Add YOLOv5-cls Model (#335)

* add yolov5cls

* fixed bugs

* fixed bugs

* fixed preprocess bug

* add yolov5cls readme

* deal with comments

* Add YOLOv5Cls Note

* add yolov5cls test

Co-authored-by: Jason <jiangjiajun@baidu.com>

fastdeploy/vision/classification/contrib/yolov5cls.h

@@ -0,0 +1,70 @@
+// 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/fastdeploy_model.h"
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+/** \brief All image classification model APIs are defined inside this namespace
+ *
+ */
+namespace classification {
+
+/*! @brief YOLOv5Cls model object used when to load a YOLOv5Cls model exported by YOLOv5
+ */
+class FASTDEPLOY_DECL YOLOv5Cls : public FastDeployModel {
+ public:
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g yolov5cls/yolov5n-cls.onnx
+   * \param[in] params_file Path of parameter file, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in `valid_cpu_backends`
+   * \param[in] model_format Model format of the loaded model, default is ONNX format
+   */
+  YOLOv5Cls(const std::string& model_file, const std::string& params_file = "",
+            const RuntimeOption& custom_option = RuntimeOption(),
+            const ModelFormat& model_format = ModelFormat::ONNX);
+
+  /// Get model's name
+  virtual std::string ModelName() const { return "yolov5cls"; }
+
+  /** \brief Predict the classification result for an input image
+   *
+   * \param[in] im The input image data, comes from cv::imread()
+   * \param[in] result The output classification result will be writen to this structure
+   * \param[in] topk Returns the topk classification result with the highest predicted probability, the default is 1
+   * \return true if the prediction successed, otherwise false
+   */
+  virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
+
+  /// Preprocess image size, the default is (224, 224)
+  std::vector<int> size;
+
+ private:
+  bool Initialize();
+  /// Preprocess an input image, and set the preprocessed results to `outputs`
+  bool Preprocess(Mat* mat, FDTensor* output,
+                  const std::vector<int>& size = {224, 224});
+
+  /// Postprocess the inferenced results, and set the final result to `result`
+  bool Postprocess(const FDTensor& infer_result, ClassifyResult* result,
+                   int topk = 1);
+};
+
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy