222 lines
12 KiB
Python
222 lines
12 KiB
Python
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_cell_based_tiny_net, get_search_spaces
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def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
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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(xloader):
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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(logits.data, base_targets.data, topk=(1, 5))
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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(logits.data, arch_targets.data, topk=(1, 5))
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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 = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))
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Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
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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(loss=base_losses, top1=base_top1, top5=base_top5)
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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(loss=arch_losses, top1=arch_top1, top5=arch_top5)
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logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
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return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
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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, _, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
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split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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elif xargs.dataset.startswith('ImageNet16'):
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split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
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imagenet16_split = load_config(split_Fpath, None, None)
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train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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else:
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raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
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config_path = 'configs/nas-benchmark/algos/GDAS.config'
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config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
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logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), config.batch_size))
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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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model_config = dict2config({'name': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,
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'max_nodes': xargs.max_nodes, 'num_classes': class_num,
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'space' : search_space}, None)
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search_model = get_cell_based_tiny_net(model_config)
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w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
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a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)
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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('{:}'.format(search_model))
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logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
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logger.log('search_space : {:}'.format(search_space))
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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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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("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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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("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
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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 = 0, {'best': -1}, {}
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# start training
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start_time, epoch_time, total_epoch = time.time(), AverageMeter(), config.epochs + config.warmup
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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( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
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epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
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search_model.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )
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logger.log('\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())))
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search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5 \
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= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
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logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
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logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss , valid_a_top1 , valid_a_top5 ))
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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: find_best = False
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genotypes[epoch] = search_model.genotype()
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logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
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# save checkpoint
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save_path = save_checkpoint({'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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model_base_path, logger)
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last_info = save_checkpoint({
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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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}, logger.path('info'), logger)
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if find_best:
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logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
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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('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
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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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"""
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if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
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logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))
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else:
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nas_bench = NASBenchmarkAPI(xargs.arch_nas_dataset)
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geno = genotypes[total_epoch-1]
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logger.log('The last model is {:}'.format(geno))
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info = nas_bench.query_by_arch( geno )
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if info is None: logger.log('Did not find this architecture : {:}.'.format(geno))
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else : logger.log('{:}'.format(info))
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logger.log('-'*100)
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"""
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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='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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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('--num_cells', type=int, help='The number of cells in one stage.')
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# architecture leraning rate
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parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
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parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
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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('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
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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: args.rand_seed = random.randint(1, 100000)
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main(args)
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