def _predict_fy_samples_cuda(self, attr):
n, dx, dy = 5, 3, 2
x, y = np.random.randn(n, dx), np.random.randn(n, dy)
kern = Rbf(dx, ARD=True)
gp = GPR(x, y, kern)
f = getattr(gp, attr)
n_test = 5
x_test = np.random.randn(n_test, dx)
x_test_torch = TensorType(x_test)
gp.cuda()
# Numpy input:
samples_cuda_np = f(x_test)
assert isinstance(samples_cuda_np, np.ndarray)
# PyTorch (cpu) input
samples_cuda_torch = f(x_test_torch)
assert samples_cuda_torch.device == x_test_torch.device
# PyTorch (GPU) input
samples_cuda_gpu = f(x_test_torch.to("cuda"))
assert samples_cuda_gpu.is_cuda
def _predict_fy_cuda(self, attr):
"""
attr='predict_f' or 'predict_y'
"""
gp = self._get_model()
f = getattr(gp, attr)
x_test = np.random.randn(5, gp.input_dimension)
x_test_torch = TensorType(x_test)
# Test that CUDA works in all cases:
gp.cuda()
# Numpy input:
cuda_np = f(x_test)
for result in cuda_np:
assert isinstance(result, np.ndarray)
# PyTorch (cpu) input
cuda_torch = f(x_test_torch)
for result in cuda_torch:
assert result.device == x_test_torch.device
# PyTorch (GPU) input
cuda_gpu = f(x_test_torch.to("cuda"))
for result in cuda_gpu:
assert result.is_cuda
