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 test_random_shape_per_tensor(batch_size, min_shape, max_shape):
old_seed = torch.initial_seed()
torch.manual_seed(0)
shape_per_tensor = random_shape_per_tensor(batch_size, min_shape,
max_shape)
if min_shape is None:
min_shape = tuple([1] * len(max_shape))
min_shape = torch.tensor(min_shape).unsqueeze(0)
max_shape = torch.tensor(max_shape).unsqueeze(0)
assert shape_per_tensor.shape[0] == batch_size
assert (min_shape <= shape_per_tensor).all() and (shape_per_tensor <=
max_shape).all()
def shape_per_tensor(self, batch_size, min_shape, max_shape):
return random_shape_per_tensor(batch_size, min_shape=min_shape,
max_shape=max_shape)