!18733 fix bug retinanet and cnn_direction_model

Merge pull request !18733 from Maige/bug

model_zoo/official/cv/cnn_direction_model/scripts/run_standalone_train_ascend.sh

@@ -55,13 +55,14 @@ fi
 mkdir ./train
 cp ./*.py ./train
 cp -r ./scripts ./train
+cp -r ./src ./train
 cp ./*yaml ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
 if [ $# == 2 ]
 then
-    python train.py --train_dataset_path=$PATH1 &> log &
+    python train.py --train_dataset_path=$PATH1 &> train.log &
 fi
 
 if [ $# == 3 ]

model_zoo/official/cv/retinanet/default_config.yaml

@@ -111,6 +111,12 @@ file_format: "MINDIR"
 export_batch_size: 1
 file_name: "retinanet"
 
+# ======================================================================================
+# postprocess options
+result_path: ""
+img_path: ""
+img_id_file: ""
+
 ---
 # Help description for each configuration
 enable_modelarts: "Whether training on modelarts default: False"
@@ -134,4 +140,7 @@ dataset: "Dataset, default is coco."
 device_id: "Device id, default is 0."
 file_format: "file format choices [AIR, MINDIR]"
 file_name: "output file name."
-export_batch_size: "batch size"
+export_batch_size: "batch size"
+result_path: "result file path."
+img_path: "image file path."
+img_id_file: "image id file."

model_zoo/official/cv/retinanet/postprocess.py

@@ -16,16 +16,11 @@
 """Evaluation for retinanet"""
 
 import os
-import argparse
 import numpy as np
 from PIL import Image
 from src.coco_eval import metrics
+from src.model_utils.config import config
 
-parser = argparse.ArgumentParser(description='retinanet evaluation')
-parser.add_argument("--result_path", type=str, required=True, help="result file path.")
-parser.add_argument("--img_path", type=str, required=True, help="image file path.")
-parser.add_argument("--img_id_file", type=str, required=True, help="image id file.")
-args = parser.parse_args()
 
 def get_pred(result_path, img_id):
     boxes_file = os.path.join(result_path, img_id + '_0.bin')
@@ -35,10 +30,12 @@ def get_pred(result_path, img_id):
     scores = np.fromfile(scores_file, dtype=np.float32).reshape(67995, 81)
     return boxes, scores
 
+
 def get_img_size(file_name):
     img = Image.open(file_name)
     return img.size
 
+
 def get_img_id(img_id_file):
     f = open(img_id_file)
     lines = f.readlines()
@@ -49,6 +46,7 @@ def get_img_id(img_id_file):
 
     return ids
 
+
 def cal_acc(result_path, img_path, img_id_file):
     ids = get_img_id(img_id_file)
     imgs = os.listdir(img_path)
@@ -70,5 +68,6 @@ def cal_acc(result_path, img_path, img_id_file):
     mAP = metrics(pred_data)
     print(f"mAP: {mAP}")
 
+
 if __name__ == '__main__':
-    cal_acc(args.result_path, args.img_path, args.img_id_file)
+    cal_acc(config.result_path, config.img_path, config.img_id_file)