diff --git a/utils/learning_rates.py b/learning_rates.py
similarity index 100%
rename from utils/learning_rates.py
rename to learning_rates.py
diff --git a/main.py b/main.py
index 2f949ad43569a8f8e31b5339d55b09de41e7f5b3..69a972b79ebe4e1e9616c3df0d3dd2fefa6d67ac 100644
--- a/main.py
+++ b/main.py
@@ -1,20 +1,15 @@
-import os
-import mindspore
 from mindspore import context
-from mindspore.context import ParallelMode
 import mindspore.nn as nn
 import mindspore.dataset as ds
-from PIL import Image
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.nn import Accuracy
 from mindspore.train.callback import TimeMonitor, LossMonitor
 from mindspore import Model
 
-from unet_medical.unet_model import UNetMedical
 from nets.deeplab_v3 import deeplab_v3
 from dataset import GetDatasetGenerator
 from loss import SoftmaxCrossEntropyLoss
-import utils
+from learning_rates import exponential_lr
 
 context.set_context(mode=context.PYNATIVE_MODE, save_graphs=False,
                     device_target='Ascend', device_id=7)
@@ -25,7 +20,7 @@ train_dataset_generator = GetDatasetGenerator('./datasets', 'train')
 train_dataset = ds.GeneratorDataset(train_dataset_generator, ["data", "label"], shuffle=True)
 train_dataset = train_dataset.batch(4, drop_remainder=True)
 
-lr_iter = utils.learning_rates.exponential_lr(3e-5, 20, 0.98, 500, staircase=True)
+lr_iter = exponential_lr(3e-5, 20, 0.98, 500, staircase=True)
 
 net_loss = SoftmaxCrossEntropyLoss(6, 255)
 net_opt = nn.Adam(net.trainable_params(), learning_rate=lr_iter)
diff --git a/utils/__init__.py b/utils/__init__.py
deleted file mode 100644
index 5d1904a29f49c18a032d733aed1e62dbf70b2a1a..0000000000000000000000000000000000000000
--- a/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from learning_rates import cosine_lr, poly_lr, exponential_lr