!19058 add prarmeter for ssd-resnet50 inference

Merge pull request !19058 from 于振华/fix_ssd_resnet50_master

model_zoo/research/cv/ssd_resnet50/README.md

@@ -300,7 +300,7 @@ Current batch_ Size can only be set to 1.
 
 ```shell
 # Ascend310 inference
-bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP]  [ANNO_FILE]  [DEVICE_ID]
 ```
 
 Inference result will be stored in the example path, you can find result like the followings in acc.log.

model_zoo/research/cv/ssd_resnet50/README_CN.md

@@ -256,6 +256,13 @@ python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_
 
 在还行推理之前我们需要先导出模型。Air模型只能在昇腾910环境上导出，mindir可以在任意环境上导出。batch_size只支持1。
 
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP]  [ANNO_FILE]  [DEVICE_ID]
+```
+
+推理结果被保存到了当前目录，可以在acc.log中获得类似下面的结果。
+
 ```shell
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.327
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.475

model_zoo/research/cv/ssd_resnet50/postprocess.py

@@ -25,6 +25,7 @@ batch_size = 1
 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("--anno_file", type=str, required=True, help="annotation file.")
 parser.add_argument("--drop", action="store_true", help="drop iscrowd images or not.")
 args = parser.parse_args()
 
@@ -34,15 +35,13 @@ def get_imgSize(file_name):
 
 def get_result(result_path, img_id_file_path):
     """print the mAP"""
-    anno_json = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
-
     if args.drop:
         from pycocotools.coco import COCO
         train_cls = config.classes
         train_cls_dict = {}
         for i, cls in enumerate(train_cls):
             train_cls_dict[cls] = i
-        coco = COCO(anno_json)
+        coco = COCO(args.anno_file)
         classs_dict = {}
         cat_ids = coco.loadCats(coco.getCatIds())
         for cat in cat_ids:
@@ -83,7 +82,7 @@ def get_result(result_path, img_id_file_path):
             "img_id": img_id,
             "image_shape": image_shape
         })
-    mAP = metrics(pred_data, anno_json)
+    mAP = metrics(pred_data, args.anno_file)
     print(f" mAP:{mAP}")
 
 if __name__ == '__main__':

model_zoo/research/cv/ssd_resnet50/scripts/run_infer_310.sh

@@ -14,9 +14,10 @@
 # limitations under the License.
 # ============================================================================
 
-if [[ $# -lt 3 || $# -gt 4 ]]; then
-    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+if [[ $# -lt 4 || $# -gt 5 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [ANNO_FILE] [DEVICE_ID]
     DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
+    ANNO_PATH is mandatory, and should specify annotation file path of your data including file name.
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
 fi
@@ -31,15 +32,17 @@ get_real_path(){
 model=$(get_real_path $1)
 data_path=$(get_real_path $2)
 DVPP=${3^^}
+anno=$(get_real_path $4)
 
 device_id=0
-if [ $# == 4 ]; then
-    device_id=$4
+if [ $# == 5 ]; then
+    device_id=$5
 fi
 
 echo "mindir name: "$model
 echo "dataset path: "$data_path
 echo "image process mode: "$DVPP
+echo "anno file: "$anno
 echo "device id: "$device_id
 
 export ASCEND_HOME=/usr/local/Ascend/
@@ -85,7 +88,7 @@ function infer()
 
 function cal_acc()
 {
-    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --drop &> acc.log &
+    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --anno_file=$anno --drop &> acc.log &
 }
 
 compile_app