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Remove Paddle Reader (#1813)

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* Remove Paddle Reader

* support pp-infer c++14

* disable trt cache

---------

Co-authored-by: wang-xinyu <wangxinyu_es@163.com>
This commit is contained in:
Jason
2023-04-20 21:12:43 +08:00
committed by GitHub
parent 5e2ff374ce
commit f3d44785c4
7 changed files with 59 additions and 24 deletions
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71
fastdeploy/runtime/backends/paddle/paddle_backend.cc
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@@ -148,11 +148,9 @@ bool PaddleBackend::InitFromPaddle(const std::string& model,
FDASSERT(ReadBinaryFromFile(model, &model_content),
"Failed to read file %s.", model.c_str());
}
auto reader =
paddle2onnx::PaddleReader(model_content.c_str(), model_content.size());
// If it's a quantized model, and use cpu with mkldnn, automaticaly switch to
// int8 mode
if (reader.is_quantize_model) {
if (option.is_quantize_model) {
if (option.device == Device::GPU) {
FDWARNING << "The loaded model is a quantized model, while inference on "
"GPU, please use TensorRT backend to get better performance."
@@ -184,25 +182,25 @@ bool PaddleBackend::InitFromPaddle(const std::string& model,
}
}
inputs_desc_.resize(reader.num_inputs);
for (int i = 0; i < reader.num_inputs; ++i) {
std::string name(reader.inputs[i].name);
std::vector<int64_t> shape(reader.inputs[i].shape,
reader.inputs[i].shape + reader.inputs[i].rank);
inputs_desc_[i].name = name;
inputs_desc_[i].shape.assign(shape.begin(), shape.end());
inputs_desc_[i].dtype = ReaderDataTypeToFD(reader.inputs[i].dtype);
}
outputs_desc_.resize(reader.num_outputs);
for (int i = 0; i < reader.num_outputs; ++i) {
std::string name(reader.outputs[i].name);
std::vector<int64_t> shape(
reader.outputs[i].shape,
reader.outputs[i].shape + reader.outputs[i].rank);
outputs_desc_[i].name = name;
outputs_desc_[i].shape.assign(shape.begin(), shape.end());
outputs_desc_[i].dtype = ReaderDataTypeToFD(reader.outputs[i].dtype);
}
// inputs_desc_.resize(reader.num_inputs);
// for (int i = 0; i < reader.num_inputs; ++i) {
// std::string name(reader.inputs[i].name);
// std::vector<int64_t> shape(reader.inputs[i].shape,
// reader.inputs[i].shape + reader.inputs[i].rank);
// inputs_desc_[i].name = name;
// inputs_desc_[i].shape.assign(shape.begin(), shape.end());
// inputs_desc_[i].dtype = ReaderDataTypeToFD(reader.inputs[i].dtype);
// }
// outputs_desc_.resize(reader.num_outputs);
// for (int i = 0; i < reader.num_outputs; ++i) {
// std::string name(reader.outputs[i].name);
// std::vector<int64_t> shape(
// reader.outputs[i].shape,
// reader.outputs[i].shape + reader.outputs[i].rank);
// outputs_desc_[i].name = name;
// outputs_desc_[i].shape.assign(shape.begin(), shape.end());
// outputs_desc_[i].dtype = ReaderDataTypeToFD(reader.outputs[i].dtype);
// }
if (option.collect_trt_shape) {
// Set the shape info file.
std::string curr_model_dir = "./";
@@ -253,6 +251,35 @@ bool PaddleBackend::InitFromPaddle(const std::string& model,
}
}
predictor_ = paddle_infer::CreatePredictor(config_);
auto input_names = predictor_->GetInputNames();
auto output_names = predictor_->GetOutputNames();
auto input_dtypes = predictor_->GetInputTypes();
auto output_dtypes = predictor_->GetOutputTypes();
auto input_shapes = predictor_->GetInputTensorShape();
auto output_shapes = predictor_->GetOutputTensorShape();
inputs_desc_.resize(input_names.size());
for (int i = 0; i < input_names.size(); ++i) {
inputs_desc_[i].name = input_names[i];
auto iter = input_shapes.find(inputs_desc_[i].name);
FDASSERT(iter != input_shapes.end(), "Cannot find shape for input %s.", inputs_desc_[i].name.c_str());
inputs_desc_[i].shape.assign(iter->second.begin(), iter->second.end());
auto iter1 = input_dtypes.find(inputs_desc_[i].name);
FDASSERT(iter1 != input_dtypes.end(), "Cannot find data type for input %s.", inputs_desc_[i].name.c_str());
inputs_desc_[i].dtype = PaddleDataTypeToFD(iter1->second);
}
outputs_desc_.resize(output_names.size());
for (int i = 0; i < output_names.size(); ++i) {
outputs_desc_[i].name = output_names[i];
auto iter = output_shapes.find(outputs_desc_[i].name);
FDASSERT(iter != output_shapes.end(), "Cannot find shape for output %s.", outputs_desc_[i].name.c_str());
outputs_desc_[i].shape.assign(iter->second.begin(), iter->second.end());
auto iter1 = output_dtypes.find(outputs_desc_[i].name);
FDASSERT(iter1 != output_dtypes.end(), "Cannot find data type for output %s.", outputs_desc_[i].name.c_str());
outputs_desc_[i].dtype = PaddleDataTypeToFD(iter1->second);
}
initialized_ = true;
return true;
}