Add NanoDet-Plus Model support

examples/CMakeLists.txt

@@ -1,3 +1,17 @@
+# 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.
+
 function(add_fastdeploy_executable FIELD CC_FILE)
   # temp target name/file var in function scope
   set(TEMP_TARGET_FILE ${CC_FILE})

examples/vision/rangilyu_nanodet_plus.cc

@@ -0,0 +1,53 @@
+// 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.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+
+  std::string model_file = "../resources/models/nanodet-plus-m_320.onnx";
+  std::string img_path = "../resources/images/bus.jpg";
+  std::string vis_path =
+      "../resources/outputs/rangilyu_nanodet_plus_vis_result.jpg";
+
+  auto model = vis::rangilyu::NanoDetPlus(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed! Model: " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "Init Done! Model:" << model_file << std::endl;
+  }
+  model.EnableDebug();
+
+  cv::Mat im = cv::imread(img_path);
+  cv::Mat vis_im = im.clone();
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  } else {
+    std::cout << "Prediction Done!" << std::endl;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisDetection(&vis_im, res);
+  cv::imwrite(vis_path, vis_im);
+  std::cout << "Detect Done! Saved: " << vis_path << std::endl;
+  return 0;
+}

fastdeploy/vision.h

@@ -19,6 +19,7 @@
 #include "fastdeploy/vision/meituan/yolov6.h"
 #include "fastdeploy/vision/ppcls/model.h"
 #include "fastdeploy/vision/ppdet/ppyoloe.h"
+#include "fastdeploy/vision/rangilyu/nanodet_plus.h"
 #include "fastdeploy/vision/ppseg/model.h"
 #include "fastdeploy/vision/ultralytics/yolov5.h"
 #include "fastdeploy/vision/wongkinyiu/yolor.h"

fastdeploy/vision/__init__.py

@@ -22,3 +22,4 @@
 from . import megvii
 from . import visualize
 from . import wongkinyiu
+from . import rangilyu

fastdeploy/vision/megvii/__init__.py

@@ -28,8 +28,8 @@ def __init__(self,
         # 初始化后的option保存在self._runtime_option
         super(YOLOX, self).__init__(runtime_option)
 
-        self._model = C.vision.megvii.YOLOX(
-            model_file, params_file, self._runtime_option, model_format)
+        self._model = C.vision.megvii.YOLOX(model_file, params_file,
+                                            self._runtime_option, model_format)
         # 通过self.initialized判断整个模型的初始化是否成功
         assert self.initialized, "YOLOX initialize failed."
 
@@ -53,8 +53,8 @@ def is_decode_exported(self):
 
     @property
     def downsample_strides(self):
-        return self._model.downsample_strides        
-    
+        return self._model.downsample_strides
+
     @property
     def max_wh(self):
         return self._model.max_wh
@@ -78,16 +78,16 @@ def padding_value(self, value):
     @is_decode_exported.setter
     def is_decode_exported(self, value):
         assert isinstance(
-            value, 
+            value,
             bool), "The value to set `is_decode_exported` must be type of bool."
-        self._model.max_wh = value
+        self._model.is_decode_exported = value
 
     @downsample_strides.setter
     def downsample_strides(self, value):
         assert isinstance(
             value,
             list), "The value to set `downsample_strides` must be type of list."
-        self._model.downsample_strides = value          
+        self._model.downsample_strides = value
 
     @max_wh.setter
     def max_wh(self, value):

fastdeploy/vision/rangilyu/__init__.py

@@ -0,0 +1,105 @@
+# 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.
+
+from __future__ import absolute_import
+import logging
+from ... import FastDeployModel, Frontend
+from ... import fastdeploy_main as C
+
+
+class NanoDetPlus(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=Frontend.ONNX):
+        # 调用基函数进行backend_option的初始化
+        # 初始化后的option保存在self._runtime_option
+        super(NanoDetPlus, self).__init__(runtime_option)
+
+        self._model = C.vision.rangilyu.NanoDetPlus(
+            model_file, params_file, self._runtime_option, model_format)
+        # 通过self.initialized判断整个模型的初始化是否成功
+        assert self.initialized, "NanoDetPlus initialize failed."
+
+    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
+        return self._model.predict(input_image, conf_threshold,
+                                   nms_iou_threshold)
+
+    # 一些跟NanoDetPlus模型有关的属性封装
+    # 多数是预处理相关，可通过修改如model.size = [416, 416]改变预处理时resize的大小（前提是模型支持）
+    @property
+    def size(self):
+        return self._model.size
+
+    @property
+    def padding_value(self):
+        return self._model.padding_value
+
+    @property
+    def keep_ratio(self):
+        return self._model.keep_ratio
+
+    @property
+    def downsample_strides(self):
+        return self._model.downsample_strides
+
+    @property
+    def max_wh(self):
+        return self._model.max_wh
+
+    @property
+    def reg_max(self):
+        return self._model.reg_max
+
+    @size.setter
+    def size(self, wh):
+        assert isinstance(wh, [list, tuple]),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._model.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._model.padding_value = value
+
+    @keep_ratio.setter
+    def keep_ratio(self, value):
+        assert isinstance(
+            value, bool), "The value to set `keep_ratio` must be type of bool."
+        self._model.keep_ratio = value
+
+    @downsample_strides.setter
+    def downsample_strides(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `downsample_strides` must be type of list."
+        self._model.downsample_strides = value
+
+    @max_wh.setter
+    def max_wh(self, value):
+        assert isinstance(
+            value, float), "The value to set `max_wh` must be type of float."
+        self._model.max_wh = value
+
+    @reg_max.setter
+    def reg_max(self, value):
+        assert isinstance(
+            value, int), "The value to set `reg_max` must be type of int."
+        self._model.reg_max = value