fix act some bug (#1421)

example/auto_compression/image_classification/eval.py

@@ -98,7 +98,8 @@ def eval():
 
 def main(args):
     global global_config
-    global_config = load_slim_config(args.config_path)
+    all_config = load_slim_config(args.config_path)
+    global_config = all_config["Global"]
 
     global data_dir
     data_dir = global_config['data_dir']

example/auto_compression/pytorch_yolo_series/eval.py

@@ -55,7 +55,7 @@ def eval():
         score_threshold=0.001,
         nms_threshold=0.65,
         multi_label=True,
-        num_top_k=global_config.get('nms_num_top_k', 30000))
+        nms_top_k=global_config.get('nms_num_top_k', 30000))
 
     bboxes_list, bbox_nums_list, image_id_list = [], [], []
     with tqdm(

example/auto_compression/pytorch_yolo_series/post_process.py

@@ -80,7 +80,7 @@ class YOLOPostProcess(object):
         multi_label(bool): Whether keep multi label in boxes.
         keep_top_k(int): Number of total bboxes to be kept per image after NMS
                 step. -1 means keeping all bboxes after NMS step.
-        num_top_k(int): Maximum number of boxes put into torchvision.ops.nums()
+        nms_top_k(int): Maximum number of boxes put into nums.
     """
 
     def __init__(self,
@@ -88,12 +88,12 @@ class YOLOPostProcess(object):
                  nms_threshold=0.5,
                  multi_label=False,
                  keep_top_k=300,
-                 num_top_k=30000):
+                 nms_top_k=30000):
         self.score_threshold = score_threshold
         self.nms_threshold = nms_threshold
         self.multi_label = multi_label
         self.keep_top_k = keep_top_k
-        self.num_top_k = num_top_k
+        self.nms_top_k = nms_top_k
 
     def _xywh2xyxy(self, x):
         # Convert from [x, y, w, h] to [x1, y1, x2, y2]

example/auto_compression/pytorch_yolo_series/run.py

@@ -71,7 +71,10 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
                            feed={test_feed_names[0]: data_all['image']},
                            fetch_list=test_fetch_list,
                            return_numpy=False)
-            res = postprocess(np.array(outs[0]), data_all['scale_factor'])
+            res = postprocess(
+                np.array(outs[0]),
+                data_all['scale_factor'],
+                nms_top_k=global_config.get('nms_num_top_k', 30000))
             bboxes_list.append(res['bbox'])
             bbox_nums_list.append(res['bbox_num'])
             image_id_list.append(np.array(data_all['im_id']))

example/full_quantization/image_classification/eval.py

@@ -98,7 +98,8 @@ def eval():
 
 def main(args):
     global global_config
-    global_config = load_slim_config(args.config_path)
+    all_config = load_slim_config(args.config_path)
+    global_config = all_config["Global"]
 
     global data_dir
     data_dir = global_config['data_dir']