Fix `norm_decay` in config files (#2539)

* Fix `norm_decay` in config files

should be float value now

* Clean up a bit

mostly max line length related

PaddleCV/object_detection/configs/faster_rcnn_r50_fpn_1x.yml

@@ -21,7 +21,7 @@ FasterRCNN:
 
 ResNet:
   norm_type: affine_channel
-  norm_decay: true
+  norm_decay: 0.
   depth: 50
   feature_maps: [2, 3, 4, 5]
   freeze_at: 2

PaddleCV/object_detection/configs/faster_rcnn_r50_fpn_2x.yml

@@ -21,7 +21,7 @@ FasterRCNN:
 
 ResNet:
   norm_type: affine_channel
-  norm_decay: true
+  norm_decay: 0.
   depth: 50
   feature_maps: [2, 3, 4, 5]
   freeze_at: 2

PaddleCV/object_detection/configs/mask_rcnn_r50_2x.yml

@@ -23,7 +23,7 @@ MaskRCNN:
 
 ResNet:
   norm_type: affine_channel
-  norm_decay: true
+  norm_decay: 0.
   depth: 50
   feature_maps: 4
   freeze_at: 2

PaddleCV/object_detection/ppdet/modeling/architectures/mask_rcnn.py

@@ -64,13 +64,16 @@ class MaskRCNN(object):
 
     def build(self, feed_vars, mode='train'):
         im = feed_vars['image']
-        assert mode in ['train', 'test'], "only support 'train' and 'test' mode"
+        assert mode in ['train', 'test'], \
+            "only 'train' and 'test' mode is supported"
         if mode == 'train':
-            required_fields = ['gt_label', 'gt_box', 'gt_mask', 'is_crowd', 'im_info']
+            required_fields = ['gt_label', 'gt_box', 'gt_mask',
+                               'is_crowd', 'im_info']
         else:
             required_fields = ['im_shape', 'im_info']
         for var in required_fields:
-            assert var in feed_vars, "{} has no {} field".format(feed_vars, var)
+            assert var in feed_vars, \
+                "{} has no {} field".format(feed_vars, var)
         im_info = feed_vars['im_info']
 
         body_feats = self.backbone(im)
@@ -144,7 +147,8 @@ class MaskRCNN(object):
                 with switch.case(cond):
                     fluid.layers.assign(input=bbox_pred, output=mask_pred)
                 with switch.default():
-                    bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6])
+                    bbox = fluid.layers.slice(bbox_pred, [1],
+                                              starts=[2], ends=[6])
 
                     im_scale = fluid.layers.slice(
                         im_info, [1], starts=[2], ends=[3])
@@ -155,8 +159,8 @@ class MaskRCNN(object):
                         mask_feat = self.roi_extractor(last_feat, mask_rois)
                         mask_feat = self.bbox_head.get_head_feat(mask_feat)
                     else:
-                        mask_feat = self.roi_extractor(body_feats, mask_rois,
-                                                       spatial_scale, is_mask=True)
+                        mask_feat = self.roi_extractor(
+                            body_feats, mask_rois, spatial_scale, is_mask=True)
 
                     mask_out = self.mask_head.get_prediction(mask_feat, bbox)
                     fluid.layers.assign(input=mask_out, output=mask_pred)

PaddleCV/object_detection/ppdet/modeling/backbones/resnet.py

@@ -39,11 +39,11 @@ class ResNet(object):
     Args:
         depth (int): ResNet depth, should be 18, 34, 50, 101, 152.
         freeze_at (int): freeze the backbone at which stage
-        norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
+        norm_type (str): normalization type, 'bn'/'sync_bn'/'affine_channel'
         freeze_norm (bool): freeze normalization layers
         norm_decay (float): weight decay for normalization layer weights
         variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
-        feature_maps (list): index of the stages whose feature maps are returned
+        feature_maps (list): index of stages whose feature maps are returned
     """
 
     def __init__(self,
@@ -330,6 +330,7 @@ class ResNetC5(ResNet):
                  norm_decay=0.,
                  variant='b',
                  feature_maps=[5]):
-        super(ResNetC5, self).__init__(depth, freeze_at, norm_type, freeze_norm,
-                                       norm_decay, variant, feature_maps)
+        super(ResNetC5, self).__init__(
+            depth, freeze_at, norm_type, freeze_norm, norm_decay,
+            variant, feature_maps)
         self.severed_head = True