[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/postprocessor.cc

@@ -0,0 +1,58 @@
+// 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/postprocessor.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace classification {
+
+YOLOv5ClsPostprocessor::YOLOv5ClsPostprocessor() {
+  topk_ = 1;
+}
+
+bool YOLOv5ClsPostprocessor::Run(
+    const std::vector<FDTensor> &tensors, std::vector<ClassifyResult> *results,
+    const std::vector<std::map<std::string, std::array<float, 2>>> &ims_info) {
+  int batch = tensors[0].shape[0];
+  FDTensor infer_result = tensors[0];
+  FDTensor infer_result_softmax;
+  function::Softmax(infer_result, &infer_result_softmax, 1);
+  results->resize(batch);
+
+  for (size_t bs = 0; bs < batch; ++bs) {
+    (*results)[bs].Clear();
+    // output (1,1000) score classnum 1000
+    int num_classes = infer_result_softmax.shape[1];
+    const float* infer_result_buffer =
+        reinterpret_cast<const float*>(infer_result_softmax.Data()) + bs * infer_result_softmax.shape[1];
+    topk_ = std::min(num_classes, topk_);
+    (*results)[bs].label_ids =
+        utils::TopKIndices(infer_result_buffer, num_classes, topk_);
+    (*results)[bs].scores.resize(topk_);
+    for (int i = 0; i < topk_; ++i) {
+      (*results)[bs].scores[i] = *(infer_result_buffer + (*results)[bs].label_ids[i]);
+    }
+
+    if ((*results)[bs].label_ids.size() == 0) {
+      return true;
+    }
+  }
+  return true;
+}
+
+} // namespace classification
+} // namespace vision
+} // namespace fastdeploy