def test_get_shape_per_tensor_fail(self):
tensor_list = [
random_tensor(0, 32, shape=(2, 2, 2)),
random_tensor(0, 32, shape=(3, 3, 3, 3))
]
with pytest.raises(ValueError,
match='Expected all tensors to have 3 dimensions but got 4 at index 1'):
output_shape_per_tensor = batch.get_shape_per_tensor(tensor_list)
def test_list_to_packed_fail1(self):
tensor_list = [
random_tensor(0, 32, shape=(2, 2, 2)),
random_tensor(0, 32, shape=(3, 3, 3))
]
with pytest.raises(ValueError,
match='Expected all tensor to have last dimension 2 but '
'got 3 at index 1'):
_ = batch.list_to_packed(tensor_list)
def test_list_to_packed_fail2(self):
tensor_list = [
random_tensor(0, 32, shape=(2, 2, 2), dtype=torch.long,
device='cpu'),
random_tensor(0, 32, shape=(2, 2, 2), dtype=torch.float,
device='cuda')
]
with pytest.raises(ValueError,
match='Expected all tensor to have type torch.LongTensor but '
'got torch.cuda.FloatTensor at index 1'):
_ = batch.list_to_packed(tensor_list)
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 inputs(self, high_val, total_numel, dtype, device):
return random_tensor(0,
high_val,
shape=(total_numel, 1),
dtype=dtype,
device=device)
def test_random_tensor(low, high, shape, dtype, device):
tensor = random_tensor(low, high, shape, dtype, device)
check_tensor(tensor, shape, dtype, device)
assert (low <= tensor).all()
assert (tensor <= high).all()
def tensor_list(self, dtype, device, high_val, shape_per_tensor, last_dim):
return [random_tensor(0, high_val, shape=tuple(shape) + (last_dim,),
dtype=dtype, device=device)
for shape in shape_per_tensor]
def inputs(self, high_val, numel_per_tensor, dtype, device):
return random_tensor(0, high_val, shape=(numel_per_tensor.shape[0],),
dtype=dtype, device=device)
def tensor(self, shape, dtype, device):
return random_tensor(0, 256, shape=shape, dtype=dtype, device=device)
def packed_tensor(self, total_numel, last_dim, dtype, device):
return random_tensor(0,
256,
shape=(total_numel, last_dim),
dtype=dtype,
device=device)