def test_conversion_cpu_to_gpuD():
rational = Rational(version='D', cuda=False, trainable=False)
rational.cuda()
params = np.all(['cuda' in str(para.device) for para in rational.parameters()])
cpu_f = "CUDA_D" in rational.activation_function.__qualname__
new_res = rational(cuda_inp).clone().detach().cpu().numpy()
coherent_compute = np.all(np.isclose(new_res, expected_res, atol=5e-02))
assert params and cpu_f and coherent_compute
from rational.torch import Rational
rational_function = Rational() # Initialized closed to Leaky ReLU
print(rational_function)
# Pade Activation Unit (version A) of degrees (5, 4) running on cuda:0
# or Pade Activation Unit (version A) of degrees (5, 4) running on cpu
rational_function.cpu()
rational_function.cuda()
print(rational_function.degrees)
# (5, 4)
print(rational_function.version)
# A
print(rational_function.training)
# True
import torch
import torch.nn as nn
class RationalNetwork(nn.Module):
n_features = 512
def __init__(self,
input_shape,
output_shape,
recurrent=False,
cuda=False,
**kwargs):
super().__init__()