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https://github.com/PaddlePaddle/FastDeploy.git
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d96e98cd4d
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support
83 lines
2.4 KiB
C++
83 lines
2.4 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <iostream>
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#include <memory>
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#include <string>
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#include <vector>
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#include "fastdeploy/backends/backend.h"
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#include "paddle_inference_api.h" // NOLINT
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namespace fastdeploy {
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struct PaddleBackendOption {
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#ifdef WITH_GPU
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bool use_gpu = true;
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#else
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bool use_gpu = false;
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#endif
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bool enable_mkldnn = true;
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bool enable_log_info = false;
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int mkldnn_cache_size = 1;
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int cpu_thread_num = 8;
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// initialize memory size(MB) for GPU
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int gpu_mem_init_size = 100;
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// gpu device id
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int gpu_id = 0;
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std::vector<std::string> delete_pass_names = {};
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};
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// Share memory buffer with paddle_infer::Tensor from fastdeploy::FDTensor
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void ShareTensorFromCpu(paddle_infer::Tensor* tensor, FDTensor& fd_tensor);
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// Copy memory data from paddle_infer::Tensor to fastdeploy::FDTensor
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void CopyTensorToCpu(std::unique_ptr<paddle_infer::Tensor>& tensor,
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FDTensor* fd_tensor);
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// Convert data type from paddle inference to fastdeploy
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FDDataType PaddleDataTypeToFD(const paddle_infer::DataType& dtype);
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class PaddleBackend : public BaseBackend {
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public:
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PaddleBackend() {}
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virtual ~PaddleBackend() = default;
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void BuildOption(const PaddleBackendOption& option);
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bool InitFromPaddle(
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const std::string& model_file, const std::string& params_file,
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const PaddleBackendOption& option = PaddleBackendOption());
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bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs);
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int NumInputs() const { return inputs_desc_.size(); }
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int NumOutputs() const { return outputs_desc_.size(); }
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TensorInfo GetInputInfo(int index);
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TensorInfo GetOutputInfo(int index);
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private:
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paddle_infer::Config config_;
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std::shared_ptr<paddle_infer::Predictor> predictor_;
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std::vector<TensorInfo> inputs_desc_;
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std::vector<TensorInfo> outputs_desc_;
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};
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} // namespace fastdeploy
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