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)