292 lines
11 KiB
Python
292 lines
11 KiB
Python
#####################################################
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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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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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from xautodl.datasets import get_datasets
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from xautodl.config_utils import load_config, obtain_basic_args as obtain_args
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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.models import obtain_model
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from xautodl.nas_infer_model import obtain_nas_infer_model
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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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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(
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train_data,
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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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)
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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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# get configures
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model_config = load_config(args.model_config, {"class_num": class_num}, logger)
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optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
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if args.model_source == "normal":
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base_model = obtain_model(model_config)
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elif args.model_source == "nas":
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base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
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elif args.model_source == "autodl-searched":
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base_model = obtain_model(model_config, args.extra_model_path)
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else:
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raise ValueError("invalid model-source : {:}".format(args.model_source))
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flop, param = get_model_infos(base_model, xshape)
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logger.log("model ====>>>>:\n{:}".format(base_model))
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logger.log("model information : {:}".format(base_model.get_message()))
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logger.log("-" * 50)
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logger.log(
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"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
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param, flop, flop / 1e3
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)
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)
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logger.log("-" * 50)
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logger.log("train_data : {:}".format(train_data))
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logger.log("valid_data : {:}".format(valid_data))
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optimizer, scheduler, criterion = get_optim_scheduler(
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base_model.parameters(), optim_config
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)
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logger.log("optimizer : {:}".format(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(base_model).cuda(), criterion.cuda()
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start".format(last_info)
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)
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last_infox = torch.load(last_info)
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start_epoch = last_infox["epoch"] + 1
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last_checkpoint_path = last_infox["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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last_info.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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checkpoint = torch.load(last_checkpoint_path)
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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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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last_info, start_epoch
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)
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)
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elif args.resume is not None:
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assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
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args.resume
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)
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checkpoint = torch.load(args.resume)
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start_epoch = checkpoint["epoch"] + 1
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
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args.resume, start_epoch
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)
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)
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elif args.init_model is not None:
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assert Path(
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args.init_model
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).exists(), "Can not find the initialization file : {:}".format(args.init_model)
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checkpoint = torch.load(args.init_model)
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base_model.load_state_dict(checkpoint["base-model"])
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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logger.log("=> initialize the model from {:}".format(args.init_model))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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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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# Main Training and Evaluation Loop
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start_time = time.time()
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epoch_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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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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# set-up drop-out ratio
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if hasattr(base_model, "update_drop_path"):
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base_model.update_drop_path(
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model_config.drop_path_prob * epoch / total_epoch
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)
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logger.log(
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"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
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time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
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)
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)
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# train for one epoch
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train_loss, train_acc1, train_acc5 = train_func(
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train_loader,
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network,
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criterion,
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scheduler,
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optimizer,
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optim_config,
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epoch_str,
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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 [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
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time_string(), epoch_str, train_loss, train_acc1, train_acc5
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)
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)
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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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valid_loader,
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network,
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criterion,
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optim_config,
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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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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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num_bytes = (
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torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
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)
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logger.log(
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"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
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next(network.parameters()).device,
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int(num_bytes),
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num_bytes / 1e3,
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num_bytes / 1e6,
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num_bytes / 1e9,
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)
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)
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max_bytes[epoch] = num_bytes
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if epoch % 10 == 0:
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torch.cuda.empty_cache()
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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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"max_bytes": deepcopy(max_bytes),
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"FLOP": flop,
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"PARAM": param,
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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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"base-model": base_model.state_dict(),
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"scheduler": scheduler.state_dict(),
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"optimizer": optimizer.state_dict(),
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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("\n" + "-" * 200)
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logger.log(
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"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
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convert_secs2time(epoch_time.sum, True),
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max(v for k, v in max_bytes.items()) / 1e6,
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logger.path("info"),
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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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if __name__ == "__main__":
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args = obtain_args()
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main(args)
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