From bd29052e332f2e562e2feac2e228fa078202285c Mon Sep 17 00:00:00 2001
From: lijianshe02 <48898730+lijianshe02@users.noreply.github.com>
Date: Fri, 11 Dec 2020 10:23:05 +0800
Subject: [PATCH] fix random seed in nll_loss unitest test=develop (#29538)

* fix random seed in nll_loss unitest test=develop
---
 .../fluid/tests/unittests/test_nll_loss.py    | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/python/paddle/fluid/tests/unittests/test_nll_loss.py b/python/paddle/fluid/tests/unittests/test_nll_loss.py
index aa64a35564b..ee7e3a65283 100644
--- a/python/paddle/fluid/tests/unittests/test_nll_loss.py
+++ b/python/paddle/fluid/tests/unittests/test_nll_loss.py
@@ -72,6 +72,7 @@ def nll_loss_2d(logs, targets, weight=None, reduction='mean',
 
 class TestNLLLoss(unittest.TestCase):
     def test_NLLLoss_1D_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         prog = fluid.Program()
@@ -105,6 +106,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_1D_sum(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         prog = fluid.Program()
@@ -138,6 +140,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_1D_with_weight_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         weight_np = np.random.random(size=(10, )).astype(np.float64)
@@ -176,6 +179,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_1D_with_weight_sum(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         weight_np = np.random.random(size=(10, )).astype(np.float64)
@@ -215,6 +219,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_1D_with_weight_mean_cpu(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         weight_np = np.random.random(size=(10, )).astype(np.float64)
@@ -251,6 +256,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_1D_with_weight_no_reduce_cpu(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(10, 10)).astype(np.float64)
         label_np = np.random.randint(0, 10, size=(10, )).astype(np.int64)
         weight_np = np.random.random(size=(10, )).astype(np.float64)
@@ -288,6 +294,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_2D_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
         prog = fluid.Program()
@@ -323,6 +330,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_2D_sum(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
         prog = fluid.Program()
@@ -358,6 +366,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_2D_with_weight_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -399,6 +408,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_2D_with_weight_mean_cpu(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -438,6 +448,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_2D_with_weight_sum(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -479,6 +490,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_in_dims_not_2or4_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
         prog = fluid.Program()
@@ -519,6 +531,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_in_dims_not_2or4_with_weight_mean(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -565,6 +578,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_in_dims_not_2or4_with_weight_sum(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -614,6 +628,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_in_dims_not_2or4_with_weight_no_reduce(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -664,6 +679,7 @@ class TestNLLLoss(unittest.TestCase):
         self.assertTrue(np.allclose(dy_result, expected))
 
     def test_NLLLoss_in_dims_not_2or4_with_weight_no_reduce_cpu(self):
+        np.random.seed(200)
         input_np = np.random.random(size=(5, 3, 5, 5, 5)).astype(np.float64)
         label_np = np.random.randint(0, 3, size=(5, 5, 5, 5)).astype(np.int64)
         weight_np = np.random.random(size=(3, )).astype(np.float64)
@@ -717,6 +733,7 @@ class TestNLLLossOp1DWithReduce(OpTest):
         self.init_test_case()
         self.op_type = "nll_loss"
         self.with_weight = False
+        np.random.seed(200)
         input_np = np.random.uniform(0.1, 0.8,
                                      self.input_shape).astype("float64")
         label_np = np.random.randint(0, self.input_shape[1],
@@ -758,6 +775,7 @@ class TestNLLLossOp1DNoReduce(OpTest):
         self.init_test_case()
         self.op_type = "nll_loss"
         self.with_weight = False
+        np.random.seed(200)
         input_np = np.random.uniform(0.1, 0.8,
                                      self.input_shape).astype("float64")
         label_np = np.random.randint(0, self.input_shape[1],
@@ -844,6 +862,7 @@ class TestNLLLossOp2DNoReduce(OpTest):
         self.init_test_case()
         self.op_type = "nll_loss"
         self.with_weight = False
+        np.random.seed(200)
         input_np = np.random.uniform(0.1, 0.8,
                                      self.input_shape).astype("float64")
         label_np = np.random.randint(0, self.input_shape[1],
-- 
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