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(
+        if historical_x is not None:
-        args.dataset, args.data_path, args.cutout_length
+            mean, std = historical_x.mean().item(), historical_x.std().item()
-    )
+        else:
-    train_loader = torch.utils.data.DataLoader(
+            mean, std = 0, 1
-        train_data,
+        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
-        batch_size=args.batch_size,
+        model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
-        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 args.model_source == "normal":
+        # create the current data loader
-        base_model = obtain_model(model_config)
+        if historical_x is not None:
-    elif args.model_source == "nas":
+            train_dataset = torch.utils.data.TensorDataset(historical_x, historical_y)
-        base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
+            train_loader = torch.utils.data.DataLoader(
-    elif args.model_source == "autodl-searched":
+                train_dataset,
-        base_model = obtain_model(model_config, args.extra_model_path)
+                batch_size=args.batch_size,
-    else:
+                shuffle=True,
-        raise ValueError("invalid model-source : {:}".format(args.model_source))
+                num_workers=args.workers,
-    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)
             )
-            last_checkpoint_path = (
+            optimizer = torch.optim.Adam(
-                last_info.parent
+                model.parameters(), lr=args.init_lr, amsgrad=True
-                / last_checkpoint_path.parent.name
-                / last_checkpoint_path.name
             )
-        checkpoint = torch.load(last_checkpoint_path)
+            criterion = torch.nn.MSELoss()
-        base_model.load_state_dict(checkpoint["base-model"])
+            lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
-        scheduler.load_state_dict(checkpoint["scheduler"])
+                optimizer,
-        optimizer.load_state_dict(checkpoint["optimizer"])
+                milestones=[
-        valid_accuracies = checkpoint["valid_accuracies"]
+                    int(args.epochs * 0.25),
-        max_bytes = checkpoint["max_bytes"]
+                    int(args.epochs * 0.5),
-        logger.log(
+                    int(args.epochs * 0.75),
-            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
+                ],
-                last_info, start_epoch
+                gamma=0.3,
             )
-        )
+            for _iepoch in range(args.epochs):
-    elif args.resume is not None:
+                results = basic_train_fn(
-        assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
+                    train_loader, model, criterion, optimizer, MSEMetric(), logger
-            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"],
                 )
-            )
+                lr_scheduler.step()
-            if valid_acc1 > valid_accuracies["best"]:
+                if _iepoch % args.log_per_epoch == 0:
-                valid_accuracies["best"] = valid_acc1
+                    log_str = (
-                find_best = True
+                        "[{:}]".format(time_string())
-                logger.log(
+                        + " [{:04d}/{:04d}][{:04d}/{:04d}]".format(
-                    "Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
+                            idx, len(dynamic_env), _iepoch, args.epochs
-                        epoch,
+                        )
-                        valid_acc1,
+                        + " mse: {:.5f}, lr: {:.4f}".format(
-                        valid_acc5,
+                            results["mse"], min(lr_scheduler.get_last_lr())
-                        100 - valid_acc1,
+                        )
-                        100 - valid_acc5,
-                        model_best_path,
                     )
-                )
+                    logger.log(log_str)
-            num_bytes = (
+            results = basic_eval_fn(train_loader, model, MSEMetric(), logger)
-                torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
-            )
             logger.log(
-                "[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
+                "[{:}] [{:04d}/{:04d}] train-mse: {:.5f}".format(
-                    next(network.parameters()).device,
+                    time_string(), idx, len(dynamic_env), results["mse"]
-                    int(num_bytes),
-                    num_bytes / 1e3,
-                    num_bytes / 1e6,
-                    num_bytes / 1e9,
                 )
             )
-            max_bytes[epoch] = num_bytes
-        if epoch % 10 == 0:
-            torch.cuda.empty_cache()
 
-        # save checkpoint
+        metric = ComposeMetric(MSEMetric(), SaveMetric())
-        save_path = save_checkpoint(
+        eval_dataset = torch.utils.data.TensorDataset(allx, ally)
-            {
+        eval_loader = torch.utils.data.DataLoader(
-                "epoch": epoch,
+            eval_dataset,
-                "args": deepcopy(args),
+            batch_size=args.batch_size,
-                "max_bytes": deepcopy(max_bytes),
+            shuffle=False,
-                "FLOP": flop,
+            num_workers=args.workers,
-                "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,
         )
-        if find_best:
+        results = basic_eval_fn(eval_loader, model, metric, logger)
-            copy_checkpoint(model_base_path, model_best_path, logger)
+        log_str = (
-        last_info = save_checkpoint(
+            "[{:}]".format(time_string())
-            {
+            + " [{:04d}/{:04d}]".format(idx, len(dynamic_env))
-                "epoch": epoch,
+            + " eval-mse: {:.5f}".format(results["mse"])
-                "args": deepcopy(args),
+        )
-                "last_checkpoint": save_path,
+        logger.log(log_str)
-            },
+
-            logger.path("info"),
+        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
+        # Update historical data
-        epoch_time.update(time.time() - start_time)
+        if historical_x is None:
-        start_time = time.time()
+            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,
+        default=4,
-        help="number of data loading workers (default: 8)",
+        help="The number of data loading workers (default: 4)",
     )
     # Random Seed
     parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")

lib/log_utils/logger.py

@@ -59,8 +59,10 @@ class Logger(object):
         )
 
     def path(self, mode):
-        valids = ("model", "best", "info", "log")
+        valids = ("model", "best", "info", "log", None)
-        if mode == "model":
+        if mode is None:
+            return self.log_dir
+        elif mode == "model":
             return self.model_dir / "seed-{:}-basic.pth".format(self.seed)
         elif mode == "best":
             return self.model_dir / "seed-{:}-best.pth".format(self.seed)

lib/models/xcore.py

@@ -4,12 +4,14 @@
 # Use module in xlayers to construct different models #
 #######################################################
 from typing import List, Text, Dict, Any
+import torch
 
 __all__ = ["get_model"]
 
 
-from xlayers.super_core import SuperSequential, SuperMLPv1
+from xlayers.super_core import SuperSequential
 from xlayers.super_core import SuperSimpleNorm
+from xlayers.super_core import SuperLeakyReLU
 from xlayers.super_core import SuperLinear
 
 
@@ -19,9 +21,9 @@ def get_model(config: Dict[Text, Any], **kwargs):
         model = SuperSequential(
             SuperSimpleNorm(kwargs["mean"], kwargs["std"]),
             SuperLinear(kwargs["input_dim"], 200),
-            torch.nn.LeakyReLU(),
+            SuperLeakyReLU(),
             SuperLinear(200, 100),
-            torch.nn.LeakyReLU(),
+            SuperLeakyReLU(),
             SuperLinear(100, kwargs["output_dim"]),
         )
     else:

lib/procedures/advanced_main.py

@@ -12,49 +12,48 @@ from log_utils import time_string
 from .eval_funcs import obtain_accuracy
 
 
-def basic_train(
+def get_device(tensors):
+    if isinstance(tensors, (list, tuple)):
+        return get_device(tensors[0])
+    elif isinstance(tensors, dict):
+        for key, value in tensors.items():
+            return get_device(value)
+    else:
+        return tensors.device
+
+
+def basic_train_fn(
     xloader,
     network,
     criterion,
-    scheduler,
     optimizer,
-    optim_config,
+    metric,
-    extra_info,
-    print_freq,
     logger,
 ):
-    loss, acc1, acc5 = procedure(
+    results = procedure(
         xloader,
         network,
         criterion,
-        scheduler,
         optimizer,
+        metric,
         "train",
-        optim_config,
-        extra_info,
-        print_freq,
         logger,
     )
-    return loss, acc1, acc5
+    return results
 
 
-def basic_valid(
+def basic_eval_fn(xloader, network, metric, logger):
-    xloader, network, criterion, optim_config, extra_info, print_freq, logger
-):
     with torch.no_grad():
-        loss, acc1, acc5 = procedure(
+        results = procedure(
             xloader,
             network,
-            criterion,
             None,
             None,
+            metric,
             "valid",
-            None,
-            extra_info,
-            print_freq,
             logger,
         )
-    return loss, acc1, acc5
+    return results
 
 
 def procedure(
@@ -62,12 +61,11 @@ def procedure(
     network,
     criterion,
     optimizer,
-    eval_metric,
+    metric,
     mode: Text,
-    print_freq: int = 100,
     logger_fn: Callable = None,
 ):
-    data_time, batch_time, losses = AverageMeter(), AverageMeter(), AverageMeter()
+    data_time, batch_time = AverageMeter(), AverageMeter()
     if mode.lower() == "train":
         network.train()
     elif mode.lower() == "valid":
@@ -80,49 +78,23 @@ def procedure(
         # measure data loading time
         data_time.update(time.time() - end)
         # calculate prediction and loss
-        targets = targets.cuda(non_blocking=True)
 
         if mode == "train":
             optimizer.zero_grad()
 
         outputs = network(inputs)
-        loss = criterion(outputs, targets)
+        targets = targets.to(get_device(outputs))
 
         if mode == "train":
+            loss = criterion(outputs, targets)
             loss.backward()
             optimizer.step()
 
         # record
-        metrics = eval_metric(logits.data, targets.data)
+        with torch.no_grad():
-        prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
+            results = metric(outputs, targets)
-        losses.update(loss.item(), inputs.size(0))
-        top1.update(prec1.item(), inputs.size(0))
-        top5.update(prec5.item(), inputs.size(0))
 
         # measure elapsed time
         batch_time.update(time.time() - end)
         end = time.time()
-
+    return metric.get_info()
-        if i % print_freq == 0 or (i + 1) == len(xloader):
-            Sstr = (
-                " {:5s} ".format(mode.upper())
-                + time_string()
-                + " [{:}][{:03d}/{:03d}]".format(extra_info, i, len(xloader))
-            )
-            Lstr = "Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})".format(
-                loss=losses, top1=top1, top5=top5
-            )
-            Istr = "Size={:}".format(list(inputs.size()))
-            logger.log(Sstr + " " + Tstr + " " + Lstr + " " + Istr)
-
-    logger.log(
-        " **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
-            mode=mode.upper(),
-            top1=top1,
-            top5=top5,
-            error1=100 - top1.avg,
-            error5=100 - top5.avg,
-            loss=losses.avg,
-        )
-    )
-    return losses.avg, top1.avg, top5.avg

lib/procedures/eval_funcs.py

@@ -18,11 +18,3 @@ def obtain_accuracy(output, target, topk=(1,)):
         correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res
-
-
-class EvaluationMetric(abc.ABC):
-    def __init__(self):
-        self._total_metrics = 0
-
-    def __len__(self):
-        return self._total_metrics

lib/procedures/metric_utils.py

@@ -0,0 +1,134 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.04 #
+#####################################################
+import abc
+import numpy as np
+import torch
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0.0
+        self.avg = 0.0
+        self.sum = 0.0
+        self.count = 0.0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __repr__(self):
+        return "{name}(val={val}, avg={avg}, count={count})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+
+class Metric(abc.ABC):
+    """The default meta metric class."""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        raise NotImplementedError
+
+    def __call__(self, predictions, targets):
+        raise NotImplementedError
+
+    def get_info(self):
+        raise NotImplementedError
+
+    def __repr__(self):
+        return "{name}({inner})".format(
+            name=self.__class__.__name__, inner=self.inner_repr()
+        )
+
+    def inner_repr(self):
+        return ""
+
+
+class ComposeMetric(Metric):
+    """The composed metric class."""
+
+    def __init__(self, *metric_list):
+        self.reset()
+        for metric in metric_list:
+            self.append(metric)
+
+    def reset(self):
+        self._metric_list = []
+
+    def append(self, metric):
+        if not isinstance(metric, Metric):
+            raise ValueError(
+                "The input metric is not correct: {:}".format(type(metric))
+            )
+        self._metric_list.append(metric)
+
+    def __len__(self):
+        return len(self._metric_list)
+
+    def __call__(self, predictions, targets):
+        results = list()
+        for metric in self._metric_list:
+            results.append(metric(predictions, targets))
+        return results
+
+    def get_info(self):
+        results = dict()
+        for metric in self._metric_list:
+            for key, value in metric.get_info().items():
+                results[key] = value
+        return results
+
+    def inner_repr(self):
+        xlist = []
+        for metric in self._metric_list:
+            xlist.append(str(metric))
+        return ",".join(xlist)
+
+
+class MSEMetric(Metric):
+    """The metric for mse."""
+
+    def reset(self):
+        self._mse = AverageMeter()
+
+    def __call__(self, predictions, targets):
+        if isinstance(predictions, torch.Tensor) and isinstance(targets, torch.Tensor):
+            batch = predictions.shape[0]
+            loss = torch.nn.functional.mse_loss(predictions.data, targets.data)
+            loss = loss.item()
+            self._mse.update(loss, batch)
+            return loss
+        else:
+            raise NotImplementedError
+
+    def get_info(self):
+        return {"mse": self._mse.avg}
+
+
+class SaveMetric(Metric):
+    """The metric for mse."""
+
+    def reset(self):
+        self._predicts = []
+
+    def __call__(self, predictions, targets=None):
+        if isinstance(predictions, torch.Tensor):
+            predicts = predictions.cpu().numpy()
+            self._predicts.append(predicts)
+            return predicts
+        else:
+            raise NotImplementedError
+
+    def get_info(self):
+        all_predicts = np.concatenate(self._predicts)
+        return {"predictions": all_predicts}

lib/xlayers/super_activations.py

@@ -17,7 +17,7 @@ from .super_module import BoolSpaceType
 class SuperReLU(SuperModule):
     """Applies a the rectified linear unit function element-wise."""
 
-    def __init__(self, inplace=False) -> None:
+    def __init__(self, inplace: bool = False) -> None:
         super(SuperReLU, self).__init__()
         self._inplace = inplace
 
@@ -33,3 +33,26 @@ class SuperReLU(SuperModule):
 
     def extra_repr(self) -> str:
         return "inplace=True" if self._inplace else ""
+
+
+class SuperLeakyReLU(SuperModule):
+    """https://pytorch.org/docs/stable/_modules/torch/nn/modules/activation.html#LeakyReLU"""
+
+    def __init__(self, negative_slope: float = 1e-2, inplace: bool = False) -> None:
+        super(SuperLeakyReLU, self).__init__()
+        self._negative_slope = negative_slope
+        self._inplace = inplace
+
+    @property
+    def abstract_search_space(self):
+        return spaces.VirtualNode(id(self))
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.leaky_relu(input, self._negative_slope, self._inplace)
+
+    def extra_repr(self) -> str:
+        inplace_str = "inplace=True" if self._inplace else ""
+        return "negative_slope={}{}".format(self._negative_slope, inplace_str)

lib/xlayers/super_core.py

@@ -15,6 +15,7 @@ from .super_attention import SuperAttention
 from .super_transformer import SuperTransformerEncoderLayer
 
 from .super_activations import SuperReLU
+from .super_activations import SuperLeakyReLU
 
 from .super_trade_stem import SuperAlphaEBDv1
 from .super_positional_embedding import SuperPositionalEncoder