def test_should_raise_on_empty_dtype(self):
tensor = inference_pb2.Tensor(dtype="",
shape=[
inference_pb2.NamedInt(size=1),
inference_pb2.NamedInt(size=2)
])
with pytest.raises(ValueError):
pb_tensor_to_xarray(tensor)
def Predict(self, request: inference_pb2.PredictRequest,
context) -> inference_pb2.PredictResponse:
session = self._getModelSession(context, request.modelSessionId)
arr = converters.pb_tensor_to_xarray(request.tensor)
res = session.client.forward(arr)
pb_tensor = converters.xarray_to_pb_tensor(res)
return inference_pb2.PredictResponse(tensor=pb_tensor)
def test_should_same_data(self, shape):
arr = xr.DataArray(np.random.random(shape))
tensor = self.to_pb_tensor(arr)
result_arr = pb_tensor_to_xarray(tensor)
assert_array_equal(arr, result_arr)
def test_should_same_shape(self, shape):
arr = xr.DataArray(np.zeros(shape))
tensor = self.to_pb_tensor(arr)
result_arr = pb_tensor_to_xarray(tensor)
assert arr.shape == result_arr.shape
def test_should_have_same_dtype(self, np_dtype, dtype_str):
arr = xr.DataArray(np.arange(9, dtype=np_dtype))
tensor = self.to_pb_tensor(arr)
result_arr = pb_tensor_to_xarray(tensor)
assert arr.dtype == result_arr.dtype
def test_should_return_ndarray(self):
arr = xr.DataArray(np.arange(9))
parsed = self.to_pb_tensor(arr)
result_arr = pb_tensor_to_xarray(parsed)
assert isinstance(result_arr, xr.DataArray)
def test_should_raise_on_empty_shape(self):
tensor = inference_pb2.Tensor(dtype="int64", shape=[])
with pytest.raises(ValueError):
pb_tensor_to_xarray(tensor)
