diff --git a/dygraph/main.py b/dygraph/main.py
deleted file mode 100644
index 6ae620e31ca613cddf9fa395bd2f5f3b4c1785c1..0000000000000000000000000000000000000000
--- a/dygraph/main.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from datasets import Dataset
-import transforms
-import paddle.fluid as fluid
-from models import UNet
-
-data_dir = '/ssd1/chenguowei01/dataset/optic_disc_seg'
-train_list = '/ssd1/chenguowei01/dataset/optic_disc_seg/train_list.txt'
-val_list = '/ssd1/chenguowei01/dataset/optic_disc_seg/val_list.txt'
-img_file = data_dir + '/JPEGImages/H0005.jpg'
-
-train_transforms = transforms.Compose([
-    transforms.Resize((192, 192)),
-    transforms.RandomHorizontalFlip(),
-    transforms.Normalize()
-])
-
-train_dataset = Dataset(
-    data_dir=data_dir,
-    file_list=train_list,
-    transforms=train_transforms,
-    num_workers='auto',
-    buffer_size=100,
-    parallel_method='thread',
-    shuffle=True)
-
-eval_transforms = transforms.Compose(
-    [transforms.Resize((192, 192)),
-     transforms.Normalize()])
-
-eval_dataset = Dataset(
-    data_dir=data_dir,
-    file_list=val_list,
-    transforms=eval_transforms,
-    num_workers='auto',
-    buffer_size=100,
-    parallel_method='thread',
-    shuffle=True)
-
-model = UNet(num_classes=2)
-with fluid.dygraph.guard(model.places):
-    model.build_model()
-    #model.load_model('output/epoch_10/')
-    model.train(
-        num_epochs=10, train_dataset=train_dataset, eval_dataset=eval_dataset)
-    model.evaluate(eval_dataset)
-    model.predict(img_file, eval_transforms)