344 lines
12 KiB
Python
344 lines
12 KiB
Python
#####################################################
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# Learning to Generate Model One Step Ahead #
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#####################################################
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# python exps/GeMOSA/lfna.py --env_version v1 --workers 0
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# python exps/GeMOSA/lfna.py --env_version v1 --device cuda --lr 0.001
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# python exps/GeMOSA/main.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128
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# python exps/GeMOSA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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from copy import deepcopy
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from pathlib import Path
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from torch.nn import functional as F
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lib_dir = (Path(__file__).parent / ".." / "..").resolve()
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print("LIB-DIR: {:}".format(lib_dir))
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from xautodl.procedures import (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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)
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from xautodl.log_utils import time_string
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from xautodl.log_utils import AverageMeter, convert_secs2time
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from xautodl.utils import split_str2indexes
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from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
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from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from xautodl.datasets.synthetic_core import get_synthetic_env
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from xautodl.models.xcore import get_model
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from xautodl.xlayers import super_core, trunc_normal_
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from lfna_utils import lfna_setup, train_model, TimeData
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from lfna_meta_model import MetaModelV1
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def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=False):
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logger.log("Online evaluate: {:}".format(env))
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loss_meter = AverageMeter()
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w_containers = dict()
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for idx, (future_time, (future_x, future_y)) in enumerate(env):
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with torch.no_grad():
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meta_model.eval()
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base_model.eval()
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_, [future_container], time_embeds = meta_model(
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future_time.to(args.device).view(1, 1), None, False
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)
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if save:
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w_containers[idx] = future_container.no_grad_clone()
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future_x, future_y = future_x.to(args.device), future_y.to(args.device)
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future_y_hat = base_model.forward_with_container(future_x, future_container)
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future_loss = criterion(future_y_hat, future_y)
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loss_meter.update(future_loss.item())
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refine, post_refine_loss = meta_model.adapt(
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base_model,
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criterion,
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future_time.item(),
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future_x,
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future_y,
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args.refine_lr,
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args.refine_epochs,
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{"param": time_embeds, "loss": future_loss.item()},
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)
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logger.log(
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"[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format(
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idx, len(env), future_loss.item()
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)
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+ ", post-loss={:.4f}".format(post_refine_loss if refine else -1)
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)
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meta_model.clear_fixed()
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meta_model.clear_learnt()
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return w_containers, loss_meter
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def meta_train_procedure(base_model, meta_model, criterion, xenv, args, logger):
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base_model.train()
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meta_model.train()
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optimizer = torch.optim.Adam(
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meta_model.get_parameters(True, True, True),
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lr=args.lr,
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weight_decay=args.weight_decay,
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amsgrad=True,
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)
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logger.log("Pre-train the meta-model")
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logger.log("Using the optimizer: {:}".format(optimizer))
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meta_model.set_best_dir(logger.path(None) / "ckps-pretrain-v2")
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final_best_name = "final-pretrain-{:}.pth".format(args.rand_seed)
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if meta_model.has_best(final_best_name):
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meta_model.load_best(final_best_name)
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logger.log("Directly load the best model from {:}".format(final_best_name))
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return
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total_indexes = list(range(meta_model.meta_length))
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meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
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last_success_epoch, early_stop_thresh = 0, args.pretrain_early_stop_thresh
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per_epoch_time, start_time = AverageMeter(), time.time()
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device = args.device
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for iepoch in range(args.epochs):
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left_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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optimizer.zero_grad()
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generated_time_embeds = meta_model(meta_model.meta_timestamps, None, True)
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batch_indexes = random.choices(total_indexes, k=args.meta_batch)
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raw_time_steps = meta_model.meta_timestamps[batch_indexes]
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regularization_loss = F.l1_loss(
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generated_time_embeds, meta_model.super_meta_embed, reduction="mean"
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)
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# future loss
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total_future_losses, total_present_losses = [], []
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_, future_containers, _ = meta_model(
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None, generated_time_embeds[batch_indexes], False
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)
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_, present_containers, _ = meta_model(
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None, meta_model.super_meta_embed[batch_indexes], False
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)
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for ibatch, time_step in enumerate(raw_time_steps.cpu().tolist()):
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_, (inputs, targets) = xenv(time_step)
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inputs, targets = inputs.to(device), targets.to(device)
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predictions = base_model.forward_with_container(
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inputs, future_containers[ibatch]
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)
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total_future_losses.append(criterion(predictions, targets))
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predictions = base_model.forward_with_container(
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inputs, present_containers[ibatch]
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)
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total_present_losses.append(criterion(predictions, targets))
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with torch.no_grad():
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meta_std = torch.stack(total_future_losses).std().item()
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loss_future = torch.stack(total_future_losses).mean()
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loss_present = torch.stack(total_present_losses).mean()
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total_loss = loss_future + loss_present + regularization_loss
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total_loss.backward()
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optimizer.step()
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# success
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success, best_score = meta_model.save_best(-total_loss.item())
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logger.log(
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"{:} [META {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f}".format(
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time_string(),
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iepoch,
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args.epochs,
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total_loss.item(),
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meta_std,
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loss_future.item(),
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loss_present.item(),
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regularization_loss.item(),
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)
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+ ", batch={:}".format(len(total_future_losses))
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+ ", success={:}, best={:.4f}".format(success, -best_score)
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+ ", LS={:}/{:}".format(iepoch - last_success_epoch, early_stop_thresh)
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+ ", {:}".format(left_time)
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)
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if success:
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last_success_epoch = iepoch
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if iepoch - last_success_epoch >= early_stop_thresh:
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logger.log("Early stop the pre-training 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_model.load_best()
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# save to the final model
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meta_model.set_best_name(final_best_name)
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success, _ = meta_model.save_best(best_score + 1e-6)
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assert success
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logger.log("Save the best model into {:}".format(final_best_name))
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def main(args):
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logger, model_kwargs = lfna_setup(args)
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train_env = get_synthetic_env(mode="train", version=args.env_version)
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valid_env = get_synthetic_env(mode="valid", version=args.env_version)
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trainval_env = get_synthetic_env(mode="trainval", version=args.env_version)
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all_env = get_synthetic_env(mode=None, version=args.env_version)
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logger.log("The training enviornment: {:}".format(train_env))
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logger.log("The validation enviornment: {:}".format(valid_env))
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logger.log("The trainval enviornment: {:}".format(trainval_env))
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logger.log("The total enviornment: {:}".format(all_env))
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base_model = get_model(**model_kwargs)
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base_model = base_model.to(args.device)
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criterion = torch.nn.MSELoss()
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shape_container = base_model.get_w_container().to_shape_container()
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# pre-train the hypernetwork
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timestamps = trainval_env.get_timestamp(None)
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meta_model = MetaModelV1(
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shape_container,
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args.layer_dim,
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args.time_dim,
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timestamps,
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seq_length=args.seq_length,
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interval=trainval_env.time_interval,
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)
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meta_model = meta_model.to(args.device)
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logger.log("The base-model has {:} weights.".format(base_model.numel()))
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logger.log("The meta-model has {:} weights.".format(meta_model.numel()))
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logger.log("The base-model is\n{:}".format(base_model))
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logger.log("The meta-model is\n{:}".format(meta_model))
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meta_train_procedure(base_model, meta_model, criterion, trainval_env, args, logger)
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# try to evaluate once
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# online_evaluate(train_env, meta_model, base_model, criterion, args, logger)
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# online_evaluate(valid_env, meta_model, base_model, criterion, args, logger)
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w_containers, loss_meter = online_evaluate(
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all_env, meta_model, base_model, criterion, args, logger, True
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)
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logger.log("In this enviornment, the loss-meter is {:}".format(loss_meter))
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save_checkpoint(
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{"w_containers": w_containers},
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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logger.log("-" * 200 + "\n")
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(".")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./outputs/lfna-synthetic/lfna-battle",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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"--env_version",
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type=str,
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required=True,
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help="The synthetic enviornment version.",
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)
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parser.add_argument(
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"--hidden_dim",
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type=int,
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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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"--layer_dim",
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type=int,
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default=16,
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help="The layer chunk dimension.",
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)
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parser.add_argument(
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"--time_dim",
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type=int,
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default=16,
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help="The timestamp dimension.",
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)
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#####
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parser.add_argument(
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"--lr",
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type=float,
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default=0.002,
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help="The initial learning rate for the optimizer (default is Adam)",
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)
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parser.add_argument(
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"--weight_decay",
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type=float,
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default=0.00001,
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help="The weight decay for the optimizer (default is Adam)",
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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=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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"--sampler_enlarge",
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type=int,
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default=5,
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help="Enlarge the #iterations for an epoch",
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)
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parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
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parser.add_argument(
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"--refine_lr",
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type=float,
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default=0.001,
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help="The learning rate for the optimizer, during refine",
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)
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parser.add_argument(
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"--refine_epochs", type=int, default=150, help="The final refine #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=20,
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help="The #epochs for early stop.",
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)
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parser.add_argument(
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"--pretrain_early_stop_thresh",
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type=int,
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default=300,
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help="The #epochs for early stop.",
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)
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parser.add_argument(
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"--seq_length", type=int, default=10, help="The sequence length."
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)
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parser.add_argument(
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"--workers", type=int, default=4, help="The number of workers in parallel."
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cpu",
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help="",
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)
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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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 = "{:}-bs{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
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args.save_dir,
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args.meta_batch,
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args.hidden_dim,
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args.layer_dim,
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args.time_dim,
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args.seq_length,
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args.lr,
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args.weight_decay,
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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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