Update DEBUG INFO

exps/LFNA/lfna-debug.py

@@ -0,0 +1,256 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/lfna-debug.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
+
+
+from lfna_utils import lfna_setup, train_model, TimeData
+
+
+class LFNAmlp:
+    """A LFNA meta-model that uses the MLP as delta-net."""
+
+    def __init__(self, obs_dim, hidden_sizes, act_name, criterion):
+        self.delta_net = super_core.SuperSequential(
+            super_core.SuperLinear(obs_dim, hidden_sizes[0]),
+            super_core.super_name2activation[act_name](),
+            super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
+            super_core.super_name2activation[act_name](),
+            super_core.SuperLinear(hidden_sizes[1], 1),
+        )
+        self.meta_optimizer = torch.optim.Adam(
+            self.delta_net.parameters(), lr=0.01, amsgrad=True
+        )
+        self.criterion = criterion
+
+    def adapt(self, model, seq_flatten_w):
+        delta_inputs = torch.stack(seq_flatten_w, dim=-1)
+        delta = self.delta_net(delta_inputs)
+        container = model.get_w_container()
+        unflatten_delta = container.unflatten(delta)
+        future_container = container.create_container(unflatten_delta)
+        return future_container
+
+    def step(self):
+        torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
+        self.meta_optimizer.step()
+
+    def zero_grad(self):
+        self.meta_optimizer.zero_grad()
+        self.delta_net.zero_grad()
+
+    def state_dict(self):
+        return dict(
+            delta_net=self.delta_net.state_dict(),
+            meta_optimizer=self.meta_optimizer.state_dict(),
+        )
+
+
+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)
+
+    total_time = env_info["total"]
+    for i in range(total_time):
+        for xkey in ("timestamp", "x", "y"):
+            nkey = "{:}-{:}".format(i, xkey)
+            assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
+    train_time_bar = total_time // 2
+    network = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+
+    criterion = torch.nn.MSELoss()
+    logger.log("There are {:} weights.".format(network.get_w_container().numel()))
+
+    adaptor = LFNAmlp(args.meta_seq, (200, 200), "leaky_relu", criterion)
+
+    # pre-train the model
+    init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
+    init_loss = train_model(network, init_dataset, args.init_lr, args.epochs)
+    logger.log("The pre-training loss is {:.4f}".format(init_loss))
+
+    all_past_containers = []
+    ground_truth_path = (
+        logger.path(None) / ".." / "use-same-timestamp-v1-d16" / "final-ckp.pth"
+    )
+    ground_truth_data = torch.load(ground_truth_path)
+    all_gt_containers = ground_truth_data["w_container_per_epoch"]
+    all_gt_flattens = dict()
+    for idx, container in all_gt_containers.items():
+        all_gt_flattens[idx] = container.no_grad_clone().flatten()
+
+    # LFNA meta-training
+    meta_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)
+        )
+        logger.log(
+            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+            + need_time
+        )
+
+        adaptor.zero_grad()
+
+        meta_losses = []
+        for ibatch in range(args.meta_batch):
+            future_timestamp = random.randint(args.meta_seq, train_time_bar)
+            future_dataset = TimeData(
+                future_timestamp,
+                env_info["{:}-x".format(future_timestamp)],
+                env_info["{:}-y".format(future_timestamp)],
+            )
+            seq_datasets = []
+            for iseq in range(args.meta_seq):
+                cur_time = future_timestamp - iseq - 1
+                cur_x = env_info["{:}-x".format(cur_time)]
+                cur_y = env_info["{:}-y".format(cur_time)]
+                seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
+            seq_datasets.reverse()
+            seq_flatten_w = [
+                all_gt_flattens[dataset.timestamp] for dataset in seq_datasets
+            ]
+            future_container = adaptor.adapt(network, seq_flatten_w)
+            """
+            future_y_hat = network.forward_with_container(
+                future_dataset.x, future_container
+            )
+            future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
+            """
+            future_loss = adaptor.criterion(
+                future_container.flatten(), all_gt_flattens[future_timestamp]
+            )
+            # import pdb; pdb.set_trace()
+            meta_losses.append(future_loss)
+        meta_loss = torch.stack(meta_losses).mean()
+        meta_loss.backward()
+        adaptor.step()
+
+        meta_loss_meter.update(meta_loss.item())
+
+        logger.log(
+            "meta-loss: {:.4f} ({:.4f}) ".format(
+                meta_loss_meter.avg, meta_loss_meter.val
+            )
+        )
+        if iepoch % 200 == 0:
+            save_checkpoint(
+                {"adaptor": adaptor.state_dict(), "iepoch": iepoch},
+                logger.path("model"),
+                logger,
+            )
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    w_container_per_epoch = dict()
+    # import pdb; pdb.set_trace()
+    for idx in range(1, env_info["total"]):
+        future_time = env_info["{:}-timestamp".format(idx)]
+        future_x = env_info["{:}-x".format(idx)]
+        future_y = env_info["{:}-y".format(idx)]
+        seq_datasets = []
+        for iseq in range(1, args.meta_seq + 1):
+            cur_time = future_timestamp - iseq - 1
+            if cur_time < 0:
+                cur_time = 0
+            cur_x = env_info["{:}-x".format(cur_time)]
+            cur_y = env_info["{:}-y".format(cur_time)]
+            seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
+        seq_datasets.reverse()
+        seq_flatten_w = [all_gt_flattens[dataset.timestamp] for dataset in seq_datasets]
+        future_container = adaptor.adapt(network, seq_flatten_w)
+        w_container_per_epoch[idx] = future_container.no_grad_clone()
+        with torch.no_grad():
+            future_y_hat = network.forward_with_container(
+                future_x, w_container_per_epoch[idx]
+            )
+            future_loss = adaptor.criterion(future_y_hat, future_y)
+        logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
+
+    save_checkpoint(
+        {"w_container_per_epoch": w_container_per_epoch},
+        logger.path(None) / "final-ckp.pth",
+        logger,
+    )
+
+    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-debug",
+        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=32,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--meta_seq",
+        type=int,
+        default=10,
+        help="The length of the sequence for 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.save_dir = "{:}-{:}-d{:}".format(
+        args.save_dir, args.env_version, args.hidden_dim
+    )
+    main(args)

exps/LFNA/lfna-fix-init.py

@@ -0,0 +1,239 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+# python exps/LFNA/lfna-fix-init.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
+
+
+from lfna_utils import lfna_setup, train_model, TimeData
+
+
+class LFNAmlp:
+    """A LFNA meta-model that uses the MLP as delta-net."""
+
+    def __init__(self, obs_dim, hidden_sizes, act_name, criterion):
+        self.delta_net = super_core.SuperSequential(
+            super_core.SuperLinear(obs_dim, hidden_sizes[0]),
+            super_core.super_name2activation[act_name](),
+            super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
+            super_core.super_name2activation[act_name](),
+            super_core.SuperLinear(hidden_sizes[1], 1),
+        )
+        self.meta_optimizer = torch.optim.Adam(
+            self.delta_net.parameters(), lr=0.001, amsgrad=True
+        )
+        self.criterion = criterion
+
+    def adapt(self, model, seq_datasets):
+        delta_inputs = []
+        container = model.get_w_container()
+        for iseq, dataset in enumerate(seq_datasets):
+            y_hat = model.forward_with_container(dataset.x, container)
+            loss = self.criterion(y_hat, dataset.y)
+            gradients = torch.autograd.grad(loss, container.parameters())
+            with torch.no_grad():
+                flatten_g = container.flatten(gradients)
+                delta_inputs.append(flatten_g)
+        flatten_w = container.no_grad_clone().flatten()
+        delta_inputs.append(flatten_w)
+        delta_inputs = torch.stack(delta_inputs, dim=-1)
+        delta = self.delta_net(delta_inputs)
+
+        delta = torch.clamp(delta, -0.8, 0.8)
+        unflatten_delta = container.unflatten(delta)
+        future_container = container.no_grad_clone().additive(unflatten_delta)
+        return future_container
+
+    def step(self):
+        torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
+        self.meta_optimizer.step()
+
+    def zero_grad(self):
+        self.meta_optimizer.zero_grad()
+        self.delta_net.zero_grad()
+
+    def state_dict(self):
+        return dict(
+            delta_net=self.delta_net.state_dict(),
+            meta_optimizer=self.meta_optimizer.state_dict(),
+        )
+
+
+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)
+
+    total_time = env_info["total"]
+    for i in range(total_time):
+        for xkey in ("timestamp", "x", "y"):
+            nkey = "{:}-{:}".format(i, xkey)
+            assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
+    train_time_bar = total_time // 2
+    network = get_model(dict(model_type="simple_mlp"), **model_kwargs)
+
+    criterion = torch.nn.MSELoss()
+    logger.log("There are {:} weights.".format(network.get_w_container().numel()))
+
+    adaptor = LFNAmlp(1 + args.meta_seq, (20, 20), "leaky_relu", criterion)
+
+    # pre-train the model
+    init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
+    init_loss = train_model(network, init_dataset, args.init_lr, args.epochs)
+    logger.log("The pre-training loss is {:.4f}".format(init_loss))
+
+    # LFNA meta-training
+    meta_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)
+        )
+        logger.log(
+            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
+            + need_time
+        )
+
+        adaptor.zero_grad()
+
+        batch_indexes, meta_losses = [], []
+        for ibatch in range(args.meta_batch):
+            sampled_timestamp = random.random() * train_time_bar
+            batch_indexes.append("{:.3f}".format(sampled_timestamp))
+            seq_datasets = []
+            for iseq in range(args.meta_seq + 1):
+                cur_time = sampled_timestamp + iseq * dynamic_env.timestamp_interval
+                cur_time, (x, y) = dynamic_env(cur_time)
+                seq_datasets.append(TimeData(cur_time, x, y))
+            history_datasets, future_dataset = seq_datasets[:-1], seq_datasets[-1]
+            future_container = adaptor.adapt(network, history_datasets)
+            future_y_hat = network.forward_with_container(
+                future_dataset.x, future_container
+            )
+            future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
+            meta_losses.append(future_loss)
+        meta_loss = torch.stack(meta_losses).mean()
+        meta_loss.backward()
+        adaptor.step()
+
+        meta_loss_meter.update(meta_loss.item())
+
+        logger.log(
+            "meta-loss: {:.4f} ({:.4f}) batch: {:}".format(
+                meta_loss_meter.avg, meta_loss_meter.val, ",".join(batch_indexes[:5])
+            )
+        )
+        if iepoch % 200 == 0:
+            save_checkpoint(
+                {"adaptor": adaptor.state_dict(), "iepoch": iepoch},
+                logger.path("model"),
+                logger,
+            )
+        per_epoch_time.update(time.time() - start_time)
+        start_time = time.time()
+
+    w_container_per_epoch = dict()
+    for idx in range(1, env_info["total"]):
+        future_time = env_info["{:}-timestamp".format(idx)]
+        future_x = env_info["{:}-x".format(idx)]
+        future_y = env_info["{:}-y".format(idx)]
+        seq_datasets = []
+        for iseq in range(1, args.meta_seq + 1):
+            cur_time = future_time - iseq * dynamic_env.timestamp_interval
+            cur_time, (x, y) = dynamic_env(cur_time)
+            seq_datasets.append(TimeData(cur_time, x, y))
+        seq_datasets.reverse()
+        future_container = adaptor.adapt(network, seq_datasets)
+        w_container_per_epoch[idx] = future_container.no_grad_clone()
+        with torch.no_grad():
+            future_y_hat = network.forward_with_container(
+                future_x, w_container_per_epoch[idx]
+            )
+            future_loss = adaptor.criterion(future_y_hat, future_y)
+        logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
+
+    save_checkpoint(
+        {"w_container_per_epoch": w_container_per_epoch},
+        logger.path(None) / "final-ckp.pth",
+        logger,
+    )
+
+    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-fix-init",
+        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=32,
+        help="The batch size for the meta-model",
+    )
+    parser.add_argument(
+        "--meta_seq",
+        type=int,
+        default=10,
+        help="The length of the sequence for meta-model.",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=1000,
+        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.save_dir = "{:}-{:}-d{:}".format(
+        args.save_dir, args.env_version, args.hidden_dim
+    )
+    main(args)

exps/LFNA/lfna-v0.py

@@ -1,272 +0,0 @@
-#####################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
-#####################################################
-# python exps/LFNA/lfna-v0.py
-#####################################################
-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
-
-
-class LFNAmlp:
-    """A LFNA meta-model that uses the MLP as delta-net."""
-
-    def __init__(self, obs_dim, hidden_sizes, act_name):
-        self.delta_net = super_core.SuperSequential(
-            super_core.SuperLinear(obs_dim, hidden_sizes[0]),
-            super_core.super_name2activation[act_name](),
-            super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
-            super_core.super_name2activation[act_name](),
-            super_core.SuperLinear(hidden_sizes[1], 1),
-        )
-        self.meta_optimizer = torch.optim.Adam(
-            self.delta_net.parameters(), lr=0.01, amsgrad=True
-        )
-
-    def adapt(self, model, criterion, w_container, seq_datasets):
-        w_container.requires_grad_(True)
-        containers = [w_container]
-        for idx, dataset in enumerate(seq_datasets):
-            x, y = dataset.x, dataset.y
-            y_hat = model.forward_with_container(x, containers[-1])
-            loss = criterion(y_hat, y)
-            gradients = torch.autograd.grad(loss, containers[-1].tensors)
-            with torch.no_grad():
-                flatten_w = containers[-1].flatten().view(-1, 1)
-                flatten_g = containers[-1].flatten(gradients).view(-1, 1)
-                input_statistics = torch.tensor([x.mean(), x.std()]).view(1, 2)
-                input_statistics = input_statistics.expand(flatten_w.numel(), -1)
-            delta_inputs = torch.cat((flatten_w, flatten_g, input_statistics), dim=-1)
-            delta = self.delta_net(delta_inputs).view(-1)
-            delta = torch.clamp(delta, -0.5, 0.5)
-            unflatten_delta = containers[-1].unflatten(delta)
-            future_container = containers[-1].no_grad_clone().additive(unflatten_delta)
-            # future_container = containers[-1].additive(unflatten_delta)
-            containers.append(future_container)
-        # containers = containers[1:]
-        meta_loss = []
-        temp_containers = []
-        for idx, dataset in enumerate(seq_datasets):
-            if idx == 0:
-                continue
-            current_container = containers[idx]
-            y_hat = model.forward_with_container(dataset.x, current_container)
-            loss = criterion(y_hat, dataset.y)
-            meta_loss.append(loss)
-            temp_containers.append((dataset.timestamp, current_container, -loss.item()))
-        meta_loss = sum(meta_loss)
-        w_container.requires_grad_(False)
-        # meta_loss.backward()
-        # self.meta_optimizer.step()
-        return meta_loss, temp_containers
-
-    def step(self):
-        torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
-        self.meta_optimizer.step()
-
-    def zero_grad(self):
-        self.meta_optimizer.zero_grad()
-        self.delta_net.zero_grad()
-
-
-class TimeData:
-    def __init__(self, timestamp, xs, ys):
-        self._timestamp = timestamp
-        self._xs = xs
-        self._ys = ys
-
-    @property
-    def x(self):
-        return self._xs
-
-    @property
-    def y(self):
-        return self._ys
-
-    @property
-    def timestamp(self):
-        return self._timestamp
-
-
-class Population:
-    """A population used to maintain models at different timestamps."""
-
-    def __init__(self):
-        self._time2model = dict()
-        self._time2score = dict()  # higher is better
-
-    def append(self, timestamp, model, score):
-        if timestamp in self._time2model:
-            if self._time2score[timestamp] > score:
-                return
-        self._time2model[timestamp] = model.no_grad_clone()
-        self._time2score[timestamp] = score
-
-    def query(self, timestamp):
-        closet_timestamp = None
-        for xtime, model in self._time2model.items():
-            if closet_timestamp is None or (
-                xtime < timestamp and timestamp - closet_timestamp >= timestamp - xtime
-            ):
-                closet_timestamp = xtime
-        return self._time2model[closet_timestamp], closet_timestamp
-
-    def debug_info(self, timestamps):
-        xstrs = []
-        for timestamp in timestamps:
-            if timestamp in self._time2score:
-                xstrs.append(
-                    "{:04d}: {:.4f}".format(timestamp, self._time2score[timestamp])
-                )
-        return ", ".join(xstrs)
-
-
-def main(args):
-    prepare_seed(args.rand_seed)
-    logger = prepare_logger(args)
-
-    cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
-    if cache_path.exists():
-        env_info = torch.load(cache_path)
-    else:
-        env_info = dict()
-        dynamic_env = get_synthetic_env()
-        env_info["total"] = len(dynamic_env)
-        for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
-            env_info["{:}-timestamp".format(idx)] = timestamp
-            env_info["{:}-x".format(idx)] = _allx
-            env_info["{:}-y".format(idx)] = _ally
-        env_info["dynamic_env"] = dynamic_env
-        torch.save(env_info, cache_path)
-
-    total_time = env_info["total"]
-    for i in range(total_time):
-        for xkey in ("timestamp", "x", "y"):
-            nkey = "{:}-{:}".format(i, xkey)
-            assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
-    train_time_bar = total_time // 2
-    base_model = get_model(
-        dict(model_type="simple_mlp"),
-        act_cls="leaky_relu",
-        norm_cls="identity",
-        input_dim=1,
-        output_dim=1,
-    )
-
-    w_container = base_model.get_w_container()
-    criterion = torch.nn.MSELoss()
-    print("There are {:} weights.".format(w_container.numel()))
-
-    adaptor = LFNAmlp(4, (50, 20), "leaky_relu")
-
-    pool = Population()
-    pool.append(0, w_container, -100)
-
-    # LFNA meta-training
-    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)
-        )
-        logger.log(
-            "[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
-            + need_time
-        )
-
-        adaptor.zero_grad()
-
-        debug_timestamp = set()
-        all_meta_losses = []
-        for ibatch in range(args.meta_batch):
-            sampled_timestamp = random.randint(0, train_time_bar)
-            query_w_container, query_timestamp = pool.query(sampled_timestamp)
-            # def adapt(self, model, w_container, xs, ys):
-            seq_datasets = []
-            # xs, ys = [], []
-            for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
-                xs = env_info["{:}-x".format(it)]
-                ys = env_info["{:}-y".format(it)]
-                seq_datasets.append(TimeData(it, xs, ys))
-            temp_meta_loss, temp_containers = adaptor.adapt(
-                base_model, criterion, query_w_container, seq_datasets
-            )
-            all_meta_losses.append(temp_meta_loss)
-            for temp_time, temp_container, temp_score in temp_containers:
-                pool.append(temp_time, temp_container, temp_score)
-                debug_timestamp.add(temp_time)
-        meta_loss = torch.stack(all_meta_losses).mean()
-        meta_loss.backward()
-        adaptor.step()
-
-        debug_str = pool.debug_info(debug_timestamp)
-        logger.log("meta-loss: {:.4f}".format(meta_loss.item()))
-
-        per_epoch_time.update(time.time() - start_time)
-        start_time = time.time()
-
-    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-v1",
-        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(
-        "--meta_batch",
-        type=int,
-        default=5,
-        help="The batch size for the meta-model",
-    )
-    parser.add_argument(
-        "--epochs",
-        type=int,
-        default=1000,
-        help="The total number of epochs.",
-    )
-    parser.add_argument(
-        "--max_seq",
-        type=int,
-        default=5,
-        help="The maximum length of the sequence.",
-    )
-    parser.add_argument(
-        "--workers",
-        type=int,
-        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")
-    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"
-    main(args)

exps/LFNA/lfna_utils.py

@@ -1,6 +1,7 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
+import copy
 import torch
 from tqdm import tqdm
 from procedures import prepare_seed, prepare_logger
@@ -37,6 +38,24 @@ def lfna_setup(args):
     return logger, env_info, model_kwargs
 
 
+def train_model(model, dataset, lr, epochs):
+    criterion = torch.nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
+    best_loss, best_param = None, None
+    for _iepoch in range(epochs):
+        preds = model(dataset.x)
+        optimizer.zero_grad()
+        loss = criterion(preds, dataset.y)
+        loss.backward()
+        optimizer.step()
+        # save best
+        if best_loss is None or best_loss > loss.item():
+            best_loss = loss.item()
+            best_param = copy.deepcopy(model.state_dict())
+    model.load_state_dict(best_param)
+    return best_loss
+
+
 class TimeData:
     def __init__(self, timestamp, xs, ys):
         self._timestamp = timestamp
@@ -56,6 +75,6 @@ class TimeData:
         return self._timestamp
 
     def __repr__(self):
-        return "{name}(timestamp={:}, with {num} samples)".format(
+        return "{name}(timestamp={timestamp}, with {num} samples)".format(
             name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
         )

exps/LFNA/vis-synthetic.py

@@ -237,6 +237,8 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"):
     alg_name2dir["Optimal"] = "use-same-timestamp"
     alg_name2dir["Supervised Learning (History Data)"] = "use-all-past-data"
     alg_name2dir["MAML"] = "use-maml-s1"
+    alg_name2dir["LFNA (fix init)"] = "lfna-fix-init"
+    alg_name2dir["LFNA (debug)"] = "lfna-debug"
     alg_name2all_containers = OrderedDict()
     if version == "v1":
         poststr = "v1-d16"
@@ -256,7 +258,7 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"):
     )
 
     alg2xs, alg2ys = defaultdict(list), defaultdict(list)
-    colors = ["r", "g", "b"]
+    colors = ["r", "g", "b", "m", "y"]
 
     dynamic_env = env_info["dynamic_env"]
     min_t, max_t = dynamic_env.min_timestamp, dynamic_env.max_timestamp

lib/datasets/synthetic_env.py

@@ -51,6 +51,10 @@ class SyntheticDEnv(data.Dataset):
     def max_timestamp(self):
         return self._timestamp_generator.max_timestamp
 
+    @property
+    def timestamp_interval(self):
+        return self._timestamp_generator.interval
+
     def set_oracle_map(self, functor):
         self._oracle_map = functor
 
@@ -67,6 +71,9 @@ class SyntheticDEnv(data.Dataset):
     def __getitem__(self, index):
         assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
         index, timestamp = self._timestamp_generator[index]
+        return self.__call__(timestamp)
+
+    def __call__(self, timestamp):
         mean_list = [functor(timestamp) for functor in self._mean_functors]
         cov_matrix = [
             [abs(cov_gen(timestamp)) for cov_gen in cov_functor]

lib/datasets/synthetic_utils.py

@@ -60,6 +60,10 @@ class TimeStamp(UnifiedSplit, data.Dataset):
     @property
     def max_timestamp(self):
         return self._max_timestamp
+  
+    @property
+    def interval(self):
+        return self._interval
 
     def __iter__(self):
         self._iter_num = 0

lib/xlayers/super_module.py

@@ -46,6 +46,13 @@ class TensorContainer:
             result.append(name, new_tensor, self._param_or_buffers[index])
         return result
 
+    def create_container(self, tensors):
+        result = TensorContainer()
+        for index, name in enumerate(self._names):
+            new_tensor = tensors[index]
+            result.append(name, new_tensor, self._param_or_buffers[index])
+        return result
+
     def no_grad_clone(self):
         result = TensorContainer()
         with torch.no_grad():