[Model] Refactor PaddleClas module (#505)

* Refactor the PaddleClas module

* fix bug

* remove debug code

* clean unused code

* support pybind

* Update fd_tensor.h

* Update fd_tensor.cc

* temporary revert python api

* fix ci error

* fix code style problem

fastdeploy/vision/classification/ppcls/preprocessor.h

@@ -0,0 +1,50 @@
+// 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.
+
+#pragma once
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+
+namespace classification {
+/*! @brief Preprocessor object for PaddleClas serials model.
+ */
+class FASTDEPLOY_DECL PaddleClasPreprocessor {
+ public:
+  /** \brief Create a preprocessor instance for PaddleClas serials model
+   *
+   * \param[in] config_file Path of configuration file for deployment, e.g resnet/infer_cfg.yml
+   */
+  explicit PaddleClasPreprocessor(const std::string& config_file);
+
+  /** \brief Process the input image and prepare input tensors for runtime
+   *
+   * \param[in] images The input image data list, all the elements are returned by cv::imread()
+   * \param[in] outputs The output tensors which will feed in runtime
+   * \return true if the preprocess successed, otherwise false
+   */
+  bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
+
+
+ private:
+  bool BuildPreprocessPipelineFromConfig(const std::string& config_file);
+  std::vector<std::shared_ptr<Processor>> processors_;
+  bool initialized_ = false;
+};
+
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy