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Updated to use flatdata on the python

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This commit is contained in:
Alejandro Saucedo 2021-03-07 11:12:01 +00:00
parent a2ee928f4c
commit 6a7f410675
1 changed files with 18 additions and 18 deletions
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36
python/src/main.cpp
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@ -169,12 +169,13 @@ PYBIND11_MODULE(kp, m) {
.def("sequence", &kp::Manager::sequence, DOC(kp, Manager, sequence),
py::arg("queue_index") = 0, py::arg("total_timestamps") = 0)
.def("tensor", [np](kp::Manager& self,
const py::array_t<float> data,
const py::array_t<float>& data,
kp::Tensor::TensorTypes tensor_type) {
const py::buffer_info info = data.request();
const py::array_t<float>& flatdata = np.attr("ravel")(data);
const py::buffer_info info = flatdata.request();
return self.tensor(
info.ptr,
data.size(),
flatdata.size(),
sizeof(float),
kp::Tensor::TensorDataTypes::eFloat,
tensor_type);
@ -182,27 +183,26 @@ PYBIND11_MODULE(kp, m) {
DOC(kp, Manager, tensor),
py::arg("data"), py::arg("tensor_type") = kp::Tensor::TensorTypes::eDevice)
.def("tensor_t", [np](kp::Manager& self,
const py::array data,
const py::array& data,
kp::Tensor::TensorTypes tensor_type) {
// TODO: confirm if ravel is required as numpy data is always flat
//const py::array_t<float> flatdata = np.attr("ravel")(data);
//const py::buffer_info info = flatdata.request();
const py::buffer_info info = data.request();
if (data.dtype() == py::dtype::of<std::float_t>()) {
// TODO: Suppport strides in numpy format
const py::array_t<float>& flatdata = np.attr("ravel")(data);
const py::buffer_info info = flatdata.request();
if (flatdata.dtype() == py::dtype::of<std::float_t>()) {
return self.tensor(
info.ptr, data.size(), sizeof(float), kp::Tensor::TensorDataTypes::eFloat, tensor_type);
} else if (data.dtype() == py::dtype::of<std::uint32_t>()) {
info.ptr, flatdata.size(), sizeof(float), kp::Tensor::TensorDataTypes::eFloat, tensor_type);
} else if (flatdata.dtype() == py::dtype::of<std::uint32_t>()) {
return self.tensor(
info.ptr, data.size(), sizeof(uint32_t), kp::Tensor::TensorDataTypes::eUnsignedInt, tensor_type);
} else if (data.dtype() == py::dtype::of<std::int32_t>()) {
info.ptr, flatdata.size(), sizeof(uint32_t), kp::Tensor::TensorDataTypes::eUnsignedInt, tensor_type);
} else if (flatdata.dtype() == py::dtype::of<std::int32_t>()) {
return self.tensor(
info.ptr, data.size(), sizeof(int32_t), kp::Tensor::TensorDataTypes::eInt, tensor_type);
} else if (data.dtype() == py::dtype::of<std::double_t>()) {
info.ptr, flatdata.size(), sizeof(int32_t), kp::Tensor::TensorDataTypes::eInt, tensor_type);
} else if (flatdata.dtype() == py::dtype::of<std::double_t>()) {
return self.tensor(
info.ptr, data.size(), sizeof(double), kp::Tensor::TensorDataTypes::eDouble, tensor_type);
} else if (data.dtype() == py::dtype::of<bool>()) {
info.ptr, flatdata.size(), sizeof(double), kp::Tensor::TensorDataTypes::eDouble, tensor_type);
} else if (flatdata.dtype() == py::dtype::of<bool>()) {
return self.tensor(
info.ptr, data.size(), sizeof(bool), kp::Tensor::TensorDataTypes::eBool, tensor_type);
info.ptr, flatdata.size(), sizeof(bool), kp::Tensor::TensorDataTypes::eBool, tensor_type);
} else {
throw std::runtime_error("Kompute Python no valid dtype supported");
}