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from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
import torch.nn.functional as F
from .common import read_json, seed_everything, write_json
from .energy import (
projection_quantile_target,
prototype_manifold_energy,
relation_field_energy,
retrieval_metrics,
sliced_distribution_energy,
standardized_relation,
)
from .io import load_feature_pair, select_rows
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--manifest", default="artifacts/manifest.json")
parser.add_argument("--vision", default="artifacts/vision.pt")
parser.add_argument("--text", default="artifacts/text.pt")
parser.add_argument(
"--text-orbits",
help=(
"Optional multi-description feature cache. When supplied, each "
"language particle is the normalized mean of its observation "
"orbit."
),
)
parser.add_argument("--prototypes", default="artifacts/gw.pt")
parser.add_argument("--split", choices=["val", "test"], default="test")
parser.add_argument("--samples", type=int, default=256)
parser.add_argument("--steps", type=int, default=100)
parser.add_argument("--lr", type=float, default=0.03)
parser.add_argument("--projections", type=int, default=128)
parser.add_argument("--relation-weight-start", type=float, default=0.4)
parser.add_argument("--relation-weight-end", type=float, default=2.0)
parser.add_argument("--conditional-weight-start", type=float, default=0.05)
parser.add_argument("--conditional-weight-end", type=float, default=0.2)
parser.add_argument("--distribution-weight", type=float, default=80.0)
parser.add_argument("--manifold-weight", type=float, default=1.0)
parser.add_argument("--noise", type=float, default=0.01)
parser.add_argument(
"--shuffle-visual-energy",
action="store_true",
help=(
"Evaluation control: permute image particles before constructing "
"the energy while leaving evaluation rows unchanged."
),
)
parser.add_argument("--device", default="cuda:1")
parser.add_argument("--seed", type=int, default=20260729)
parser.add_argument(
"--output", default="artifacts/energy_free_test.pt"
)
parser.add_argument(
"--metrics-output", default="artifacts/energy_free_test.json"
)
return parser.parse_args()
def main() -> None:
args = parse_args()
seed_everything(args.seed)
manifest = read_json(args.manifest)
vision, text, vision_lookup, text_lookup = load_feature_pair(
args.vision, args.text
)
rows = manifest[args.split][: args.samples]
visual = select_rows(
vision["features"], vision_lookup, rows
).to(args.device)
paired_text = select_rows(
text["features"], text_lookup, rows
).to(args.device)
text_population = select_rows(
text["features"], text_lookup, manifest["text_only_train"]
).to(args.device)
if args.text_orbits:
orbit_state = torch.load(
args.text_orbits, map_location="cpu", weights_only=False
)
orbit_lookup = {
int(row): index
for index, row in enumerate(orbit_state["rows"])
}
orbit_mean = F.normalize(
orbit_state["features"].float().mean(1), dim=-1
)
paired_text = select_rows(
orbit_mean, orbit_lookup, rows
).to(args.device)
text_population = select_rows(
orbit_mean, orbit_lookup, manifest["text_only_train"]
).to(args.device)
if args.shuffle_visual_energy:
control_generator = torch.Generator(
device=args.device
).manual_seed(args.seed + 10_000)
visual = visual[
torch.randperm(
len(visual),
generator=control_generator,
device=args.device,
)
]
prototype_state = torch.load(
args.prototypes, map_location="cpu", weights_only=False
)
prototypes = prototype_state["text_centers"].to(args.device)
if prototypes.shape[-1] != text_population.shape[-1]:
raise ValueError(
"Prototype dimension does not match text features; use the GW "
"cache built from the selected text backbone."
)
generator = torch.Generator(device=args.device).manual_seed(args.seed)
initial_indices = torch.randperm(
len(text_population),
generator=generator,
device=args.device,
)[: len(visual)]
initial = text_population[initial_indices].clone()
initial = initial + args.noise * torch.randn(
initial.shape, generator=generator, device=args.device
)
particles = torch.nn.Parameter(F.normalize(initial, dim=-1))
directions, target_quantiles = projection_quantile_target(
text_population,
particles=len(particles),
projections=args.projections,
generator=generator,
)
visual_relation, visual_standardized = standardized_relation(visual)
optimizer = torch.optim.Adam([particles], lr=args.lr)
def energy_values() -> tuple[torch.Tensor, ...]:
relation, conditional = relation_field_energy(
visual_relation, visual_standardized, particles
)
distribution = sliced_distribution_energy(
particles, directions, target_quantiles
)
manifold = prototype_manifold_energy(particles, prototypes)
return relation, conditional, distribution, manifold
history: list[dict] = []
initial_cpu = F.normalize(particles.detach(), dim=-1).cpu()
for step in range(args.steps + 1):
relation, conditional, distribution, manifold = energy_values()
progress = min(step / max(args.steps, 1), 1.0)
relation_weight = (
args.relation_weight_start
+ progress
* (args.relation_weight_end - args.relation_weight_start)
)
conditional_weight = (
args.conditional_weight_start
+ progress
* (
args.conditional_weight_end
- args.conditional_weight_start
)
)
loss = (
relation_weight * relation
+ conditional_weight * conditional
+ args.distribution_weight * distribution
+ args.manifold_weight * manifold
)
if step % 20 == 0 or step == args.steps:
history.append(
{
"step": step,
"total": float(loss.detach()),
"relation": float(relation.detach()),
"conditional": float(conditional.detach()),
"distribution": float(distribution.detach()),
"manifold": float(manifold.detach()),
"paired_evaluation_only": retrieval_metrics(
particles.detach(), paired_text
),
}
)
print(json.dumps(history[-1]))
if step == args.steps:
break
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_([particles], 2.0)
optimizer.step()
with torch.no_grad():
particles.copy_(F.normalize(particles, dim=-1))
with torch.no_grad():
oracle_relation, oracle_conditional = relation_field_energy(
visual_relation, visual_standardized, paired_text
)
oracle_distribution = sliced_distribution_energy(
paired_text, directions, target_quantiles
)
oracle_manifold = prototype_manifold_energy(
paired_text, prototypes
)
result = {
"protocol": (
"No cross-modal map and no image-text pair is used by the energy "
"or optimizer. Paired text is loaded only for trajectory and "
"oracle diagnostics; the final step is fixed by CLI arguments."
),
"mode": "free_language_latent_particles",
"split": args.split,
"rows": rows,
"vision_model": vision["model"],
"text_model": text["model"],
"text_observation": (
"multi-description orbit mean"
if args.text_orbits
else "single description"
),
"args": vars(args),
"history": history,
"oracle_energy_components": {
"relation": float(oracle_relation),
"conditional": float(oracle_conditional),
"distribution": float(oracle_distribution),
"manifold": float(oracle_manifold),
},
}
state = {
**result,
"initial_particles": initial_cpu,
"final_particles": F.normalize(
particles.detach(), dim=-1
).cpu(),
}
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
torch.save(state, args.output)
write_json(args.metrics_output, result)
print(f"Wrote {args.output} and {args.metrics_output}")
if __name__ == "__main__":
main()
|
