mirror of
https://github.com/jung-geun/PSO.git
synced 2026-09-20 14:11:48 +09:00
jung-geun
813433000a
Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery. Constraint: Preserve one-shot official-test sealing and auditable research artifacts Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local Confidence: high Scope-risk: broad Not-tested: Production CUDA run on pieroot-server
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
import argparse
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from pso import Optimizer
|
|
from cli import add_pso_args, build_optimizer_kwargs
|
|
|
|
|
|
def get_data(seed: int = 42):
|
|
from sklearn.decomposition import PCA
|
|
from torchvision.datasets import MNIST
|
|
|
|
train_dataset = MNIST(root="./data", train=True, download=True)
|
|
test_dataset = MNIST(root="./data", train=False, download=True)
|
|
|
|
x_train_raw = (train_dataset.data[:3000].float() / 255.0).reshape(3000, -1).numpy()
|
|
y_train = train_dataset.targets[:3000].long()
|
|
|
|
x_test_raw = (test_dataset.data[:1000].float() / 255.0).reshape(1000, -1).numpy()
|
|
y_test = test_dataset.targets[:1000].long()
|
|
|
|
pca = PCA(n_components=32, whiten=True, random_state=seed)
|
|
x_train_pca = pca.fit_transform(x_train_raw)
|
|
x_test_pca = pca.transform(x_test_raw)
|
|
|
|
x_train = torch.tensor(x_train_pca, dtype=torch.float32)
|
|
x_test = torch.tensor(x_test_pca, dtype=torch.float32)
|
|
|
|
print(f"x_train : {x_train.shape} | y_train : {y_train.shape}")
|
|
print(f"x_test : {x_test.shape} | y_test : {y_test.shape}")
|
|
|
|
return x_train, y_train, x_test, y_test
|
|
|
|
|
|
def make_model(seed: int = 42):
|
|
torch.manual_seed(seed)
|
|
return nn.Linear(32, 10)
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="PSO MNIST Benchmark Script")
|
|
add_pso_args(
|
|
parser,
|
|
defaults={
|
|
"method": "inertia",
|
|
"initialization": "model_noise",
|
|
"evaluation": "fixed_subset",
|
|
"convergence": "none",
|
|
"refinement": "adam",
|
|
"n_particles": 30,
|
|
"c0": None,
|
|
"c1": None,
|
|
"w_min": None,
|
|
"w_max": None,
|
|
"negative_swarm": 0.0,
|
|
"mutation_swarm": 0.02,
|
|
"particle_min": -3.0,
|
|
"particle_max": 3.0,
|
|
"velocity_limit_ratio": 0.025,
|
|
"boundary_strategy": "reflect",
|
|
"initial_position_noise": 0.05,
|
|
"seed": 42,
|
|
"epochs": 80,
|
|
"batch_size": 1000,
|
|
"fitness_size": 2000,
|
|
"renewal": "loss",
|
|
"output_dir": "output/mnist",
|
|
"checkpoint_interval": 25,
|
|
"refinement_epochs": 100,
|
|
"refinement_lr": 0.01,
|
|
},
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
model = make_model(seed=args.seed)
|
|
x_train, y_train, x_test, y_test = get_data(seed=args.seed)
|
|
|
|
fitness_size = args.fitness_size if args.evaluation == "fixed_subset" else None
|
|
refinement_epochs = args.refinement_epochs if args.refinement == "adam" else 0
|
|
|
|
kwargs = build_optimizer_kwargs(
|
|
args,
|
|
model=model,
|
|
loss=nn.CrossEntropyLoss(),
|
|
task="multiclass",
|
|
inertia_profile={"c0": 1.49618, "c1": 1.49618, "w_min": 0.7298, "w_max": 0.7298},
|
|
)
|
|
pso_mnist = Optimizer(**kwargs)
|
|
|
|
print(f"Optimizer device: {pso_mnist.device}")
|
|
|
|
best_score = pso_mnist.fit(
|
|
x_train,
|
|
y_train,
|
|
epochs=args.epochs,
|
|
batch_size=args.batch_size,
|
|
fitness_size=fitness_size,
|
|
renewal=args.renewal,
|
|
validation_data=(x_test, y_test),
|
|
output_dir=args.output_dir,
|
|
checkpoint_interval=25,
|
|
save_info=True,
|
|
refinement_epochs=refinement_epochs,
|
|
refinement_lr=args.refinement_lr,
|
|
)
|
|
|
|
print(f"Done! Best score: {best_score}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|