[Model] Refactoring code of YOLOv5Cls with new model type (PaddlePadd…

…le#1237)

* Refactoring code of YOLOv5Cls with new model type

* fix reviewed problem

* Normalize&HWC2CHW -> NormalizeAndPermute

* remove cast()

examples/vision/classification/yolov5cls/cpp/infer.cc

@@ -27,10 +27,9 @@ void CpuInfer(const std::string& model_file, const std::string& image_file) {
   }
 
   auto im = cv::imread(image_file);
-  auto im_bak = im.clone();
 
   fastdeploy::vision::ClassifyResult res;
-  if (!model.Predict(&im, &res)) {
+  if (!model.Predict(im, &res)) {
     std::cerr << "Failed to predict." << std::endl;
     return;
   }
@@ -48,10 +47,9 @@ void GpuInfer(const std::string& model_file, const std::string& image_file) {
   }
 
   auto im = cv::imread(image_file);
-  auto im_bak = im.clone();
 
   fastdeploy::vision::ClassifyResult res;
-  if (!model.Predict(&im, &res)) {
+  if (!model.Predict(im, &res)) {
     std::cerr << "Failed to predict." << std::endl;
     return;
   }
@@ -71,10 +69,9 @@ void TrtInfer(const std::string& model_file, const std::string& image_file) {
   }
 
   auto im = cv::imread(image_file);
-  auto im_bak = im.clone();
 
   fastdeploy::vision::ClassifyResult res;
-  if (!model.Predict(&im, &res)) {
+  if (!model.Predict(im, &res)) {
     std::cerr << "Failed to predict." << std::endl;
     return;
   }

examples/vision/classification/yolov5cls/python/infer.py

@@ -44,8 +44,9 @@ def build_option(args):
 runtime_option = build_option(args)
 model = fd.vision.classification.YOLOv5Cls(
     args.model, runtime_option=runtime_option)
+model.postprocessor.topk = args.topk
 
 # 预测图片分类结果
 im = cv2.imread(args.image)
-result = model.predict(im, args.topk)
+result = model.predict(im)
 print(result)

fastdeploy/vision.h

@@ -16,7 +16,7 @@
 #include "fastdeploy/core/config.h"
 #ifdef ENABLE_VISION
 #include "fastdeploy/vision/classification/contrib/resnet.h"
-#include "fastdeploy/vision/classification/contrib/yolov5cls.h"
+#include "fastdeploy/vision/classification/contrib/yolov5cls/yolov5cls.h"
 #include "fastdeploy/vision/classification/ppcls/model.h"
 #include "fastdeploy/vision/detection/contrib/nanodet_plus.h"
 #include "fastdeploy/vision/detection/contrib/scaledyolov4.h"

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

fastdeploy/vision/classification/contrib/yolov5cls/postprocessor.h

@@ -0,0 +1,56 @@
+// 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 Postprocessor object for YOLOv5Cls serials model.
+ */
+class FASTDEPLOY_DECL YOLOv5ClsPostprocessor {
+ public:
+  /** \brief Create a postprocessor instance for YOLOv5Cls serials model
+   */
+  YOLOv5ClsPostprocessor();
+
+  /** \brief Process the result of runtime and fill to ClassifyResult structure
+   *
+   * \param[in] tensors The inference result from runtime
+   * \param[in] result The output result of classification
+   * \param[in] ims_info The shape info list, record input_shape and output_shape
+   * \return true if the postprocess successed, otherwise false
+   */
+  bool Run(const std::vector<FDTensor>& tensors,
+      std::vector<ClassifyResult>* results,
+      const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info);
+
+  /// Set topk, default 1
+  void SetTopK(const int& topk) {
+    topk_ = topk;
+  }
+
+  /// Get topk, default 1
+  float GetTopK() const { return topk_; }
+
+ protected:
+  int topk_;
+};
+
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy