def solve(file):
instance = CVRPInstance.from_file(file)
logger.info("Finetunning on evaluation instance.")
model_finetuned = finetune(model, instance)
logger.info("Starting to dynamic route.")
for delivery in tqdm(instance.deliveries):
model_finetuned = route(model_finetuned, delivery)
solution = finish(instance, model_finetuned)
solution.to_file(output_dir / f"{instance.name}.json")
eval_files = ([eval_path]
if eval_path.is_file() else list(eval_path.iterdir()))
train_path = Path(args.train_instances)
train_path_dir = train_path if train_path.is_dir() else train_path.parent
train_files = ([train_path]
if train_path.is_file() else list(train_path.iterdir()))
# params = params_class.from_file(args.params) if args.params else None
params = None
output_dir = Path(args.output or ".")
output_dir.mkdir(parents=True, exist_ok=True)
train_instances = [CVRPInstance.from_file(f) for f in train_files[:240]]
logger.info("Pretraining on training instances.")
model = pretrain(train_instances)
def solve(file):
instance = CVRPInstance.from_file(file)
logger.info("Finetunning on evaluation instance.")
model_finetuned = finetune(model, instance)
logger.info("Starting to dynamic route.")
for delivery in tqdm(instance.deliveries):
model_finetuned = route(model_finetuned, delivery)
solution = finish(instance, model_finetuned)
def solve(file):
instance = CVRPInstance.from_file(file)
solution = method(instance, params)
solution.to_file(output_dir / f"{instance.name}.json")
