def test_can_reset_shape_after_decreasing_on_preallocated_memory():
ones_arr = np.ones(shape=(1, 3, 32, 32), dtype=np.float32)
ones_arr = np.ascontiguousarray(ones_arr)
ov_tensor = Tensor(ones_arr, shared_memory=True)
ref_shape_1 = [1, 3, 24, 24]
ref_shape_2 = [1, 3, 32, 32]
assert np.shares_memory(ones_arr, ov_tensor.data)
ov_tensor.shape = ref_shape_1
assert list(ov_tensor.shape) == ref_shape_1
ov_tensor.shape = ref_shape_2
assert list(ov_tensor.shape) == ref_shape_2
def test_set_shape(ov_type, numpy_dtype):
shape = ov.Shape([1, 3, 32, 32])
ref_shape = ov.Shape([1, 3, 48, 48])
ref_shape_np = [1, 3, 28, 28]
ov_tensor = Tensor(ov_type, shape)
ov_tensor.shape = ref_shape
assert list(ov_tensor.shape) == list(ref_shape)
ones_arr = np.ones(list(ov_tensor.shape), numpy_dtype)
ov_tensor.data[:] = ones_arr
assert np.array_equal(ov_tensor.data, ones_arr)
ov_tensor.shape = ref_shape_np
assert list(ov_tensor.shape) == ref_shape_np
zeros = np.zeros(ref_shape_np, numpy_dtype)
ov_tensor.data[:] = zeros
assert np.array_equal(ov_tensor.data, zeros)
def test_can_set_smaller_or_same_shape_on_preallocated_memory(ref_shape):
ones_arr = np.ones(shape=(1, 3, 32, 32), dtype=np.float32)
ones_arr = np.ascontiguousarray(ones_arr)
ov_tensor = Tensor(ones_arr, shared_memory=True)
assert np.shares_memory(ones_arr, ov_tensor.data)
ov_tensor.shape = ref_shape
assert list(ov_tensor.shape) == ref_shape
def test_cannot_set_shape_on_preallocated_memory(ref_shape):
ones_arr = np.ones(shape=(1, 3, 32, 32), dtype=np.float32)
ones_arr = np.ascontiguousarray(ones_arr)
ov_tensor = Tensor(ones_arr, shared_memory=True)
assert np.shares_memory(ones_arr, ov_tensor.data)
with pytest.raises(RuntimeError) as e:
ov_tensor.shape = ref_shape
assert "Cannot call setShape for Blobs created on top of preallocated memory" in str(e.value)
def test_cannot_set_shape_incorrect_dims():
ov_tensor = Tensor(np.float32, [1, 3, 48, 48])
with pytest.raises(RuntimeError) as e:
ov_tensor.shape = [3, 28, 28]
assert "Dims and format are inconsistent" in str(e.value)
