260 lines
9.7 KiB
Python
260 lines
9.7 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
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#####################################################
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# python exps/basic/xmain.py --save_dir outputs/x #
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#####################################################
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import os, sys, time, torch, random, argparse
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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 import xmisc
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def main(args):
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train_data = xmisc.nested_call_by_yaml(args.train_data_config, args.data_path)
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valid_data = xmisc.nested_call_by_yaml(args.valid_data_config, args.data_path)
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logger = xmisc.Logger(args.save_dir, prefix="seed-{:}-".format(args.rand_seed))
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logger.log("Create the logger: {:}".format(logger))
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logger.log("Arguments : -------------------------------")
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for name, value in args._get_kwargs():
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logger.log("{:16} : {:}".format(name, value))
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logger.log("Python Version : {:}".format(sys.version.replace("\n", " ")))
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logger.log("PyTorch Version : {:}".format(torch.__version__))
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logger.log("cuDNN Version : {:}".format(torch.backends.cudnn.version()))
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logger.log("CUDA available : {:}".format(torch.cuda.is_available()))
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logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
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logger.log(
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"CUDA_VISIBLE_DEVICES : {:}".format(
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os.environ["CUDA_VISIBLE_DEVICES"]
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if "CUDA_VISIBLE_DEVICES" in os.environ
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else "None"
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)
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)
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logger.log("The training data is:\n{:}".format(train_data))
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logger.log("The validation data is:\n{:}".format(valid_data))
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model = xmisc.nested_call_by_yaml(args.model_config)
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logger.log("The model is:\n{:}".format(model))
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logger.log("The model size is {:.4f} M".format(xmisc.count_parameters(model)))
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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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logger.log("The training loader: {:}".format(train_loader))
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logger.log("The validation loader: {:}".format(valid_loader))
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optimizer = xmisc.nested_call_by_yaml(
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args.optim_config,
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model.parameters(),
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lr=args.lr,
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weight_decay=args.weight_decay,
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)
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loss = xmisc.nested_call_by_yaml(args.loss_config)
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logger.log("The optimizer is:\n{:}".format(optimizer))
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logger.log("The loss is {:}".format(loss))
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model, loss = torch.nn.DataParallel(model).cuda(), loss.cuda()
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import pdb
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pdb.set_trace()
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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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parser = argparse.ArgumentParser(
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description="Train a classification model with a loss function.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--save_dir", type=str, help="Folder to save checkpoints and log."
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)
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parser.add_argument("--resume", type=str, help="Resume path.")
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parser.add_argument("--init_model", type=str, help="The initialization model path.")
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parser.add_argument("--model_config", type=str, help="The path to the model config")
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parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
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parser.add_argument("--loss_config", type=str, help="The loss config file.")
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parser.add_argument(
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"--train_data_config", type=str, help="The training dataset config path."
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)
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parser.add_argument(
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"--valid_data_config", type=str, help="The validation dataset config path."
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)
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parser.add_argument("--data_path", type=str, help="The path to the dataset.")
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parser.add_argument("--algorithm", type=str, help="The algorithm.")
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# Optimization options
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parser.add_argument("--lr", type=float, help="The learning rate")
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parser.add_argument("--weight_decay", type=float, help="The weight decay")
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parser.add_argument("--batch_size", type=int, default=2, help="The batch size.")
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parser.add_argument("--workers", type=int, default=4, help="The number of workers")
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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if args.save_dir is None:
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raise ValueError("The save-path argument can not be None")
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main(args)
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