Use black for lib/models

exps/LFNA/lfna-tall-hpnet.py

@@ -0,0 +1,179 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16
+#####################################################
+import sys, time, copy, torch, random, argparse
+from tqdm import tqdm
+from copy import deepcopy
+from pathlib import Path
+
+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 time_string
+from log_utils import AverageMeter, convert_secs2time
+
+from utils import split_str2indexes
+
+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
+from xlayers import super_core, trunc_normal_
+
+
+from lfna_utils import lfna_setup, train_model, TimeData
+
+# from lfna_models import HyperNet_VX as HyperNet
+from lfna_models import HyperNet
+
+
+def main(args):
+    logger, env_info, model_kwargs = lfna_setup(args)
+    dynamic_env = env_info["dynamic_env"]
+    model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+    criterion = torch.nn.MSELoss()
+
+    logger.log("There are {:} weights.".format(model.get_w_container().numel()))
+
+    shape_container = model.get_w_container().to_shape_container()
+    hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
+    total_bar = env_info["total"] - 1
+    task_embeds = []
+    for i in range(total_bar):
+        task_embeds.append(torch.nn.Parameter(torch.Tensor(1, args.task_dim)))
+    for task_embed in task_embeds:
+        trunc_normal_(task_embed, std=0.02)
+
+    parameters = list(hypernet.parameters()) + task_embeds
+    optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
+    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+        optimizer,
+        milestones=[
+            int(args.epochs * 0.8),
+            int(args.epochs * 0.9),
+        ],
+        gamma=0.1,
+    )
+
+    # LFNA meta-training
+    loss_meter = AverageMeter()
+    per_epoch_time, start_time = AverageMeter(), time.time()
+    for iepoch in range(args.epochs):
+
+        need_time = "Time Left: {:}".format(
+            convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
+        )
+        head_str = (
+            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+            + need_time
+        )
+
+        limit_bar = float(iepoch + 1) / args.epochs * total_bar
+        limit_bar = min(max(0, int(limit_bar)), total_bar)
+        losses = []
+        for ibatch in range(args.meta_batch):
+            cur_time = random.randint(0, limit_bar)
+            cur_task_embed = task_embeds[cur_time]
+            cur_container = hypernet(cur_task_embed)
+            cur_x = env_info["{:}-x".format(cur_time)]
+            cur_y = env_info["{:}-y".format(cur_time)]
+            cur_dataset = TimeData(cur_time, cur_x, cur_y)
+
+            preds = model.forward_with_container(cur_dataset.x, cur_container)
+            optimizer.zero_grad()
+            loss = criterion(preds, cur_dataset.y)
+
+            losses.append(loss)
+
+        final_loss = torch.stack(losses).mean()
+        final_loss.backward()
+        optimizer.step()
+        lr_scheduler.step()
+
+        loss_meter.update(final_loss.item())
+        if iepoch % 200 == 0:
+            logger.log(
+                head_str
+                + "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}, limit={:}".format(
+                    loss_meter.avg,
+                    loss_meter.val,
+                    min(lr_scheduler.get_last_lr()),
+                    len(losses),
+                    limit_bar,
+                )
+            )
+
+            save_checkpoint(
+                {
+                    "hypernet": hypernet.state_dict(),
+                    "task_embed": task_embed,
+                    "lr_scheduler": lr_scheduler.state_dict(),
+                    "iepoch": iepoch,
+                },
+                logger.path("model"),
+                logger,
+            )
+            loss_meter.reset()
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    print(model)
+    print(hypernet)
+
+    logger.log("-" * 200 + "\n")
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Use the data in the past.")
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        default="./outputs/lfna-synthetic/lfna-tall-hpnet",
+        help="The checkpoint directory.",
+    )
+    parser.add_argument(
+        "--env_version",
+        type=str,
+        required=True,
+        help="The synthetic enviornment version.",
+    )
+    parser.add_argument(
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
+    )
+    #####
+    parser.add_argument(
+        "--init_lr",
+        type=float,
+        default=0.1,
+        help="The initial learning rate for the optimizer (default is Adam)",
+    )
+    parser.add_argument(
+        "--meta_batch",
+        type=int,
+        default=64,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
+    args = parser.parse_args()
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "The save dir argument can not be None"
+    args.task_dim = args.hidden_dim
+    args.save_dir = "{:}-{:}-d{:}".format(
+        args.save_dir, args.env_version, args.hidden_dim
+    )
+    main(args)

exps/LFNA/lfna-test-hpnet.py

@@ -41,10 +41,14 @@ def main(args):
     shape_container = model.get_w_container().to_shape_container()
     hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
     # task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
-    task_embed = torch.nn.Parameter(torch.Tensor(1, args.task_dim))
-    trunc_normal_(task_embed, std=0.02)
+    total_bar = 10
+    task_embeds = []
+    for i in range(total_bar):
+        task_embeds.append(torch.nn.Parameter(torch.Tensor(1, args.task_dim)))
+    for task_embed in task_embeds:
+        trunc_normal_(task_embed, std=0.02)
 
-    parameters = list(hypernet.parameters()) + [task_embed]
+    parameters = list(hypernet.parameters()) + task_embeds
     optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
@@ -56,7 +60,6 @@ def main(args):
     )
 
     # total_bar = env_info["total"] - 1
-    total_bar = 1
     # LFNA meta-training
     loss_meter = AverageMeter()
     per_epoch_time, start_time = AverageMeter(), time.time()
@@ -74,7 +77,7 @@ def main(args):
         # for ibatch in range(args.meta_batch):
         for cur_time in range(total_bar):
             # cur_time = random.randint(0, total_bar)
-            cur_task_embed = task_embed
+            cur_task_embed = task_embeds[cur_time]
             cur_container = hypernet(cur_task_embed)
             cur_x = env_info["{:}-x".format(cur_time)]
             cur_y = env_info["{:}-y".format(cur_time)]
@@ -98,7 +101,7 @@ def main(args):
                 + "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
                     loss_meter.avg,
                     loss_meter.val,
-                    min(lr_scheduler.get_lr()),
+                    min(lr_scheduler.get_last_lr()),
                     len(losses),
                 )
             )

exps/LFNA/lfna_models.py

@@ -28,6 +28,15 @@ class HyperNet(super_core.SuperModule):
         )
         trunc_normal_(self._super_layer_embed, std=0.02)
 
+        model_kwargs = dict(
+            input_dim=layer_embeding + task_embedding,
+            output_dim=max(self._numel_per_layer),
+            hidden_dims=[layer_embeding * 4] * 4,
+            act_cls="gelu",
+            norm_cls="layer_norm_1d",
+        )
+        self._generator = get_model(dict(model_type="norm_mlp"), **model_kwargs)
+        """
         model_kwargs = dict(
             input_dim=layer_embeding + task_embedding,
             output_dim=max(self._numel_per_layer),
@@ -36,6 +45,7 @@ class HyperNet(super_core.SuperModule):
             norm_cls="identity",
         )
         self._generator = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+        """
         self._return_container = return_container
         print("generator: {:}".format(self._generator))