s.similarity(im, bm)))
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out.write(stream)
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out.write(bytes(s.done()))
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93
screen.py
93
screen.py
@ -5,8 +5,12 @@ import functools
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import enum
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from typing import Set, Iterator, Union, Tuple
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from ortools.constraint_solver import pywrapcp
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from ortools.constraint_solver import routing_enums_pb2
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import numpy as np
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@functools.lru_cache(None)
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def hamming_weight(n: int) -> int:
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"""Compute hamming weight of 8-bit int"""
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@ -142,6 +146,77 @@ class Screen:
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sorted(self.index_changes(delta, target), reverse=True)
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)[:est_opcodes]
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for b in self._heuristic_opcode_scheduler(changes):
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yield b
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def _tsp_opcode_scheduler(self, changes):
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# Build distance matrix for pairs of changes based on number of
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# opcodes it would cost for opcodes to emit target change given source
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dist = np.zeros(shape=(len(changes), len(changes)), dtype=np.int)
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for i1, ch1 in enumerate(changes):
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_, page1, _, content1 = ch1
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for i2, ch2 in enumerate(changes):
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if ch1 == ch2:
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continue
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_, page2, _, content2 = ch2
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cost = self.CYCLES[0] # Emit the target content byte
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if page1 != page2:
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cost += self.CYCLES[Opcode.SET_PAGE]
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if content1 != content2:
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cost += self.CYCLES[Opcode.SET_CONTENT]
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dist[i1][i2] = cost
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dist[i2][i1] = cost
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def create_distance_callback(dist_matrix):
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# Create a callback to calculate distances between cities.
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def distance_callback(from_node, to_node):
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return int(dist_matrix[from_node][to_node])
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return distance_callback
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routing = pywrapcp.RoutingModel(len(changes), 1, 0)
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search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
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# Create the distance callback.
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dist_callback = create_distance_callback(dist)
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routing.SetArcCostEvaluatorOfAllVehicles(dist_callback)
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assignment = routing.SolveWithParameters(search_parameters)
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if assignment:
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# Solution distance.
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print("Total cycles: " + str(assignment.ObjectiveValue()))
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# Display the solution.
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# Only one route here; otherwise iterate from 0 to routing.vehicles() - 1
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route_number = 0
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index = routing.Start(route_number) # Index of the variable for the starting node.
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page = 0x20
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content = 0x7f
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# TODO: I think this will end by visiting the origin node which
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# is not what we want
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while not routing.IsEnd(index):
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_, new_page, offset, new_content = changes[index]
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if new_page != page:
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page = new_page
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yield self._emit(Opcode.SET_PAGE)
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yield page
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if new_content != content:
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content = new_content
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yield self._emit(Opcode.SET_CONTENT)
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yield content
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self._write(page << 8 | offset, content)
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yield self._emit(offset)
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index = assignment.Value(routing.NextVar(index))
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else:
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raise ValueError('No solution found.')
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def _heuristic_opcode_scheduler(self, changes):
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# Heuristic: group by content byte first then page
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data = {}
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for ch in changes:
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@ -159,6 +234,24 @@ class Screen:
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self._write(page << 8 | offset, content)
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yield self._emit(offset)
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def _heuristic_page_first_opcode_scheduler(self, changes):
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# Heuristic: group by page first then content byte
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data = {}
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for ch in changes:
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xor_weight, page, offset, content = ch
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data.setdefault(page, {}).setdefault(content, set()).add(offset)
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for page, content_offsets in data.items():
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yield self._emit(Opcode.SET_PAGE)
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yield page
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for content, offsets in content_offsets.items():
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yield self._emit(Opcode.SET_CONTENT)
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yield content
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for offset in offsets:
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self._write(page << 8 | offset, content)
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yield self._emit(offset)
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def index_changes(self, deltas: np.array,
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memmap: np.array) -> Set[Tuple[int, int, int, int]]:
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"""Transform encoded screen to sequence of change tuples.
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