Fix path errors in TAS due to lib->xautodl
This commit is contained in:
D-X-Y
382
exps/TAS/search-shape.py
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382
exps/TAS/search-shape.py
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@@ -0,0 +1,382 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import sys, time, torch, random, argparse
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from PIL import ImageFile
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from os import path as osp
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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import numpy as np
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from copy import deepcopy
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from pathlib import Path
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from xautodl.config_utils import (
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load_config,
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configure2str,
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obtain_search_single_args as obtain_args,
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)
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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.procedures import get_optim_scheduler, get_procedures
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from xautodl.datasets import get_datasets, SearchDataset
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from xautodl.models import obtain_search_model, obtain_model, change_key
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from xautodl.utils import get_model_infos
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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def main(args):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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# torch.set_num_threads(args.workers)
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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# prepare dataset
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train_data, valid_data, xshape, class_num = get_datasets(
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args.dataset, args.data_path, args.cutout_length
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)
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# train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.workers,
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pin_memory=True,
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)
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split_file_path = Path(args.split_path)
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assert split_file_path.exists(), "{:} does not exist".format(split_file_path)
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split_info = torch.load(split_file_path)
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train_split, valid_split = split_info["train"], split_info["valid"]
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assert (
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len(set(train_split).intersection(set(valid_split))) == 0
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), "There should be 0 element that belongs to both train and valid"
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assert len(train_split) + len(valid_split) == len(
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train_data
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), "{:} + {:} vs {:}".format(len(train_split), len(valid_split), len(train_data))
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search_dataset = SearchDataset(args.dataset, train_data, train_split, valid_split)
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search_train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=args.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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pin_memory=True,
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num_workers=args.workers,
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)
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search_valid_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=args.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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pin_memory=True,
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num_workers=args.workers,
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)
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search_loader = torch.utils.data.DataLoader(
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search_dataset,
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batch_size=args.batch_size,
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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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sampler=None,
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)
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# get configures
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model_config = load_config(
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args.model_config,
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{"class_num": class_num, "search_mode": args.search_shape},
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logger,
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)
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# obtain the model
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search_model = obtain_search_model(model_config)
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MAX_FLOP, param = get_model_infos(search_model, xshape)
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optim_config = load_config(
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args.optim_config, {"class_num": class_num, "FLOP": MAX_FLOP}, logger
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)
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logger.log("Model Information : {:}".format(search_model.get_message()))
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logger.log("MAX_FLOP = {:} M".format(MAX_FLOP))
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logger.log("Params = {:} M".format(param))
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logger.log("train_data : {:}".format(train_data))
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logger.log("search-data: {:}".format(search_dataset))
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logger.log("search_train_loader : {:} samples".format(len(train_split)))
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logger.log("search_valid_loader : {:} samples".format(len(valid_split)))
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base_optimizer, scheduler, criterion = get_optim_scheduler(
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search_model.base_parameters(), optim_config
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)
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arch_optimizer = torch.optim.Adam(
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search_model.arch_parameters(),
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lr=optim_config.arch_LR,
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betas=(0.5, 0.999),
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weight_decay=optim_config.arch_decay,
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)
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logger.log("base-optimizer : {:}".format(base_optimizer))
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logger.log("arch-optimizer : {:}".format(arch_optimizer))
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logger.log("scheduler : {:}".format(scheduler))
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logger.log("criterion : {:}".format(criterion))
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last_info, model_base_path, model_best_path = (
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logger.path("info"),
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logger.path("model"),
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logger.path("best"),
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)
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network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
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# load checkpoint
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if last_info.exists() or (
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args.resume is not None and osp.isfile(args.resume)
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): # automatically resume from previous checkpoint
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if args.resume is not None and osp.isfile(args.resume):
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resume_path = Path(args.resume)
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elif last_info.exists():
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resume_path = last_info
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else:
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raise ValueError("Something is wrong.")
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(resume_path)
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)
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checkpoint = torch.load(resume_path)
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if "last_checkpoint" in checkpoint:
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last_checkpoint_path = checkpoint["last_checkpoint"]
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if not last_checkpoint_path.exists():
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logger.log(
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"Does not find {:}, try another path".format(last_checkpoint_path)
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)
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last_checkpoint_path = (
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resume_path.parent
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/ last_checkpoint_path.parent.name
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/ last_checkpoint_path.name
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)
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assert (
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last_checkpoint_path.exists()
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), "can not find the checkpoint from {:}".format(last_checkpoint_path)
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checkpoint = torch.load(last_checkpoint_path)
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start_epoch = checkpoint["epoch"] + 1
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search_model.load_state_dict(checkpoint["search_model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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base_optimizer.load_state_dict(checkpoint["base_optimizer"])
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arch_optimizer.load_state_dict(checkpoint["arch_optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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arch_genotypes = checkpoint["arch_genotypes"]
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discrepancies = checkpoint["discrepancies"]
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
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resume_path, start_epoch
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)
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)
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else:
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logger.log(
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"=> do not find the last-info file : {:} or resume : {:}".format(
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last_info, args.resume
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)
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)
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start_epoch, valid_accuracies, arch_genotypes, discrepancies = (
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0,
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{"best": -1},
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{},
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{},
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)
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# main procedure
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train_func, valid_func = get_procedures(args.procedure)
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total_epoch = optim_config.epochs + optim_config.warmup
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start_time, epoch_time = time.time(), AverageMeter()
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for epoch in range(start_epoch, total_epoch):
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scheduler.update(epoch, 0.0)
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search_model.set_tau(
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args.gumbel_tau_max, args.gumbel_tau_min, epoch * 1.0 / total_epoch
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)
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need_time = "Time Left: {:}".format(
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convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
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)
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epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
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LRs = scheduler.get_lr()
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find_best = False
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logger.log(
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"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}".format(
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time_string(),
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epoch_str,
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need_time,
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min(LRs),
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max(LRs),
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scheduler,
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search_model.tau,
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MAX_FLOP,
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)
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)
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# train for one epoch
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train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(
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search_loader,
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network,
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criterion,
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scheduler,
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base_optimizer,
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arch_optimizer,
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optim_config,
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{
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"epoch-str": epoch_str,
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"FLOP-exp": MAX_FLOP * args.FLOP_ratio,
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"FLOP-weight": args.FLOP_weight,
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"FLOP-tolerant": MAX_FLOP * args.FLOP_tolerant,
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},
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args.print_freq,
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logger,
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)
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# log the results
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logger.log(
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"***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
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time_string(),
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epoch_str,
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train_base_loss,
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train_arch_loss,
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train_acc1,
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train_acc5,
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)
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)
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cur_FLOP, genotype = search_model.get_flop(
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"genotype", model_config._asdict(), None
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)
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arch_genotypes[epoch] = genotype
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arch_genotypes["last"] = genotype
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logger.log("[{:}] genotype : {:}".format(epoch_str, genotype))
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arch_info, discrepancy = search_model.get_arch_info()
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logger.log(arch_info)
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discrepancies[epoch] = discrepancy
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logger.log(
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"[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}".format(
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epoch_str,
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cur_FLOP,
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cur_FLOP / MAX_FLOP,
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args.FLOP_ratio,
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np.mean(discrepancy),
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)
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)
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# if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
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# init_flop_weight = init_flop_weight * args.FLOP_decay
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# else:
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# init_flop_weight = init_flop_weight / args.FLOP_decay
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# evaluate the performance
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if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
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logger.log("-" * 150)
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valid_loss, valid_acc1, valid_acc5 = valid_func(
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search_valid_loader,
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network,
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criterion,
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epoch_str,
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args.print_freq_eval,
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logger,
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)
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valid_accuracies[epoch] = valid_acc1
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logger.log(
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"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
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time_string(),
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epoch_str,
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valid_loss,
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valid_acc1,
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valid_acc5,
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valid_accuracies["best"],
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100 - valid_accuracies["best"],
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)
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)
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if valid_acc1 > valid_accuracies["best"]:
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valid_accuracies["best"] = valid_acc1
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arch_genotypes["best"] = genotype
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find_best = True
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logger.log(
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"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
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epoch,
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valid_acc1,
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valid_acc5,
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100 - valid_acc1,
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100 - valid_acc5,
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model_best_path,
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)
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)
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# save checkpoint
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save_path = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"valid_accuracies": deepcopy(valid_accuracies),
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"model-config": model_config._asdict(),
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"optim-config": optim_config._asdict(),
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"search_model": search_model.state_dict(),
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"scheduler": scheduler.state_dict(),
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"base_optimizer": base_optimizer.state_dict(),
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"arch_optimizer": arch_optimizer.state_dict(),
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"arch_genotypes": arch_genotypes,
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"discrepancies": discrepancies,
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},
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model_base_path,
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logger,
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)
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if find_best:
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copy_checkpoint(model_base_path, model_best_path, logger)
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last_info = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"last_checkpoint": save_path,
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},
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logger.path("info"),
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logger,
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)
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log("")
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logger.log("-" * 100)
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last_config_path = logger.path("log") / "seed-{:}-last.config".format(
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args.rand_seed
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)
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configure2str(arch_genotypes["last"], str(last_config_path))
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logger.log(
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"save the last config int {:} :\n{:}".format(
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last_config_path, arch_genotypes["last"]
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)
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)
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best_arch, valid_acc = arch_genotypes["best"], valid_accuracies["best"]
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for key, config in arch_genotypes.items():
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if key == "last":
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continue
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FLOP_ratio = config["estimated_FLOP"] / MAX_FLOP
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if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
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if valid_acc < valid_accuracies[key]:
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best_arch, valid_acc = config, valid_accuracies[key]
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print(
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"Best-Arch : {:}\nRatio={:}, Valid-ACC={:}".format(
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best_arch, best_arch["estimated_FLOP"] / MAX_FLOP, valid_acc
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)
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)
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best_config_path = logger.path("log") / "seed-{:}-best.config".format(
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args.rand_seed
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)
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configure2str(best_arch, str(best_config_path))
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logger.log(
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"save the last config int {:} :\n{:}".format(best_config_path, best_arch)
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)
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logger.log("\n" + "-" * 200)
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logger.log(
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"Finish training/validation in {:}, and save final checkpoint into {:}".format(
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convert_secs2time(epoch_time.sum, True), logger.path("info")
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)
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)
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logger.close()
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if __name__ == "__main__":
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args = obtain_args()
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main(args)
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420
exps/TAS/search-transformable.py
Normal file
420
exps/TAS/search-transformable.py
Normal file
@@ -0,0 +1,420 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#######################################################################
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# Network Pruning via Transformable Architecture Search, NeurIPS 2019 #
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#######################################################################
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import sys, time, torch, random, argparse
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from PIL import ImageFile
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from os import path as osp
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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import numpy as np
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from copy import deepcopy
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from pathlib import Path
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from xautodl.config_utils import (
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load_config,
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configure2str,
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obtain_search_args as obtain_args,
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)
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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.procedures import get_optim_scheduler, get_procedures
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from xautodl.datasets import get_datasets, SearchDataset
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from xautodl.models import obtain_search_model, obtain_model, change_key
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from xautodl.utils import get_model_infos
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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def main(args):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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torch.set_num_threads(args.workers)
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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# prepare dataset
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train_data, valid_data, xshape, class_num = get_datasets(
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args.dataset, args.data_path, args.cutout_length
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)
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# train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=args.batch_size,
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shuffle=False,
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num_workers=args.workers,
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pin_memory=True,
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)
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split_file_path = Path(args.split_path)
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assert split_file_path.exists(), "{:} does not exist".format(split_file_path)
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split_info = torch.load(split_file_path)
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train_split, valid_split = split_info["train"], split_info["valid"]
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assert (
|
||||
len(set(train_split).intersection(set(valid_split))) == 0
|
||||
), "There should be 0 element that belongs to both train and valid"
|
||||
assert len(train_split) + len(valid_split) == len(
|
||||
train_data
|
||||
), "{:} + {:} vs {:}".format(len(train_split), len(valid_split), len(train_data))
|
||||
search_dataset = SearchDataset(args.dataset, train_data, train_split, valid_split)
|
||||
|
||||
search_train_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=args.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
|
||||
pin_memory=True,
|
||||
num_workers=args.workers,
|
||||
)
|
||||
search_valid_loader = torch.utils.data.DataLoader(
|
||||
train_data,
|
||||
batch_size=args.batch_size,
|
||||
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
|
||||
pin_memory=True,
|
||||
num_workers=args.workers,
|
||||
)
|
||||
search_loader = torch.utils.data.DataLoader(
|
||||
search_dataset,
|
||||
batch_size=args.batch_size,
|
||||
shuffle=True,
|
||||
num_workers=args.workers,
|
||||
pin_memory=True,
|
||||
sampler=None,
|
||||
)
|
||||
# get configures
|
||||
if args.ablation_num_select is None or args.ablation_num_select <= 0:
|
||||
model_config = load_config(
|
||||
args.model_config, {"class_num": class_num, "search_mode": "shape"}, logger
|
||||
)
|
||||
else:
|
||||
model_config = load_config(
|
||||
args.model_config,
|
||||
{
|
||||
"class_num": class_num,
|
||||
"search_mode": "ablation",
|
||||
"num_random_select": args.ablation_num_select,
|
||||
},
|
||||
logger,
|
||||
)
|
||||
|
||||
# obtain the model
|
||||
search_model = obtain_search_model(model_config)
|
||||
MAX_FLOP, param = get_model_infos(search_model, xshape)
|
||||
optim_config = load_config(
|
||||
args.optim_config, {"class_num": class_num, "FLOP": MAX_FLOP}, logger
|
||||
)
|
||||
logger.log("Model Information : {:}".format(search_model.get_message()))
|
||||
logger.log("MAX_FLOP = {:} M".format(MAX_FLOP))
|
||||
logger.log("Params = {:} M".format(param))
|
||||
logger.log("train_data : {:}".format(train_data))
|
||||
logger.log("search-data: {:}".format(search_dataset))
|
||||
logger.log("search_train_loader : {:} samples".format(len(train_split)))
|
||||
logger.log("search_valid_loader : {:} samples".format(len(valid_split)))
|
||||
base_optimizer, scheduler, criterion = get_optim_scheduler(
|
||||
search_model.base_parameters(), optim_config
|
||||
)
|
||||
arch_optimizer = torch.optim.Adam(
|
||||
search_model.arch_parameters(optim_config.arch_LR),
|
||||
lr=optim_config.arch_LR,
|
||||
betas=(0.5, 0.999),
|
||||
weight_decay=optim_config.arch_decay,
|
||||
)
|
||||
logger.log("base-optimizer : {:}".format(base_optimizer))
|
||||
logger.log("arch-optimizer : {:}".format(arch_optimizer))
|
||||
logger.log("scheduler : {:}".format(scheduler))
|
||||
logger.log("criterion : {:}".format(criterion))
|
||||
|
||||
last_info, model_base_path, model_best_path = (
|
||||
logger.path("info"),
|
||||
logger.path("model"),
|
||||
logger.path("best"),
|
||||
)
|
||||
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
|
||||
|
||||
# load checkpoint
|
||||
if last_info.exists() or (
|
||||
args.resume is not None and osp.isfile(args.resume)
|
||||
): # automatically resume from previous checkpoint
|
||||
if args.resume is not None and osp.isfile(args.resume):
|
||||
resume_path = Path(args.resume)
|
||||
elif last_info.exists():
|
||||
resume_path = last_info
|
||||
else:
|
||||
raise ValueError("Something is wrong.")
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start".format(resume_path)
|
||||
)
|
||||
checkpoint = torch.load(resume_path)
|
||||
if "last_checkpoint" in checkpoint:
|
||||
last_checkpoint_path = checkpoint["last_checkpoint"]
|
||||
if not last_checkpoint_path.exists():
|
||||
logger.log(
|
||||
"Does not find {:}, try another path".format(last_checkpoint_path)
|
||||
)
|
||||
last_checkpoint_path = (
|
||||
resume_path.parent
|
||||
/ last_checkpoint_path.parent.name
|
||||
/ last_checkpoint_path.name
|
||||
)
|
||||
assert (
|
||||
last_checkpoint_path.exists()
|
||||
), "can not find the checkpoint from {:}".format(last_checkpoint_path)
|
||||
checkpoint = torch.load(last_checkpoint_path)
|
||||
start_epoch = checkpoint["epoch"] + 1
|
||||
# for key, value in checkpoint['search_model'].items():
|
||||
# print('K {:} = Shape={:}'.format(key, value.shape))
|
||||
search_model.load_state_dict(checkpoint["search_model"])
|
||||
scheduler.load_state_dict(checkpoint["scheduler"])
|
||||
base_optimizer.load_state_dict(checkpoint["base_optimizer"])
|
||||
arch_optimizer.load_state_dict(checkpoint["arch_optimizer"])
|
||||
valid_accuracies = checkpoint["valid_accuracies"]
|
||||
arch_genotypes = checkpoint["arch_genotypes"]
|
||||
discrepancies = checkpoint["discrepancies"]
|
||||
max_bytes = checkpoint["max_bytes"]
|
||||
logger.log(
|
||||
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||
resume_path, start_epoch
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.log(
|
||||
"=> do not find the last-info file : {:} or resume : {:}".format(
|
||||
last_info, args.resume
|
||||
)
|
||||
)
|
||||
start_epoch, valid_accuracies, arch_genotypes, discrepancies, max_bytes = (
|
||||
0,
|
||||
{"best": -1},
|
||||
{},
|
||||
{},
|
||||
{},
|
||||
)
|
||||
|
||||
# main procedure
|
||||
train_func, valid_func = get_procedures(args.procedure)
|
||||
total_epoch = optim_config.epochs + optim_config.warmup
|
||||
start_time, epoch_time = time.time(), AverageMeter()
|
||||
for epoch in range(start_epoch, total_epoch):
|
||||
scheduler.update(epoch, 0.0)
|
||||
search_model.set_tau(
|
||||
args.gumbel_tau_max, args.gumbel_tau_min, epoch * 1.0 / total_epoch
|
||||
)
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
|
||||
)
|
||||
epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
|
||||
LRs = scheduler.get_lr()
|
||||
find_best = False
|
||||
|
||||
logger.log(
|
||||
"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}".format(
|
||||
time_string(),
|
||||
epoch_str,
|
||||
need_time,
|
||||
min(LRs),
|
||||
max(LRs),
|
||||
scheduler,
|
||||
search_model.tau,
|
||||
MAX_FLOP,
|
||||
)
|
||||
)
|
||||
|
||||
# train for one epoch
|
||||
train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(
|
||||
search_loader,
|
||||
network,
|
||||
criterion,
|
||||
scheduler,
|
||||
base_optimizer,
|
||||
arch_optimizer,
|
||||
optim_config,
|
||||
{
|
||||
"epoch-str": epoch_str,
|
||||
"FLOP-exp": MAX_FLOP * args.FLOP_ratio,
|
||||
"FLOP-weight": args.FLOP_weight,
|
||||
"FLOP-tolerant": MAX_FLOP * args.FLOP_tolerant,
|
||||
},
|
||||
args.print_freq,
|
||||
logger,
|
||||
)
|
||||
# log the results
|
||||
logger.log(
|
||||
"***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
|
||||
time_string(),
|
||||
epoch_str,
|
||||
train_base_loss,
|
||||
train_arch_loss,
|
||||
train_acc1,
|
||||
train_acc5,
|
||||
)
|
||||
)
|
||||
cur_FLOP, genotype = search_model.get_flop(
|
||||
"genotype", model_config._asdict(), None
|
||||
)
|
||||
arch_genotypes[epoch] = genotype
|
||||
arch_genotypes["last"] = genotype
|
||||
logger.log("[{:}] genotype : {:}".format(epoch_str, genotype))
|
||||
# save the configuration
|
||||
configure2str(
|
||||
genotype,
|
||||
str(logger.path("log") / "seed-{:}-temp.config".format(args.rand_seed)),
|
||||
)
|
||||
arch_info, discrepancy = search_model.get_arch_info()
|
||||
logger.log(arch_info)
|
||||
discrepancies[epoch] = discrepancy
|
||||
logger.log(
|
||||
"[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}".format(
|
||||
epoch_str,
|
||||
cur_FLOP,
|
||||
cur_FLOP / MAX_FLOP,
|
||||
args.FLOP_ratio,
|
||||
np.mean(discrepancy),
|
||||
)
|
||||
)
|
||||
|
||||
# if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
|
||||
# init_flop_weight = init_flop_weight * args.FLOP_decay
|
||||
# else:
|
||||
# init_flop_weight = init_flop_weight / args.FLOP_decay
|
||||
|
||||
# evaluate the performance
|
||||
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
|
||||
logger.log("-" * 150)
|
||||
valid_loss, valid_acc1, valid_acc5 = valid_func(
|
||||
search_valid_loader,
|
||||
network,
|
||||
criterion,
|
||||
epoch_str,
|
||||
args.print_freq_eval,
|
||||
logger,
|
||||
)
|
||||
valid_accuracies[epoch] = valid_acc1
|
||||
logger.log(
|
||||
"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
|
||||
time_string(),
|
||||
epoch_str,
|
||||
valid_loss,
|
||||
valid_acc1,
|
||||
valid_acc5,
|
||||
valid_accuracies["best"],
|
||||
100 - valid_accuracies["best"],
|
||||
)
|
||||
)
|
||||
if valid_acc1 > valid_accuracies["best"]:
|
||||
valid_accuracies["best"] = valid_acc1
|
||||
arch_genotypes["best"] = genotype
|
||||
find_best = True
|
||||
logger.log(
|
||||
"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
|
||||
epoch,
|
||||
valid_acc1,
|
||||
valid_acc5,
|
||||
100 - valid_acc1,
|
||||
100 - valid_acc5,
|
||||
model_best_path,
|
||||
)
|
||||
)
|
||||
# log the GPU memory usage
|
||||
# num_bytes = torch.cuda.max_memory_allocated( next(network.parameters()).device ) * 1.0
|
||||
num_bytes = (
|
||||
torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
|
||||
)
|
||||
logger.log(
|
||||
"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
|
||||
next(network.parameters()).device,
|
||||
int(num_bytes),
|
||||
num_bytes / 1e3,
|
||||
num_bytes / 1e6,
|
||||
num_bytes / 1e9,
|
||||
)
|
||||
)
|
||||
max_bytes[epoch] = num_bytes
|
||||
|
||||
# save checkpoint
|
||||
save_path = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch,
|
||||
"args": deepcopy(args),
|
||||
"max_bytes": deepcopy(max_bytes),
|
||||
"valid_accuracies": deepcopy(valid_accuracies),
|
||||
"model-config": model_config._asdict(),
|
||||
"optim-config": optim_config._asdict(),
|
||||
"search_model": search_model.state_dict(),
|
||||
"scheduler": scheduler.state_dict(),
|
||||
"base_optimizer": base_optimizer.state_dict(),
|
||||
"arch_optimizer": arch_optimizer.state_dict(),
|
||||
"arch_genotypes": arch_genotypes,
|
||||
"discrepancies": discrepancies,
|
||||
},
|
||||
model_base_path,
|
||||
logger,
|
||||
)
|
||||
if find_best:
|
||||
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||
last_info = save_checkpoint(
|
||||
{
|
||||
"epoch": epoch,
|
||||
"args": deepcopy(args),
|
||||
"last_checkpoint": save_path,
|
||||
},
|
||||
logger.path("info"),
|
||||
logger,
|
||||
)
|
||||
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
logger.log("")
|
||||
logger.log("-" * 100)
|
||||
last_config_path = logger.path("log") / "seed-{:}-last.config".format(
|
||||
args.rand_seed
|
||||
)
|
||||
configure2str(arch_genotypes["last"], str(last_config_path))
|
||||
logger.log(
|
||||
"save the last config int {:} :\n{:}".format(
|
||||
last_config_path, arch_genotypes["last"]
|
||||
)
|
||||
)
|
||||
|
||||
best_arch, valid_acc = arch_genotypes["best"], valid_accuracies["best"]
|
||||
for key, config in arch_genotypes.items():
|
||||
if key == "last":
|
||||
continue
|
||||
FLOP_ratio = config["estimated_FLOP"] / MAX_FLOP
|
||||
if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
|
||||
if valid_acc <= valid_accuracies[key]:
|
||||
best_arch, valid_acc = config, valid_accuracies[key]
|
||||
print(
|
||||
"Best-Arch : {:}\nRatio={:}, Valid-ACC={:}".format(
|
||||
best_arch, best_arch["estimated_FLOP"] / MAX_FLOP, valid_acc
|
||||
)
|
||||
)
|
||||
best_config_path = logger.path("log") / "seed-{:}-best.config".format(
|
||||
args.rand_seed
|
||||
)
|
||||
configure2str(best_arch, str(best_config_path))
|
||||
logger.log(
|
||||
"save the last config int {:} :\n{:}".format(best_config_path, best_arch)
|
||||
)
|
||||
logger.log("\n" + "-" * 200)
|
||||
logger.log(
|
||||
"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} GB, and save final checkpoint into {:}".format(
|
||||
convert_secs2time(epoch_time.sum, True),
|
||||
max(v for k, v in max_bytes.items()) / 1e9,
|
||||
logger.path("info"),
|
||||
)
|
||||
)
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = obtain_args()
|
||||
main(args)
|
||||
Reference in New Issue
Block a user