560 lines
21 KiB
Python
560 lines
21 KiB
Python
#####################################################
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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
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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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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, EnvSampler
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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 LFNA_Meta
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def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
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base_model.train()
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meta_model.train()
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loss_meter = AverageMeter()
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for ibatch, batch_data in enumerate(loader):
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timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
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timestamps = timestamps.squeeze(dim=-1).to(device)
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batch_seq_inputs = batch_seq_inputs.to(device)
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batch_seq_targets = batch_seq_targets.to(device)
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optimizer.zero_grad()
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batch_seq_containers = meta_model(timestamps)
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losses = []
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for seq_containers, seq_inputs, seq_targets in zip(
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batch_seq_containers, batch_seq_inputs, batch_seq_targets
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):
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for container, inputs, targets in zip(
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seq_containers, seq_inputs, seq_targets
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):
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predictions = base_model.forward_with_container(inputs, container)
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loss = criterion(predictions, targets)
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losses.append(loss)
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final_loss = torch.stack(losses).mean()
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final_loss.backward()
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optimizer.step()
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loss_meter.update(final_loss.item())
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return loss_meter
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def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
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with torch.no_grad():
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base_model.eval()
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meta_model.eval()
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loss_meter = AverageMeter()
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for ibatch, batch_data in enumerate(loader):
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timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
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timestamps = timestamps.squeeze(dim=-1).to(device)
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batch_seq_inputs = batch_seq_inputs.to(device)
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batch_seq_targets = batch_seq_targets.to(device)
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batch_seq_containers = meta_model(timestamps)
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losses = []
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for seq_containers, seq_inputs, seq_targets in zip(
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batch_seq_containers, batch_seq_inputs, batch_seq_targets
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):
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for container, inputs, targets in zip(
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seq_containers, seq_inputs, seq_targets
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):
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predictions = base_model.forward_with_container(inputs, container)
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loss = criterion(predictions, targets)
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losses.append(loss)
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final_loss = torch.stack(losses).mean()
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loss_meter.update(final_loss.item())
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return loss_meter
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def online_evaluate(env, meta_model, base_model, criterion, args, logger):
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logger.log("Online evaluate: {:}".format(env))
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for idx, (timestamp, (future_x, future_y)) in enumerate(env):
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future_time = timestamp.item()
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time_seqs = [
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future_time - iseq * env.timestamp_interval
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for iseq in range(args.seq_length * 2)
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]
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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, None)
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future_container = seq_containers[-2]
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_, (future_x, future_y) = env(time_seqs[0, -2].item())
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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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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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)
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import pdb
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pdb.set_trace()
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for iseq in range(args.seq_length):
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time_seqs.append(future_time - iseq * eval_env.timestamp_interval)
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print("-")
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def pretrain_v2(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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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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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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total_meta_v1_losses, total_meta_v2_losses, total_match_losses = [], [], []
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optimizer.zero_grad()
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for ibatch in range(args.meta_batch):
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rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
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timestamps = meta_model.meta_timestamps[
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rand_index : rand_index + xenv.seq_length
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]
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meta_embeds = meta_model.super_meta_embed[
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rand_index : rand_index + xenv.seq_length
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]
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_, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
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seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
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args.device
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)
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# generate models one step ahead
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[seq_containers], time_embeds = meta_model(
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torch.unsqueeze(timestamps, dim=0), None
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)
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for container, inputs, targets in zip(
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seq_containers, seq_inputs, seq_targets
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):
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predictions = base_model.forward_with_container(inputs, container)
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total_meta_v1_losses.append(criterion(predictions, targets))
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# the matching loss
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match_loss = criterion(torch.squeeze(time_embeds, dim=0), meta_embeds)
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total_match_losses.append(match_loss)
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# generate models via memory
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[seq_containers], _ = meta_model(None, torch.unsqueeze(meta_embeds, dim=0))
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for container, inputs, targets in zip(
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seq_containers, seq_inputs, seq_targets
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):
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predictions = base_model.forward_with_container(inputs, container)
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total_meta_v2_losses.append(criterion(predictions, targets))
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with torch.no_grad():
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meta_std = torch.stack(total_meta_v1_losses).std().item()
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meta_v1_loss = torch.stack(total_meta_v1_losses).mean()
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meta_v2_loss = torch.stack(total_meta_v2_losses).mean()
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match_loss = torch.stack(total_match_losses).mean()
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total_loss = meta_v1_loss + meta_v2_loss + match_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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"{:} [Pre-V2 {:04d}/{:}] loss : {:.4f} +- {:.4f} = {:.4f} + {:.4f} + {:.4f} (match)".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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meta_v1_loss.item(),
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meta_v2_loss.item(),
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match_loss.item(),
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)
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+ ", batch={:}".format(len(total_meta_v1_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, env_info, 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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logger.log("training enviornment: {:}".format(train_env))
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logger.log("validation enviornment: {:}".format(valid_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 = train_env.get_timestamp(None)
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meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps)
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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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batch_sampler = EnvSampler(train_env, args.meta_batch, args.sampler_enlarge)
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train_env.reset_max_seq_length(args.seq_length)
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# valid_env.reset_max_seq_length(args.seq_length)
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valid_env_loader = torch.utils.data.DataLoader(
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valid_env,
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batch_size=args.meta_batch,
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shuffle=True,
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num_workers=args.workers,
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pin_memory=True,
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)
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train_env_loader = torch.utils.data.DataLoader(
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train_env,
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batch_sampler=batch_sampler,
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num_workers=args.workers,
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pin_memory=True,
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)
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pretrain_v2(base_model, meta_model, criterion, train_env, args, logger)
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# try to evaluate once
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online_evaluate(valid_env, meta_model, base_model, criterion, args, logger)
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import pdb
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pdb.set_trace()
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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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w_container_per_epoch = dict()
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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.timestamp_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.timestamp_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(
|
|
"post-meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
|
|
)
|
|
with torch.no_grad():
|
|
meta_model.replace_append_learnt(None, None)
|
|
meta_model.append_fixed(torch.Tensor([future_time]), new_param)
|
|
|
|
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(".")
|
|
parser.add_argument(
|
|
"--save_dir",
|
|
type=str,
|
|
default="./outputs/lfna-synthetic/lfna-battle",
|
|
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,
|
|
default=16,
|
|
help="The hidden dimension.",
|
|
)
|
|
parser.add_argument(
|
|
"--layer_dim",
|
|
type=int,
|
|
default=16,
|
|
help="The layer chunk dimension.",
|
|
)
|
|
parser.add_argument(
|
|
"--time_dim",
|
|
type=int,
|
|
default=16,
|
|
help="The timestamp dimension.",
|
|
)
|
|
#####
|
|
parser.add_argument(
|
|
"--lr",
|
|
type=float,
|
|
default=0.002,
|
|
help="The initial learning rate for the optimizer (default is Adam)",
|
|
)
|
|
parser.add_argument(
|
|
"--weight_decay",
|
|
type=float,
|
|
default=0.00001,
|
|
help="The weight decay for the optimizer (default is Adam)",
|
|
)
|
|
parser.add_argument(
|
|
"--meta_batch",
|
|
type=int,
|
|
default=64,
|
|
help="The batch size for the meta-model",
|
|
)
|
|
parser.add_argument(
|
|
"--sampler_enlarge",
|
|
type=int,
|
|
default=5,
|
|
help="Enlarge the #iterations for an epoch",
|
|
)
|
|
parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
|
|
parser.add_argument(
|
|
"--refine_lr",
|
|
type=float,
|
|
default=0.001,
|
|
help="The learning rate for the optimizer, during refine",
|
|
)
|
|
parser.add_argument(
|
|
"--refine_epochs", type=int, default=1000, help="The final refine #epochs."
|
|
)
|
|
parser.add_argument(
|
|
"--early_stop_thresh",
|
|
type=int,
|
|
default=20,
|
|
help="The #epochs for early stop.",
|
|
)
|
|
parser.add_argument(
|
|
"--pretrain_early_stop_thresh",
|
|
type=int,
|
|
default=300,
|
|
help="The #epochs for early stop.",
|
|
)
|
|
parser.add_argument(
|
|
"--seq_length", type=int, default=10, help="The sequence length."
|
|
)
|
|
parser.add_argument(
|
|
"--workers", type=int, default=4, help="The number of workers in parallel."
|
|
)
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default="cpu",
|
|
help="",
|
|
)
|
|
# 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{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
|
|
args.save_dir,
|
|
args.hidden_dim,
|
|
args.layer_dim,
|
|
args.time_dim,
|
|
args.seq_length,
|
|
args.lr,
|
|
args.weight_decay,
|
|
args.epochs,
|
|
args.env_version,
|
|
)
|
|
main(args)
|