def _run_test(dev, singa_op, np_op, s1, s2):
x_0 = np.random.random(s1).astype(np.float32)
y_0 = np.random.random(s2).astype(np.float32)
x0 = tensor.Tensor(device=dev, data=x_0)
y0 = tensor.Tensor(device=dev, data=y_0)
z0 = tensor._call_singa_func(singa_op, x0.data, y0.data)
z0.to_host()
np.testing.assert_array_almost_equal(tensor.to_numpy(z0),
np_op(x_0, y_0))
return
def _round_even(self, dev=gpu_dev):
q=np.array([0.1, 0.5, 0.9, 1.2, 1.5,
1.8, 2.3, 2.5, 2.7, -1.1,
-1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float32)
ans = np.array([0., 0., 1., 1., 2.,
2., 2., 2., 3., -1.,
-2., -2., -2., -2., -3.]).astype(np.float32)
x = tensor.Tensor(shape=q.shape, device=dev)
x.copy_from_numpy(q)
y = tensor._call_singa_func(singa_api.RoundE, x.data)
np.testing.assert_array_almost_equal(ans, tensor.to_numpy(y))
def _run_test(org_shape, axis, aft_shape):
x_0 = np.random.random(org_shape).astype(np.float32)
x_0 = x_0 + 1000
x0 = tensor.Tensor(device=gpu_dev, data=x_0)
# test with axis
y0 = tensor._call_singa_func(singa_api.SoftMax, x0.data, axis)
# test with numpy
x_0 = x_0.reshape(aft_shape)
x_0 = x_0 - np.max(x_0)
y1 = np.divide(np.exp(x_0), np.sum(np.exp(x_0),axis=1).reshape(x_0.shape[0],1) ) # 2d softmax
y1 = y1.reshape(org_shape)
np.testing.assert_array_almost_equal(tensor.to_numpy(y0), y1)
def _test(s1, s2, axis1, axis2, s3, s_op, n_op, dev):
x_0 = np.random.random(s1).astype(np.float32)
y_0 = np.random.random(s2).astype(np.float32)
x0 = tensor.Tensor(device=dev, data=x_0)
y0 = tensor.Tensor(device=dev, data=y_0)
x1 = x0.transpose(axis1)
y1 = y0.transpose(axis2)
z0 = tensor._call_singa_func(s_op, x1.data, y1.data)
z0.to_host()
np.testing.assert_array_almost_equal(
tensor.to_numpy(z0),
n_op(x_0.transpose(axis1), y_0.transpose(axis2)))
np.testing.assert_array_almost_equal(z0.shape, s3)
return
def _round(self, dev=gpu_dev):
x = tensor.Tensor(shape=(3,4,5), device=dev).gaussian(0, 1)
y = tensor._call_singa_func(singa_api.Round, x.data)
np.testing.assert_array_almost_equal(np.round(tensor.to_numpy(x)),
tensor.to_numpy(y))
