add deeplabv3 and ssd_vgg16 310 inference

modified:   model_zoo/official/cv/deeplabv3/README.md
	modified:   model_zoo/official/cv/deeplabv3/README_CN.md

model_zoo/official/cv/deeplabv3/README.md

@@ -482,6 +482,8 @@ Note: There OS is output stride, and MS is multiscale.
 
 ## [Export MindIR](#contents)
 
+Currently, batchsize can only set to 1.
+
 ```shell
 python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
 ```

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -508,7 +508,6 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [
 
 ### 用法
 
-在执行推理前，air文件必须在910上通过export.py文件导出。
 目前仅可处理batch_Size为1。
 
 ```shell

model_zoo/official/cv/deeplabv3/ascend310_infer/src/CMakeLists.txt → model_zoo/official/cv/deeplabv3/ascend310_infer/CMakeLists.txt

@@ -1,14 +1,14 @@
 cmake_minimum_required(VERSION 3.14.1)
-project(MindSporeCxxTestcase[CXX])
+project(Ascend310Infer)
 add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
 set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
 option(MINDSPORE_PATH "mindspore install path" "")
 include_directories(${MINDSPORE_PATH})
 include_directories(${MINDSPORE_PATH}/include)
-include_directories(${PROJECT_SRC_ROOT}/../inc)
+include_directories(${PROJECT_SRC_ROOT})
 find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
 file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
 
-add_executable(main main.cc utils.cc)
+add_executable(main src/main.cc src/utils.cc)
 target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

model_zoo/official/cv/deeplabv3/ascend310_infer/src/build.sh → model_zoo/official/cv/deeplabv3/ascend310_infer/build.sh

@@ -13,6 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+if [ ! -d out ]; then
+    mkdir out
+fi
 
-cmake . -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
-make
+cd out
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake .. \
+    -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/deeplabv3/ascend310_infer/fusion_switch.cfg

@@ -0,0 +1 @@
+ConvBatchnormFusionPass:off

model_zoo/official/cv/deeplabv3/ascend310_infer/src/main.cc

@@ -52,6 +52,7 @@ using mindspore::dataset::vision::Decode;
 
 DEFINE_string(mindir_path, "", "mindir path");
 DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_string(fusion_switch_path, ".", "fusion switch path");
 DEFINE_int32(device_id, 0, "device id");
 
 int PadImage(const MSTensor &input, MSTensor *output) {
@@ -122,11 +123,17 @@ int main(int argc, char **argv) {
     std::cout << "Invalid mindir" << std::endl;
     return 1;
   }
-
+  if (RealPath(FLAGS_fusion_switch_path).empty()) {
+    std::cout << "Invalid fusion switch path" << std::endl;
+    return 1;
+  }
   GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
   GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
   auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
   auto model_context = std::make_shared<mindspore::Context>();
+  if (!FLAGS_fusion_switch_path.empty()) {
+    ModelContext::SetFusionSwitchConfigPath(model_context, FLAGS_fusion_switch_path);
+  }
   Model model(GraphCell(graph), model_context);
   Status ret = model.Build();
   if (ret != kSuccess) {

model_zoo/official/cv/deeplabv3/postprocess.py

@@ -75,6 +75,8 @@ def eval_batch_scales(args, eval_net, img_lst, scales,
 
 def acc_cal():
     args = parse_args()
+    args.image_mean = [103.53, 116.28, 123.675]
+    args.image_std = [57.375, 57.120, 58.395]
     # data list
     with open(args.data_lst) as f:
         img_lst = f.readlines()

model_zoo/official/cv/deeplabv3/scripts/run_infer_310.sh

@@ -60,10 +60,7 @@ fi
 
 function compile_app()
 {
-    cd ../ascend310_infer/src
-    if [ -f "Makefile" ]; then
-        make clean
-    fi
+    cd ../ascend310_infer
     bash build.sh &> build.log
 }
 
@@ -78,7 +75,7 @@ function infer()
     fi
     mkdir result_Files
     mkdir time_Result
-    ../ascend310_infer/src/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
+    ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --fusion_switch_path=../ascend310_infer/fusion_switch.cfg &> infer.log
 }
 
 function cal_acc()

model_zoo/official/cv/ssd/README.md

@@ -410,10 +410,11 @@ Current batch_Size can only be set to 1. The precision calculation process needs
 
 ```shell
 # Ascend310 inference
-bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
 ```
 
-`DEVICE_ID` is optional, default value is 0.
+- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. Note that the image shape of ssd_vgg16 inference is [300, 300], The DVPP hardware restricts width 16-alignment and height even-alignment. Therefore, the network needs to use the CPU operator to process images.
+- `DEVICE_ID` is optional, default value is 0.
 
 ### result
 

model_zoo/official/cv/ssd/ascend310_infer/src/main.cc

@@ -29,7 +29,7 @@
 #include "include/api/serialization.h"
 #include "include/minddata/dataset/include/vision_ascend.h"
 #include "include/minddata/dataset/include/execute.h"
-
+#include "include/minddata/dataset/include/vision.h"
 #include "inc/utils.h"
 
 using mindspore::GlobalContext;
@@ -42,13 +42,20 @@ using mindspore::GraphCell;
 using mindspore::kSuccess;
 using mindspore::MSTensor;
 using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
 using mindspore::dataset::vision::DvppDecodeResizeJpeg;
-
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::HWC2CHW;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::Decode;
 
 DEFINE_string(mindir_path, "", "mindir path");
 DEFINE_string(dataset_path, ".", "dataset path");
 DEFINE_int32(device_id, 0, "device id");
 DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
+DEFINE_string(cpu_dvpp, "DVPP", "cpu or dvpp process");
+DEFINE_int32(image_height, 640, "image height");
+DEFINE_int32(image_width, 640, "image width");
 
 int main(int argc, char **argv) {
   gflags::ParseCommandLineFlags(&argc, &argv, true);
@@ -56,17 +63,18 @@ int main(int argc, char **argv) {
     std::cout << "Invalid mindir" << std::endl;
     return 1;
   }
-  if (RealPath(FLAGS_aipp_path).empty()) {
-    std::cout << "Invalid aipp path" << std::endl;
-    return 1;
-  }
 
   GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
   GlobalContext::SetGlobalDeviceID(FLAGS_device_id);
   auto graph = Serialization::LoadModel(FLAGS_mindir_path, ModelType::kMindIR);
   auto model_context = std::make_shared<mindspore::ModelContext>();
-  if (!FLAGS_aipp_path.empty()) {
-    ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
+  if (FLAGS_cpu_dvpp == "DVPP") {
+    if (RealPath(FLAGS_aipp_path).empty()) {
+      std::cout << "Invalid aipp path" << std::endl;
+      return 1;
+    } else {
+      ModelContext::SetInsertOpConfigPath(model_context, FLAGS_aipp_path);
+    }
   }
 
   Model model(GraphCell(graph), model_context);
@@ -84,7 +92,7 @@ int main(int argc, char **argv) {
 
   std::map<double, double> costTime_map;
   size_t size = all_files.size();
-  Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg({640, 640})));
+
   for (size_t i = 0; i < size; ++i) {
     struct timeval start = {0};
     struct timeval end = {0};
@@ -93,11 +101,33 @@ int main(int argc, char **argv) {
     std::vector<MSTensor> inputs;
     std::vector<MSTensor> outputs;
     std::cout << "Start predict input files:" << all_files[i] << std::endl;
-    auto imgDvpp = std::make_shared<MSTensor>();
-    resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
-
-    inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
+    if (FLAGS_cpu_dvpp == "DVPP") {
+      auto resizeShape = {static_cast <uint32_t>(FLAGS_image_height), static_cast <uint32_t>(FLAGS_image_width)};
+      Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg(resizeShape)));
+      auto imgDvpp = std::make_shared<MSTensor>();
+      resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
+      inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
                         imgDvpp->Data().get(), imgDvpp->DataSize());
+    } else {
+      std::shared_ptr<TensorTransform> decode(new Decode());
+      std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
+      std::shared_ptr<TensorTransform> normalize(
+      new Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375}));
+      auto resizeShape = {FLAGS_image_height, FLAGS_image_width};
+      std::shared_ptr<TensorTransform> resize(new Resize(resizeShape));
+      Execute composeDecode({decode, resize, normalize, hwc2chw});
+      auto img = MSTensor();
+      auto image = ReadFileToTensor(all_files[i]);
+      composeDecode(image, &img);
+      std::vector<MSTensor> model_inputs = model.GetInputs();
+      if (model_inputs.empty()) {
+        std::cout << "Invalid model, inputs is empty." << std::endl;
+        return 1;
+      }
+      inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                       img.Data().get(), img.DataSize());
+    }
+
     gettimeofday(&start, nullptr);
     ret = model.Predict(inputs, &outputs);
     gettimeofday(&end, nullptr);

model_zoo/official/cv/ssd/export.py

@@ -28,7 +28,7 @@ parser.add_argument("--device_id", type=int, default=0, help="Device id")
 parser.add_argument("--batch_size", type=int, default=1, help="batch size")
 parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
 parser.add_argument("--file_name", type=str, default="ssd", help="output file name.")
-parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
 parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
                     help="device target")
 args = parser.parse_args()

model_zoo/official/cv/ssd/postprocess.py

@@ -22,7 +22,7 @@ from src.config import config
 from src.eval_utils import metrics
 
 batch_size = 1
-parser = argparse.ArgumentParser(description="ssd_mobilenet_v1_fpn inference")
+parser = argparse.ArgumentParser(description="ssd acc calculation")
 parser.add_argument("--result_path", type=str, required=True, help="result files path.")
 parser.add_argument("--img_path", type=str, required=True, help="image file path.")
 parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
@@ -73,9 +73,8 @@ def get_result(result_path, img_id_file_path):
         image_shape = np.array([img_size[1], img_size[0]])
         result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
         result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
-
-        boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(51150, 4)
-        box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(51150, 81)
+        boxes = np.fromfile(result_path_0, dtype=np.float32).reshape(config.num_ssd_boxes, 4)
+        box_scores = np.fromfile(result_path_1, dtype=np.float32).reshape(config.num_ssd_boxes, config.num_classes)
 
         pred_data.append({
             "boxes": boxes,

model_zoo/official/cv/ssd/scripts/run_infer_310.sh

@@ -14,8 +14,9 @@
 # limitations under the License.
 # ============================================================================
 
-if [[ $# -lt 2 || $# -gt 3 ]]; then
-    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+if [[ $# -lt 3 || $# -gt 4 ]]; then
+    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+    DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
 fi
@@ -29,14 +30,16 @@ get_real_path(){
 }
 model=$(get_real_path $1)
 data_path=$(get_real_path $2)
+DVPP=${3^^}
 
 device_id=0
-if [ $# == 3 ]; then    
-    device_id=$3
+if [ $# == 4 ]; then
+    device_id=$4
 fi
 
 echo "mindir name: "$model
 echo "dataset path: "$data_path
+echo "image process mode: "$DVPP
 echo "device id: "$device_id
 
 export ASCEND_HOME=/usr/local/Ascend/
@@ -56,10 +59,7 @@ fi
 function compile_app()
 {
     cd ../ascend310_infer
-    if [ -f "Makefile" ]; then
-        make clean
-    fi
-    sh build.sh &> build.log    
+    sh build.sh &> build.log
 }
 
 function infer()
@@ -73,7 +73,14 @@ function infer()
     fi
     mkdir result_Files
     mkdir time_Result
-    ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --aipp_path ../src/aipp.cfg &> infer.log
+    if [ "$DVPP" == "DVPP" ];then
+      ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --cpu_dvpp=$DVPP --aipp_path=../ascend310_infer/aipp.cfg --image_height=640 --image_width=640 &> infer.log
+    elif [ "$DVPP" == "CPU"  ]; then
+      ../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --cpu_dvpp=$DVPP --device_id=$device_id --image_height=300 --image_width=300 &> infer.log
+    else
+      echo "image process mode must be in [DVPP|CPU]"
+      exit 1
+    fi
 }
 
 function cal_acc()