train.py

# MIT License

# Copyright (c) 2024 Ysobel Sims

# Permission is hereby granted, free of charge, to any person obtaining a copy
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# ==============================================================================

# The entry point for the program to train a model for the ZeroDiffusion method.
# Runs ten random seeds of repeated training.

from setup import setup
from configs import CGDN_CONFIG
from train_cgdn import run_trials

# Get non-tuning params
params = setup()

config = dict(CGDN_CONFIG)
config["classifier_dataset_size"] = params.cls_dataset_size  # this is per-class

n_trials = 10
print("Running {} trials.".format(n_trials))

summary = run_trials(
    params.data,
    params.dataset,
    params.split,
    params.val_classes,
    params.test_classes,
    params.device,
    config,
    method="cgdn",
    n_trials=n_trials,
)

print("Mean acc is", summary["mean_accuracy_mean"])
print("Standard deviation acc is", summary["mean_accuracy_std"])
print("Best acc is {}".format(summary["mean_accuracy_best"]))

print("Experiment was type {} : folder {}".format(params.split, params.data))