update all

test.py

@@ -11,30 +11,26 @@
 
 from    model import NetworkCIFAR as Network
 
-parser = argparse.ArgumentParser("cifar")
+parser = argparse.ArgumentParser("cifar10")
 parser.add_argument('--data', type=str, default='../data', help='location of the data corpus')
-parser.add_argument('--batchsz', type=int, default=16, help='batch size')
+parser.add_argument('--batchsz', type=int, default=36, help='batch size')
 parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
 parser.add_argument('--gpu', type=int, default=0, help='gpu device id')
-parser.add_argument('--init_ch', type=int, default=16, help='num of init channels')
-parser.add_argument('--layers', type=int, default=8, help='total number of layers')
-parser.add_argument('--exp_path', type=str, default='exp1/model.pt', help='path of pretrained model')
+parser.add_argument('--init_ch', type=int, default=36, help='num of init channels')
+parser.add_argument('--layers', type=int, default=20, help='total number of layers')
+parser.add_argument('--exp_path', type=str, default='exp/model.pt', help='path of pretrained model')
 parser.add_argument('--auxiliary', action='store_true', default=False, help='use auxiliary tower')
 parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
 parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
 parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path probability')
 parser.add_argument('--seed', type=int, default=0, help='random seed')
-parser.add_argument('--arch', type=str, default='MyDARTS', help='which architecture to use')
+parser.add_argument('--arch', type=str, default='DARTS', help='which architecture to use')
 args = parser.parse_args()
 
 log_format = '%(asctime)s %(message)s'
 logging.basicConfig(stream=sys.stdout, level=logging.INFO,
                     format=log_format, datefmt='%m/%d %I:%M:%S %p')
 
-CIFAR_CLASSES = 10
-
-
-
 
 
 def main():
@@ -68,6 +64,7 @@ def main():
 
 
 def infer(test_queue, model, criterion):
+
     objs = utils.AverageMeter()
     top1 = utils.AverageMeter()
     top5 = utils.AverageMeter()

train.py

@@ -17,7 +17,7 @@
 
 parser = argparse.ArgumentParser("cifar10")
 parser.add_argument('--data', type=str, default='../data', help='location of the data corpus')
-parser.add_argument('--batchsz', type=int, default=48, help='batch size')
+parser.add_argument('--batchsz', type=int, default=30, help='batch size')
 parser.add_argument('--lr', type=float, default=0.025, help='init learning rate')
 parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
 parser.add_argument('--wd', type=float, default=3e-4, help='weight decay')
@@ -91,11 +91,13 @@ def main():
         logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
         model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
 
+        valid_acc, valid_obj = infer(valid_queue, model, criterion)
+        logging.info('valid_acc: %f', valid_acc)
+
         train_acc, train_obj = train(train_queue, model, criterion, optimizer)
         logging.info('train_acc: %f', train_acc)
 
-        valid_acc, valid_obj = infer(valid_queue, model, criterion)
-        logging.info('valid_acc: %f', valid_acc)
+
 
         utils.save(model, os.path.join(args.save, 'trained.pt'))
         print('saved to: trained.pt')