def _infer_step_implementation(self, batch):
# Get only batch size from real batch
batch_size = batch.shape[0]
noise = R.gaussian(shape=[batch_size, self.nz])
fake_images = self.forward(noise)
return fake_images
def test_random_dynamic_same_result():
R.manual_seed(0)
a = R.uniform(5) + R.gaussian(5)
R.manual_seed(0)
b = R.uniform(5) + R.gaussian(5)
assert np.all(a.numpy() == b.numpy())
def test_random_dynamic_diff_result():
a = R.uniform(5) + R.gaussian(5)
b = R.uniform(5) + R.gaussian(5)
assert np.any(a.numpy() != b.numpy())
def graph_b():
R.manual_seed(731)
return R.uniform(5) + R.gaussian(5)
def graph_b():
return R.uniform(5) + R.gaussian(5)
