提交 6461e800 编写于 作者: K Krzysztof Binias

Inheritance added for MKLDNN tests

上级 d8bd436f
...@@ -507,58 +507,46 @@ class TestSwish(OpTest): ...@@ -507,58 +507,46 @@ class TestSwish(OpTest):
#--------------------test MKLDNN-------------------- #--------------------test MKLDNN--------------------
class TestMKLDNNRelu(OpTest): class TestMKLDNNRelu(TestRelu):
def setUp(self): def setUp(self):
self.op_type = "relu" super(TestMKLDNNRelu, self).setUp()
x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32") x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs # The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02 x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x} out = np.maximum(x, 0)
self.outputs = {'Out': np.maximum(self.inputs['X'], 0)}
self.attrs = {"use_mkldnn": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self): self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.check_grad(['X'], 'Out', max_relative_error=0.007) self.outputs = {'Out': out}
self.attrs = {"use_mkldnn": True}
class TestMKLDNNTanh(OpTest): class TestMKLDNNTanh(TestTanh):
def setUp(self): def setUp(self):
self.op_type = "tanh" super(TestMKLDNNTanh, self).setUp()
self.inputs = { self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32") 'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
} }
self.outputs = {'Out': np.tanh(self.inputs['X'])} self.outputs = {'Out': np.tanh(self.inputs['X'])}
self.attrs = {"use_mkldnn": True} self.attrs = {"use_mkldnn": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self): class TestMKLDNNSqrt(TestSqrt):
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestMKLDNNSqrt(OpTest):
def setUp(self): def setUp(self):
self.op_type = "sqrt" super(TestMKLDNNSqrt, self).setUp()
self.inputs = { self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32") 'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
} }
self.outputs = {'Out': np.sqrt(self.inputs['X'])} self.outputs = {'Out': np.sqrt(self.inputs['X'])}
self.attrs = {"use_mkldnn": True} self.attrs = {"use_mkldnn": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out', max_relative_error=0.007)
class TestMKLDNNAbs(OpTest): class TestMKLDNNAbs(TestAbs):
def setUp(self): def setUp(self):
self.op_type = "abs" super(TestMKLDNNAbs, self).setUp()
x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32") x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs # The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02 x[np.abs(x) < 0.005] = 0.02
...@@ -566,12 +554,6 @@ class TestMKLDNNAbs(OpTest): ...@@ -566,12 +554,6 @@ class TestMKLDNNAbs(OpTest):
self.outputs = {'Out': np.abs(self.inputs['X'])} self.outputs = {'Out': np.abs(self.inputs['X'])}
self.attrs = {"use_mkldnn": True} self.attrs = {"use_mkldnn": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out', max_relative_error=0.007)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()
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