fix bug ctpn eval.py and modify openpose train param

model_zoo/official/cv/ctpn/eval.py

@@ -15,6 +15,7 @@
 
 """Evaluation for CTPN"""
 import os
+from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import set_seed
 from src.ctpn import CTPN
@@ -22,12 +23,13 @@ from src.dataset import create_ctpn_dataset
 from src.eval_utils import eval_for_ctpn
 from src.model_utils.config import config
 from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id
 
 
 set_seed(1)
 
 
-context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id)
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
 
 
 def modelarts_pre_process():

model_zoo/official/cv/openpose/README.md

@@ -49,7 +49,7 @@ In the currently provided training script, the coco2017 data set is used as an e
     Run python gen_ignore_mask.py
 
 ````python
-    python gen_ignore_mask.py --train_ann ../dataset/annotations/person_keypoints_train2017.json --val_ann ../dataset/annotations/person_keypoints_val2017.json --train_dir train2017 --val_dir val2017
+    python gen_ignore_mask.py --train_ann ../dataset/annotations/person_keypoints_train2017.json --val_ann ../dataset/annotations/person_keypoints_val2017.json --train_dir ../dataset/train2017 --val_dir ../dataset/val2017
 ````
 
 - The dataset folder is generated in the root directory and contains the following files:
@@ -90,10 +90,10 @@ After installing MindSpore via the official website, you can start training and
 
   ```python
   # run training example
-  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 > train.log 2>&1 &
+  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 --vgg_path ./vgg19-0-97_5004.ckpt > train.log 2>&1 &
 
   # run distributed training example
-  bash run_distribute_train.sh [RANK_TABLE_FILE] [IMGPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]
+  bash run_distribute_train.sh [RANK_TABLE_FILE] [IMGPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN] [VGG_PATH]
 
   # run evaluation example
   python eval.py --model_path path_to_eval_model.ckpt --imgpath_val ./dataset/val2017 --ann ./dataset/annotations/person_keypoints_val2017.json > eval.log 2>&1 &
@@ -165,7 +165,7 @@ For more configuration details, please refer the script `default_config.yaml`.
 - running on Ascend
 
   ```python
-  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 > train.log 2>&1 &
+  python train.py --imgpath_train ./train2017 --jsonpath_train ./person_keypoints_train2017.json --maskpath_train ./ignore_mask_train2017 --vgg_path ./vgg19-0-97_5004.ckpt > train.log 2>&1 &
   ```
 
   The python command above will run in the background, you can view the results through the file `train.log`.

model_zoo/official/cv/openpose/default_config.yaml

@@ -10,8 +10,14 @@ output_path: "/cache/train"
 load_path: "/cache/checkpoint_path"
 device_target: "Ascend"
 enable_profiling: False
-checkpoint_path: "./checkpoint/"
-checkpoint_file: "./checkpoint/.ckpt"
+
+# ======================================================================================
+# create ignore mask options
+train_dir: ""
+val_dir: ""
+train_ann: ""
+val_ann: ""
+vis: False
 
 # ======================================================================================
 # Training options
@@ -144,9 +150,12 @@ export_batch_size: "batch size"
 file_name: "output file name"
 file_format: "file format choices[AIR, MINDIR, ONNX]"
 ckpt_file: "Checkpoint file path."
-train_dir: "train data dir"
-train_ann: "train annotations json"
 model_path: "path of testing model"
 imgpath_val: "path of testing imgs"
 ann: "path of annotations"
 output_img_path: "path of testing imgs"
+vis: "visualize annotations and ignore masks"
+val_ann: "val annotations json"
+train_ann: "train annotations json"
+train_dir: "name of train dir"
+val_dir: "name of val dir"

model_zoo/official/cv/openpose/scripts/run_distribute_train.sh

@@ -13,9 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-if [ $# != 4 ]
+if [ $# != 5 ]
 then
-    echo "Usage: sh scripts/run_distribute_train.sh [RANK_TABLE_FILE] [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]"
+    echo "Usage: sh scripts/run_distribute_train.sh [RANK_TABLE_FILE] [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN] [VGG_PATH]"
 exit 1
 fi
 
@@ -56,6 +56,7 @@ do
     python train.py \
         --imgpath_train=$2 \
         --jsonpath_train=$3 \
-        --maskpath_train=$4 > log.txt 2>&1 &
+        --maskpath_train=$4  \
+        --vgg_path=$5 > log.txt 2>&1 &
     cd ..
 done

model_zoo/official/cv/openpose/scripts/run_eval_ascend.sh

@@ -21,7 +21,7 @@ exit 1
 fi
 
 export DEVICE_ID=0
-export DEVICE_NUM=1
+export RANK_SIZE=1
 export RANK_ID=0
 python eval.py \
   --model_path=$1 \

model_zoo/official/cv/openpose/scripts/run_standalone_train.sh

@@ -14,14 +14,14 @@
 # limitations under the License.
 # ============================================================================
 
-if [ $# != 3 ]
+if [ $# != 4 ]
 then
-    echo "Usage: sh scripts/run_standalone_train.sh [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN]"
+    echo "Usage: sh scripts/run_standalone_train.sh [IAMGEPATH_TRAIN] [JSONPATH_TRAIN] [MASKPATH_TRAIN] [VGG_PATH]"
 exit 1
 fi
 
 export DEVICE_ID=0
-export DEVICE_NUM=1
+export RANK_SIZE=1
 export RANK_ID=0
 rm -rf train
 mkdir train
@@ -29,5 +29,6 @@ cp -r ./src ./train
 cp -r ./scripts ./train
 cp ./*.py ./train
 cp ./*yaml ./train
-cd ./train
-python train.py --imgpath_train=$1 --jsonpath_train=$2 --maskpath_train=$3 > train.log 2>&1 &
+cd ./train || exit
+python train.py --imgpath_train=$1 --jsonpath_train=$2 --maskpath_train=$3 --vgg_path=$4 > train.log 2>&1 &
+cd ..

model_zoo/official/cv/openpose/src/gen_ignore_mask.py

@@ -13,13 +13,12 @@
 # limitations under the License.
 # ============================================================================
 import os
-import argparse
 import cv2
 import numpy as np
 from tqdm import tqdm
 from pycocotools.coco import COCO as ReadJson
+from model_utils.config import config
 
-from config import params
 
 class DataLoader():
     def __init__(self, train_, dir_name, mode_='train'):
@@ -42,7 +41,7 @@ class DataLoader():
                 intxn = mask_all_1 & mask
                 mask_miss_1 = np.bitwise_or(mask_miss_1.astype(int), np.subtract(mask, intxn, dtype=np.int32))
                 mask_all_1 = np.bitwise_or(mask_all_1.astype(int), mask.astype(int))
-            elif ann['num_keypoints'] < params['min_keypoints'] or ann['area'] <= params['min_area']:
+            elif ann['num_keypoints'] < config.min_keypoints or ann['area'] <= config.min_area:
                 mask_all_1 = np.bitwise_or(mask_all_1.astype(int), mask.astype(int))
                 mask_miss_1 = np.bitwise_or(mask_miss_1.astype(int), mask.astype(int))
             else:
@@ -90,25 +89,18 @@ class DataLoader():
         anno_ids = self.train.getAnnIds(imgIds=[img_id_])
         annotations_ = self.train.loadAnns(anno_ids)
 
-        img_file = os.path.join(params['data_dir'], self.dir_name, self.train.loadImgs([img_id_])[0]['file_name'])
+        img_file = os.path.join(self.dir_name, self.train.loadImgs([img_id_])[0]['file_name'])
         image_ = cv2.imread(img_file)
         return image_, annotations_, img_id_
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--vis', action='store_true', help='visualize annotations and ignore masks')
-    parser.add_argument('--train_ann', type=str, help='train annotations json')
-    parser.add_argument('--val_ann', type=str, help='val annotations json')
-    parser.add_argument('--train_dir', type=str, help='name of train dir')
-    parser.add_argument('--val_dir', type=str, help='name of val dir')
-    args = parser.parse_args()
-    path_list = [args.train_ann, args.val_ann, args.train_dir, args.val_dir]
+    path_list = [config.train_ann, config.val_ann, config.train_dir, config.val_dir]
     for index, mode in enumerate(['train', 'val']):
         train = ReadJson(path_list[index])
         data_loader = DataLoader(train, path_list[index+2], mode_=mode)
 
-        save_dir = os.path.join(params['data_dir'], 'ignore_mask_{}'.format(mode))
+        save_dir = os.path.join(os.path.dirname(path_list[index+2]), 'ignore_mask_{}'.format(mode))
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)
 
@@ -116,7 +108,7 @@ if __name__ == '__main__':
             img, annotations, img_id = data_loader.get_img_annotation(ind=i)
             mask_all, mask_miss = data_loader.gen_masks(img, annotations)
 
-            if args.vis:
+            if config.vis:
                 ann_img = data_loader.draw_masks_and_keypoints(img, annotations)
                 msk_img = data_loader.dwaw_gen_masks(img, mask_miss)
                 cv2.imshow('image', np.hstack((ann_img, msk_img)))
@@ -126,7 +118,7 @@ if __name__ == '__main__':
                 elif k == ord('s'):
                     cv2.imwrite('aaa.png', np.hstack((ann_img, msk_img)))
 
-            if np.any(mask_miss) and not args.vis:
+            if np.any(mask_miss) and not config.vis:
                 mask_miss = mask_miss.astype(np.uint8) * 255
                 save_path = os.path.join(save_dir, '{:012d}.png'.format(img_id))
                 cv2.imwrite(save_path, mask_miss)