From 48d9fd08e5193a505a8dea48926f2ab2abfd129f Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 26 Feb 2019 13:49:55 +0800
Subject: [PATCH] fix default value. test=develop

---
 .../unittests/ir_memory_optimize_net_base.py  | 15 +++--
 .../test_ir_memory_optimize_ifelse_net.py     | 55 -------------------
 2 files changed, 10 insertions(+), 60 deletions(-)
 delete mode 100644 python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_net.py

diff --git a/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py b/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py
index bf6adce8aca..079f0d22056 100644
--- a/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py
+++ b/python/paddle/fluid/tests/unittests/ir_memory_optimize_net_base.py
@@ -117,7 +117,7 @@ class TestIrMemOptBase(BuildIrMemOptBase):
         self.network = None
 
     def test_network(self):
-        if self.network is None:
+        if self.network is None or not core.is_compiled_with_cuda():
             return
 
         baseline_first_loss, baseline_last_loss = None, None
@@ -139,7 +139,12 @@ class TestIrMemOptBase(BuildIrMemOptBase):
                                 self.network,
                                 use_cuda=use_cuda,
                                 memory_opt=use_python_mem_opt)
-                            self.assertAlmostEquals(np.mean(baseline_last_loss),
-                                                    np.mean(cur_last_loss), delta=1e-2)
-                            self.assertAlmostEquals(np.mean(baseline_first_loss),
-                                                    np.mean(cur_first_loss), delta=1e-2)
+
+                            self.assertAlmostEquals(
+                                np.mean(baseline_last_loss),
+                                np.mean(cur_last_loss),
+                                delta=1e-2)
+                            self.assertAlmostEquals(
+                                np.mean(baseline_first_loss),
+                                np.mean(cur_first_loss),
+                                delta=1e-2)
diff --git a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_net.py b/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_net.py
deleted file mode 100644
index 7ae7920fb69..00000000000
--- a/python/paddle/fluid/tests/unittests/test_ir_memory_optimize_ifelse_net.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright (c) 2019 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.
-
-from __future__ import print_function
-
-import paddle.fluid as fluid
-import unittest
-from ir_memory_optimize_net_base import TestIrMemOptBase
-from paddle.fluid.layers.control_flow import ConditionalBlock
-
-
-def lstm_net(data,
-             label,
-             dict_dim,
-             emb_dim=128,
-             hid_dim=128,
-             hid_dim2=96,
-             class_dim=2,
-             emb_lr=30.0):
-    emb = fluid.layers.embedding(
-        input=data,
-        size=[dict_dim, emb_dim],
-        param_attr=fluid.ParamAttr(learning_rate=emb_lr))
-    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
-
-    lstm_h, c = fluid.layers.dynamic_lstm(
-        input=fc0, size=hid_dim * 4, is_reverse=False)
-    lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
-    lstm_max_tanh = fluid.layers.tanh(lstm_max)
-    fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
-    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
-    cost = fluid.layers.cross_entropy(input=prediction, label=label)
-    avg_cost = fluid.layers.mean(x=cost)
-    return avg_cost
-
-
-class TestIrMemOptRNN(TestIrMemOptBase):
-    def setUp(self):
-        self.network = lstm_net
-        self.iter = 2
-
-
-if __name__ == "__main__":
-    unittest.main()
-- 
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