Update LFNA
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D-X-Y
@@ -1,7 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/basic-maml.py --env_version v1 --hidden_dim 16 --inner_step 5
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# python exps/LFNA/basic-maml.py --env_version v1 --inner_step 5
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# python exps/LFNA/basic-maml.py --env_version v2
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#####################################################
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import sys, time, copy, torch, random, argparse
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@@ -20,7 +20,7 @@ from utils import split_str2indexes
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from procedures.advanced_main import basic_train_fn, basic_eval_fn
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from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from datasets.synthetic_core import get_synthetic_env
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from datasets.synthetic_core import get_synthetic_env, EnvSampler
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from models.xcore import get_model
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from xlayers import super_core
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@@ -42,11 +42,10 @@ class MAML:
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self.meta_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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self.meta_optimizer,
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milestones=[
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int(epochs * 0.25),
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int(epochs * 0.5),
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int(epochs * 0.75),
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int(epochs * 0.8),
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int(epochs * 0.9),
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],
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gamma=0.3,
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gamma=0.1,
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)
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self.inner_lr = inner_lr
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self.inner_step = inner_step
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@@ -85,33 +84,27 @@ class MAML:
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self.meta_optimizer.load_state_dict(state_dict["meta_optimizer"])
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self.meta_lr_scheduler.load_state_dict(state_dict["meta_lr_scheduler"])
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def save_best(self, iepoch, score):
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if self._best_info["score"] is None or self._best_info["score"] < score:
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state_dict = dict(
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criterion=self.criterion.state_dict(),
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network=self.network.state_dict(),
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meta_optimizer=self.meta_optimizer.state_dict(),
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meta_lr_scheduler=self.meta_lr_scheduler.state_dict(),
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)
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self._best_info["state_dict"] = state_dict
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self._best_info["score"] = score
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self._best_info["iepoch"] = iepoch
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is_best = True
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else:
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is_best = False
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return self._best_info, is_best
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def state_dict(self):
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state_dict = dict()
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state_dict["criterion"] = self.criterion.state_dict()
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state_dict["network"] = self.network.state_dict()
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state_dict["meta_optimizer"] = self.meta_optimizer.state_dict()
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state_dict["meta_lr_scheduler"] = self.meta_lr_scheduler.state_dict()
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return state_dict
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def save_best(self, score):
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success, best_score = self.network.save_best(score)
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return success, best_score
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def load_best(self):
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self.network.load_best()
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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model = get_model(**model_kwargs)
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total_time = env_info["total"]
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for i in range(total_time):
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for xkey in ("timestamp", "x", "y"):
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nkey = "{:}-{:}".format(i, xkey)
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assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
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train_time_bar = total_time // 2
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dynamic_env = get_synthetic_env(mode="train", version=args.env_version)
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criterion = torch.nn.MSELoss()
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@@ -120,83 +113,65 @@ def main(args):
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)
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# meta-training
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last_success_epoch = 0
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per_epoch_time, start_time = AverageMeter(), time.time()
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# for iepoch in range(args.epochs):
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iepoch = 0
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while iepoch < args.epochs:
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for iepoch in range(args.epochs):
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need_time = "Time Left: {:}".format(
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convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
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)
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logger.log(
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head_str = (
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"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
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+ need_time
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)
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maml.zero_grad()
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batch_indexes, meta_losses = [], []
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meta_losses = []
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for ibatch in range(args.meta_batch):
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sampled_timestamp = random.randint(0, train_time_bar)
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batch_indexes.append("{:5d}".format(sampled_timestamp))
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past_dataset = TimeData(
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sampled_timestamp,
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env_info["{:}-x".format(sampled_timestamp)],
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env_info["{:}-y".format(sampled_timestamp)],
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future_timestamp = dynamic_env.random_timestamp()
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_, (future_x, future_y) = dynamic_env(future_timestamp)
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past_timestamp = (
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future_timestamp - args.prev_time * dynamic_env.timestamp_interval
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)
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future_dataset = TimeData(
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sampled_timestamp + 1,
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env_info["{:}-x".format(sampled_timestamp + 1)],
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env_info["{:}-y".format(sampled_timestamp + 1)],
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)
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future_container = maml.adapt(past_dataset)
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future_y_hat = maml.predict(future_dataset.x, future_container)
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future_loss = maml.criterion(future_y_hat, future_dataset.y)
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_, (past_x, past_y) = dynamic_env(past_timestamp)
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future_container = maml.adapt(TimeData(past_timestamp, past_x, past_y))
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future_y_hat = maml.predict(future_x, future_container)
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future_loss = maml.criterion(future_y_hat, future_y)
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meta_losses.append(future_loss)
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meta_loss = torch.stack(meta_losses).mean()
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meta_loss.backward()
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maml.step()
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logger.log(
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"meta-loss: {:.4f} batch: {:}".format(
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meta_loss.item(), ",".join(batch_indexes)
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)
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)
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best_info, is_best = maml.save_best(iepoch, -meta_loss.item())
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if is_best:
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save_checkpoint(best_info, logger.path("best"), logger)
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logger.log("Save the best into {:}".format(logger.path("best")))
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if iepoch >= 10 and (
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torch.isnan(meta_loss).item() or meta_loss.item() >= args.fail_thresh
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):
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xdata = torch.load(logger.path("best"))
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maml.load_state_dict(xdata["state_dict"])
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iepoch = xdata["iepoch"]
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logger.log(
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"The training failed, re-use the previous best epoch [{:}]".format(
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iepoch
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)
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)
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else:
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iepoch = iepoch + 1
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logger.log(head_str + " meta-loss: {:.4f}".format(meta_loss.item()))
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success, best_score = maml.save_best(-meta_loss.item())
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if success:
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logger.log("Achieve the best with best_score = {:.3f}".format(best_score))
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save_checkpoint(maml.state_dict(), logger.path("model"), logger)
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last_success_epoch = iepoch
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if iepoch - last_success_epoch >= args.early_stop_thresh:
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logger.log("Early stop at {:}".format(iepoch))
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break
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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# meta-test
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maml.load_best()
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eval_env = env_info["dynamic_env"]
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assert eval_env.timestamp_interval == dynamic_env.timestamp_interval
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w_container_per_epoch = dict()
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for idx in range(1, env_info["total"]):
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past_dataset = TimeData(
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idx - 1,
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env_info["{:}-x".format(idx - 1)],
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env_info["{:}-y".format(idx - 1)],
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for idx in range(args.prev_time, len(eval_env)):
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future_timestamp, (future_x, future_y) = eval_env[idx]
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past_timestamp = (
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future_timestamp.item() - args.prev_time * eval_env.timestamp_interval
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)
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current_container = maml.adapt(past_dataset)
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w_container_per_epoch[idx] = current_container.no_grad_clone()
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_, (past_x, past_y) = eval_env(past_timestamp)
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future_container = maml.adapt(TimeData(past_timestamp, past_x, past_y))
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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with torch.no_grad():
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current_x = env_info["{:}-x".format(idx)]
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current_y = env_info["{:}-y".format(idx)]
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current_y_hat = maml.predict(current_x, w_container_per_epoch[idx])
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current_loss = maml.criterion(current_y_hat, current_y)
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logger.log(
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"meta-test: [{:03d}] -> loss={:.4f}".format(idx, current_loss.item())
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)
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future_y_hat = maml.predict(future_x, w_container_per_epoch[idx])
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future_loss = maml.criterion(future_y_hat, future_y)
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logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
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save_checkpoint(
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{"w_container_per_epoch": w_container_per_epoch},
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logger.path(None) / "final-ckp.pth",
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@@ -224,13 +199,13 @@ if __name__ == "__main__":
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parser.add_argument(
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"--hidden_dim",
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type=int,
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required=True,
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default=16,
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help="The hidden dimension.",
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)
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parser.add_argument(
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"--meta_lr",
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type=float,
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default=0.05,
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default=0.01,
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help="The learning rate for the MAML optimizer (default is Adam)",
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)
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parser.add_argument(
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@@ -242,24 +217,36 @@ if __name__ == "__main__":
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parser.add_argument(
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"--inner_lr",
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type=float,
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default=0.01,
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default=0.005,
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help="The learning rate for the inner optimization",
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)
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parser.add_argument(
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"--inner_step", type=int, default=1, help="The inner loop steps for MAML."
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)
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parser.add_argument(
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"--prev_time",
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type=int,
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default=5,
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help="The gap between prev_time and current_timestamp",
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)
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parser.add_argument(
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"--meta_batch",
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type=int,
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default=10,
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default=64,
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help="The batch size for the meta-model",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=1000,
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default=2000,
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help="The total number of epochs.",
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)
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parser.add_argument(
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"--early_stop_thresh",
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type=int,
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default=50,
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help="The maximum epochs for early stop.",
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)
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parser.add_argument(
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"--workers",
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type=int,
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@@ -272,7 +259,13 @@ if __name__ == "__main__":
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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assert args.save_dir is not None, "The save dir argument can not be None"
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args.save_dir = "{:}-s{:}-{:}-d{:}".format(
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args.save_dir, args.inner_step, args.env_version, args.hidden_dim
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args.save_dir = "{:}-s{:}-mlr{:}-d{:}-prev{:}-e{:}-env{:}".format(
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args.save_dir,
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args.inner_step,
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args.meta_lr,
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args.hidden_dim,
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args.prev_time,
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args.epochs,
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args.env_version,
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)
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main(args)
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