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[Model] Add Paddle3D smoke model (#1766)

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* add smoke model

* add 3d vis

* update code

* update doc

* mv paddle3d from detection to perception

* update result for velocity

* update code for CI

* add set input data for TRT backend

* add serving support for smoke model

* update code

* update code

* update code

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
yeliang2258
2023-04-14 16:30:56 +08:00
committed by GitHub
parent 81fbd54c9d
commit a509dd8ec1
53 changed files with 2610 additions and 26 deletions
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42
fastdeploy/runtime/backends/tensorrt/option.h
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@@ -33,8 +33,9 @@ struct TrtBackendOption {
/// Enable log while converting onnx model to tensorrt
bool enable_log_info = false;
/// Enable half precison inference, on some device not support half precision, it will fallback to float32 mode
/// Enable half precison inference, on some device not support half precision,
/// it will fallback to float32 mode
bool enable_fp16 = false;
/** \brief Set shape range of input tensor for the model that contain dynamic input shape while using TensorRT backend
@@ -63,9 +64,44 @@ struct TrtBackendOption {
max_shape[tensor_name].assign(max.begin(), max.end());
}
}
/// Set cache file path while use TensorRT backend. Loadding a Paddle/ONNX model and initialize TensorRT will take a long time, by this interface it will save the tensorrt engine to `cache_file_path`, and load it directly while execute the code again
/** \brief Set Input data for input tensor for the model while using TensorRT backend
*
* \param[in] tensor_name The name of input for the model which is dynamic shape
* \param[in] min_data The input data for minimal shape for the input tensor
* \param[in] opt_data The input data for optimized shape for the input tensor
* \param[in] max_data The input data for maximum shape for the input tensor, if set as default value, it will keep same with min_data
*/
void SetInputData(const std::string& tensor_name,
const std::vector<float> min_data,
const std::vector<float> opt_data = std::vector<float>(),
const std::vector<float> max_data = std::vector<float>()) {
max_input_data[tensor_name].clear();
min_input_data[tensor_name].clear();
opt_input_data[tensor_name].clear();
min_input_data[tensor_name].assign(min_data.begin(), min_data.end());
if (opt_data.empty()) {
opt_input_data[tensor_name].assign(min_data.begin(), min_data.end());
} else {
opt_input_data[tensor_name].assign(opt_data.begin(), opt_data.end());
}
if (max_data.empty()) {
max_input_data[tensor_name].assign(min_data.begin(), min_data.end());
} else {
max_input_data[tensor_name].assign(max_data.begin(), max_data.end());
}
}
/// Set cache file path while use TensorRT backend.
/// Loadding a Paddle/ONNX model and initialize TensorRT will
/// take a long time,
/// by this interface it will save the tensorrt engine to `cache_file_path`,
/// and load it directly while execute the code again
std::string serialize_file = "";
std::map<std::string, std::vector<float>> max_input_data;
std::map<std::string, std::vector<float>> min_input_data;
std::map<std::string, std::vector<float>> opt_input_data;
// The below parameters may be removed in next version, please do not
// visit or use them directly
std::map<std::string, std::vector<int32_t>> max_shape;