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FastDeploy/fastdeploy/pybind/runtime.cc
huangjianhui 291db315c8 [Other]Fastdeploy supports set_model_buffer function for encrypted model (#930) 
* Update keypointdetection result docs

* Update im.copy() to im in examples

* Update new Api, fastdeploy::vision::Visualize to fastdeploy::vision

* Update SwapBackgroundSegmentation && SwapBackgroundMatting to SwapBackground

* Update README_CN.md

* Update README_CN.md

* Support set_model_buffer function
2022-12-21 14:21:28 +08:00

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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/pybind/main.h"
namespace fastdeploy {
void BindRuntime(pybind11::module& m) {
pybind11::class_<RuntimeOption>(m, "RuntimeOption")
.def(pybind11::init())
.def("set_model_path", &RuntimeOption::SetModelPath)
.def("set_model_buffer", &RuntimeOption::SetModelBuffer)
.def("use_gpu", &RuntimeOption::UseGpu)
.def("use_cpu", &RuntimeOption::UseCpu)
.def("use_rknpu2", &RuntimeOption::UseRKNPU2)
.def("use_xpu", &RuntimeOption::UseXpu)
.def("set_external_stream", &RuntimeOption::SetExternalStream)
.def("set_cpu_thread_num", &RuntimeOption::SetCpuThreadNum)
.def("use_paddle_backend", &RuntimeOption::UsePaddleBackend)
.def("use_poros_backend", &RuntimeOption::UsePorosBackend)
.def("use_ort_backend", &RuntimeOption::UseOrtBackend)
.def("set_ort_graph_opt_level", &RuntimeOption::SetOrtGraphOptLevel)
.def("use_trt_backend", &RuntimeOption::UseTrtBackend)
.def("use_openvino_backend", &RuntimeOption::UseOpenVINOBackend)
.def("use_lite_backend", &RuntimeOption::UseLiteBackend)
.def("set_paddle_mkldnn", &RuntimeOption::SetPaddleMKLDNN)
.def("set_openvino_device", &RuntimeOption::SetOpenVINODevice)
.def("set_openvino_shape_info", &RuntimeOption::SetOpenVINOShapeInfo)
.def("set_openvino_cpu_operators",
&RuntimeOption::SetOpenVINOCpuOperators)
.def("enable_paddle_log_info", &RuntimeOption::EnablePaddleLogInfo)
.def("disable_paddle_log_info", &RuntimeOption::DisablePaddleLogInfo)
.def("set_paddle_mkldnn_cache_size",
&RuntimeOption::SetPaddleMKLDNNCacheSize)
.def("enable_lite_fp16", &RuntimeOption::EnableLiteFP16)
.def("disable_lite_fp16", &RuntimeOption::DisableLiteFP16)
.def("set_lite_power_mode", &RuntimeOption::SetLitePowerMode)
.def("set_trt_input_shape", &RuntimeOption::SetTrtInputShape)
.def("set_trt_max_workspace_size", &RuntimeOption::SetTrtMaxWorkspaceSize)
.def("set_trt_max_batch_size", &RuntimeOption::SetTrtMaxBatchSize)
.def("enable_paddle_to_trt", &RuntimeOption::EnablePaddleToTrt)
.def("enable_trt_fp16", &RuntimeOption::EnableTrtFP16)
.def("disable_trt_fp16", &RuntimeOption::DisableTrtFP16)
.def("set_trt_cache_file", &RuntimeOption::SetTrtCacheFile)
.def("enable_pinned_memory", &RuntimeOption::EnablePinnedMemory)
.def("disable_pinned_memory", &RuntimeOption::DisablePinnedMemory)
.def("enable_paddle_trt_collect_shape",
&RuntimeOption::EnablePaddleTrtCollectShape)
.def("disable_paddle_trt_collect_shape",
&RuntimeOption::DisablePaddleTrtCollectShape)
.def("use_ipu", &RuntimeOption::UseIpu)
.def("set_ipu_config", &RuntimeOption::SetIpuConfig)
.def("delete_paddle_backend_pass",
&RuntimeOption::DeletePaddleBackendPass)
.def("disable_paddle_trt_ops", &RuntimeOption::DisablePaddleTrtOPs)
.def_readwrite("model_file", &RuntimeOption::model_file)
.def_readwrite("params_file", &RuntimeOption::params_file)
.def_readwrite("model_format", &RuntimeOption::model_format)
.def_readwrite("backend", &RuntimeOption::backend)
.def_readwrite("external_stream", &RuntimeOption::external_stream_)
.def_readwrite("model_from_memory", &RuntimeOption::model_from_memory_)
.def_readwrite("model_buffer_size", &RuntimeOption::model_buffer_size_)
.def_readwrite("params_buffer_size", &RuntimeOption::params_buffer_size_)
.def_readwrite("cpu_thread_num", &RuntimeOption::cpu_thread_num)
.def_readwrite("device_id", &RuntimeOption::device_id)
.def_readwrite("device", &RuntimeOption::device)
.def_readwrite("ort_graph_opt_level", &RuntimeOption::ort_graph_opt_level)
.def_readwrite("ort_inter_op_num_threads",
&RuntimeOption::ort_inter_op_num_threads)
.def_readwrite("ort_execution_mode", &RuntimeOption::ort_execution_mode)
.def_readwrite("trt_max_shape", &RuntimeOption::trt_max_shape)
.def_readwrite("trt_opt_shape", &RuntimeOption::trt_opt_shape)
.def_readwrite("trt_min_shape", &RuntimeOption::trt_min_shape)
.def_readwrite("trt_serialize_file", &RuntimeOption::trt_serialize_file)
.def_readwrite("trt_enable_fp16", &RuntimeOption::trt_enable_fp16)
.def_readwrite("trt_enable_int8", &RuntimeOption::trt_enable_int8)
.def_readwrite("trt_max_batch_size", &RuntimeOption::trt_max_batch_size)
.def_readwrite("trt_max_workspace_size",
&RuntimeOption::trt_max_workspace_size)
.def_readwrite("is_dynamic", &RuntimeOption::is_dynamic)
.def_readwrite("long_to_int", &RuntimeOption::long_to_int)
.def_readwrite("use_nvidia_tf32", &RuntimeOption::use_nvidia_tf32)
.def_readwrite("unconst_ops_thres", &RuntimeOption::unconst_ops_thres)
.def_readwrite("poros_file", &RuntimeOption::poros_file)
.def_readwrite("ipu_device_num", &RuntimeOption::ipu_device_num)
.def_readwrite("ipu_micro_batch_size",
&RuntimeOption::ipu_micro_batch_size)
.def_readwrite("ipu_enable_pipelining",
&RuntimeOption::ipu_enable_pipelining)
.def_readwrite("ipu_batches_per_step",
&RuntimeOption::ipu_batches_per_step)
.def_readwrite("ipu_enable_fp16", &RuntimeOption::ipu_enable_fp16)
.def_readwrite("ipu_replica_num", &RuntimeOption::ipu_replica_num)
.def_readwrite("ipu_available_memory_proportion",
&RuntimeOption::ipu_available_memory_proportion)
.def_readwrite("ipu_enable_half_partial",
&RuntimeOption::ipu_enable_half_partial)
.def_readwrite("xpu_l3_workspace_size",
&RuntimeOption::xpu_l3_workspace_size)
.def_readwrite("xpu_locked",
&RuntimeOption::xpu_locked)
.def_readwrite("xpu_autotune",
&RuntimeOption::xpu_autotune)
.def_readwrite("xpu_autotune_file",
&RuntimeOption::xpu_autotune_file)
.def_readwrite("xpu_precision",
&RuntimeOption::xpu_precision)
.def_readwrite("xpu_adaptive_seqlen",
&RuntimeOption::xpu_adaptive_seqlen)
.def_readwrite("xpu_enable_multi_stream",
&RuntimeOption::xpu_enable_multi_stream);
pybind11::class_<TensorInfo>(m, "TensorInfo")
.def_readwrite("name", &TensorInfo::name)
.def_readwrite("shape", &TensorInfo::shape)
.def_readwrite("dtype", &TensorInfo::dtype);
pybind11::class_<Runtime>(m, "Runtime")
.def(pybind11::init())
.def("init", &Runtime::Init)
.def("compile",
[](Runtime& self,
std::vector<std::vector<pybind11::array>>& warm_datas,
const RuntimeOption& _option) {
size_t rows = warm_datas.size();
size_t columns = warm_datas[0].size();
std::vector<std::vector<FDTensor>> warm_tensors(
rows, std::vector<FDTensor>(columns));
for (size_t i = 0; i < rows; ++i) {
for (size_t j = 0; j < columns; ++j) {
auto dtype =
NumpyDataTypeToFDDataType(warm_datas[i][j].dtype());
std::vector<int64_t> data_shape;
data_shape.insert(data_shape.begin(), warm_datas[i][j].shape(),
warm_datas[i][j].shape() +
warm_datas[i][j].ndim());
warm_tensors[i][j].Resize(data_shape, dtype);
memcpy(warm_tensors[i][j].MutableData(),
warm_datas[i][j].mutable_data(),
warm_datas[i][j].nbytes());
}
}
return self.Compile(warm_tensors, _option);
})
.def("infer",
[](Runtime& self, std::map<std::string, pybind11::array>& data) {
std::vector<FDTensor> inputs(data.size());
int index = 0;
for (auto iter = data.begin(); iter != data.end(); ++iter) {
std::vector<int64_t> data_shape;
data_shape.insert(data_shape.begin(), iter->second.shape(),
iter->second.shape() + iter->second.ndim());
auto dtype = NumpyDataTypeToFDDataType(iter->second.dtype());
// TODO(jiangjiajun) Maybe skip memory copy is a better choice
// use SetExternalData
inputs[index].Resize(data_shape, dtype);
memcpy(inputs[index].MutableData(), iter->second.mutable_data(),
iter->second.nbytes());
inputs[index].name = iter->first;
index += 1;
}
std::vector<FDTensor> outputs(self.NumOutputs());
self.Infer(inputs, &outputs);
std::vector<pybind11::array> results;
results.reserve(outputs.size());
for (size_t i = 0; i < outputs.size(); ++i) {
auto numpy_dtype = FDDataTypeToNumpyDataType(outputs[i].dtype);
results.emplace_back(
pybind11::array(numpy_dtype, outputs[i].shape));
memcpy(results[i].mutable_data(), outputs[i].Data(),
outputs[i].Numel() * FDDataTypeSize(outputs[i].dtype));
}
return results;
})
.def("infer",
[](Runtime& self, std::map<std::string, FDTensor>& data) {
std::vector<FDTensor> inputs;
inputs.reserve(data.size());
for (auto iter = data.begin(); iter != data.end(); ++iter) {
FDTensor tensor;
tensor.SetExternalData(iter->second.Shape(),
iter->second.Dtype(), iter->second.Data(),
iter->second.device);
tensor.name = iter->first;
inputs.push_back(tensor);
}
std::vector<FDTensor> outputs;
if (!self.Infer(inputs, &outputs)) {
throw std::runtime_error("Failed to inference with Runtime.");
}
return outputs;
})
.def("infer",
[](Runtime& self, std::vector<FDTensor>& inputs) {
std::vector<FDTensor> outputs;
return self.Infer(inputs, &outputs);
})
.def("bind_input_tensor", &Runtime::BindInputTensor)
.def("infer", [](Runtime& self) { self.Infer(); })
.def("get_output_tensor",
[](Runtime& self, const std::string& name) {
FDTensor* output = self.GetOutputTensor(name);
if (output == nullptr) {
return pybind11::cast(nullptr);
}
return pybind11::cast(*output);
})
.def("num_inputs", &Runtime::NumInputs)
.def("num_outputs", &Runtime::NumOutputs)
.def("get_input_info", &Runtime::GetInputInfo)
.def("get_output_info", &Runtime::GetOutputInfo)
.def_readonly("option", &Runtime::option);
pybind11::enum_<Backend>(m, "Backend", pybind11::arithmetic(),
"Backend for inference.")
.value("UNKOWN", Backend::UNKNOWN)
.value("ORT", Backend::ORT)
.value("TRT", Backend::TRT)
.value("POROS", Backend::POROS)
.value("PDINFER", Backend::PDINFER)
.value("RKNPU2", Backend::RKNPU2)
.value("LITE", Backend::LITE);
pybind11::enum_<ModelFormat>(m, "ModelFormat", pybind11::arithmetic(),
"ModelFormat for inference.")
.value("PADDLE", ModelFormat::PADDLE)
.value("TORCHSCRIPT", ModelFormat::TORCHSCRIPT)
.value("RKNN", ModelFormat::RKNN)
.value("ONNX", ModelFormat::ONNX);
pybind11::enum_<Device>(m, "Device", pybind11::arithmetic(),
"Device for inference.")
.value("CPU", Device::CPU)
.value("GPU", Device::GPU)
.value("IPU", Device::IPU)
.value("RKNPU", Device::RKNPU);
pybind11::enum_<FDDataType>(m, "FDDataType", pybind11::arithmetic(),
"Data type of FastDeploy.")
.value("BOOL", FDDataType::BOOL)
.value("INT8", FDDataType::INT8)
.value("INT16", FDDataType::INT16)
.value("INT32", FDDataType::INT32)
.value("INT64", FDDataType::INT64)
.value("FP16", FDDataType::FP16)
.value("FP32", FDDataType::FP32)
.value("FP64", FDDataType::FP64)
.value("UINT8", FDDataType::UINT8);
m.def("get_available_backends", []() { return GetAvailableBackends(); });
}
} // namespace fastdeploy
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