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fix random seed in nll_loss unitest test=develop (#29538)

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* fix random seed in nll_loss unitest test=develop
revert-31562-mean
lijianshe02 4 years ago committed by GitHub
parent 0ce6d7fa77
commit bd29052e33
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1 changed files with 19 additions and 0 deletions
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python/paddle/fluid/tests/unittests/test_nll_loss.py
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@ -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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