updates
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D-X-Y
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MIT License
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Copyright (c) 2019 Xuanyi Dong
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Copyright (c) 2019 Xuanyi Dong [GitHub: https://github.com/D-X-Y]
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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@ -10,7 +10,7 @@ This project contains the following neural architecture search algorithms, imple
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## Requirements and Preparation
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Please install `PyTorch>=1.0.1`, `Python>=3.6`, and `opencv`.
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Please install `PyTorch>=1.1.0`, `Python>=3.6`, and `opencv`.
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The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
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Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
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import os, sys, time, queue, torch
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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 log_utils import time_string
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from models import CellStructure
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def get_unique_matrix(archs, consider_zero):
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UniquStrs = [arch.to_unique_str(consider_zero) for arch in archs]
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print ('{:} create unique-string done'.format(time_string()))
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sm_matrix = torch.eye(len(archs)).bool()
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for i, _ in enumerate(UniquStrs):
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for j in range(i):
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sm_matrix[i,j] = sm_matrix[j,i] = UniquStrs[i] == UniquStrs[j]
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unique_ids, unique_num = [-1 for _ in archs], 0
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for i in range(len(unique_ids)):
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if unique_ids[i] > -1: continue
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neighbours = sm_matrix[i].nonzero().view(-1).tolist()
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for nghb in neighbours:
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assert unique_ids[nghb] == -1, 'impossible'
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unique_ids[nghb] = unique_num
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unique_num += 1
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return sm_matrix, unique_ids, unique_num
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def check_unique_arch():
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print ('{:} start'.format(time_string()))
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meta_info = torch.load('./output/AA-NAS-BENCH-4/meta-node-4.pth')
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arch_strs = meta_info['archs']
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archs = [CellStructure.str2structure(arch_str) for arch_str in arch_strs]
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_, _, unique_num = get_unique_matrix(archs, False)
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print ('{:} There are {:} unique architectures (not considering zero).'.format(time_string(), unique_num))
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_, _, unique_num = get_unique_matrix(archs, True)
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print ('{:} There are {:} unique architectures (considering zero).'.format(time_string(), unique_num))
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if __name__ == '__main__':
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check_unique_arch()
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@ -1,124 +0,0 @@
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, torch
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from procedures import prepare_seed, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from config_utils import dict2config
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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
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__all__ = ['evaluate_for_seed', 'pure_evaluate']
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def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
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data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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latencies = []
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network.eval()
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with torch.no_grad():
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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targets = targets.cuda(non_blocking=True)
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inputs = inputs.cuda(non_blocking=True)
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data_time.update(time.time() - end)
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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batch_time.update(time.time() - end)
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if batch is None or batch == inputs.size(0):
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batch = inputs.size(0)
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latencies.append( batch_time.val - data_time.val )
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# record loss and accuracy
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update (prec1.item(), inputs.size(0))
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top5.update (prec5.item(), inputs.size(0))
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end = time.time()
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if len(latencies) > 2: latencies = latencies[1:]
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return losses.avg, top1.avg, top5.avg, latencies
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def procedure(xloader, network, criterion, scheduler, optimizer, mode):
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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if mode == 'train' : network.train()
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elif mode == 'valid': network.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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for i, (inputs, targets) in enumerate(xloader):
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if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
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targets = targets.cuda(non_blocking=True)
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if mode == 'train': optimizer.zero_grad()
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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# backward
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if mode == 'train':
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loss.backward()
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optimizer.step()
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# record loss and accuracy
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update (prec1.item(), inputs.size(0))
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top5.update (prec5.item(), inputs.size(0))
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return losses.avg, top1.avg, top5.avg
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def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loader, seed, logger):
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prepare_seed(seed) # random seed
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net = get_cell_based_tiny_net(dict2config({'name': 'infer.tiny',
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'C': arch_config['channel'], 'N': arch_config['num_cells'],
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'genotype': arch, 'num_classes': config.class_num}
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, None)
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)
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#net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
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flop, param = get_model_infos(net, config.xshape)
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logger.log('Network : {:}'.format(net.get_message()), False)
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logger.log('Seed-------------------------- {:} --------------------------'.format(seed))
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logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
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# train and valid
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optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
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network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
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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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train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
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for epoch in range(total_epoch):
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scheduler.update(epoch, 0.0)
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train_loss, train_acc1, train_acc5 = procedure(train_loader, network, criterion, scheduler, optimizer, 'train')
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with torch.no_grad():
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valid_loss, valid_acc1, valid_acc5 = procedure(valid_loader, network, criterion, None, None, 'valid')
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train_losses[epoch] = train_loss
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train_acc1es[epoch] = train_acc1
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train_acc5es[epoch] = train_acc5
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valid_losses[epoch] = valid_loss
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valid_acc1es[epoch] = valid_acc1
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valid_acc5es[epoch] = valid_acc5
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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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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (total_epoch-epoch-1), True) )
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logger.log('{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%]'.format(time_string(), need_time, epoch, total_epoch, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5))
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info_seed = {'flop' : flop,
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'param': param,
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'channel' : arch_config['channel'],
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'num_cells' : arch_config['num_cells'],
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'config' : config._asdict(),
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'total_epoch' : total_epoch ,
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'train_losses': train_losses,
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'train_acc1es': train_acc1es,
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'train_acc5es': train_acc5es,
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'valid_losses': valid_losses,
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'valid_acc1es': valid_acc1es,
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'valid_acc5es': valid_acc5es,
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'net_state_dict': net.state_dict(),
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'net_string' : '{:}'.format(net),
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'finish-train': True
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}
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return info_seed
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