Fix small bugs

.gitignore

@@ -103,3 +103,7 @@ main_main.py
 scripts-nas/.nfs00*
 */.nfs00*
 *.DS_Store
+
+# logs and snapshots
+output
+logs

exps-cnn/train_utils_imagenet.py

@@ -108,11 +108,11 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
   for epoch in range(start_epoch, config.epochs):
     scheduler.step()
 
-    need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
-    print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size), log)
-
     basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
 
+    need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
+    print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}, Drop-Path-Prob={:}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size, basemodel.get_drop_path()), log)
+
     train_acc1, train_acc5, train_los = _train(train_queue, model, criterion_smooth, optimizer, 'train', epoch, config, args.print_freq, log)
 
     with torch.no_grad():

lib/datasets/get_dataset_with_transform.py

@@ -67,7 +67,7 @@ def get_datasets(name, root, cutout):
     test_data  = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
   elif name == 'imagenet-1k' or name == 'imagenet-100':
     train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
-    test_data  = dset.ImageFolder(osp.join(root, 'val'), train_transform)
+    test_data  = dset.ImageFolder(osp.join(root, 'val'),   test_transform)
   else: raise TypeError("Unknow dataset : {:}".format(name))
   
   class_num = Dataset2Class[name]

lib/nas/ImageNet.py

@@ -80,6 +80,9 @@ class NetworkImageNet(nn.Module):
   def update_drop_path(self, drop_path_prob):
     self.drop_path_prob = drop_path_prob
 
+  def get_drop_path(self):
+    return self.drop_path_prob
+
   def auxiliary_param(self):
     if self.auxiliary_head is None: return []
     else: return list( self.auxiliary_head.parameters() )