refine unit test

python/paddle/fluid/tests/unittests/test_parallel_executor.py

@@ -722,9 +722,10 @@ def test_update_dense_parameter(self):
 # test fetch all the variables of global_block
 
 import paddle.dataset.flowers as flowers
+import math
 
 
-def lenet(data, class_dim):
+def Lenet(data, class_dim):
     conv1 = fluid.layers.conv2d(data, 32, 5, 1, act=None)
     bn1 = fluid.layers.batch_norm(conv1, act='relu')
     pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
@@ -774,25 +775,25 @@ def parallel_exe(self, train_inputs, seed):
             fetch_list = []
             all_vars = main.global_block().vars
             for k, v in all_vars.iteritems():
-                if 'velocity' not in k:
+                if 'tmp' not in k and k[0] is not '_' or v.persistable:
                     fetch_list.append(k)
 
             for data in train_inputs:
                 ret = pe.run(fetch_list, feed=feeder.feed(data))
                 for i in range(len(fetch_list)):
-                    print("%s - %s" % (fetch_list[i], np.sum(ret[i])))
+                    assert not math.isnan(np.sum(ret[i])) and \
+                           not math.isinf(np.sum(ret[i]))
 
     def test_update_sparse_parameter(self):
         tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
         tst_reader_iter = tst_reader()
 
-        seed = 100
-        iters = 4
+        iters = 3
         train_inputs = []
         for i in range(iters):
             train_inputs.append(tst_reader_iter.next())
 
-        self.parallel_exe(train_inputs, seed)
+        self.parallel_exe(train_inputs, seed=1)
 
 
 if __name__ == '__main__':