LFNA -> GMOA
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D-X-Y
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@ -1,9 +1,10 @@
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#####################################################
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# Learning to Generate Model One Step Ahead #
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#####################################################
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# python exps/LFNA/lfna.py --env_version v1 --workers 0
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# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
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# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002 --meta_batch 128
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# python exps/GMOA/lfna.py --env_version v1 --workers 0
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# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.001
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# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128
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# python exps/GMOA/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 pdb, sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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@ -33,7 +34,7 @@ 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 LFNA_Meta
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from lfna_meta_model import MetaModelV1
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def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
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@ -240,7 +241,7 @@ def main(args):
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# pre-train the hypernetwork
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timestamps = trainval_env.get_timestamp(None)
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meta_model = LFNA_Meta(
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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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@ -270,179 +271,6 @@ def main(args):
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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return
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"""
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optimizer = torch.optim.Adam(
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meta_model.get_parameters(True, True, False), # fix hypernet
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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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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[1, 2, 3, 4, 5],
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gamma=0.2,
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)
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logger.log("The optimizer is\n{:}".format(optimizer))
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logger.log("The scheduler is\n{:}".format(lr_scheduler))
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logger.log("Per epoch iterations = {:}".format(len(train_env_loader)))
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if logger.path("model").exists():
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ckp_data = torch.load(logger.path("model"))
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base_model.load_state_dict(ckp_data["base_model"])
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meta_model.load_state_dict(ckp_data["meta_model"])
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optimizer.load_state_dict(ckp_data["optimizer"])
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lr_scheduler.load_state_dict(ckp_data["lr_scheduler"])
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last_success_epoch = ckp_data["last_success_epoch"]
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start_epoch = ckp_data["iepoch"] + 1
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check_strs = [
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"epochs",
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"env_version",
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"hidden_dim",
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"lr",
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"layer_dim",
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"time_dim",
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"seq_length",
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]
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for xstr in check_strs:
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cx = getattr(args, xstr)
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px = getattr(ckp_data["args"], xstr)
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assert cx == px, "[{:}] {:} vs {:}".format(xstr, cx, ps)
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success, _ = meta_model.save_best(ckp_data["cur_score"])
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logger.log("Load ckp from {:}".format(logger.path("model")))
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if success:
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logger.log(
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"Re-save the best model with score={:}".format(ckp_data["cur_score"])
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)
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else:
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start_epoch, last_success_epoch = 0, 0
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# LFNA meta-train
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meta_model.set_best_dir(logger.path(None) / "checkpoint")
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per_epoch_time, start_time = AverageMeter(), time.time()
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for iepoch in range(start_epoch, args.epochs):
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head_str = "[{:}] [{:04d}/{:04d}] ".format(
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time_string(), iepoch, args.epochs
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) + "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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loss_meter = epoch_train(
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train_env_loader,
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meta_model,
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base_model,
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optimizer,
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criterion,
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args.device,
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logger,
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)
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valid_loss_meter = epoch_evaluate(
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valid_env_loader, meta_model, base_model, criterion, args.device, logger
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)
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logger.log(
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head_str
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+ " meta-train-loss: {meter.avg:.4f} ({meter.count:.0f})".format(
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meter=loss_meter
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)
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+ " meta-valid-loss: {meter.val:.4f}".format(meter=valid_loss_meter)
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+ " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
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+ " :: last-success={:}".format(last_success_epoch)
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)
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success, best_score = meta_model.save_best(-loss_meter.avg)
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if success:
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logger.log("Achieve the best with best-score = {:.5f}".format(best_score))
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last_success_epoch = iepoch
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save_checkpoint(
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{
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"meta_model": meta_model.state_dict(),
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"base_model": base_model.state_dict(),
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"optimizer": optimizer.state_dict(),
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"lr_scheduler": lr_scheduler.state_dict(),
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"last_success_epoch": last_success_epoch,
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"cur_score": -loss_meter.avg,
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"iepoch": iepoch,
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"args": args,
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},
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logger.path("model"),
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logger,
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)
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if iepoch - last_success_epoch >= args.early_stop_thresh:
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if lr_scheduler.last_epoch > 4:
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logger.log("Early stop at {:}".format(iepoch))
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break
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else:
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last_success_epoch = iepoch
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lr_scheduler.step()
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logger.log("Decay the lr [{:}]".format(lr_scheduler.last_epoch))
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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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meta_model.load_best()
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eval_env = env_info["dynamic_env"]
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for idx in range(args.seq_length, len(eval_env)):
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# build-timestamp
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future_time = env_info["{:}-timestamp".format(idx)].item()
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time_seqs = []
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for iseq in range(args.seq_length):
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time_seqs.append(future_time - iseq * eval_env.time_interval)
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time_seqs.reverse()
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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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time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
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[seq_containers] = meta_model(time_seqs)
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future_container = seq_containers[-1]
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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# evaluation
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future_x = env_info["{:}-x".format(idx)].to(args.device)
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future_y = env_info["{:}-y".format(idx)].to(args.device)
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future_y_hat = base_model.forward_with_container(
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future_x, w_container_per_epoch[idx]
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)
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future_loss = criterion(future_y_hat, future_y)
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logger.log(
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"meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
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)
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# creating the new meta-time-embedding
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distance = meta_model.get_closest_meta_distance(future_time)
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if distance < eval_env.time_interval:
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continue
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#
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new_param = meta_model.create_meta_embed()
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optimizer = torch.optim.Adam(
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[new_param], lr=args.refine_lr, weight_decay=1e-5, amsgrad=True
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)
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meta_model.replace_append_learnt(
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torch.Tensor([future_time]).to(args.device), new_param
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)
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meta_model.eval()
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base_model.train()
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for iepoch in range(args.refine_epochs):
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optimizer.zero_grad()
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[seq_containers] = meta_model(time_seqs)
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future_container = seq_containers[-1]
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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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future_loss.backward()
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optimizer.step()
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logger.log(
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"post-meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
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)
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with torch.no_grad():
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meta_model.replace_append_learnt(None, None)
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meta_model.append_fixed(torch.Tensor([future_time]), new_param)
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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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logger,
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)
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"""
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logger.log("-" * 200 + "\n")
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logger.close()
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@ -513,7 +341,7 @@ if __name__ == "__main__":
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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=50, help="The final refine #epochs."
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"--refine_epochs", type=int, default=100, 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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@ -10,8 +10,8 @@ from xautodl.xlayers import trunc_normal_
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from xautodl.models.xcore import get_model
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class LFNA_Meta(super_core.SuperModule):
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"""Learning to Forecast Neural Adaptation (Meta Model Design)."""
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class MetaModelV1(super_core.SuperModule):
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"""Learning to Generate Models One Step Ahead (Meta Model Design)."""
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def __init__(
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self,
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