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
|
"""Blind recovery on synthetic set-kernel fields: the end-to-end test.
Builds the connected-component descriptor field and the phrase
bag-of-words field, hides the text order behind a shuffle, and runs
parallel tempering on the relational energy alone. Recovery accuracy
against the hidden truth is the first end-to-end measurement of world
matching in the closed world.
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
from tqdm import tqdm
from .blind_recovery import arm_tempering, permutation_energy_batch
from .common import read_json, seed_everything, write_json
from .manifold_gate import standardize_relation
from .synth_cc_battery import (
component_descriptors,
moment_field,
onehot_descriptors,
phrase_bow_sets,
)
from .synth_set_battery import set_similarity_field
from .synth_towers import load_image
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", default="artifacts/synth_v0")
parser.add_argument("--split", choices=["val", "test"], default="test")
parser.add_argument("--samples", type=int, default=512)
parser.add_argument("--merge-distance", type=float, default=30.0)
parser.add_argument("--vision-views", type=int, default=4)
parser.add_argument("--replicas", type=int, default=8)
parser.add_argument("--tempering-rounds", type=int, default=60000)
parser.add_argument("--temp-high", type=float, default=3e-3)
parser.add_argument("--temp-low", type=float, default=1e-5)
parser.add_argument("--exchange-every", type=int, default=20)
parser.add_argument("--unary-weight", type=float, default=0.0)
parser.add_argument("--init", default="random")
parser.add_argument("--residualize-size", action="store_true", default=False)
parser.add_argument(
"--features", choices=["descriptors", "onehot"], default="onehot"
)
parser.add_argument("--kernel", choices=["matching", "moment"], default="moment")
parser.add_argument("--device", default="cuda:3")
parser.add_argument("--seed", type=int, default=20260731)
parser.add_argument(
"--output", default="artifacts/synth_v0/recovery_end_to_end.json"
)
return parser.parse_args()
def residualize(field: torch.Tensor, sizes: torch.Tensor) -> torch.Tensor:
"""Regress the set-size nuisance out of a matching-value field.
Set sizes are unimodal observables; their sum, difference, and product
explain a size-driven component that differs between modalities and
is exploitable by counterfeit assignments.
"""
n = len(field)
mask = ~torch.eye(n, dtype=torch.bool)
features = torch.stack(
[
(sizes[:, None] + sizes[None, :])[mask],
(sizes[:, None] - sizes[None, :]).abs()[mask],
(sizes[:, None] * sizes[None, :])[mask],
torch.ones(int(mask.sum()), dtype=torch.float64),
],
dim=1,
)
values = field.double()[mask]
solution = torch.linalg.lstsq(features, values[:, None]).solution
residual = values - (features @ solution).squeeze(1)
output = field.double().clone()
output[mask] = residual
output.fill_diagonal_(0.0)
return output.float()
def main() -> None:
args = parse_args()
seed_everything(args.seed)
manifest = read_json(Path(args.data_dir, "manifest.json"))
captions = read_json(Path(args.data_dir, "captions.json"))["captions"]
rows = manifest[args.split][: args.samples]
image_dir = Path(manifest["image_dir"])
import torch.nn.functional as F
per_view_fields = []
for view in range(args.vision_views):
raw_sets = []
for row in tqdm(rows, desc=f"cc v{view}"):
image = load_image(image_dir / f"scene{row:06d}_v{view}.png")
sprites, _ = component_descriptors(image, args.merge_distance)
raw_sets.append(sprites)
if args.features == "onehot":
vision_sets = [
F.normalize(v, dim=-1) for v in onehot_descriptors(raw_sets)
]
else:
vision_sets = [F.normalize(v, dim=-1) for v in raw_sets]
if args.kernel == "moment":
per_view_fields.append(moment_field(vision_sets))
else:
per_view_fields.append(set_similarity_field(vision_sets))
visual_field = torch.stack(per_view_fields).mean(0)
text_sets = phrase_bow_sets(rows, captions, manifest["vocabulary"])
if args.kernel == "moment":
text_field = moment_field(text_sets)
else:
text_field = set_similarity_field(text_sets)
if args.residualize_size:
vision_sizes = torch.tensor(
[len(s) for s in vision_sets], dtype=torch.float64
)
text_sizes = torch.tensor([len(s) for s in text_sets], dtype=torch.float64)
visual_field = residualize(visual_field, vision_sizes)
text_field = residualize(text_field, text_sizes)
device = torch.device(args.device)
size = len(rows)
generator = torch.Generator().manual_seed(args.seed)
hidden = torch.randperm(size, generator=generator)
truth = torch.argsort(hidden)
text_input = text_field[hidden][:, hidden]
text_standardized = standardize_relation(text_input.double())[0].float().to(device)
visual_standardized = (
standardize_relation(visual_field.double())[0].float().to(device)
)
true_energy = float(
permutation_energy_batch(
text_standardized, visual_standardized, truth[None].to(device)
)[0]
)
report = {
"protocol": (
"Set-kernel fields from released renders and captions; text "
"order hidden behind a shuffle; tempering sees no truth. "
"Hidden truth scores the outcome only."
),
"split": args.split,
"samples": size,
"true_energy": true_energy,
"chance_accuracy": 1.0 / size,
"tempering": arm_tempering(
text_standardized,
visual_standardized,
truth,
true_energy,
args,
generator,
unary=None,
),
}
best = max(
report["tempering"]["replicas"] + [report["tempering"]["best"]],
key=lambda c: c["accuracy"],
)
by_energy = min(
report["tempering"]["replicas"] + [report["tempering"]["best"]],
key=lambda c: c["energy"],
)
report["summary"] = {
"best_accuracy": best["accuracy"],
"best_accuracy_energy_over_true": best["energy_over_true"],
"lowest_energy_accuracy": by_energy["accuracy"],
"lowest_energy_over_true": by_energy["energy_over_true"],
}
print(json.dumps({"summary": report["summary"]}))
write_json(args.output, report)
print(f"Wrote {args.output}")
if __name__ == "__main__":
main()
|
