def test_random_tensor_seed(low, high, shape):
threshold = shape[0] * shape[1] * 0.9
manual_seed(0)
tensor1 = random_tensor(low, high, shape)
tensor2 = random_tensor(low, high, shape)
assert torch.sum(tensor1 != tensor2) > threshold
manual_seed(0)
tensor3 = random_tensor(low, high, shape)
assert torch.equal(tensor1, tensor3)
manual_seed(1)
tensor4 = random_tensor(low, high, shape)
assert torch.sum(tensor1 != tensor4) > threshold
def test_random_shape_per_tensor_seed(batch_size, min_shape, max_shape):
threshold = batch_size * len(max_shape) * 0.9
manual_seed(0)
shape_per_tensor1 = random_shape_per_tensor(batch_size, min_shape,
max_shape)
shape_per_tensor2 = random_shape_per_tensor(batch_size, min_shape,
max_shape)
assert torch.sum(shape_per_tensor1 != shape_per_tensor2) > threshold
manual_seed(0)
shape_per_tensor3 = random_shape_per_tensor(batch_size, min_shape,
max_shape)
assert torch.equal(shape_per_tensor1, shape_per_tensor3)
manual_seed(1)
shape_per_tensor4 = random_shape_per_tensor(batch_size, min_shape,
max_shape)
assert torch.sum(shape_per_tensor1 != shape_per_tensor4) > threshold
def orig_test_wrapper(*args, **kwargs):
torch_state, random_state, np_state = random.get_state()
random.manual_seed(torch_seed, numpy_seed, random_seed)
output = orig_test(*args, **kwargs)
random.set_state(torch_state, random_state, np_state)
return output