#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import unittest
import numpy as np
from op_test import OpTest


class TestConcatOp(OpTest):
    def setUp(self):
        self.op_type = "concat"
        x0 = np.random.random((2, 1, 4, 5)).astype('float32')
        x1 = np.random.random((2, 2, 4, 5)).astype('float32')
        x2 = np.random.random((2, 3, 4, 5)).astype('float32')
        axis = 1
        self.inputs = {'X': [('x0', x0), ('x1', x1), ('x2', x2)]}
        self.attrs = {'axis': axis}
        self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['x0'], 'Out')


if __name__ == '__main__':
    unittest.main()

@@ -763,202 +763,29 @@
     
-x0 = np.random.random((2, 1, 4, 5)).astype('float32')%0A        x1 = np.random.random((2, 2, 4, 5)).astype('float32')%0A        x2 = np.random.random((2, 3, 4, 5)).astype('float32')%0A        axis = 1
+self.init_test_data()
 %0A   
@@ -817,16 +817,21 @@
 %5B('x0', 
+self.
 x0), ('x
@@ -834,16 +834,21 @@
  ('x1', 
+self.
 x1), ('x
@@ -851,16 +851,21 @@
  ('x2', 
+self.
 x2)%5D%7D%0A  
@@ -892,16 +892,21 @@
 'axis': 
+self.
 axis%7D%0A  
@@ -927,16 +927,29 @@
 puts = %7B
+%0A            
 'Out': n
@@ -966,32 +966,78 @@
 ate(
-(x0, x1, x2), axis=axis)
+%0A                (self.x0, self.x1, self.x2), axis=self.axis)%0A        
 %7D%0A%0A 
@@ -1167,16 +1167,632 @@
  'Out')%0A
+        self.check_grad(%5B'x1'%5D, 'Out')%0A        self.check_grad(%5B'x2'%5D, 'Out')%0A%0A    def init_test_data(self):%0A        self.x0 = np.random.random((2, 1, 4, 5)).astype('float32')%0A        self.x1 = np.random.random((2, 2, 4, 5)).astype('float32')%0A        self.x2 = np.random.random((2, 3, 4, 5)).astype('float32')%0A        self.axis = 1%0A%0A%0Aclass TestConcatOp2(OpTest):%0A    def init_test_data(self):%0A        self.x0 = np.random.random((2, 3, 4, 5)).astype('float32')%0A        self.x1 = np.random.random((2, 3, 4, 5)).astype('float32')%0A        self.x2 = np.random.random((2, 3, 4, 5)).astype('float32')%0A        self.axis = 1%0A
 %0A%0Aif __n

add unit test