train1 to trainA

train_autodeeplab.py

@@ -30,7 +30,7 @@ def __init__(self, args):
         self.writer = self.summary.create_summary()
 
         kwargs = {'num_workers': args.workers, 'pin_memory': True}
-        self.train_loader1, self.train_loader2, self.val_loader, self.test_loader, self.nclass = make_data_loader(args, **kwargs)
+        self.train_loaderA, self.train_loaderB, self.val_loader, self.test_loader, self.nclass = make_data_loader(args, **kwargs)
 
         if args.use_balanced_weights:
             classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy')
@@ -45,7 +45,7 @@ def __init__(self, args):
         self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type)
 
         # Define network
-        model = AutoDeeplab (num_classes=self.nclass, num_layers=12, criterion=self.criterion)
+        model = AutoDeeplab (num_classes=self.nclass, num_layers=12, criterion=self.criterion, filter_multiplier=self.args.filter_multiplier)
         optimizer = torch.optim.SGD(
                 model.parameters(),
                 args.lr,
@@ -58,7 +58,7 @@ def __init__(self, args):
         self.evaluator = Evaluator(self.nclass)
         # Define lr scheduler
         self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr,
-                                            args.epochs, len(self.train_loader1))
+                                      args.epochs, len(self.train_loaderA))
 
         self.architect = Architect (self.model, args)
 
@@ -113,8 +113,8 @@ def __init__(self, args):
     def training(self, epoch):
         train_loss = 0.0
         self.model.train()
-        tbar = tqdm(self.train_loader1)
-        num_img_tr = len(self.train_loader1)
+        tbar = tqdm(self.train_loaderA)
+        num_img_tr = len(self.train_loaderA)
         for i, sample in enumerate(tbar):
             image, target = sample['image'], sample['label']
             if self.args.cuda:
@@ -127,7 +127,7 @@ def training(self, epoch):
             self.optimizer.step()
 
             if epoch > self.args.alpha_epoch:
-                search = next(iter(self.train_loader2))
+                search = next(iter(self.train_loaderB))
                 image_search, target_search = search['image'], search['label']
                 if self.args.cuda:
                     image_search, target_search = image_search.cuda (), target_search.cuda ()
@@ -247,6 +247,7 @@ def main():
                         help='number of epochs to train (default: auto)')
     parser.add_argument('--start_epoch', type=int, default=0,
                         metavar='N', help='start epochs (default:0)')
+    parser.add_argument('--filter_multiplier', type=int, default=8)
     parser.add_argument('--alpha_epoch', type=int, default=5,
                         metavar='N', help='epoch to start training alphas')
     parser.add_argument('--batch-size', type=int, default=1,

train_cityscapes.sh

@@ -1,2 +1,2 @@
 CUDA_VISIBLE_DEVICES=0,1 python train_autodeeplab.py --batch-size 2 \
---dataset cityscapes --checkname July22  --alpha_epoch 20
+--dataset cityscapes --checkname July22  --alpha_epoch 20 --filter_multiplier 4