def test_empty_tensor_tensorlist():
arr = np.array([], dtype=np.float32)
tensor = TensorCPU(arr, "NHWC")
tensorlist = TensorListCPU(arr, "NHWC")
assert_array_equal(np.array(tensor), tensorlist.as_array())
assert (np.array(tensor).shape == (0, ))
assert (tensorlist.as_array().shape == (0, ))
def test_data_ptr_tensor_list_cpu():
arr = np.random.rand(3, 5, 6)
tensorlist = TensorListCPU(arr, "NHWC")
tensor = tensorlist.as_tensor()
from_tensor_list = py_buffer_from_address(
tensorlist.data_ptr(), tensor.shape(),
types.to_numpy_type(tensor.dtype))
assert (np.array_equal(arr, from_tensor_list))
def test_tensorlist_shape():
shapes = [(3, 4, 5, 6), (1, 8, 7, 6, 5), (1, ), (1, 1)]
for shape in shapes:
arr = np.empty(shape)
tl = TensorListCPU(arr)
tl_gpu = tl._as_gpu()
assert tl.shape() == [shape[1:]] * shape[0]
assert tl_gpu.shape() == [shape[1:]] * shape[0]
def test_tensorlist_dtype():
dali_types = types._all_types
np_types = list(map(dali_type_to_np, dali_types))
for dali_type, np_type in zip(dali_types, np_types):
tl = TensorListCPU([TensorCPU(np.zeros((1), dtype=np_type))])
assert tl.dtype == dali_type
assert tl._as_gpu().dtype == dali_type
def test_tensorlist_getitem_cpu():
arr = np.random.rand(3, 5, 6)
tensorlist = TensorListCPU(arr, "NHWC")
list_of_tensors = [x for x in tensorlist]
assert (type(tensorlist.at(0)) == np.ndarray)
assert (type(tensorlist[0]) != np.ndarray)
assert (type(tensorlist[0]) == TensorCPU)
assert (type(tensorlist[-3]) == TensorCPU)
assert (len(list_of_tensors) == len(tensorlist))
with assert_raises(IndexError, glob="out of range"):
tensorlist[len(tensorlist)]
with assert_raises(IndexError, glob="out of range"):
tensorlist[-len(tensorlist) - 1]
def test_tl_from_list_of_tensors_empty():
with assert_raises(RuntimeError,
glob='Cannot create TensorList from an empty list.'):
TensorListCPU([])
with assert_raises(RuntimeError,
glob='Cannot create TensorList from an empty list.'):
TensorListGPU([])
def test_tensorlist_str_scalars():
arr = np.arange(10)
tl = TensorListCPU(arr)
params = [
arr, 'DALIDataType.INT64', 10,
'[(), (), (), (), (), (), (), (), (), ()]'
]
_test_str(tl, params, _expected_tensorlist_str)
def test_array_interface_tensor_cpu():
arr = np.random.rand(3, 5, 6)
tensorlist = TensorListCPU(arr, "NHWC")
assert tensorlist[0].__array_interface__['data'][0] == tensorlist[
0].data_ptr()
assert tensorlist[0].__array_interface__['data'][1] == True
assert np.array_equal(tensorlist[0].__array_interface__['shape'],
tensorlist[0].shape())
assert np.dtype(tensorlist[0].__array_interface__['typestr']) == np.dtype(
types.to_numpy_type(tensorlist[0].dtype))
def test_tl_from_list_of_tensors_different_dtypes():
np_types = [np.float32, np.float16, np.int16, np.int8, np.uint16, np.uint8]
for dtypes in np.random.choice(np_types, size=(3, 2), replace=False):
t1 = TensorCPU(np.zeros((1), dtype=dtypes[0]))
t2 = TensorCPU(np.zeros((1), dtype=dtypes[1]))
with assert_raises(
TypeError,
glob=
f"Tensors cannot have different data types. Tensor at position "
"1 has type '*' expected to have type '*'."):
TensorListCPU([t1, t2])
def test_tl_from_list_of_tensors_different_backends():
t1 = TensorCPU(np.zeros((1)))
t2 = TensorCPU(np.zeros((1)))._as_gpu()
with assert_raises(
TypeError,
glob='Object at position 1 cannot be converted to TensorCPU'):
TensorListCPU([t1, t2])
with assert_raises(
TypeError,
glob='Object at position 1 cannot be converted to TensorGPU'):
TensorListGPU([t2, t1])
def test_tl_from_list_of_tensors_different_shapes():
shapes = [(1, 2, 3), (4, 5, 6), (128, 128, 128), (8, 8, 8), (13, 47, 131)]
for size in [10, 5, 36, 1]:
np_arrays = [
np.random.rand(*shapes[i])
for i in np.random.choice(range(len(shapes)), size=size)
]
tl_cpu = TensorListCPU([TensorCPU(a) for a in np_arrays])
tl_gpu = TensorListGPU([TensorCPU(a)._as_gpu() for a in np_arrays])
for arr, tensor_cpu, tensor_gpu in zip(np_arrays, tl_cpu, tl_gpu):
np.testing.assert_array_equal(arr, tensor_cpu)
np.testing.assert_array_equal(arr, tensor_gpu.as_cpu())
def test_tl_from_list_of_tensors_same_shape():
for shape in [(10, 1), (4, 5, 6), (13, 1), (1, 1)]:
arr = np.random.rand(*shape)
tl_cpu_from_np = TensorListCPU(arr)
tl_cpu_from_tensors = TensorListCPU([TensorCPU(a) for a in arr])
np.testing.assert_array_equal(tl_cpu_from_np.as_array(),
tl_cpu_from_tensors.as_array())
tl_gpu_from_np = tl_cpu_from_np._as_gpu()
tl_gpu_from_tensors = TensorListGPU(
[TensorCPU(a)._as_gpu() for a in arr])
np.testing.assert_array_equal(tl_gpu_from_np.as_cpu().as_array(),
tl_gpu_from_tensors.as_cpu().as_array())
def test_create_tensorlist_as_tensor():
arr = np.random.rand(3, 5, 6)
tensorlist = TensorListCPU(arr, "NHWC")
tensor = tensorlist.as_tensor()
assert_array_equal(np.array(tensor), tensorlist.as_array())
def test_tensorlist_str_empty():
tl = TensorListCPU(np.empty(0))
params = [[], 'DALIDataType.FLOAT64', 0, []]
_test_str(tl, params, _expected_tensorlist_str)