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Remove self-set accuracy parameters of op tests: max_relative_error (#21744)

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* Remove self-set accuracy parameters of op tests: max_relative_error
test=develop

* fix errors
test=develop
1.6.2
GaoWei8 6 years ago committed by Tao Luo
parent 3c33417905
commit 187d1c38ef
1 changed files with 26 additions and 26 deletions
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52
python/paddle/fluid/tests/unittests/test_activation_op.py
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@ -58,7 +58,7 @@ class TestActivation(OpTest):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
def init_dtype(self):
self.dtype = np.float32
@ -115,7 +115,7 @@ class TestTanh(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestAtan(TestActivation):
@ -132,7 +132,7 @@ class TestAtan(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestTanhShrink(TestActivation):
@ -149,7 +149,7 @@ class TestTanhShrink(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.008)
self.check_grad(['X'], 'Out')
class TestHardShrink(TestActivation):
@ -169,7 +169,7 @@ class TestHardShrink(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.005)
self.check_grad(['X'], 'Out')
class TestSoftShrink(TestActivation):
@ -190,7 +190,7 @@ class TestSoftShrink(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestSqrt(TestActivation):
@ -207,7 +207,7 @@ class TestSqrt(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestRsqrt(TestActivation):
@ -246,7 +246,7 @@ class TestAbs(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestCeil(TestActivation):
@ -297,7 +297,7 @@ class TestCos(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestAcos(TestActivation):
@ -314,7 +314,7 @@ class TestAcos(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestSin(TestActivation):
@ -331,7 +331,7 @@ class TestSin(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestAsin(TestActivation):
@ -348,7 +348,7 @@ class TestAsin(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestRound(TestActivation):
@ -382,7 +382,7 @@ class TestRelu(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestLeakyRelu(TestActivation):
@ -401,7 +401,7 @@ class TestLeakyRelu(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestGelu(TestActivation):
@ -418,7 +418,7 @@ class TestGelu(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestBRelu(TestActivation):
@ -443,7 +443,7 @@ class TestBRelu(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
class TestRelu6(TestActivation):
@ -465,7 +465,7 @@ class TestRelu6(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
class TestHardSwish(TestActivation):
@ -489,7 +489,7 @@ class TestHardSwish(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
class TestSoftRelu(TestActivation):
@ -534,7 +534,7 @@ class TestELU(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
class TestELUOpError(unittest.TestCase):
@ -580,7 +580,7 @@ class TestLog(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestSquare(TestActivation):
@ -615,7 +615,7 @@ class TestPow(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
class TestPow_factor_tensor(TestActivation):
@ -640,7 +640,7 @@ class TestPow_factor_tensor(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.02)
self.check_grad(['X'], 'Out')
def test_api(self):
import paddle.fluid as fluid
@ -680,7 +680,7 @@ class TestSTanh(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestSoftplus(TestActivation):
@ -698,7 +698,7 @@ class TestSoftplus(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestSoftsign(TestActivation):
@ -715,7 +715,7 @@ class TestSoftsign(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
self.check_grad(['X'], 'Out')
class TestThresholdedRelu(TestActivation):
@ -738,7 +738,7 @@ class TestThresholdedRelu(TestActivation):
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=self.relative_error)
self.check_grad(['X'], 'Out')
class TestHardSigmoid(TestActivation):

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