fix bugs if fitting darknet

fluid/PaddleCV/yolov3/config/config.py

@@ -80,7 +80,7 @@ _C.warm_up_factor = 0.
 
 # lr steps_with_decay
 _C.lr_steps = [300000, 400000, 450000]
-_C.lr_gamma = [0.2, 0.5, 0.1]
+_C.lr_gamma = [0.2, 0.1, 0.01]
 
 # L2 regularization hyperparameter
 _C.weight_decay = 0.0005

fluid/PaddleCV/yolov3/models.py

@@ -210,7 +210,7 @@ class YOLOv3(object):
                             class_num=class_num,
                             ignore_thresh=ignore_thresh,
                             downsample=self.downsample,
-                            use_label_smooth=cfg.label_smooth,
+                            use_label_smooth=False,
                             name="yolo_loss"+str(i))
                     self.losses.append(fluid.layers.reduce_mean(loss))
                     self.downsample //= 2

fluid/PaddleCV/yolov3/reader.py

@@ -172,15 +172,15 @@ class DataSetReader(object):
             gt_labels = img['gt_labels'].copy()
             gt_scores = np.ones_like(gt_labels)
 
-            if mixup_img:
-                mixup_im = cv2.imread(mixup_img['image'])
-                mixup_im = cv2.cvtColor(mixup_im, cv2.COLOR_BGR2RGB)
-                mixup_gt_boxes = mixup_img['gt_boxes'].copy()
-                mixup_gt_labels = mixup_img['gt_labels'].copy()
-                mixup_gt_scores = np.ones_like(mixup_gt_labels)
-                im, gt_boxes, gt_labels, gt_scores = image_utils.image_mixup(im, gt_boxes, \
-                        gt_labels, gt_scores, mixup_im, mixup_gt_boxes, mixup_gt_labels, \
-                        mixup_gt_scores)
+            # if mixup_img:
+            #     mixup_im = cv2.imread(mixup_img['image'])
+            #     mixup_im = cv2.cvtColor(mixup_im, cv2.COLOR_BGR2RGB)
+            #     mixup_gt_boxes = mixup_img['gt_boxes'].copy()
+            #     mixup_gt_labels = mixup_img['gt_labels'].copy()
+            #     mixup_gt_scores = np.ones_like(mixup_gt_labels)
+            #     im, gt_boxes, gt_labels, gt_scores = image_utils.image_mixup(im, gt_boxes, \
+            #             gt_labels, gt_scores, mixup_im, mixup_gt_boxes, mixup_gt_labels, \
+            #             mixup_gt_scores)
 
             im, gt_boxes, gt_labels, gt_scores = image_utils.image_augment(im, gt_boxes, gt_labels, gt_scores, size, mean)
 

fluid/PaddleCV/yolov3/train.py

@@ -57,7 +57,10 @@ def train():
     boundaries = cfg.lr_steps
     gamma = cfg.lr_gamma
     step_num = len(cfg.lr_steps)
-    values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
+    if isinstance(gamma, list):
+        values = [learning_rate * g for g in gamma]
+    else:
+        values = [learning_rate * (gamma**i) for i in range(step_num + 1)]
 
     optimizer = fluid.optimizer.Momentum(
         learning_rate=exponential_with_warmup_decay(