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[Other] Unify initialize api for lite/trt backend (#1249)
* Unify initialize api for lite/trt backend * Unify initialize api for lite/trt backend
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@@ -113,6 +113,50 @@ bool TrtBackend::LoadTrtCache(const std::string& trt_engine_file) {
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return true;
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}
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bool TrtBackend::Init(const RuntimeOption& runtime_option) {
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if (runtime_option.device != Device::GPU) {
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FDERROR << "TrtBackend only supports Device::GPU, but now it's "
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<< runtime_option.device << "." << std::endl;
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return false;
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}
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if (runtime_option.model_format != ModelFormat::PADDLE &&
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runtime_option.model_format != ModelFormat::ONNX) {
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FDERROR
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<< "TrtBackend only supports model format PADDLE/ONNX, but now it's "
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<< runtime_option.model_format << "." << std::endl;
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return false;
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}
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if (runtime_option.model_format == ModelFormat::PADDLE) {
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if (runtime_option.model_from_memory_) {
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return InitFromPaddle(runtime_option.model_file,
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runtime_option.params_file,
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runtime_option.trt_option);
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} else {
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std::string model_buffer;
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std::string params_buffer;
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FDASSERT(ReadBinaryFromFile(runtime_option.model_file, &model_buffer),
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"Failed to read model file %s.",
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runtime_option.model_file.c_str());
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FDASSERT(ReadBinaryFromFile(runtime_option.params_file, ¶ms_buffer),
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"Failed to read parameters file %s.",
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runtime_option.params_file.c_str());
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return InitFromPaddle(model_buffer, params_buffer,
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runtime_option.trt_option);
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}
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} else {
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if (runtime_option.model_from_memory_) {
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return InitFromOnnx(runtime_option.model_file, runtime_option.trt_option);
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} else {
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std::string model_buffer;
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FDASSERT(ReadBinaryFromFile(runtime_option.model_file, &model_buffer),
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"Failed to read model file %s.",
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runtime_option.model_file.c_str());
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return InitFromOnnx(model_buffer, runtime_option.trt_option);
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}
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}
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return true;
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}
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bool TrtBackend::InitFromPaddle(const std::string& model_buffer,
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const std::string& params_buffer,
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const TrtBackendOption& option, bool verbose) {
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@@ -291,14 +335,14 @@ bool TrtBackend::Infer(std::vector<FDTensor>& inputs,
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cudaSetDevice(option_.gpu_id);
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SetInputs(inputs);
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AllocateOutputsBuffer(outputs, copy_to_fd);
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RUNTIME_PROFILE_LOOP_BEGIN(1)
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if (!context_->enqueueV2(bindings_.data(), stream_, nullptr)) {
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FDERROR << "Failed to Infer with TensorRT." << std::endl;
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return false;
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}
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RUNTIME_PROFILE_LOOP_END
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for (size_t i = 0; i < outputs->size(); ++i) {
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// if the final output tensor's dtype is different from the model output
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// tensor's dtype, then we need cast the data to the final output's dtype
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