Initial commit

3rdParty/InstallOpenCVDependences.sh

+#! /bin/sh
+
+############### Ubuntu  ###############
+# 参考：https://docs.opencv.org/3.4.11/d7/d9f/tutorial_linux_install.html
+# apt-get install build-essential -y
+# apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev -y
+# apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev -y # 处理图像所需的包，可选
+
+############### CentOS ###############
+yum install gcc gcc-c++ gtk2-devel gimp-devel gimp-devel-tools gimp-help-browser zlib-devel libtiff-devel libjpeg-devel libpng-devel gstreamer-devel libavc1394-devel libraw1394-devel libdc1394-devel jasper-devel jasper-utils swig python libtool nasm -y
\ No newline at end of file

3rdParty/InstallRBuild.sh

+
+############################ 在线安装依赖 ###############################
+#cd ./3rdParty
+#pip install rbuild-master.tar.gz
+
+
+############################ 离线安装依赖 ###############################
+# 安装依赖
+cd ./3rdParty/rbuild_depend
+pip install click-6.6-py2.py3-none-any.whl
+pip install six-1.15.0-py2.py3-none-any.whl
+pip install subprocess32-3.5.4.tar.gz
+pip install cget-0.1.9.tar.gz
+
+# 安装rbuild
+cd ../
+pip install rbuild-master.tar.gz
+
+

CMakeLists.txt

+# 设置cmake的最低版本
+cmake_minimum_required(VERSION 3.5)
+
+# 设置项目名
+project(ResNet50)
+
+# 设置编译器
+set(CMAKE_CXX_COMPILER g++)
+set(CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS} -std=c++17) # 2.2版本以上需要c++17
+set(CMAKE_BUILD_TYPE release)
+
+# 添加头文件路径
+set(INCLUDE_PATH    ${CMAKE_CURRENT_SOURCE_DIR}/Src/
+                    ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/ 
+                    $ENV{DTKROOT}/include/
+                    ${CMAKE_CURRENT_SOURCE_DIR}/depend/include/)
+include_directories(${INCLUDE_PATH})
+
+# 添加依赖库路径
+set(LIBRARY_PATH ${CMAKE_CURRENT_SOURCE_DIR}/depend/lib64/
+                $ENV{DTKROOT}/lib/)
+link_directories(${LIBRARY_PATH})
+
+# 添加依赖库
+set(LIBRARY opencv_core
+            opencv_imgproc
+            opencv_imgcodecs
+            opencv_dnn
+            onnxruntime
+            )
+link_libraries(${LIBRARY})
+
+# 添加源文件
+set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/Src/main.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/Src/Classifier.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/CommonUtility.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/Filesystem.cpp)
+
+# 添加可执行目标
+add_executable(ResNet50 ${SOURCE_FILES})

Doc/Tutorial_Cpp.md

+# 分类器
+
+
+
+本示例通过ResNet50模型说明如何使用OnnxRunTime C++ API进行分类模型的推理，包括如何预处理、推理并获取推理结果。
+
+
+
+## 模型简介
+
+本示例使用了经典的ResNet50模型，onnx文件在Resource/Models/文件夹下，模型结构可以通过netron (https://netron.app/) 查看，该模型的输入shape为[1,3,224,224] ，数据排布为NCHW。
+
+​																														
+
+## 预处理
+
+在将数据输入到模型之前，需要对图像做如下预处理操作：
+
+- 图像格式转换，BGR转换为RGB
+
+- 调整图像大小，并在中心窗口位置裁剪出224x224大小的图像
+
+- normalize操作，对图像减均值再除方差
+
+- 转换数据排布为NCHW
+
+  
+
+本示例代码主要采用了OpenCV实现了预处理操作：
+
+```c++
+ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector<std::vector<ResultOfPrediction>> &predictions)
+{
+	...
+	
+    // 预处理
+    std::vector<cv::Mat> image;
+    for(int i =0;i<srcImages.size();++i)
+    {
+        //BGR转换为RGB
+        cv::Mat imgRGB;
+        cv::cvtColor(srcImages[i], imgRGB, cv::COLOR_BGR2RGB);
+
+        // 调整大小，保持长宽比
+        cv::Mat shrink;
+        float ratio = (float)256 / min(imgRGB.cols, imgRGB.rows);
+        if(imgRGB.rows > imgRGB.cols)
+        {
+            cv::resize(imgRGB, shrink, cv::Size(256, int(ratio * imgRGB.rows)), 0, 0);
+        }
+        else
+        {
+            cv::resize(imgRGB, shrink, cv::Size(int(ratio * imgRGB.cols), 256), 0, 0);
+        }
+
+        // 裁剪中心窗口
+        int start_x = shrink.cols/2 - 224/2;
+        int start_y = shrink.rows/2 - 224/2;
+        cv::Rect rect(start_x, start_y, 224, 224);
+        cv::Mat images = shrink(rect);
+        image.push_back(images);
+    }
+
+    // normalize并转换为NCHW
+    cv::Mat inputBlob;
+    Image2BlobParams image2BlobParams;
+    image2BlobParams.scalefactor=cv::Scalar(1/58.395, 1/57.12, 1/57.375);
+    image2BlobParams.mean=cv::Scalar(123.675, 116.28, 103.53);
+    image2BlobParams.swapRB=false;
+    blobFromImagesWithParams(image,inputBlob,image2BlobParams);
+                    
+     ...
+}
+```
+
+blobFromImagesWithParams()函数支持多个输入图像，首先对输入图像各通道减对应的均值，然后乘以各通道对应的缩放系数，最后转换为NCHW，最终将转换好的数据保存到inputBlob中，然后就可以输入到模型中执行推理了。
+
+
+
+## 推理
+
+完成预处理后，就可以执行推理了：
+
+```c++
+ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector<std::vector<ResultOfPrediction>> &predictions)
+{
+	...
+	
+	// 预处理
+
+
+    // 创建输入数据
+    std::array<int64_t, 4> inputShape{1, 3, 224, 224};
+    auto memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+    std::array<float, 3 * 224 * 224> input_image{};
+    float* input_test = (float*)inputBlob.data;
+    Ort::Value inputTensor = Ort::Value::CreateTensor<float>(memoryInfo, input_test, input_image.size(), inputShape.data(), inputShape.size());
+    std::vector<Ort::Value> intput_tensors;
+    intput_tensors.push_back(std::move(inputTensor));
+    
+    // 推理
+    auto output_tensors = session->Run(Ort::RunOptions{nullptr}, input_node_names.data(), intput_tensors.data(), 1, output_node_names.data(), 1);
+    
+    // 获取输出节点的属性
+    const float* pdata = output_tensors[0].GetTensorMutableData<float>(); // 获取第一个输出节点的数据
+   	
+   	...
+   	
+}
+```
+
+- input_node_names为一个字符串数组，包含输入节点的名称。intput_tensors表示一个OrtValue类型的指针数组，包含输入节点的值。"1"为输入节点的数量。output_node_names为一个字符串数组，包含输出节点的名称。最后一个1为输出节点的数量。
+- session->Run(...)返回模型的推理结果，由于这里只有一个输出节点，所以返回数据中只有一个数据，output_tensors[0]表示第一个输出节点，这里对应resnetv24_dense0_fwd节点，获取输出数据。
+
+

Doc/Tutorial_Python.md

+# 分类器
+
+本示例通过ResNet50模型说明如何使用OnnxRunTime Python API进行分类模型的推理，包括预处理、推理并获取推理结果。
+
+
+
+## 模型简介
+
+本示例使用了经典的ResNet50模型，onnx文件在Resource/Models/文件夹下，模型结构可以通过netron (https://netron.app/) 查看，该模型的输入shape为[1,3,224,224] ，数据排布为NCHW。
+
+​																														
+
+## 预处理
+
+在将数据输入到模型之前，需要对图像做如下预处理操作：
+
+- 图像格式转换，将BGR转换为RGB
+- 调整图像大小，使短边为 256，长边等比例缩放
+- 裁剪图像，在中心窗口位置裁剪出224x224大小的图像
+- normalize操作，对图像减均值再除方差
+- 调整输入数据的尺寸为（1, 3, 224, 224）
+
+本示例代码采用了OpenCV实现了预处理操作：
+
+```
+def Preprocessing(pathOfImage):
+    
+    image = cv2.imread(pathOfImage, cv2.IMREAD_COLOR)    
+    
+    # 转换为RGB格式
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # 调整大小，保持长宽比
+    ratio = float(256) / min(image.shape[0], image.shape[1])
+    if image.shape[0] > image.shape[1]:
+        new_size = [int(round(ratio * image.shape[0])), 256]
+    else:
+        new_size = [256, int(round(ratio * image.shape[1]))]
+    image = np.array(cv2.resize(image, (new_size[1],new_size[0])))    # w h 格式
+    
+    # 裁剪中心窗口
+    h, w, c = image.shape
+    start_x = w//2 - 224//2
+    start_y = h//2 - 224//2
+    image = image[start_y:start_y+224, start_x:start_x+224, :]
+    
+    # transpose
+    image = image.transpose(2, 0, 1)
+    
+    # 将输入数据转换为float32
+    img_data = image.astype('float32')
+    
+    # normalize
+    mean_vec = np.array([123.675, 116.28, 103.53])
+    stddev_vec = np.array([58.395, 57.12, 57.375])
+    norm_img_data = np.zeros(img_data.shape).astype('float32')
+    for i in range(img_data.shape[0]):
+        norm_img_data[i,:,:] = (img_data[i,:,:] - mean_vec[i]) / stddev_vec[i]
+    
+    # 调整输入数据的尺寸
+    norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
+    return norm_img_data
+```
+
+
+
+## 推理
+
+完成预处理后，就可以执行推理了：
+
+```
+def ort_seg_dcu(model_path,image):
+    
+    #创建sess_options
+    sess_options = ort.SessionOptions()
+
+    #设置图优化
+    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+
+    #是否开启profiling
+    sess_options.enable_profiling = False
+    dcu_session = ort.InferenceSession(model_path,sess_options,providers=['ROCMExecutionProvider'],)
+    input_name=dcu_session.get_inputs()[0].name
+
+    results = dcu_session.run(None, input_feed={input_name:image })
+    scores=np.array(results[0])
+    # print("ort result.shape:",scores.shape)
+
+    return scores
+```
+
+- Preprocessing()函数返回输入数据（numpy类型），然后通过构造一个字典输入模型执行推理，如果模型有多个输入，则在字典中需要添加多个输入数据。
+- model.run()返回模型的推理结果，返回结果是一个list类型，results[0]表示第一个输出节点的输出。由于示例模型只有一个输出节点，所以results[0]对应resnetv24_dense0_fwd节点。最后，通过np.array(result)获取分类结果。

Python/Classifier.py

+# -*- coding: utf-8 -*-
+"""
+分类器示例
+"""
+
+import cv2
+import numpy as np
+
+import onnxruntime as ort
+
+def Preprocessing(pathOfImage):
+    # 读取图像
+    image = cv2.imread(pathOfImage, cv2.IMREAD_COLOR)             
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # 调整大小，使短边为256，保持长宽比
+    ratio = float(256) / min(image.shape[0], image.shape[1])
+    if image.shape[0] > image.shape[1]:
+        new_size = [int(round(ratio * image.shape[0])), 256]
+    else:
+        new_size = [256, int(round(ratio * image.shape[1]))]
+    image = np.array(cv2.resize(image, (new_size[1],new_size[0])))
+    
+    # 裁剪中心窗口为224*224
+    h, w, c = image.shape
+    start_x = w//2 - 224//2
+    start_y = h//2 - 224//2
+    image = image[start_y:start_y+224, start_x:start_x+224, :]
+    
+    # transpose
+    image = image.transpose(2, 0, 1)
+    
+    # 将输入数据转换为float32
+    img_data = image.astype('float32')
+    
+    # normalize
+    mean_vec = np.array([123.675, 116.28, 103.53])
+    stddev_vec = np.array([58.395, 57.12, 57.375])
+    norm_img_data = np.zeros(img_data.shape).astype('float32')
+    for i in range(img_data.shape[0]):
+        norm_img_data[i,:,:] = (img_data[i,:,:] - mean_vec[i]) / stddev_vec[i]
+    
+    # 调整尺寸
+    norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
+    return norm_img_data
+
+
+def postprocess(scores):
+    '''
+    Postprocessing with mxnet gluon
+    The function takes scores generated by the network and returns the class IDs in decreasing order
+    of probability
+    '''
+    with open('../Resource/synset.txt', 'r') as f:
+        labels = [l.rstrip() for l in f]
+    preds = np.squeeze(scores)
+    a = np.argsort(preds)[::-1]
+    print('class=%s ; probability=%f' %(labels[a[0]],preds[a[0]]))
+
+
+def ort_seg_dcu(model_path,image):
+    
+    #创建sess_options
+    sess_options = ort.SessionOptions()
+
+    #设置图优化
+    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+
+    #是否开启profiling
+    sess_options.enable_profiling = False
+    dcu_session = ort.InferenceSession(model_path,sess_options,providers=['ROCMExecutionProvider'],)
+    input_name=dcu_session.get_inputs()[0].name
+
+    results = dcu_session.run(None, input_feed={input_name:image })
+    scores=np.array(results[0])
+    print("ort result.shape:",scores.shape)
+
+    return scores
+
+if __name__ == '__main__':
+    
+    pathOfImage ="../Resource/Images/ImageNet_01.jpg"
+    image = Preprocessing(pathOfImage)
+    model_path = "../Resource/Models/resnet50-v2-7.onnx"
+    
+    # 推理
+    result = ort_seg_dcu(model_path,image)
+    
+    # 解析分类结果
+    postprocess(result)

Python/requirements.txt

+opencv-python
+numpy
\ No newline at end of file

README.md

+# ResNet50
+
+## 模型介绍
+使用ONNXRunTime推理框架对ResNet50模型进行推理。
+
+## 模型结构
+ResNet50模型包含了49个卷积层、一个全连接层。
+
+## Python版本推理
+采用ONNXRunTime框架使用DCU进行推理，模型文件下载链接：https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet50-v2-7.onnx ，并将resnet50-v2-7.onnx模型文件保存在Resource/文件夹下。下面介绍如何运行python代码示例，Python示例的详细说明见Doc目录下的Tutorial_Python.md。
+### 下载镜像
+
+在光源中下载ONNXRunTime推理框架镜像： 
+
+```python
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1
+```
+
+### 设置Python环境变量
+
+```
+export PYTHONPATH=/opt/dtk/lib:$PYTHONPATH
+```
+
+### 安装依赖
+
+```python
+# 进入resnet50 migraphx工程根目录
+cd <path_to_resnet50_ort> 
+
+# 进入示例程序目录
+cd Python/
+
+# 安装依赖
+pip install -r requirements.txt
+```
+
+### 运行示例
+
+```python
+python Classifier.py
+```
+
+## C++版本推理
+
+下面介绍如何运行C++代码示例，C++示例的详细说明见Doc目录下的Tutorial_Cpp.md。
+
+### 下载镜像
+
+在光源中下载ONNXRunTime推理框架镜像： 
+
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1
+```
+
+### 安装Opencv依赖
+
+```python
+cd <path_to_resnet50_ort>
+sh ./3rdParty/InstallOpenCVDependences.sh
+```
+
+
+### 安装OpenCV并构建工程
+
+```
+source /opt/dtk/env.sh
+
+rbuild build -d depend
+```
+
+### 设置环境变量
+
+将依赖库依赖加入环境变量LD_LIBRARY_PATH，在~/.bashrc中添加如下语句：
+
+```
+export LD_LIBRARY_PATH=<path_to_resnet50_migraphx>/depend/lib64/:$LD_LIBRARY_PATH
+```
+
+然后执行:
+
+```
+source ~/.bashrc
+```
+
+### 运行示例
+
+```python
+# 进入resnet50 migraphx工程根目录
+cd <path_to_resnet50_ort> 
+
+# 进入build目录
+cd build/
+
+# 执行示例程序
+./ResNet50
+```
+## 性能和准确率数据
+测试数据使用的是ImageNet-1k，使用的加速卡是DCU Z100。
+
+| Engine | Model Path| Model Format | accuracy (%) | Speed(qps) |
+| :------: | :------: | :------: | :------: |:------: |
+| ONNXRuntime | resnet50-v2-7.onnx | onnx | 74.83 |  149.83 |
+
+
+
+## 源码仓库及问题反馈
+
+https://developer.hpccube.com/codes/modelzoo/resnet50_ort
+
+## 参考
+
+https://github.com/onnx/models/tree/main/vision/classification/resnet
+

Resource/Configuration.xml

+<?xml version="1.0" encoding="GB2312"?>
+<opencv_storage>
+
+	<!--分类器-->
+	<Classifier>
+		<ModelPath>"../Resource/Models/resnet50-v2-7.onnx"</ModelPath>
+		<UseInt8>0</UseInt8><!--是否使用int8,不支持-->
+		<UseFP16>0</UseFP16><!--是否使用FP16-->
+	</Classifier>
+
+</opencv_storage>

Resource/synset.txt

+n01440764 tench, Tinca tinca
+n01443537 goldfish, Carassius auratus
+n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+n01491361 tiger shark, Galeocerdo cuvieri
+n01494475 hammerhead, hammerhead shark
+n01496331 electric ray, crampfish, numbfish, torpedo
+n01498041 stingray
+n01514668 cock
+n01514859 hen
+n01518878 ostrich, Struthio camelus
+n01530575 brambling, Fringilla montifringilla
+n01531178 goldfinch, Carduelis carduelis
+n01532829 house finch, linnet, Carpodacus mexicanus
+n01534433 junco, snowbird
+n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea
+n01558993 robin, American robin, Turdus migratorius
+n01560419 bulbul
+n01580077 jay
+n01582220 magpie
+n01592084 chickadee
+n01601694 water ouzel, dipper
+n01608432 kite
+n01614925 bald eagle, American eagle, Haliaeetus leucocephalus
+n01616318 vulture
+n01622779 great grey owl, great gray owl, Strix nebulosa
+n01629819 European fire salamander, Salamandra salamandra
+n01630670 common newt, Triturus vulgaris
+n01631663 eft
+n01632458 spotted salamander, Ambystoma maculatum
+n01632777 axolotl, mud puppy, Ambystoma mexicanum
+n01641577 bullfrog, Rana catesbeiana
+n01644373 tree frog, tree-frog
+n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+n01664065 loggerhead, loggerhead turtle, Caretta caretta
+n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+n01667114 mud turtle
+n01667778 terrapin
+n01669191 box turtle, box tortoise
+n01675722 banded gecko
+n01677366 common iguana, iguana, Iguana iguana
+n01682714 American chameleon, anole, Anolis carolinensis
+n01685808 whiptail, whiptail lizard
+n01687978 agama
+n01688243 frilled lizard, Chlamydosaurus kingi
+n01689811 alligator lizard
+n01692333 Gila monster, Heloderma suspectum
+n01693334 green lizard, Lacerta viridis
+n01694178 African chameleon, Chamaeleo chamaeleon
+n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+n01697457 African crocodile, Nile crocodile, Crocodylus niloticus
+n01698640 American alligator, Alligator mississipiensis
+n01704323 triceratops
+n01728572 thunder snake, worm snake, Carphophis amoenus
+n01728920 ringneck snake, ring-necked snake, ring snake
+n01729322 hognose snake, puff adder, sand viper
+n01729977 green snake, grass snake
+n01734418 king snake, kingsnake
+n01735189 garter snake, grass snake
+n01737021 water snake
+n01739381 vine snake
+n01740131 night snake, Hypsiglena torquata
+n01742172 boa constrictor, Constrictor constrictor
+n01744401 rock python, rock snake, Python sebae
+n01748264 Indian cobra, Naja naja
+n01749939 green mamba
+n01751748 sea snake
+n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus
+n01756291 sidewinder, horned rattlesnake, Crotalus cerastes
+n01768244 trilobite
+n01770081 harvestman, daddy longlegs, Phalangium opilio
+n01770393 scorpion
+n01773157 black and gold garden spider, Argiope aurantia
+n01773549 barn spider, Araneus cavaticus
+n01773797 garden spider, Aranea diademata
+n01774384 black widow, Latrodectus mactans
+n01774750 tarantula
+n01775062 wolf spider, hunting spider
+n01776313 tick
+n01784675 centipede
+n01795545 black grouse
+n01796340 ptarmigan
+n01797886 ruffed grouse, partridge, Bonasa umbellus
+n01798484 prairie chicken, prairie grouse, prairie fowl
+n01806143 peacock
+n01806567 quail
+n01807496 partridge
+n01817953 African grey, African gray, Psittacus erithacus
+n01818515 macaw
+n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+n01820546 lorikeet
+n01824575 coucal
+n01828970 bee eater
+n01829413 hornbill
+n01833805 hummingbird
+n01843065 jacamar
+n01843383 toucan
+n01847000 drake
+n01855032 red-breasted merganser, Mergus serrator
+n01855672 goose
+n01860187 black swan, Cygnus atratus
+n01871265 tusker
+n01872401 echidna, spiny anteater, anteater
+n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+n01877812 wallaby, brush kangaroo
+n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+n01883070 wombat
+n01910747 jellyfish
+n01914609 sea anemone, anemone
+n01917289 brain coral
+n01924916 flatworm, platyhelminth
+n01930112 nematode, nematode worm, roundworm
+n01943899 conch
+n01944390 snail
+n01945685 slug
+n01950731 sea slug, nudibranch
+n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore
+n01968897 chambered nautilus, pearly nautilus, nautilus
+n01978287 Dungeness crab, Cancer magister
+n01978455 rock crab, Cancer irroratus
+n01980166 fiddler crab
+n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus
+n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+n01985128 crayfish, crawfish, crawdad, crawdaddy
+n01986214 hermit crab
+n01990800 isopod
+n02002556 white stork, Ciconia ciconia
+n02002724 black stork, Ciconia nigra
+n02006656 spoonbill
+n02007558 flamingo
+n02009229 little blue heron, Egretta caerulea
+n02009912 American egret, great white heron, Egretta albus
+n02011460 bittern
+n02012849 crane
+n02013706 limpkin, Aramus pictus
+n02017213 European gallinule, Porphyrio porphyrio
+n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana
+n02018795 bustard
+n02025239 ruddy turnstone, Arenaria interpres
+n02027492 red-backed sandpiper, dunlin, Erolia alpina
+n02028035 redshank, Tringa totanus
+n02033041 dowitcher
+n02037110 oystercatcher, oyster catcher
+n02051845 pelican
+n02056570 king penguin, Aptenodytes patagonica
+n02058221 albatross, mollymawk
+n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+n02074367 dugong, Dugong dugon
+n02077923 sea lion
+n02085620 Chihuahua
+n02085782 Japanese spaniel
+n02085936 Maltese dog, Maltese terrier, Maltese
+n02086079 Pekinese, Pekingese, Peke
+n02086240 Shih-Tzu
+n02086646 Blenheim spaniel
+n02086910 papillon
+n02087046 toy terrier
+n02087394 Rhodesian ridgeback
+n02088094 Afghan hound, Afghan
+n02088238 basset, basset hound
+n02088364 beagle
+n02088466 bloodhound, sleuthhound
+n02088632 bluetick
+n02089078 black-and-tan coonhound
+n02089867 Walker hound, Walker foxhound
+n02089973 English foxhound
+n02090379 redbone
+n02090622 borzoi, Russian wolfhound
+n02090721 Irish wolfhound
+n02091032 Italian greyhound
+n02091134 whippet
+n02091244 Ibizan hound, Ibizan Podenco
+n02091467 Norwegian elkhound, elkhound
+n02091635 otterhound, otter hound
+n02091831 Saluki, gazelle hound
+n02092002 Scottish deerhound, deerhound
+n02092339 Weimaraner
+n02093256 Staffordshire bullterrier, Staffordshire bull terrier
+n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+n02093647 Bedlington terrier
+n02093754 Border terrier
+n02093859 Kerry blue terrier
+n02093991 Irish terrier
+n02094114 Norfolk terrier
+n02094258 Norwich terrier
+n02094433 Yorkshire terrier
+n02095314 wire-haired fox terrier
+n02095570 Lakeland terrier
+n02095889 Sealyham terrier, Sealyham
+n02096051 Airedale, Airedale terrier
+n02096177 cairn, cairn terrier
+n02096294 Australian terrier
+n02096437 Dandie Dinmont, Dandie Dinmont terrier
+n02096585 Boston bull, Boston terrier
+n02097047 miniature schnauzer
+n02097130 giant schnauzer
+n02097209 standard schnauzer
+n02097298 Scotch terrier, Scottish terrier, Scottie
+n02097474 Tibetan terrier, chrysanthemum dog
+n02097658 silky terrier, Sydney silky
+n02098105 soft-coated wheaten terrier
+n02098286 West Highland white terrier
+n02098413 Lhasa, Lhasa apso
+n02099267 flat-coated retriever
+n02099429 curly-coated retriever
+n02099601 golden retriever
+n02099712 Labrador retriever
+n02099849 Chesapeake Bay retriever
+n02100236 German short-haired pointer
+n02100583 vizsla, Hungarian pointer
+n02100735 English setter
+n02100877 Irish setter, red setter
+n02101006 Gordon setter
+n02101388 Brittany spaniel
+n02101556 clumber, clumber spaniel
+n02102040 English springer, English springer spaniel
+n02102177 Welsh springer spaniel
+n02102318 cocker spaniel, English cocker spaniel, cocker
+n02102480 Sussex spaniel
+n02102973 Irish water spaniel
+n02104029 kuvasz
+n02104365 schipperke
+n02105056 groenendael
+n02105162 malinois
+n02105251 briard
+n02105412 kelpie
+n02105505 komondor
+n02105641 Old English sheepdog, bobtail
+n02105855 Shetland sheepdog, Shetland sheep dog, Shetland
+n02106030 collie
+n02106166 Border collie
+n02106382 Bouvier des Flandres, Bouviers des Flandres
+n02106550 Rottweiler
+n02106662 German shepherd, German shepherd dog, German police dog, alsatian
+n02107142 Doberman, Doberman pinscher
+n02107312 miniature pinscher
+n02107574 Greater Swiss Mountain dog
+n02107683 Bernese mountain dog
+n02107908 Appenzeller
+n02108000 EntleBucher
+n02108089 boxer
+n02108422 bull mastiff
+n02108551 Tibetan mastiff
+n02108915 French bulldog
+n02109047 Great Dane
+n02109525 Saint Bernard, St Bernard
+n02109961 Eskimo dog, husky
+n02110063 malamute, malemute, Alaskan malamute
+n02110185 Siberian husky
+n02110341 dalmatian, coach dog, carriage dog
+n02110627 affenpinscher, monkey pinscher, monkey dog
+n02110806 basenji
+n02110958 pug, pug-dog
+n02111129 Leonberg
+n02111277 Newfoundland, Newfoundland dog
+n02111500 Great Pyrenees
+n02111889 Samoyed, Samoyede
+n02112018 Pomeranian
+n02112137 chow, chow chow
+n02112350 keeshond
+n02112706 Brabancon griffon
+n02113023 Pembroke, Pembroke Welsh corgi
+n02113186 Cardigan, Cardigan Welsh corgi
+n02113624 toy poodle
+n02113712 miniature poodle
+n02113799 standard poodle
+n02113978 Mexican hairless
+n02114367 timber wolf, grey wolf, gray wolf, Canis lupus
+n02114548 white wolf, Arctic wolf, Canis lupus tundrarum
+n02114712 red wolf, maned wolf, Canis rufus, Canis niger
+n02114855 coyote, prairie wolf, brush wolf, Canis latrans
+n02115641 dingo, warrigal, warragal, Canis dingo
+n02115913 dhole, Cuon alpinus
+n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+n02117135 hyena, hyaena
+n02119022 red fox, Vulpes vulpes
+n02119789 kit fox, Vulpes macrotis
+n02120079 Arctic fox, white fox, Alopex lagopus
+n02120505 grey fox, gray fox, Urocyon cinereoargenteus
+n02123045 tabby, tabby cat
+n02123159 tiger cat
+n02123394 Persian cat
+n02123597 Siamese cat, Siamese
+n02124075 Egyptian cat
+n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+n02127052 lynx, catamount
+n02128385 leopard, Panthera pardus
+n02128757 snow leopard, ounce, Panthera uncia
+n02128925 jaguar, panther, Panthera onca, Felis onca
+n02129165 lion, king of beasts, Panthera leo
+n02129604 tiger, Panthera tigris
+n02130308 cheetah, chetah, Acinonyx jubatus
+n02132136 brown bear, bruin, Ursus arctos
+n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus
+n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+n02134418 sloth bear, Melursus ursinus, Ursus ursinus
+n02137549 mongoose
+n02138441 meerkat, mierkat
+n02165105 tiger beetle
+n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+n02167151 ground beetle, carabid beetle
+n02168699 long-horned beetle, longicorn, longicorn beetle
+n02169497 leaf beetle, chrysomelid
+n02172182 dung beetle
+n02174001 rhinoceros beetle
+n02177972 weevil
+n02190166 fly
+n02206856 bee
+n02219486 ant, emmet, pismire
+n02226429 grasshopper, hopper
+n02229544 cricket
+n02231487 walking stick, walkingstick, stick insect
+n02233338 cockroach, roach
+n02236044 mantis, mantid
+n02256656 cicada, cicala
+n02259212 leafhopper
+n02264363 lacewing, lacewing fly
+n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+n02268853 damselfly
+n02276258 admiral
+n02277742 ringlet, ringlet butterfly
+n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+n02280649 cabbage butterfly
+n02281406 sulphur butterfly, sulfur butterfly
+n02281787 lycaenid, lycaenid butterfly
+n02317335 starfish, sea star
+n02319095 sea urchin
+n02321529 sea cucumber, holothurian
+n02325366 wood rabbit, cottontail, cottontail rabbit
+n02326432 hare
+n02328150 Angora, Angora rabbit
+n02342885 hamster
+n02346627 porcupine, hedgehog
+n02356798 fox squirrel, eastern fox squirrel, Sciurus niger
+n02361337 marmot
+n02363005 beaver
+n02364673 guinea pig, Cavia cobaya
+n02389026 sorrel
+n02391049 zebra
+n02395406 hog, pig, grunter, squealer, Sus scrofa
+n02396427 wild boar, boar, Sus scrofa
+n02397096 warthog
+n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius
+n02403003 ox
+n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+n02410509 bison
+n02412080 ram, tup
+n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+n02417914 ibex, Capra ibex
+n02422106 hartebeest
+n02422699 impala, Aepyceros melampus
+n02423022 gazelle
+n02437312 Arabian camel, dromedary, Camelus dromedarius
+n02437616 llama
+n02441942 weasel
+n02442845 mink
+n02443114 polecat, fitch, foulmart, foumart, Mustela putorius
+n02443484 black-footed ferret, ferret, Mustela nigripes
+n02444819 otter
+n02445715 skunk, polecat, wood pussy
+n02447366 badger
+n02454379 armadillo
+n02457408 three-toed sloth, ai, Bradypus tridactylus
+n02480495 orangutan, orang, orangutang, Pongo pygmaeus
+n02480855 gorilla, Gorilla gorilla
+n02481823 chimpanzee, chimp, Pan troglodytes
+n02483362 gibbon, Hylobates lar
+n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus
+n02484975 guenon, guenon monkey
+n02486261 patas, hussar monkey, Erythrocebus patas
+n02486410 baboon
+n02487347 macaque
+n02488291 langur
+n02488702 colobus, colobus monkey
+n02489166 proboscis monkey, Nasalis larvatus
+n02490219 marmoset
+n02492035 capuchin, ringtail, Cebus capucinus
+n02492660 howler monkey, howler
+n02493509 titi, titi monkey
+n02493793 spider monkey, Ateles geoffroyi
+n02494079 squirrel monkey, Saimiri sciureus
+n02497673 Madagascar cat, ring-tailed lemur, Lemur catta
+n02500267 indri, indris, Indri indri, Indri brevicaudatus
+n02504013 Indian elephant, Elephas maximus
+n02504458 African elephant, Loxodonta africana
+n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+n02514041 barracouta, snoek
+n02526121 eel
+n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+n02606052 rock beauty, Holocanthus tricolor
+n02607072 anemone fish
+n02640242 sturgeon
+n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus
+n02643566 lionfish
+n02655020 puffer, pufferfish, blowfish, globefish
+n02666196 abacus
+n02667093 abaya
+n02669723 academic gown, academic robe, judge's robe
+n02672831 accordion, piano accordion, squeeze box
+n02676566 acoustic guitar
+n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier
+n02690373 airliner
+n02692877 airship, dirigible
+n02699494 altar
+n02701002 ambulance
+n02704792 amphibian, amphibious vehicle
+n02708093 analog clock
+n02727426 apiary, bee house
+n02730930 apron
+n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+n02749479 assault rifle, assault gun
+n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack
+n02776631 bakery, bakeshop, bakehouse
+n02777292 balance beam, beam
+n02782093 balloon
+n02783161 ballpoint, ballpoint pen, ballpen, Biro
+n02786058 Band Aid
+n02787622 banjo
+n02788148 bannister, banister, balustrade, balusters, handrail
+n02790996 barbell
+n02791124 barber chair
+n02791270 barbershop
+n02793495 barn
+n02794156 barometer
+n02795169 barrel, cask
+n02797295 barrow, garden cart, lawn cart, wheelbarrow
+n02799071 baseball
+n02802426 basketball
+n02804414 bassinet
+n02804610 bassoon
+n02807133 bathing cap, swimming cap
+n02808304 bath towel
+n02808440 bathtub, bathing tub, bath, tub
+n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+n02814860 beacon, lighthouse, beacon light, pharos
+n02815834 beaker
+n02817516 bearskin, busby, shako
+n02823428 beer bottle
+n02823750 beer glass
+n02825657 bell cote, bell cot
+n02834397 bib
+n02835271 bicycle-built-for-two, tandem bicycle, tandem
+n02837789 bikini, two-piece
+n02840245 binder, ring-binder
+n02841315 binoculars, field glasses, opera glasses
+n02843684 birdhouse
+n02859443 boathouse
+n02860847 bobsled, bobsleigh, bob
+n02865351 bolo tie, bolo, bola tie, bola
+n02869837 bonnet, poke bonnet
+n02870880 bookcase
+n02871525 bookshop, bookstore, bookstall
+n02877765 bottlecap
+n02879718 bow
+n02883205 bow tie, bow-tie, bowtie
+n02892201 brass, memorial tablet, plaque
+n02892767 brassiere, bra, bandeau
+n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty
+n02895154 breastplate, aegis, egis
+n02906734 broom
+n02909870 bucket, pail
+n02910353 buckle
+n02916936 bulletproof vest
+n02917067 bullet train, bullet
+n02927161 butcher shop, meat market
+n02930766 cab, hack, taxi, taxicab
+n02939185 caldron, cauldron
+n02948072 candle, taper, wax light
+n02950826 cannon
+n02951358 canoe
+n02951585 can opener, tin opener
+n02963159 cardigan
+n02965783 car mirror
+n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig
+n02966687 carpenter's kit, tool kit
+n02971356 carton
+n02974003 car wheel
+n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+n02978881 cassette
+n02979186 cassette player
+n02980441 castle
+n02981792 catamaran
+n02988304 CD player
+n02992211 cello, violoncello
+n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone
+n02999410 chain
+n03000134 chainlink fence
+n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+n03000684 chain saw, chainsaw
+n03014705 chest
+n03016953 chiffonier, commode
+n03017168 chime, bell, gong
+n03018349 china cabinet, china closet
+n03026506 Christmas stocking
+n03028079 church, church building
+n03032252 cinema, movie theater, movie theatre, movie house, picture palace
+n03041632 cleaver, meat cleaver, chopper
+n03042490 cliff dwelling
+n03045698 cloak
+n03047690 clog, geta, patten, sabot
+n03062245 cocktail shaker
+n03063599 coffee mug
+n03063689 coffeepot
+n03065424 coil, spiral, volute, whorl, helix
+n03075370 combination lock
+n03085013 computer keyboard, keypad
+n03089624 confectionery, confectionary, candy store
+n03095699 container ship, containership, container vessel
+n03100240 convertible
+n03109150 corkscrew, bottle screw
+n03110669 cornet, horn, trumpet, trump
+n03124043 cowboy boot
+n03124170 cowboy hat, ten-gallon hat
+n03125729 cradle
+n03126707 crane
+n03127747 crash helmet
+n03127925 crate
+n03131574 crib, cot
+n03133878 Crock Pot
+n03134739 croquet ball
+n03141823 crutch
+n03146219 cuirass
+n03160309 dam, dike, dyke
+n03179701 desk
+n03180011 desktop computer
+n03187595 dial telephone, dial phone
+n03188531 diaper, nappy, napkin
+n03196217 digital clock
+n03197337 digital watch
+n03201208 dining table, board
+n03207743 dishrag, dishcloth
+n03207941 dishwasher, dish washer, dishwashing machine
+n03208938 disk brake, disc brake
+n03216828 dock, dockage, docking facility
+n03218198 dogsled, dog sled, dog sleigh
+n03220513 dome
+n03223299 doormat, welcome mat
+n03240683 drilling platform, offshore rig
+n03249569 drum, membranophone, tympan
+n03250847 drumstick
+n03255030 dumbbell
+n03259280 Dutch oven
+n03271574 electric fan, blower
+n03272010 electric guitar
+n03272562 electric locomotive
+n03290653 entertainment center
+n03291819 envelope
+n03297495 espresso maker
+n03314780 face powder
+n03325584 feather boa, boa
+n03337140 file, file cabinet, filing cabinet
+n03344393 fireboat
+n03345487 fire engine, fire truck
+n03347037 fire screen, fireguard
+n03355925 flagpole, flagstaff
+n03372029 flute, transverse flute
+n03376595 folding chair
+n03379051 football helmet
+n03384352 forklift
+n03388043 fountain
+n03388183 fountain pen
+n03388549 four-poster
+n03393912 freight car
+n03394916 French horn, horn
+n03400231 frying pan, frypan, skillet
+n03404251 fur coat
+n03417042 garbage truck, dustcart
+n03424325 gasmask, respirator, gas helmet
+n03425413 gas pump, gasoline pump, petrol pump, island dispenser
+n03443371 goblet
+n03444034 go-kart
+n03445777 golf ball
+n03445924 golfcart, golf cart
+n03447447 gondola
+n03447721 gong, tam-tam
+n03450230 gown
+n03452741 grand piano, grand
+n03457902 greenhouse, nursery, glasshouse
+n03459775 grille, radiator grille
+n03461385 grocery store, grocery, food market, market
+n03467068 guillotine
+n03476684 hair slide
+n03476991 hair spray
+n03478589 half track
+n03481172 hammer
+n03482405 hamper
+n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier
+n03485407 hand-held computer, hand-held microcomputer
+n03485794 handkerchief, hankie, hanky, hankey
+n03492542 hard disc, hard disk, fixed disk
+n03494278 harmonica, mouth organ, harp, mouth harp
+n03495258 harp
+n03496892 harvester, reaper
+n03498962 hatchet
+n03527444 holster
+n03529860 home theater, home theatre
+n03530642 honeycomb
+n03532672 hook, claw
+n03534580 hoopskirt, crinoline
+n03535780 horizontal bar, high bar
+n03538406 horse cart, horse-cart
+n03544143 hourglass
+n03584254 iPod
+n03584829 iron, smoothing iron
+n03590841 jack-o'-lantern
+n03594734 jean, blue jean, denim
+n03594945 jeep, landrover
+n03595614 jersey, T-shirt, tee shirt
+n03598930 jigsaw puzzle
+n03599486 jinrikisha, ricksha, rickshaw
+n03602883 joystick
+n03617480 kimono
+n03623198 knee pad
+n03627232 knot
+n03630383 lab coat, laboratory coat
+n03633091 ladle
+n03637318 lampshade, lamp shade
+n03642806 laptop, laptop computer
+n03649909 lawn mower, mower
+n03657121 lens cap, lens cover
+n03658185 letter opener, paper knife, paperknife
+n03661043 library
+n03662601 lifeboat
+n03666591 lighter, light, igniter, ignitor
+n03670208 limousine, limo
+n03673027 liner, ocean liner
+n03676483 lipstick, lip rouge
+n03680355 Loafer
+n03690938 lotion
+n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+n03692522 loupe, jeweler's loupe
+n03697007 lumbermill, sawmill
+n03706229 magnetic compass
+n03709823 mailbag, postbag
+n03710193 mailbox, letter box
+n03710637 maillot
+n03710721 maillot, tank suit
+n03717622 manhole cover
+n03720891 maraca
+n03721384 marimba, xylophone
+n03724870 mask
+n03729826 matchstick
+n03733131 maypole
+n03733281 maze, labyrinth
+n03733805 measuring cup
+n03742115 medicine chest, medicine cabinet
+n03743016 megalith, megalithic structure
+n03759954 microphone, mike
+n03761084 microwave, microwave oven
+n03763968 military uniform
+n03764736 milk can
+n03769881 minibus
+n03770439 miniskirt, mini
+n03770679 minivan
+n03773504 missile
+n03775071 mitten
+n03775546 mixing bowl
+n03776460 mobile home, manufactured home
+n03777568 Model T
+n03777754 modem
+n03781244 monastery
+n03782006 monitor
+n03785016 moped
+n03786901 mortar
+n03787032 mortarboard
+n03788195 mosque
+n03788365 mosquito net
+n03791053 motor scooter, scooter
+n03792782 mountain bike, all-terrain bike, off-roader
+n03792972 mountain tent
+n03793489 mouse, computer mouse
+n03794056 mousetrap
+n03796401 moving van
+n03803284 muzzle
+n03804744 nail
+n03814639 neck brace
+n03814906 necklace
+n03825788 nipple
+n03832673 notebook, notebook computer
+n03837869 obelisk
+n03838899 oboe, hautboy, hautbois
+n03840681 ocarina, sweet potato
+n03841143 odometer, hodometer, mileometer, milometer
+n03843555 oil filter
+n03854065 organ, pipe organ
+n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO
+n03866082 overskirt
+n03868242 oxcart
+n03868863 oxygen mask
+n03871628 packet
+n03873416 paddle, boat paddle
+n03874293 paddlewheel, paddle wheel
+n03874599 padlock
+n03876231 paintbrush
+n03877472 pajama, pyjama, pj's, jammies
+n03877845 palace
+n03884397 panpipe, pandean pipe, syrinx
+n03887697 paper towel
+n03888257 parachute, chute
+n03888605 parallel bars, bars
+n03891251 park bench
+n03891332 parking meter
+n03895866 passenger car, coach, carriage
+n03899768 patio, terrace
+n03902125 pay-phone, pay-station
+n03903868 pedestal, plinth, footstall
+n03908618 pencil box, pencil case
+n03908714 pencil sharpener
+n03916031 perfume, essence
+n03920288 Petri dish
+n03924679 photocopier
+n03929660 pick, plectrum, plectron
+n03929855 pickelhaube
+n03930313 picket fence, paling
+n03930630 pickup, pickup truck
+n03933933 pier
+n03935335 piggy bank, penny bank
+n03937543 pill bottle
+n03938244 pillow
+n03942813 ping-pong ball
+n03944341 pinwheel
+n03947888 pirate, pirate ship
+n03950228 pitcher, ewer
+n03954731 plane, carpenter's plane, woodworking plane
+n03956157 planetarium
+n03958227 plastic bag
+n03961711 plate rack
+n03967562 plow, plough
+n03970156 plunger, plumber's helper
+n03976467 Polaroid camera, Polaroid Land camera
+n03976657 pole
+n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+n03980874 poncho
+n03982430 pool table, billiard table, snooker table
+n03983396 pop bottle, soda bottle
+n03991062 pot, flowerpot
+n03992509 potter's wheel
+n03995372 power drill
+n03998194 prayer rug, prayer mat
+n04004767 printer
+n04005630 prison, prison house
+n04008634 projectile, missile
+n04009552 projector
+n04019541 puck, hockey puck
+n04023962 punching bag, punch bag, punching ball, punchball
+n04026417 purse
+n04033901 quill, quill pen
+n04033995 quilt, comforter, comfort, puff
+n04037443 racer, race car, racing car
+n04039381 racket, racquet
+n04040759 radiator
+n04041544 radio, wireless
+n04044716 radio telescope, radio reflector
+n04049303 rain barrel
+n04065272 recreational vehicle, RV, R.V.
+n04067472 reel
+n04069434 reflex camera
+n04070727 refrigerator, icebox
+n04074963 remote control, remote
+n04081281 restaurant, eating house, eating place, eatery
+n04086273 revolver, six-gun, six-shooter
+n04090263 rifle
+n04099969 rocking chair, rocker
+n04111531 rotisserie
+n04116512 rubber eraser, rubber, pencil eraser
+n04118538 rugby ball
+n04118776 rule, ruler
+n04120489 running shoe
+n04125021 safe
+n04127249 safety pin
+n04131690 saltshaker, salt shaker
+n04133789 sandal
+n04136333 sarong
+n04141076 sax, saxophone
+n04141327 scabbard
+n04141975 scale, weighing machine
+n04146614 school bus
+n04147183 schooner
+n04149813 scoreboard
+n04152593 screen, CRT screen
+n04153751 screw
+n04154565 screwdriver
+n04162706 seat belt, seatbelt
+n04179913 sewing machine
+n04192698 shield, buckler
+n04200800 shoe shop, shoe-shop, shoe store
+n04201297 shoji
+n04204238 shopping basket
+n04204347 shopping cart
+n04208210 shovel
+n04209133 shower cap
+n04209239 shower curtain
+n04228054 ski
+n04229816 ski mask
+n04235860 sleeping bag
+n04238763 slide rule, slipstick
+n04239074 sliding door
+n04243546 slot, one-armed bandit
+n04251144 snorkel
+n04252077 snowmobile
+n04252225 snowplow, snowplough
+n04254120 soap dispenser
+n04254680 soccer ball
+n04254777 sock
+n04258138 solar dish, solar collector, solar furnace
+n04259630 sombrero
+n04263257 soup bowl
+n04264628 space bar
+n04265275 space heater
+n04266014 space shuttle
+n04270147 spatula
+n04273569 speedboat
+n04275548 spider web, spider's web
+n04277352 spindle
+n04285008 sports car, sport car
+n04286575 spotlight, spot
+n04296562 stage
+n04310018 steam locomotive
+n04311004 steel arch bridge
+n04311174 steel drum
+n04317175 stethoscope
+n04325704 stole
+n04326547 stone wall
+n04328186 stopwatch, stop watch
+n04330267 stove
+n04332243 strainer
+n04335435 streetcar, tram, tramcar, trolley, trolley car
+n04336792 stretcher
+n04344873 studio couch, day bed
+n04346328 stupa, tope
+n04347754 submarine, pigboat, sub, U-boat
+n04350905 suit, suit of clothes
+n04355338 sundial
+n04355933 sunglass
+n04356056 sunglasses, dark glasses, shades
+n04357314 sunscreen, sunblock, sun blocker
+n04366367 suspension bridge
+n04367480 swab, swob, mop
+n04370456 sweatshirt
+n04371430 swimming trunks, bathing trunks
+n04371774 swing
+n04372370 switch, electric switch, electrical switch
+n04376876 syringe
+n04380533 table lamp
+n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle
+n04392985 tape player
+n04398044 teapot
+n04399382 teddy, teddy bear
+n04404412 television, television system
+n04409515 tennis ball
+n04417672 thatch, thatched roof
+n04418357 theater curtain, theatre curtain
+n04423845 thimble
+n04428191 thresher, thrasher, threshing machine
+n04429376 throne
+n04435653 tile roof
+n04442312 toaster
+n04443257 tobacco shop, tobacconist shop, tobacconist
+n04447861 toilet seat
+n04456115 torch
+n04458633 totem pole
+n04461696 tow truck, tow car, wrecker
+n04462240 toyshop
+n04465501 tractor
+n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+n04476259 tray
+n04479046 trench coat
+n04482393 tricycle, trike, velocipede
+n04483307 trimaran
+n04485082 tripod
+n04486054 triumphal arch
+n04487081 trolleybus, trolley coach, trackless trolley
+n04487394 trombone
+n04493381 tub, vat
+n04501370 turnstile
+n04505470 typewriter keyboard
+n04507155 umbrella
+n04509417 unicycle, monocycle
+n04515003 upright, upright piano
+n04517823 vacuum, vacuum cleaner
+n04522168 vase
+n04523525 vault
+n04525038 velvet
+n04525305 vending machine
+n04532106 vestment
+n04532670 viaduct
+n04536866 violin, fiddle
+n04540053 volleyball
+n04542943 waffle iron
+n04548280 wall clock
+n04548362 wallet, billfold, notecase, pocketbook
+n04550184 wardrobe, closet, press
+n04552348 warplane, military plane
+n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin
+n04554684 washer, automatic washer, washing machine
+n04557648 water bottle
+n04560804 water jug
+n04562935 water tower
+n04579145 whiskey jug
+n04579432 whistle
+n04584207 wig
+n04589890 window screen
+n04590129 window shade
+n04591157 Windsor tie
+n04591713 wine bottle
+n04592741 wing
+n04596742 wok
+n04597913 wooden spoon
+n04599235 wool, woolen, woollen
+n04604644 worm fence, snake fence, snake-rail fence, Virginia fence
+n04606251 wreck
+n04612504 yawl
+n04613696 yurt
+n06359193 web site, website, internet site, site
+n06596364 comic book
+n06785654 crossword puzzle, crossword
+n06794110 street sign
+n06874185 traffic light, traffic signal, stoplight
+n07248320 book jacket, dust cover, dust jacket, dust wrapper
+n07565083 menu
+n07579787 plate
+n07583066 guacamole
+n07584110 consomme
+n07590611 hot pot, hotpot
+n07613480 trifle
+n07614500 ice cream, icecream
+n07615774 ice lolly, lolly, lollipop, popsicle
+n07684084 French loaf
+n07693725 bagel, beigel
+n07695742 pretzel
+n07697313 cheeseburger
+n07697537 hotdog, hot dog, red hot
+n07711569 mashed potato
+n07714571 head cabbage
+n07714990 broccoli
+n07715103 cauliflower
+n07716358 zucchini, courgette
+n07716906 spaghetti squash
+n07717410 acorn squash
+n07717556 butternut squash
+n07718472 cucumber, cuke
+n07718747 artichoke, globe artichoke
+n07720875 bell pepper
+n07730033 cardoon
+n07734744 mushroom
+n07742313 Granny Smith
+n07745940 strawberry
+n07747607 orange
+n07749582 lemon
+n07753113 fig
+n07753275 pineapple, ananas
+n07753592 banana
+n07754684 jackfruit, jak, jack
+n07760859 custard apple
+n07768694 pomegranate
+n07802026 hay
+n07831146 carbonara
+n07836838 chocolate sauce, chocolate syrup
+n07860988 dough
+n07871810 meat loaf, meatloaf
+n07873807 pizza, pizza pie
+n07875152 potpie
+n07880968 burrito
+n07892512 red wine
+n07920052 espresso
+n07930864 cup
+n07932039 eggnog
+n09193705 alp
+n09229709 bubble
+n09246464 cliff, drop, drop-off
+n09256479 coral reef
+n09288635 geyser
+n09332890 lakeside, lakeshore
+n09399592 promontory, headland, head, foreland
+n09421951 sandbar, sand bar
+n09428293 seashore, coast, seacoast, sea-coast
+n09468604 valley, vale
+n09472597 volcano
+n09835506 ballplayer, baseball player
+n10148035 groom, bridegroom
+n10565667 scuba diver
+n11879895 rapeseed
+n11939491 daisy
+n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+n12144580 corn
+n12267677 acorn
+n12620546 hip, rose hip, rosehip
+n12768682 buckeye, horse chestnut, conker
+n12985857 coral fungus
+n12998815 agaric
+n13037406 gyromitra
+n13040303 stinkhorn, carrion fungus
+n13044778 earthstar
+n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+n13054560 bolete
+n13133613 ear, spike, capitulum
+n15075141 toilet tissue, toilet paper, bathroom tissue
\ No newline at end of file

Src/Classifier.cpp

+#include <Classifier.h>
+#include <Filesystem.h>
+#include <SimpleLog.h>
+#include <algorithm>
+#include <CommonUtility.h>
+
+namespace ortSamples
+{
+
+Classifier::Classifier()
+{
+}
+
+Classifier::~Classifier()
+{
+    configurationFile.release();
+}
+
+ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializationParameterOfClassifier)
+{
+    // 读取配置文件
+    std::string configFilePath=initializationParameterOfClassifier.configFilePath;
+    if(Exists(configFilePath)==false)
+    {
+        LOG_ERROR(stdout, "no configuration file!\n");
+        return CONFIG_FILE_NOT_EXIST;
+    }
+    if(!configurationFile.open(configFilePath, cv::FileStorage::READ))
+    {
+       LOG_ERROR(stdout, "fail to open configuration file\n");
+       return FAIL_TO_OPEN_CONFIG_FILE;
+    }
+    LOG_INFO(stdout, "succeed to open configuration file\n");
+
+    // 获取配置文件参数
+    cv::FileNode netNode = configurationFile["Classifier"];
+    std::string modelPath=(std::string)netNode["ModelPath"];
+
+
+    // 初始化session
+    //设置DCU
+    OrtROCMProviderOptions rocm_options;
+    rocm_options.device_id = 0;
+    sessionOptions.AppendExecutionProvider_ROCM(rocm_options);
+    
+    sessionOptions.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_BASIC);
+    // sessionOptions.EnableProfiling("profile_prefix");
+
+    session = new Ort::Session(env, modelPath.c_str(), sessionOptions);
+
+    return SUCCESS;
+
+}
+
+ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector<std::vector<ResultOfPrediction>> &predictions)
+{
+    if(srcImages.size()==0||srcImages[0].empty()||srcImages[0].depth()!=CV_8U)
+    {
+        LOG_ERROR(stdout, "image error!\n");
+        return IMAGE_ERROR;
+    }
+    
+    // 数据预处理
+    std::vector<cv::Mat> image;
+    for(int i =0;i<srcImages.size();++i)
+    {
+        //BGR转换为RGB
+        cv::Mat imgRGB;
+        cv::cvtColor(srcImages[i], imgRGB, cv::COLOR_BGR2RGB);
+
+        // 调整大小，使短边为256，保持长宽比
+        cv::Mat shrink;
+        float ratio = (float)256 / min(imgRGB.cols, imgRGB.rows);
+        if(imgRGB.rows > imgRGB.cols)
+        {
+            cv::resize(imgRGB, shrink, cv::Size(256, int(ratio * imgRGB.rows)), 0, 0);
+        }
+        else
+        {
+            cv::resize(imgRGB, shrink, cv::Size(int(ratio * imgRGB.cols), 256), 0, 0);
+        }
+
+        // 裁剪中心窗口为224*224
+        int start_x = shrink.cols/2 - 224/2;
+        int start_y = shrink.rows/2 - 224/2;
+        cv::Rect rect(start_x, start_y, 224, 224);
+        cv::Mat images = shrink(rect);
+        image.push_back(images);
+    }
+
+    // normalize并转换为NCHW
+    cv::Mat inputBlob;
+    Image2BlobParams image2BlobParams;
+    image2BlobParams.scalefactor=cv::Scalar(1/58.395, 1/57.12, 1/57.375);
+    image2BlobParams.mean=cv::Scalar(123.675, 116.28, 103.53);
+    image2BlobParams.swapRB=false;
+    blobFromImagesWithParams(image,inputBlob,image2BlobParams);
+
+    // 设置onnx的输入和输出名
+    std::vector<const char*> input_node_names = {"data"};
+    std::vector<const char*> output_node_names = {"resnetv24_dense0_fwd"};
+
+    // 初始化输入数据
+    std::array<int64_t, 4> inputShape{1, 3, 224, 224};
+    auto memoryInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+    std::array<float, 3 * 224 * 224> input_image{};
+    float* input_test = (float*)inputBlob.data;
+    Ort::Value inputTensor = Ort::Value::CreateTensor<float>(memoryInfo, input_test, input_image.size(), inputShape.data(), inputShape.size());
+    std::vector<Ort::Value> intput_tensors;
+    intput_tensors.push_back(std::move(inputTensor));
+    
+    // 进行推理
+    auto output_tensors = session->Run(Ort::RunOptions{nullptr}, input_node_names.data(), intput_tensors.data(), 1, output_node_names.data(), 1);
+    
+    // 解析输出结果
+    const float* pdata = output_tensors[0].GetTensorMutableData<float>();
+    int numberOfClasses = 1000 ;
+    for(int i=0;i<srcImages.size();++i)
+    {
+        int startIndex=numberOfClasses*i;
+
+        std::vector<float> logit;
+        for(int j=0;j<numberOfClasses;++j)
+        {
+            logit.push_back(pdata[startIndex+j]);
+        }
+        
+        std::vector<ResultOfPrediction> resultOfPredictions;
+        for(int j=0;j<numberOfClasses;++j)
+        {
+            ResultOfPrediction prediction;
+            prediction.label=j;
+            prediction.confidence=logit[j];
+
+            resultOfPredictions.push_back(prediction);
+        }
+
+        predictions.push_back(resultOfPredictions);
+    }
+
+    return SUCCESS;
+
+}
+
+}

Src/Classifier.h

+#ifndef __CLASSIFIER_H__
+#define __CLASSIFIER_H__
+
+
+#include <onnxruntime/core/session/onnxruntime_cxx_api.h>
+
+#include <CommonDefinition.h>
+
+namespace ortSamples
+{
+
+class  Classifier
+{
+public:
+    Classifier();
+    
+    ~Classifier();
+
+    ErrorCode Initialize(InitializationParameterOfClassifier initializationParameterOfClassifier);
+
+    ErrorCode Classify(const std::vector<cv::Mat> &srcImages,std::vector<std::vector<ResultOfPrediction>> &predictions);
+
+private:
+    cv::FileStorage configurationFile;
+    
+
+    Ort::Session *session;
+
+    cv::Size inputSize;
+    std::string inputName;
+    Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, "ONNXRuntime");
+    Ort::SessionOptions sessionOptions = Ort::SessionOptions();
+
+    bool useInt8;
+    bool useFP16;
+    
+
+};
+
+}
+
+#endif
+

Src/Utility/CommonDefinition.h

+// 常用定义
+
+#ifndef __COMMON_DEFINITION_H__
+#define __COMMON_DEFINITION_H__
+
+#include <opencv2/opencv.hpp>
+
+namespace ortSamples
+{
+   
+// 路径分隔符(Linux:‘/’,Windows:’\\’)
+#ifdef _WIN32
+#define  PATH_SEPARATOR '\\'
+#else
+#define  PATH_SEPARATOR '/'
+#endif
+
+#define CONFIG_FILE  "../Resource/Configuration.xml"
+
+typedef enum _ErrorCode
+{
+    SUCCESS=0,  // 0
+    MODEL_NOT_EXIST, // 模型不存在
+    CONFIG_FILE_NOT_EXIST, // 配置文件不存在
+    FAIL_TO_LOAD_MODEL, // 加载模型失败
+    FAIL_TO_OPEN_CONFIG_FILE, // 加载配置文件失败
+    IMAGE_ERROR, // 图像错误
+}ErrorCode;
+
+typedef struct _ResultOfPrediction
+{
+    float confidence;
+    int label;
+    _ResultOfPrediction():confidence(0.0f),label(0){}
+
+}ResultOfPrediction;
+
+typedef struct _InitializationParameterOfClassifier
+{
+    std::string parentPath;
+    std::string configFilePath;
+}InitializationParameterOfClassifier;
+
+}
+
+#endif
+