diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..be103a6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+*.jpg
+*.png
+*.pyc
+example_weights
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..5676b26
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/home/kda/.pyenv/versions/3.6.9/bin/python"
+}
\ No newline at end of file
diff --git a/kaffe/tensorflow/network.py b/kaffe/tensorflow/network.py
index 0fe15d1..8df11e0 100644
--- a/kaffe/tensorflow/network.py
+++ b/kaffe/tensorflow/network.py
@@ -79,7 +79,7 @@ def feed(self, *args):
         assert len(args) != 0
         self.terminals = []
         for fed_layer in args:
-            if isinstance(fed_layer, basestring):
+            if isinstance(fed_layer, str):
                 try:
                     #print('Layer ' + fed_layer + ' shape')
                     #print(self.layers[fed_layer].shape)
@@ -121,7 +121,7 @@ def attention_refinment_module(self, input, name):
     @layer
     def attention_refinment_module_new(self, input, name, last_arm=False):
         global_pool = tf.reduce_mean(input, [1, 2], keep_dims=True)
-        conv_1 = keras_ly.Conv2D(input.get_shape()[3], [1, 1], padding='SAME', name=name+'_conv1')(global_pool)
+        conv_1 = keras_ly.Conv2D(int(input.get_shape()[3]), [1, 1], padding='SAME', name=name+'_conv1')(global_pool)
         with tf.variable_scope(name+'_conv1_bn') as scope:
             conv_1_bn = slim.batch_norm(conv_1, fused=True, scope=scope)
         sigmoid = tf.sigmoid(conv_1_bn, name=name+'_sigmoid')
@@ -214,7 +214,7 @@ def conv(self,
         # Verify that the padding is acceptable
         self.validate_padding(padding)
         # Get the number of channels in the input
-        c_i = input.get_shape()[-1]
+        c_i = int(input.get_shape()[-1])
         # Verify that the grouping parameter is valid
         assert c_i % group == 0
         assert c_o % group == 0
@@ -256,7 +256,7 @@ def atrous_conv(self,
         # Verify that the padding is acceptable
         self.validate_padding(padding)
         # Get the number of channels in the input
-        c_i = input.get_shape()[-1]
+        c_i = int(input.get_shape()[-1])
         # Verify that the grouping parameter is valid
         assert c_i % group == 0
         assert c_o % group == 0
diff --git a/wasr_inference_noimu_general.py b/wasr_inference_noimu_general.py
index 7034208..e97b7c9 100644
--- a/wasr_inference_noimu_general.py
+++ b/wasr_inference_noimu_general.py
@@ -124,7 +124,10 @@ def main():
         os.makedirs(args.save_dir)
 
     # Read image
-    img_in = cv2.imread(os.path.join(args.dataset_path, args.img_path))
+    # img_in = cv2.imread(os.path.join(args.dataset_path, args.img_path))
+    img_in = cv2.imread(os.path.join(args.img_path))
+    assert img_in is not None
+    img_in = cv2.resize(img_in, (IMG_SIZE[1], IMG_SIZE[0]))
 
     # Run inference
     preds = sess.run(pred, feed_dict={img_input: img_in})