[Model] Refactoring code of YOLOv5Cls with new model type (#1237)

* Refactoring code of YOLOv5Cls with new model type

* fix reviewed problem

* Normalize&HWC2CHW -> NormalizeAndPermute

* remove cast()

fastdeploy/vision/classification/contrib/yolov5cls/preprocessor.cc

@@ -0,0 +1,88 @@
+// 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.
+
+#include "fastdeploy/vision/classification/contrib/yolov5cls/preprocessor.h"
+#include "fastdeploy/function/concat.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace classification {
+
+YOLOv5ClsPreprocessor::YOLOv5ClsPreprocessor() {
+  size_ = {224, 224}; //{h,w}
+}
+
+bool YOLOv5ClsPreprocessor::Preprocess(FDMat* mat, FDTensor* output,
+            std::map<std::string, std::array<float, 2>>* im_info) {
+  // Record the shape of image and the shape of preprocessed image
+  (*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
+                               static_cast<float>(mat->Width())};
+
+  // process after image load
+  double ratio = (size_[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
+                                            static_cast<float>(mat->Width()));
+
+  // yolov5cls's preprocess steps
+  // 1. CenterCrop
+  // 2. Normalize
+  // CenterCrop
+  int crop_size = std::min(mat->Height(), mat->Width());
+  CenterCrop::Run(mat, crop_size, crop_size);
+  Resize::Run(mat, size_[0], size_[1], -1, -1, cv::INTER_LINEAR);
+  // Normalize
+  BGR2RGB::Run(mat);
+  std::vector<float> alpha = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
+  std::vector<float> beta = {0.0f, 0.0f, 0.0f};
+  Convert::Run(mat, alpha, beta);
+  std::vector<float> mean = {0.485f, 0.456f, 0.406f};
+  std::vector<float> std = {0.229f, 0.224f, 0.225f};
+  NormalizeAndPermute::Run(mat, mean, std, false);
+
+  // Record output shape of preprocessed image
+  (*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
+                                static_cast<float>(mat->Width())};
+
+  mat->ShareWithTensor(output);
+  output->ExpandDim(0);  // reshape to n, h, w, c
+  return true;
+}
+
+bool YOLOv5ClsPreprocessor::Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
+                             std::vector<std::map<std::string, std::array<float, 2>>>* ims_info) {
+  if (images->size() == 0) {
+    FDERROR << "The size of input images should be greater than 0." << std::endl;
+    return false;
+  }
+  ims_info->resize(images->size());
+  outputs->resize(1);
+  // Concat all the preprocessed data to a batch tensor
+  std::vector<FDTensor> tensors(images->size()); 
+  for (size_t i = 0; i < images->size(); ++i) {
+    if (!Preprocess(&(*images)[i], &tensors[i], &(*ims_info)[i])) {
+      FDERROR << "Failed to preprocess input image." << std::endl;
+      return false;
+    }
+  }
+
+  if (tensors.size() == 1) {
+    (*outputs)[0] = std::move(tensors[0]);
+  } else {
+    function::Concat(tensors, &((*outputs)[0]), 0);
+  }
+  return true;
+}
+
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy