From 7e6a2190ddff25362d395667e397f5174f2346a2 Mon Sep 17 00:00:00 2001
From: Zhou Wei <1183042833@qq.com>
Date: Thu, 27 Jan 2022 15:01:31 +0800
Subject: [PATCH] fix UT test_lr_scheduler random fail (#39254)

---
 .../tests/unittests/test_lr_scheduler.py      | 47 ++++++++-----------
 1 file changed, 20 insertions(+), 27 deletions(-)

diff --git a/python/paddle/fluid/tests/unittests/test_lr_scheduler.py b/python/paddle/fluid/tests/unittests/test_lr_scheduler.py
index d62a633c285..6d94144fc77 100644
--- a/python/paddle/fluid/tests/unittests/test_lr_scheduler.py
+++ b/python/paddle/fluid/tests/unittests/test_lr_scheduler.py
@@ -323,15 +323,15 @@ def step_lr(epoch_num, learning_rate, step_size, gamma=0.1, verbose=False):
 
 class TestLRScheduler(unittest.TestCase):
     def _test_static(self, python_func, paddle_api, kwarg, place):
+        scheduler = paddle_api(**kwarg)
+        adam = paddle.optimizer.Adam(learning_rate=scheduler)
+
         main_prog = paddle.static.Program()
         start_prog = paddle.static.Program()
         with paddle.static.program_guard(main_prog, start_prog):
             x = paddle.static.data(name='x', shape=[3, 4, 5])
-            y = paddle.static.data(name='y', shape=[3, 4, 5])
-            z = paddle.static.nn.fc(x, 100)
-            loss = paddle.mean(z)
-            scheduler = paddle_api(**kwarg)
-            adam = paddle.optimizer.Adam(learning_rate=scheduler)
+            loss = paddle.mean(x)
+
             adam.minimize(loss)
             lr_var = adam._global_learning_rate()
             test_prog = main_prog.clone()
@@ -339,14 +339,12 @@ class TestLRScheduler(unittest.TestCase):
         num = 0
         exe = paddle.static.Executor(place)
         exe.run(start_prog)
+
         for epoch in range(5):
             for batch_id in range(2):
                 out = exe.run(
                     main_prog,
-                    feed={
-                        'x': np.random.randn(3, 4, 5).astype('float32'),
-                        'y': np.random.randn(3, 4, 5).astype('float32')
-                    },
+                    feed={'x': np.random.randn(3, 4, 5).astype('float32')},
                     fetch_list=lr_var.name)
             self.assertEqual(out, np.array(python_func(num, **kwarg)))
             scheduler.step()
@@ -356,10 +354,7 @@ class TestLRScheduler(unittest.TestCase):
             for batch_id in range(2):
                 out = exe.run(
                     test_prog,
-                    feed={
-                        'x': np.random.randn(3, 4, 5).astype('float32'),
-                        'y': np.random.randn(3, 4, 5).astype('float32')
-                    },
+                    feed={'x': np.random.randn(3, 4, 5).astype('float32')},
                     fetch_list=lr_var.name)
             self.assertEqual(out, np.array(python_func(num, **kwarg)))
             scheduler.step()
@@ -372,13 +367,12 @@ class TestLRScheduler(unittest.TestCase):
             for epoch in range(5):
                 python_result = python_func(num, **kwarg)
                 for batch_id in range(2):
-                    _ = exe.run(
-                        compiled_train_prog,
-                        feed={
-                            'x': np.random.randn(12, 4, 5).astype('float32'),
-                            'y': np.random.randn(12, 4, 5).astype('float32')
-                        },
-                        fetch_list=lr_var.name)
+                    _ = exe.run(compiled_train_prog,
+                                feed={
+                                    'x':
+                                    np.random.randn(12, 4, 5).astype('float32')
+                                },
+                                fetch_list=lr_var.name)
                 scopes = compiled_train_prog._executor.local_scopes()
                 out = np.array(scopes[0].var(lr_var.name).get_tensor())
                 self.assertEqual(out, np.array(python_result))
@@ -399,13 +393,12 @@ class TestLRScheduler(unittest.TestCase):
             for epoch in range(5):
                 python_result = python_func(num, **kwarg)
                 for batch_id in range(2):
-                    _ = exe.run(
-                        compiled_test_prog,
-                        feed={
-                            'x': np.random.randn(12, 4, 5).astype('float32'),
-                            'y': np.random.randn(12, 4, 5).astype('float32')
-                        },
-                        fetch_list=lr_var.name)
+                    _ = exe.run(compiled_test_prog,
+                                feed={
+                                    'x':
+                                    np.random.randn(12, 4, 5).astype('float32')
+                                },
+                                fetch_list=lr_var.name)
                 scopes = compiled_test_prog._executor.local_scopes()
                 out = np.array(scopes[0].var(lr_var.name).get_tensor())
                 self.assertEqual(out, np.array(python_result))
-- 
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