Add simple baseline for LFNA

exps/LFNA/basic.py

@@ -11,270 +11,109 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
-from log_utils import AverageMeter, time_string, convert_secs2time
+from log_utils import time_string
 
+from procedures.advanced_main import basic_train_fn, basic_eval_fn
+from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
 from datasets.synthetic_core import get_synthetic_env
 from models.xcore import get_model
 
+
 def main(args):
     torch.set_num_threads(args.workers)
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)
 
     dynamic_env = get_synthetic_env()
+    historical_x, historical_y = None, None
     for idx, (timestamp, (allx, ally)) in enumerate(dynamic_env):
-        import pdb
-        pdb.set_trace()
 
-    train_data, valid_data, xshape, class_num = get_datasets(
-        args.dataset, args.data_path, args.cutout_length
-    )
-    train_loader = torch.utils.data.DataLoader(
-        train_data,
-        batch_size=args.batch_size,
-        shuffle=True,
-        num_workers=args.workers,
-        pin_memory=True,
-    )
-    valid_loader = torch.utils.data.DataLoader(
-        valid_data,
-        batch_size=args.batch_size,
-        shuffle=False,
-        num_workers=args.workers,
-        pin_memory=True,
-    )
-    # get configures
-    model_config = load_config(args.model_config, {"class_num": class_num}, logger)
-    optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
+        if historical_x is not None:
+            mean, std = historical_x.mean().item(), historical_x.std().item()
+        else:
+            mean, std = 0, 1
+        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
+        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
 
-    if args.model_source == "normal":
-        base_model = obtain_model(model_config)
-    elif args.model_source == "nas":
-        base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
-    elif args.model_source == "autodl-searched":
-        base_model = obtain_model(model_config, args.extra_model_path)
-    else:
-        raise ValueError("invalid model-source : {:}".format(args.model_source))
-    flop, param = get_model_infos(base_model, xshape)
-    logger.log("model ====>>>>:\n{:}".format(base_model))
-    logger.log("model information : {:}".format(base_model.get_message()))
-    logger.log("-" * 50)
-    logger.log(
-        "Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
-            param, flop, flop / 1e3
-        )
-    )
-    logger.log("-" * 50)
-    logger.log("train_data : {:}".format(train_data))
-    logger.log("valid_data : {:}".format(valid_data))
-    optimizer, scheduler, criterion = get_optim_scheduler(
-        base_model.parameters(), optim_config
-    )
-    logger.log("optimizer  : {:}".format(optimizer))
-    logger.log("scheduler  : {:}".format(scheduler))
-    logger.log("criterion  : {:}".format(criterion))
-
-    last_info, model_base_path, model_best_path = (
-        logger.path("info"),
-        logger.path("model"),
-        logger.path("best"),
-    )
-    network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
-
-    if last_info.exists():  # automatically resume from previous checkpoint
-        logger.log(
-            "=> loading checkpoint of the last-info '{:}' start".format(last_info)
-        )
-        last_infox = torch.load(last_info)
-        start_epoch = last_infox["epoch"] + 1
-        last_checkpoint_path = last_infox["last_checkpoint"]
-        if not last_checkpoint_path.exists():
-            logger.log(
-                "Does not find {:}, try another path".format(last_checkpoint_path)
+        # create the current data loader
+        if historical_x is not None:
+            train_dataset = torch.utils.data.TensorDataset(historical_x, historical_y)
+            train_loader = torch.utils.data.DataLoader(
+                train_dataset,
+                batch_size=args.batch_size,
+                shuffle=True,
+                num_workers=args.workers,
             )
-            last_checkpoint_path = (
-                last_info.parent
-                / last_checkpoint_path.parent.name
-                / last_checkpoint_path.name
+            optimizer = torch.optim.Adam(
+                model.parameters(), lr=args.init_lr, amsgrad=True
             )
-        checkpoint = torch.load(last_checkpoint_path)
-        base_model.load_state_dict(checkpoint["base-model"])
-        scheduler.load_state_dict(checkpoint["scheduler"])
-        optimizer.load_state_dict(checkpoint["optimizer"])
-        valid_accuracies = checkpoint["valid_accuracies"]
-        max_bytes = checkpoint["max_bytes"]
-        logger.log(
-            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
-                last_info, start_epoch
+            criterion = torch.nn.MSELoss()
+            lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+                optimizer,
+                milestones=[
+                    int(args.epochs * 0.25),
+                    int(args.epochs * 0.5),
+                    int(args.epochs * 0.75),
+                ],
+                gamma=0.3,
             )
-        )
-    elif args.resume is not None:
-        assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
-            args.resume
-        )
-        checkpoint = torch.load(args.resume)
-        start_epoch = checkpoint["epoch"] + 1
-        base_model.load_state_dict(checkpoint["base-model"])
-        scheduler.load_state_dict(checkpoint["scheduler"])
-        optimizer.load_state_dict(checkpoint["optimizer"])
-        valid_accuracies = checkpoint["valid_accuracies"]
-        max_bytes = checkpoint["max_bytes"]
-        logger.log(
-            "=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
-                args.resume, start_epoch
-            )
-        )
-    elif args.init_model is not None:
-        assert Path(
-            args.init_model
-        ).exists(), "Can not find the initialization file : {:}".format(args.init_model)
-        checkpoint = torch.load(args.init_model)
-        base_model.load_state_dict(checkpoint["base-model"])
-        start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
-        logger.log("=> initialize the model from {:}".format(args.init_model))
-    else:
-        logger.log("=> do not find the last-info file : {:}".format(last_info))
-        start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
-
-    train_func, valid_func = get_procedures(args.procedure)
-
-    total_epoch = optim_config.epochs + optim_config.warmup
-    # Main Training and Evaluation Loop
-    start_time = time.time()
-    epoch_time = AverageMeter()
-    for epoch in range(start_epoch, total_epoch):
-        scheduler.update(epoch, 0.0)
-        need_time = "Time Left: {:}".format(
-            convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
-        )
-        epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
-        LRs = scheduler.get_lr()
-        find_best = False
-        # set-up drop-out ratio
-        if hasattr(base_model, "update_drop_path"):
-            base_model.update_drop_path(
-                model_config.drop_path_prob * epoch / total_epoch
-            )
-        logger.log(
-            "\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
-                time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
-            )
-        )
-
-        # train for one epoch
-        train_loss, train_acc1, train_acc5 = train_func(
-            train_loader,
-            network,
-            criterion,
-            scheduler,
-            optimizer,
-            optim_config,
-            epoch_str,
-            args.print_freq,
-            logger,
-        )
-        # log the results
-        logger.log(
-            "***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
-                time_string(), epoch_str, train_loss, train_acc1, train_acc5
-            )
-        )
-
-        # evaluate the performance
-        if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
-            logger.log("-" * 150)
-            valid_loss, valid_acc1, valid_acc5 = valid_func(
-                valid_loader,
-                network,
-                criterion,
-                optim_config,
-                epoch_str,
-                args.print_freq_eval,
-                logger,
-            )
-            valid_accuracies[epoch] = valid_acc1
-            logger.log(
-                "***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
-                    time_string(),
-                    epoch_str,
-                    valid_loss,
-                    valid_acc1,
-                    valid_acc5,
-                    valid_accuracies["best"],
-                    100 - valid_accuracies["best"],
+            for _iepoch in range(args.epochs):
+                results = basic_train_fn(
+                    train_loader, model, criterion, optimizer, MSEMetric(), logger
                 )
-            )
-            if valid_acc1 > valid_accuracies["best"]:
-                valid_accuracies["best"] = valid_acc1
-                find_best = True
-                logger.log(
-                    "Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
-                        epoch,
-                        valid_acc1,
-                        valid_acc5,
-                        100 - valid_acc1,
-                        100 - valid_acc5,
-                        model_best_path,
+                lr_scheduler.step()
+                if _iepoch % args.log_per_epoch == 0:
+                    log_str = (
+                        "[{:}]".format(time_string())
+                        + " [{:04d}/{:04d}][{:04d}/{:04d}]".format(
+                            idx, len(dynamic_env), _iepoch, args.epochs
+                        )
+                        + " mse: {:.5f}, lr: {:.4f}".format(
+                            results["mse"], min(lr_scheduler.get_last_lr())
+                        )
                     )
-                )
-            num_bytes = (
-                torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
-            )
+                    logger.log(log_str)
+            results = basic_eval_fn(train_loader, model, MSEMetric(), logger)
             logger.log(
-                "[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
-                    next(network.parameters()).device,
-                    int(num_bytes),
-                    num_bytes / 1e3,
-                    num_bytes / 1e6,
-                    num_bytes / 1e9,
+                "[{:}] [{:04d}/{:04d}] train-mse: {:.5f}".format(
+                    time_string(), idx, len(dynamic_env), results["mse"]
                 )
             )
-            max_bytes[epoch] = num_bytes
-        if epoch % 10 == 0:
-            torch.cuda.empty_cache()
 
-        # save checkpoint
-        save_path = save_checkpoint(
-            {
-                "epoch": epoch,
-                "args": deepcopy(args),
-                "max_bytes": deepcopy(max_bytes),
-                "FLOP": flop,
-                "PARAM": param,
-                "valid_accuracies": deepcopy(valid_accuracies),
-                "model-config": model_config._asdict(),
-                "optim-config": optim_config._asdict(),
-                "base-model": base_model.state_dict(),
-                "scheduler": scheduler.state_dict(),
-                "optimizer": optimizer.state_dict(),
-            },
-            model_base_path,
-            logger,
+        metric = ComposeMetric(MSEMetric(), SaveMetric())
+        eval_dataset = torch.utils.data.TensorDataset(allx, ally)
+        eval_loader = torch.utils.data.DataLoader(
+            eval_dataset,
+            batch_size=args.batch_size,
+            shuffle=False,
+            num_workers=args.workers,
         )
-        if find_best:
-            copy_checkpoint(model_base_path, model_best_path, logger)
-        last_info = save_checkpoint(
-            {
-                "epoch": epoch,
-                "args": deepcopy(args),
-                "last_checkpoint": save_path,
-            },
-            logger.path("info"),
+        results = basic_eval_fn(eval_loader, model, metric, logger)
+        log_str = (
+            "[{:}]".format(time_string())
+            + " [{:04d}/{:04d}]".format(idx, len(dynamic_env))
+            + " eval-mse: {:.5f}".format(results["mse"])
+        )
+        logger.log(log_str)
+
+        save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
+            idx, len(dynamic_env)
+        )
+        save_checkpoint(
+            {"model": model.state_dict(), "index": idx, "timestamp": timestamp},
+            save_path,
             logger,
         )
 
-        # measure elapsed time
-        epoch_time.update(time.time() - start_time)
-        start_time = time.time()
+        # Update historical data
+        if historical_x is None:
+            historical_x, historical_y = allx, ally
+        else:
+            historical_x, historical_y = torch.cat((historical_x, allx)), torch.cat(
+                (historical_y, ally)
+            )
+        logger.log("")
 
-    logger.log("\n" + "-" * 200)
-    logger.log(
-        "Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
-            convert_secs2time(epoch_time.sum, True),
-            max(v for k, v in max_bytes.items()) / 1e6,
-            logger.path("info"),
-        )
-    )
     logger.log("-" * 200 + "\n")
     logger.close()
 
@@ -287,11 +126,35 @@ if __name__ == "__main__":
         default="./outputs/lfna-synthetic/use-all-past-data",
         help="The checkpoint directory.",
     )
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=256,
+        help="The batch size",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
+    )
+    parser.add_argument(
+        "--log_per_epoch",
+        type=int,
+        default=200,
+        help="Log the training information per __ epochs.",
+    )
     parser.add_argument(
         "--workers",
         type=int,
-        default=8,
-        help="number of data loading workers (default: 8)",
+        default=4,
+        help="The number of data loading workers (default: 4)",
     )
     # Random Seed
     parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")