fix pylint

Author: RandyShuai

tests/test_backend.py

@@ -2932,37 +2932,37 @@ def test_Conv2DBackpropInput_const_valid(self):
     def test_Conv2DBackpropInput(self):
         input_sizes_val = np.array([1, 10, 10, 3], dtype=np.int32)
         input_sizes = tf.placeholder(tf.int32, input_sizes_val.shape, name=_TFINPUT)
-        filter_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
-        filter = tf.placeholder(tf.float32, filter_val.shape, name=_TFINPUT1)
+        filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
+        filters = tf.placeholder(tf.float32, filters_val.shape, name=_TFINPUT1)
         out_backprop_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 5]).astype(np.float32)
         out_backprop = tf.placeholder(tf.float32, out_backprop_val.shape, name=_TFINPUT2)
-        _ = tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides=[1, 1, 1, 1], padding='SAME',
+        _ = tf.nn.conv2d_backprop_input(input_sizes, filters, out_backprop, strides=[1, 1, 1, 1], padding='SAME',
                                         name=_TFOUTPUT)
-        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filter_val, _INPUT2: out_backprop_val})
+        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filters_val, _INPUT2: out_backprop_val})
 
     @check_opset_min_version(10, "Conv2DBackpropInput")
     def test_Conv2DBackpropInput_strided(self):
         input_sizes_val = np.array([1, 10, 10, 3], dtype=np.int32)
         input_sizes = tf.placeholder(tf.int32, input_sizes_val.shape, name=_TFINPUT)
-        filter_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
-        filter = tf.placeholder(tf.float32, filter_val.shape, name=_TFINPUT1)
+        filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
+        filters = tf.placeholder(tf.float32, filters_val.shape, name=_TFINPUT1)
         out_backprop_val = np.random.randint(low=0, high=256, size=[1, 5, 5, 5]).astype(np.float32)
         out_backprop = tf.placeholder(tf.float32, out_backprop_val.shape, name=_TFINPUT2)
-        _ = tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides=[1, 2, 2, 1], padding='SAME',
+        _ = tf.nn.conv2d_backprop_input(input_sizes, filters, out_backprop, strides=[1, 2, 2, 1], padding='SAME',
                                         name=_TFOUTPUT)
-        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filter_val, _INPUT2: out_backprop_val})
+        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filters_val, _INPUT2: out_backprop_val})
 
     @check_opset_min_version(10, "Conv2DBackpropInput")
     def test_Conv2DBackpropInput_valid(self):
         input_sizes_val = np.array([1, 12, 12, 3], dtype=np.int32)
         input_sizes = tf.placeholder(tf.int32, input_sizes_val.shape, name=_TFINPUT)
-        filter_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
-        filter = tf.placeholder(tf.float32, filter_val.shape, name=_TFINPUT1)
+        filters_val = np.random.randint(low=0, high=256, size=[3, 3, 3, 5]).astype(np.float32)
+        filters = tf.placeholder(tf.float32, filters_val.shape, name=_TFINPUT1)
         out_backprop_val = np.random.randint(low=0, high=256, size=[1, 10, 10, 5]).astype(np.float32)
         out_backprop = tf.placeholder(tf.float32, out_backprop_val.shape, name=_TFINPUT2)
-        _ = tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides=[1, 1, 1, 1], padding='VALID',
+        _ = tf.nn.conv2d_backprop_input(input_sizes, filters, out_backprop, strides=[1, 1, 1, 1], padding='VALID',
                                         name=_TFOUTPUT)
-        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filter_val, _INPUT2: out_backprop_val})
+        self._run_test_case([_OUTPUT], {_INPUT: input_sizes_val, _INPUT1: filters_val, _INPUT2: out_backprop_val})
 
 
 if __name__ == '__main__':