update dataset using

dygraph/infer.py

@@ -43,22 +43,11 @@ def parse_args():
 
     # params of dataset
     parser.add_argument(
-        '--data_dir',
-        dest='data_dir',
-        help='The root directory of dataset',
-        type=str)
-    parser.add_argument(
-        '--test_list',
-        dest='test_list',
-        help='Val list file of dataset',
+        '--dataset',
+        dest='dataset',
+        help='The dataset you want to train',
         type=str,
-        default=None)
-    parser.add_argument(
-        '--num_classes',
-        dest='num_classes',
-        help='Number of classes',
-        type=int,
-        default=2)
+        default='OpticDiscSeg')
 
     # params of prediction
     parser.add_argument(
@@ -142,12 +131,19 @@ def main(args):
     places = fluid.CUDAPlace(ParallelEnv().dev_id) \
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
+
+    if args.dataset.lower() == 'opticdiscseg':
+        dataset = OpticDiscSeg
+    else:
+        raise Exception(
+            "The --dataset set wrong. It should be one of ('OpticDiscSeg',)")
+
     with fluid.dygraph.guard(places):
         test_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        test_dataset = OpticDiscSeg(transforms=test_transforms, mode='test')
+        test_dataset = dataset(transforms=test_transforms, mode='test')
 
         if args.model_name == 'UNet':
-            model = models.UNet(num_classes=args.num_classes)
+            model = models.UNet(num_classes=test_dataset.num_classes)
 
         infer(
             model,

dygraph/train.py

@@ -40,31 +40,15 @@ def parse_args():
         dest='model_name',
         help="Model type for traing, which is one of ('UNet')",
         type=str,
-        default='UNet')
+        default='OpticDiscSeg')
 
     # params of dataset
     parser.add_argument(
-        '--data_dir',
-        dest='data_dir',
-        help='The root directory of dataset',
-        type=str)
-    parser.add_argument(
-        '--train_list',
-        dest='train_list',
-        help='Train list file of dataset',
-        type=str)
-    parser.add_argument(
-        '--val_list',
-        dest='val_list',
-        help='Val list file of dataset',
+        '--dataset',
+        dest='dataset',
+        help='The dataset you want to train',
         type=str,
-        default=None)
-    parser.add_argument(
-        '--num_classes',
-        dest='num_classes',
-        help='Number of classes',
-        type=int,
-        default=2)
+        default='OpticDiscSeg')
 
     # params of training
     parser.add_argument(
@@ -83,7 +67,7 @@ def parse_args():
     parser.add_argument(
         '--batch_size',
         dest='batch_size',
-        help='Mini batch size',
+        help='Mini batch size of one gpu or cpu',
         type=int,
         default=2)
     parser.add_argument(
@@ -210,6 +194,12 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
+    if args.dataset.lower() == 'opticdiscseg':
+        dataset = OpticDiscSeg
+    else:
+        raise Exception(
+            "The --dataset set wrong. It should be one of ('OpticDiscSeg',)")
+
     with fluid.dygraph.guard(places):
         # Creat dataset reader
         train_transforms = T.Compose([
@@ -217,17 +207,18 @@ def main(args):
             T.RandomHorizontalFlip(),
             T.Normalize()
         ])
-        train_dataset = OpticDiscSeg(transforms=train_transforms, mode='train')
+        train_dataset = dataset(transforms=train_transforms, mode='train')
 
         eval_dataset = None
         if args.do_eval:
             eval_transforms = T.Compose(
                 [T.Resize(args.input_size),
                  T.Normalize()])
-            eval_dataset = OpticDiscSeg(transforms=eval_transforms, mode='eval')
+            eval_dataset = dataset(transforms=eval_transforms, mode='eval')
 
         if args.model_name == 'UNet':
-            model = models.UNet(num_classes=args.num_classes, ignore_index=255)
+            model = models.UNet(
+                num_classes=train_dataset.num_classes, ignore_index=255)
 
         # Creat optimizer
         num_steps_each_epoch = len(train_dataset) // args.batch_size
@@ -251,7 +242,7 @@ def main(args):
             batch_size=args.batch_size,
             pretrained_model=args.pretrained_model,
             save_interval_epochs=args.save_interval_epochs,
-            num_classes=args.num_classes,
+            num_classes=train_dataset.num_classes,
             num_workers=args.num_workers)
 
 

dygraph/val.py

@@ -44,22 +44,11 @@ def parse_args():
 
     # params of dataset
     parser.add_argument(
-        '--data_dir',
-        dest='data_dir',
-        help='The root directory of dataset',
-        type=str)
-    parser.add_argument(
-        '--val_list',
-        dest='val_list',
-        help='Val list file of dataset',
+        '--dataset',
+        dest='dataset',
+        help='The dataset you want to evaluation',
         type=str,
-        default=None)
-    parser.add_argument(
-        '--num_classes',
-        dest='num_classes',
-        help='Number of classes',
-        type=int,
-        default=2)
+        default='OpticDiscSeg')
 
     # params of evaluate
     parser.add_argument(
@@ -140,19 +129,26 @@ def main(args):
     places = fluid.CUDAPlace(ParallelEnv().dev_id) \
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
+
+    if args.dataset.lower() == 'opticdiscseg':
+        dataset = OpticDiscSeg
+    else:
+        raise Exception(
+            "The --dataset set wrong. It should be one of ('OpticDiscSeg',)")
+
     with fluid.dygraph.guard(places):
         eval_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        eval_dataset = OpticDiscSeg(transforms=eval_transforms, mode='eval')
+        eval_dataset = dataset(transforms=eval_transforms, mode='eval')
 
         if args.model_name == 'UNet':
-            model = models.UNet(num_classes=args.num_classes)
+            model = models.UNet(num_classes=eval_dataset.num_classes)
 
         evaluate(
             model,
             eval_dataset,
             places=places,
             model_dir=args.model_dir,
-            num_classes=args.num_classes,
+            num_classes=eval_dataset.num_classes,
             batch_size=args.batch_size)