refine code.

fluid/PaddleCV/yolov3/config.py

@@ -64,6 +64,16 @@ _C.pixel_means = [0.485, 0.456, 0.406]
 # pixel std values
 _C.pixel_stds = [0.229, 0.224, 0.225]
 
+# anchors box weight and height
+_C.anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
+
+# anchor mask of each yolo layer
+_C.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+
+# IoU threshold to ignore objectness loss of pred box
+_C.ignore_thresh = .7
+
+
 #
 # SOLVER options
 #

fluid/PaddleCV/yolov3/models/darknet.py

@@ -86,14 +86,11 @@ def add_DarkNet53_conv_body(body_input, is_test=True):
     conv1 = conv_bn_layer(
             body_input, ch_out=32, filter_size=3, stride=1, padding=1, is_test=is_test, name="yolo_input")
     downsample_ = downsample(conv1, ch_out=conv1.shape[1]*2, is_test=is_test, name="yolo_input.downsample")
-    index = 2
     blocks = []
     for i, stage in enumerate(stages):
         block = layer_warp(block_func, downsample_, 32 *(2**i), stage, is_test=is_test, name="stage.{}".format(i))
         blocks.append(block)
-        index += 3 * stage
         if i < len(stages) - 1: # do not downsaple in the last stage
             downsample_ = downsample(block, ch_out=block.shape[1]*2, is_test=is_test, name="stage.{}.downsample".format(i))
-        index += 1
     return blocks[-1:-4:-1]
 

fluid/PaddleCV/yolov3/models/yolov3.py

@@ -62,8 +62,6 @@ class YOLOv3(object):
         self.outputs = []
         self.losses = []
         self.downsample = 32
-        self.ignore_thresh = .7
-        self.class_num = 80
 
     def build_input(self):
         self.image_shape = [3, cfg.input_size, cfg.input_size]
@@ -132,16 +130,8 @@ class YOLOv3(object):
                 route = upsample(route)
 
 
-        anchor_mask = [6,7,8,3,4,5,0,1,2]
-        anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
-        for i,out in enumerate(self.outputs):
-            mask = anchor_mask[i*3 : (i+1)*3]
-            mask_anchors=[]
-
-            for m in mask:
-                mask_anchors.append(anchors[2 * m])
-                mask_anchors.append(anchors[2 * m + 1])
-            class_num = int(self.class_num)
+        for i, out in enumerate(self.outputs):
+            anchor_mask = cfg.anchor_masks[i]
 
             if self.is_train:
                 ignore_thresh = float(self.ignore_thresh)
@@ -150,20 +140,24 @@ class YOLOv3(object):
                         gtbox=self.gtbox,
                         gtlabel=self.gtlabel,
                         gtscore=self.gtscore,
-                        anchors=anchors,
-                        anchor_mask=mask,
-                        class_num=class_num,
-                        ignore_thresh=ignore_thresh,
+                        anchors=cfg.anchors,
+                        anchor_mask=anchor_mask,
+                        class_num=cfg.class_num,
+                        ignore_thresh=cfg.ignore_thresh,
                         downsample_ratio=self.downsample,
                         use_label_smooth=cfg.label_smooth,
                         name="yolo_loss"+str(i))
                 self.losses.append(fluid.layers.reduce_mean(loss))
             else:
+                mask_anchors=[]
+                for m in anchor_mask:
+                    mask_anchors.append(cfg.anchors[2 * m])
+                    mask_anchors.append(cfg.anchors[2 * m + 1])
                 boxes, scores = fluid.layers.yolo_box(
                         x=out,
                         img_size=self.im_shape,
                         anchors=mask_anchors,
-                        class_num=class_num,
+                        class_num=cfg.class_num,
                         conf_thresh=cfg.valid_thresh,
                         downsample_ratio=self.downsample,
                         name="yolo_box"+str(i))