clean code

official/cv/faster_rcnn/default_config.yaml

@@ -161,7 +161,6 @@ anno_path: ''
 backbone: 'resnet_v1.5_50'
 
 # eval.py FasterRcnn evaluation
-ann_file: '/cache/data/annotations/instances_val2017.json'
 checkpoint_path: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # export.py fasterrcnn_export
@@ -170,7 +169,6 @@ file_format: "MINDIR"
 ckpt_file: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # postprocess ("./src/config_50.yaml")
-#ann_file: ''
 result_path: ''
 
 ---
@@ -199,7 +197,6 @@ device_id: 'Device id, default is 0.'
 device_num: 'Use device nums, default is 1.'
 rank_id: 'Rank id, default is 0.'
 file_format: 'file format'
-ann_file: "Ann file, default is val.json."
 checkpoint_path: "Checkpoint file path."
 ckpt_file: 'fasterrcnn ckpt file.'
 result_path: "result file path."

official/cv/faster_rcnn/default_config_101.yaml

@@ -162,7 +162,6 @@ anno_path: ''
 backbone: 'resnet_v1_101'
 
 # eval.py FasterRcnn evaluation
-ann_file: '/cache/data/annotations/instances_val2017.json'
 checkpoint_path: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # export.py fasterrcnn_export
@@ -171,7 +170,6 @@ file_format: "MINDIR"
 ckpt_file: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # postprocess ("./src/config_50.yaml")
-#ann_file: ''
 result_path: ''
 
 ---
@@ -200,7 +198,6 @@ device_id: 'Device id, default is 0.'
 device_num: 'Use device nums, default is 1.'
 rank_id: 'Rank id, default is 0.'
 file_format: 'file format'
-ann_file: "Ann file, default is val.json."
 checkpoint_path: "Checkpoint file path."
 ckpt_file: 'fasterrcnn ckpt file.'
 result_path: "result file path."

official/cv/faster_rcnn/default_config_152.yaml

@@ -162,7 +162,6 @@ anno_path: ''
 backbone: 'resnet_v1_152'
 
 # eval.py FasterRcnn evaluation
-ann_file: '/cache/data/annotations/instances_val2017.json'
 checkpoint_path: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # export.py fasterrcnn_export
@@ -171,7 +170,6 @@ file_format: "MINDIR"
 ckpt_file: "/cache/train/fasterrcnn/faster_rcnn-12_7393.ckpt"
 
 # postprocess ("./src/config_50.yaml")
-#ann_file: ''
 result_path: ''
 
 ---
@@ -200,7 +198,6 @@ device_id: 'Device id, default is 0.'
 device_num: 'Use device nums, default is 1.'
 rank_id: 'Rank id, default is 0.'
 file_format: 'file format'
-ann_file: "Ann file, default is val.json."
 checkpoint_path: "Checkpoint file path."
 ckpt_file: 'fasterrcnn ckpt file.'
 result_path: "result file path."

official/cv/faster_rcnn/default_config_InceptionResnetV2.yaml

@@ -160,7 +160,6 @@ anno_path: ''
 backbone: 'inception_resnet_v2'
 
 # eval.py FasterRcnn evaluation
-ann_file: '/cache/data/annotations/instances_val2017.json'
 checkpoint_path: "/cache/checkpoint_path/faster_rcnn-20_7393.ckpt"
 
 # export.py fasterrcnn_export
@@ -169,7 +168,6 @@ file_format: "MINDIR"
 ckpt_file: ""
 
 # postprocess ("./src/default_config_InceptionResnetV2.yaml")
-#ann_file: ''
 result_path: ''
 
 ---
@@ -198,7 +196,6 @@ device_id: 'Device id, default is 0.'
 device_num: 'Use device nums, default is 1.'
 rank_id: 'Rank id, default is 0.'
 file_format: 'file format'
-ann_file: "Ann file, default is val.json."
 checkpoint_path: "Checkpoint file path."
 ckpt_file: 'fasterrcnn ckpt file.'
 result_path: "result file path."

official/cv/faster_rcnn/postprocess.py

@@ -31,14 +31,14 @@ def modelarts_pre_process():
 
 
 @moxing_wrapper(pre_process=modelarts_pre_process)
-def get_eval_result(ann_file, result_path):
+def get_eval_result(anno_path, result_path):
     """ get evaluation result of faster rcnn"""
     max_num = 128
     result_path = result_path
 
     outputs = []
 
-    dataset_coco = COCO(ann_file)
+    dataset_coco = COCO(anno_path)
     img_ids = dataset_coco.getImgIds()
 
     for img_id in img_ids:
@@ -74,4 +74,4 @@ def get_eval_result(ann_file, result_path):
 
 
 if __name__ == '__main__':
-    get_eval_result(config.ann_file, config.result_path)
+    get_eval_result(config.anno_path, config.result_path)

official/gnn/bgcf/train.py

@@ -168,9 +168,8 @@ def run_train():
                                    neg_gnew_neighs)
 
             if iter_num == num_iter:
-                print('Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num,
-                      'loss',
-                      '{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start))
+                print('Epoch: {}, iter: {}, loss: {}, epoch time: {:.4f}, per step time: {:.4f}'.format(
+                    _epoch, iter_num, train_loss, time.time() - epoch_start, (time.time()-epoch_start) / num_iter))
             iter_num += 1
 
         if _epoch % config.eval_interval == 0: