405 lines
15 KiB
Python
405 lines
15 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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###########################################################################
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# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
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###########################################################################
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import sys, time, random, argparse
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from copy import deepcopy
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import torch
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from xautodl.config_utils import load_config, dict2config
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from xautodl.datasets import get_datasets, get_nas_search_loaders
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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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get_optim_scheduler,
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)
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from xautodl.utils import get_model_infos, obtain_accuracy
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import get_cell_based_tiny_net, get_search_spaces
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from nas_201_api import NASBench201API as API
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def search_func(
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xloader,
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network,
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criterion,
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scheduler,
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w_optimizer,
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a_optimizer,
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epoch_str,
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print_freq,
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logger,
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):
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data_time, batch_time = AverageMeter(), AverageMeter()
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base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
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network.train()
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end = time.time()
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for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
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xloader
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):
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scheduler.update(None, 1.0 * step / len(xloader))
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base_targets = base_targets.cuda(non_blocking=True)
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arch_targets = arch_targets.cuda(non_blocking=True)
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# measure data loading time
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data_time.update(time.time() - end)
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# update the weights
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w_optimizer.zero_grad()
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_, logits = network(base_inputs)
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base_loss = criterion(logits, base_targets)
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base_loss.backward()
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torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
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w_optimizer.step()
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# record
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base_prec1, base_prec5 = obtain_accuracy(
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logits.data, base_targets.data, topk=(1, 5)
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)
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base_losses.update(base_loss.item(), base_inputs.size(0))
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base_top1.update(base_prec1.item(), base_inputs.size(0))
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base_top5.update(base_prec5.item(), base_inputs.size(0))
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# update the architecture-weight
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a_optimizer.zero_grad()
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_, logits = network(arch_inputs)
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arch_loss = criterion(logits, arch_targets)
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arch_loss.backward()
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a_optimizer.step()
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# record
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arch_prec1, arch_prec5 = obtain_accuracy(
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logits.data, arch_targets.data, topk=(1, 5)
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)
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arch_losses.update(arch_loss.item(), arch_inputs.size(0))
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arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
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arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if step % print_freq == 0 or step + 1 == len(xloader):
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Sstr = (
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"*SEARCH* "
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+ time_string()
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+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(xloader))
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)
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Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
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batch_time=batch_time, data_time=data_time
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)
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Wstr = "Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
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loss=base_losses, top1=base_top1, top5=base_top5
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)
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Astr = "Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]".format(
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loss=arch_losses, top1=arch_top1, top5=arch_top5
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)
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logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Astr)
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return (
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base_losses.avg,
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base_top1.avg,
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base_top5.avg,
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arch_losses.avg,
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arch_top1.avg,
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arch_top5.avg,
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)
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def main(xargs):
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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 = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.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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xargs.dataset, xargs.data_path, -1
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)
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# config_path = 'configs/nas-benchmark/algos/GDAS.config'
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config = load_config(
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xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger
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)
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search_loader, _, valid_loader = get_nas_search_loaders(
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train_data,
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valid_data,
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xargs.dataset,
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"configs/nas-benchmark/",
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config.batch_size,
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xargs.workers,
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)
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logger.log(
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"||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(search_loader), config.batch_size
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)
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)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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search_space = get_search_spaces("cell", xargs.search_space_name)
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if xargs.model_config is None:
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model_config = dict2config(
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{
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"name": "GDAS",
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"C": xargs.channel,
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"N": xargs.num_cells,
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"max_nodes": xargs.max_nodes,
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"num_classes": class_num,
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"space": search_space,
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"affine": False,
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"track_running_stats": bool(xargs.track_running_stats),
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},
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None,
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)
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else:
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model_config = load_config(
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xargs.model_config,
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{
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"num_classes": class_num,
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"space": search_space,
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"affine": False,
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"track_running_stats": bool(xargs.track_running_stats),
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},
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None,
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)
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search_model = get_cell_based_tiny_net(model_config)
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logger.log("search-model :\n{:}".format(search_model))
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logger.log("model-config : {:}".format(model_config))
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(
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search_model.get_weights(), config
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)
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a_optimizer = torch.optim.Adam(
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search_model.get_alphas(),
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lr=xargs.arch_learning_rate,
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betas=(0.5, 0.999),
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weight_decay=xargs.arch_weight_decay,
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)
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logger.log("w-optimizer : {:}".format(w_optimizer))
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logger.log("a-optimizer : {:}".format(a_optimizer))
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logger.log("w-scheduler : {:}".format(w_scheduler))
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logger.log("criterion : {:}".format(criterion))
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flop, param = get_model_infos(search_model, xshape)
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logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
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logger.log("search-space [{:} ops] : {:}".format(len(search_space), search_space))
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if xargs.arch_nas_dataset is None:
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api = None
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else:
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api = API(xargs.arch_nas_dataset)
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logger.log("{:} create API = {:} done".format(time_string(), api))
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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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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_info = torch.load(last_info)
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start_epoch = last_info["epoch"]
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checkpoint = torch.load(last_info["last_checkpoint"])
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genotypes = checkpoint["genotypes"]
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valid_accuracies = checkpoint["valid_accuracies"]
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search_model.load_state_dict(checkpoint["search_model"])
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w_scheduler.load_state_dict(checkpoint["w_scheduler"])
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w_optimizer.load_state_dict(checkpoint["w_optimizer"])
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a_optimizer.load_state_dict(checkpoint["a_optimizer"])
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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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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, genotypes = (
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0,
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{"best": -1},
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{-1: search_model.genotype()},
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)
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# start training
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start_time, search_time, epoch_time, total_epoch = (
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time.time(),
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AverageMeter(),
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AverageMeter(),
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config.epochs + config.warmup,
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)
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for epoch in range(start_epoch, total_epoch):
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w_scheduler.update(epoch, 0.0)
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need_time = "Time Left: {:}".format(
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convert_secs2time(epoch_time.val * (total_epoch - epoch), True)
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)
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epoch_str = "{:03d}-{:03d}".format(epoch, total_epoch)
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search_model.set_tau(
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xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1)
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)
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logger.log(
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"\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}".format(
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epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())
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)
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)
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(
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search_w_loss,
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search_w_top1,
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search_w_top5,
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valid_a_loss,
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valid_a_top1,
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valid_a_top5,
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) = search_func(
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search_loader,
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network,
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criterion,
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w_scheduler,
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w_optimizer,
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a_optimizer,
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epoch_str,
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xargs.print_freq,
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logger,
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)
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search_time.update(time.time() - start_time)
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logger.log(
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"[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s".format(
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epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum
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)
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)
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logger.log(
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"[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
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epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
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)
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)
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# check the best accuracy
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valid_accuracies[epoch] = valid_a_top1
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if valid_a_top1 > valid_accuracies["best"]:
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valid_accuracies["best"] = valid_a_top1
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genotypes["best"] = search_model.genotype()
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find_best = True
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else:
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find_best = False
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genotypes[epoch] = search_model.genotype()
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logger.log(
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"<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch])
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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 + 1,
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"args": deepcopy(xargs),
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"search_model": search_model.state_dict(),
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"w_optimizer": w_optimizer.state_dict(),
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"a_optimizer": a_optimizer.state_dict(),
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"w_scheduler": w_scheduler.state_dict(),
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"genotypes": genotypes,
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"valid_accuracies": valid_accuracies,
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},
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model_base_path,
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logger,
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)
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last_info = save_checkpoint(
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{
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"epoch": epoch + 1,
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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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if find_best:
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logger.log(
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"<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.".format(
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epoch_str, valid_a_top1
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)
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)
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copy_checkpoint(model_base_path, model_best_path, logger)
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with torch.no_grad():
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logger.log("{:}".format(search_model.show_alphas()))
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if api is not None:
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logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200")))
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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" + "-" * 100)
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# check the performance from the architecture dataset
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logger.log(
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"GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
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total_epoch, search_time.sum, genotypes[total_epoch - 1]
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)
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)
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if api is not None:
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logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200")))
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("GDAS")
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parser.add_argument("--data_path", type=str, help="The path to dataset")
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parser.add_argument(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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# channels and number-of-cells
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parser.add_argument("--search_space_name", type=str, help="The search space name.")
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parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
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parser.add_argument("--channel", type=int, help="The number of channels.")
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parser.add_argument(
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"--num_cells", type=int, help="The number of cells in one stage."
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)
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parser.add_argument(
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"--track_running_stats",
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type=int,
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choices=[0, 1],
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help="Whether use track_running_stats or not in the BN layer.",
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)
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parser.add_argument(
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"--config_path", type=str, help="The path of the configuration."
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)
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parser.add_argument(
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"--model_config",
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type=str,
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help="The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.",
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)
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# architecture leraning rate
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parser.add_argument(
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"--arch_learning_rate",
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type=float,
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default=3e-4,
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help="learning rate for arch encoding",
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)
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parser.add_argument(
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"--arch_weight_decay",
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type=float,
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default=1e-3,
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help="weight decay for arch encoding",
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)
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parser.add_argument("--tau_min", type=float, help="The minimum tau for Gumbel")
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parser.add_argument("--tau_max", type=float, help="The maximum tau for Gumbel")
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# log
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parser.add_argument(
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"--workers",
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type=int,
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default=2,
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help="number of data loading workers (default: 2)",
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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(
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"--arch_nas_dataset",
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type=str,
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help="The path to load the architecture dataset (tiny-nas-benchmark).",
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)
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parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
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parser.add_argument("--rand_seed", type=int, 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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main(args)
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