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Posted to dev@singa.apache.org by GitBox <gi...@apache.org> on 2019/08/25 06:12:20 UTC
[GitHub] [incubator-singa] ShichengChen commented on a change in pull
request #524: SINGA-474 prelu, add, equal, selu, elu operator
ShichengChen commented on a change in pull request #524: SINGA-474 prelu,add,equal,selu,elu operator
URL: https://github.com/apache/incubator-singa/pull/524#discussion_r317383759
##########
File path: test/python/test_operation.py
##########
@@ -1587,25 +1587,148 @@ def test_min_gpu(self):
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
-def test_HardSigmoid(self):
- def test_helper(gpu=False):
- x = np.random.randn(3, 2)
- #y = max(0, min(1, alpha * x + gamma))
- a=0.2
- g=0.5
- y = np.clip(x * 0.2 + 0.5, 0, 1)
- grad=(0<(np.clip(x * 0.2 + 0.5, 0, 1)) * (np.clip(x * 0.2 + 0.5, 0, 1)<1))*0.2
- x = tensor.from_numpy(x)
- if(gpu):
- x.to_device(gpu_dev)
- result = autograd.hardsigmoid(x,a,g)
- dy = tensor.from_numpy(np.random.randn((3,2)).astype(np.float32))
- dx = result.creator.backward(dy.data)
- np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
- np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
- test_helper(False)
- test_helper(True)
+ def test_HardSigmoid(self):
+ def test_helper(gpu=False):
+ x = np.random.randn(3, 2)
+ #y = max(0, min(1, alpha * x + gamma))
+ a=0.2
+ g=0.5
+ y = np.clip(x * 0.2 + 0.5, 0, 1)
+ dy=np.random.randn(3,2)
+ grad=(0<(np.clip(x * 0.2 + 0.5, 0, 1)) * (np.clip(x * 0.2 + 0.5, 0, 1)<1))*0.2 * dy
+ x = tensor.from_numpy(x)
+ dy = tensor.from_numpy(dy)
+ if(gpu):
+ x.to_device(gpu_dev)
+ dy.to_device(gpu_dev)
+ result = autograd.hardsigmoid(x,a,g)
+ dx = result.creator.backward(dy.data)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
+ test_helper(False)
+ test_helper(True)
+
+ @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
+ def test_prelu(self):
+ def hepler(gpu):
+ x = np.random.randn(3, 2)
+ slope = np.random.randn(3, 2)
+ y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
+ dy = np.random.randn(3, 2)
+ x0=x.copy()
+ x0[x0>0]=1
+ x0[x0<1]=0
+ grad0=(x0+(1-x0)*slope)*dy
+ grad1 = (1-x0)*x*dy
+ x = tensor.from_numpy(x)
+ slope = tensor.from_numpy(slope)
+ dy = tensor.from_numpy(dy)
+ if(gpu):
+ x.to_device(gpu_dev)
+ slope.to_device(gpu_dev)
+ dy.to_device(gpu_dev)
+ result = autograd.prelu(x,slope)
+ dx0,dx1 = result.creator.backward(dy.data)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
+ hepler(False)
+ hepler(True)
+
+ @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
+ def test_SeLU(self):
+ def test_helper(gpu):
+ x = np.random.randn(3, 2)
+ a=0.2
+ g=0.3
+ y = np.clip(x, 0, np.inf) * g + (np.exp(np.clip(x, -np.inf, 0)) - 1) * a * g
+ dy=np.random.randn(3, 2)
+ grad = (np.exp(np.clip(x, -np.inf, 0))) * g
+ grad[x<=0]=grad[x<=0]*a
+ grad*=dy
+ x = tensor.from_numpy(x)
+
+ result = autograd.selu(x,a,g)
+ dy = tensor.from_numpy(dy)
+ if(gpu):
+ dy.to_device(gpu_dev)
+ x.to_device(gpu_dev)
+ dx = result.creator.backward(dy.data)
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
+ test_helper(False)
+ test_helper(True)
+
+
+ @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
+ def test_Equal(self):
+ def test_helper(gpu):
+ x0 = np.random.randn(3, 2)
+ x1 = np.random.randn(3, 2)
+ y = np.equal(x0,x1)
+ x0 = tensor.from_numpy(x0)
+ x1 = tensor.from_numpy(x1)
+ if(gpu):
+ x0.to_device(gpu_dev)
+ x1.to_device(gpu_dev)
+
+ result = autograd.equal(x0,x1)
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
+ test_helper(False)
+ test_helper(True)
+
+ @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
+ def test_Elu(self):
+ def test_helper(gpu):
+ #f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0
+ x = np.random.randn(3, 2)
+ y = np.clip(x, 0, np.inf) + (np.exp(np.clip(x, -np.inf, 0)) - 1) * 1.0
+ dy=np.random.randn(3, 2)
+ grad=np.exp(np.clip(x, -np.inf, 0))*dy
+
+ x = tensor.from_numpy(x)
+ result = autograd.elu(x)
+ dy = tensor.from_numpy(dy)
+ if(gpu):
+ dy.to_device(gpu_dev)
+ x.to_device(gpu_dev)
+ dx = result.creator.backward(dy.data)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
+ np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
+ test_helper(False)
+ test_helper(True)
+
+ @unittest.skipIf(not singa_wrap.USE_CUDA, 'CUDA is not enabled')
Review comment:
I just changed it to if-else checks inside unit test functions
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