Provide a way to avoid duplicated config keys, e.g., `num_classes` (#2764)

* Clean up a bit * Add `__shared__` annotation for shared variables between modules * Dedup `num_classes` configurations * Document `__shared__` usage * Remove some unused variable * Improve docstring and add comments for `__shared__`

Provide a way to avoid duplicated config keys, e.g., `num_classes` (#2764)
* Clean up a bit * Add `__shared__` annotation for shared variables between modules * Dedup `num_classes` configurations * Document `__shared__` usage * Remove some unused variable * Improve docstring and add comments for `__shared__`
2953d108 · Yang Zhang · GitHub · a653012d · 2953d108 · 2953d108
33 changed file
--- a/configs/cascade_rcnn_r50_fpn_1x.yml
+++ b/configs/cascade_rcnn_r50_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 weights: output/cascade_rcnn_r50_fpn_1x/model_final
 metric: COCO
+num_classes: 81

 CascadeRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ CascadeBBoxAssigner:
  bg_thresh_hi: [0.5, 0.6, 0.7]
  fg_thresh: [0.5, 0.6, 0.7]
  fg_fraction: 0.25
-  num_classes: 81

 CascadeBBoxHead:
  head: FC6FC7Head
@@ -82,7 +82,6 @@ CascadeBBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 FC6FC7Head:
  num_chan: 1024

--- a/configs/faster_rcnn_r101_1x.yml
+++ b/configs/faster_rcnn_r101_1x.yml
@@ -10,6 +10,7 @@ snapshot_iter: 10000
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
 metric: COCO
 weights: output/faster_rcnn_r101_1x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -64,7 +65,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: ResNetC5
@@ -72,7 +72,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 LearningRate:
  base_lr: 0.01

--- a/configs/faster_rcnn_r101_fpn_1x.yml
+++ b/configs/faster_rcnn_r101_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
 weights: output/faster_rcnn_r101_fpn_1x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -73,7 +74,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -81,7 +81,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r101_fpn_2x.yml
+++ b/configs/faster_rcnn_r101_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
 weights: output/faster_rcnn_r101_fpn_2x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -73,7 +74,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -81,7 +81,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r101_vd_fpn_1x.yml
+++ b/configs/faster_rcnn_r101_vd_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
 weights: output/faster_rcnn_r101_vd_fpn_1x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -82,7 +82,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r101_vd_fpn_2x.yml
+++ b/configs/faster_rcnn_r101_vd_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
 weights: output/faster_rcnn_r101_vd_fpn_2x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -82,7 +82,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r50_1x.yml
+++ b/configs/faster_rcnn_r50_1x.yml
@@ -10,6 +10,7 @@ snapshot_iter: 10000
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/faster_rcnn_r50_1x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -64,7 +65,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: ResNetC5
@@ -72,7 +72,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 LearningRate:
  base_lr: 0.01

--- a/configs/faster_rcnn_r50_2x.yml
+++ b/configs/faster_rcnn_r50_2x.yml
@@ -10,6 +10,7 @@ snapshot_iter: 10000
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/faster_rcnn_r50_2x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -64,7 +65,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: ResNetC5
@@ -72,7 +72,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 LearningRate:
  base_lr: 0.01

--- a/configs/faster_rcnn_r50_fpn_1x.yml
+++ b/configs/faster_rcnn_r50_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/fpn/faster_rcnn_r50_fpn_1x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ BBoxAssigner:
  bg_thresh_hi: 0.5
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -82,7 +82,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r50_fpn_2x.yml
+++ b/configs/faster_rcnn_r50_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/faster_rcnn_r50_fpn_2x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ BBoxAssigner:
  bg_thresh_hi: 0.5
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -82,7 +82,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_r50_vd_1x.yml
+++ b/configs/faster_rcnn_r50_vd_1x.yml
@@ -10,6 +10,7 @@ snapshot_iter: 10000
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
 metric: COCO
 weights: output/faster_rcnn_r50_vd_1x/model_final
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -66,7 +67,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: ResNetC5
@@ -74,7 +74,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 LearningRate:
  base_lr: 0.01

--- a/configs/faster_rcnn_r50_vd_fpn_2x.yml
+++ b/configs/faster_rcnn_r50_vd_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
 weights: output/faster_rcnn_r50_vd_fpn_2x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNet
@@ -74,7 +75,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -82,7 +82,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_se154_vd_fpn_s1x.yml
+++ b/configs/faster_rcnn_se154_vd_fpn_s1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar
 weights: output/faster_rcnn_se154_vd_fpn_s1x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: SENet
@@ -76,7 +77,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -84,7 +84,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/faster_rcnn_x101_vd_64x4d_fpn_1x.yml
+++ b/configs/faster_rcnn_x101_vd_64x4d_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
 weights: output/faster_rcnn_x101_vd_64x4d_fpn_1x/model_final
 metric: COCO
+num_classes: 81

 FasterRCNN:
  backbone: ResNeXt
@@ -76,7 +77,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 BBoxHead:
  head: TwoFCHead
@@ -84,7 +84,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/mask_rcnn_r101_fpn_1x.yml
+++ b/configs/mask_rcnn_r101_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
 metric: COCO
 weights: output/mask_rcnn_r101_fpn_1x/model_final/
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -68,7 +69,6 @@ FPNRoIAlign:
 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  num_convs: 4
  resolution: 28

@@ -79,7 +79,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
  resolution: 28
@@ -90,7 +89,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/mask_rcnn_r50_1x.yml
+++ b/configs/mask_rcnn_r50_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/mask_rcnn_r50_1x/model_final
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -66,12 +67,10 @@ BBoxHead:
    nms_threshold: 0.5
    normalized: false
    score_threshold: 0.05
-  num_classes: 81

 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  resolution: 14

 BBoxAssigner:
@@ -81,10 +80,8 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
-  num_classes: 81
  resolution: 14

 LearningRate:

--- a/configs/mask_rcnn_r50_2x.yml
+++ b/configs/mask_rcnn_r50_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/mask_rcnn_r50_2x/model_final/
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -67,12 +68,10 @@ BBoxHead:
    nms_threshold: 0.5
    normalized: false
    score_threshold: 0.05
-  num_classes: 81

 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  resolution: 14

 BBoxAssigner:
@@ -82,10 +81,8 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
-  num_classes: 81
  resolution: 14

 LearningRate:

--- a/configs/mask_rcnn_r50_fpn_1x.yml
+++ b/configs/mask_rcnn_r50_fpn_1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 metric: COCO
 weights: output/mask_rcnn_r50_fpn_1x/model_final/
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -68,7 +69,6 @@ FPNRoIAlign:
 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  num_convs: 4
  resolution: 28

@@ -79,7 +79,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
  resolution: 28
@@ -90,7 +89,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/mask_rcnn_r50_fpn_2x.yml
+++ b/configs/mask_rcnn_r50_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 weights: output/mask_rcnn_r50_fpn_2x/model_final/
 metric: COCO
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -68,7 +69,6 @@ FPNRoIAlign:
 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  num_convs: 4
  resolution: 28

@@ -79,7 +79,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
  resolution: 28
@@ -90,7 +89,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/mask_rcnn_r50_vd_fpn_2x.yml
+++ b/configs/mask_rcnn_r50_vd_fpn_2x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
 metric: COCO
 weights: output/mask_rcnn_r50_vd_fpn_2x/model_final/
+num_classes: 81

 MaskRCNN:
  backbone: ResNet
@@ -69,7 +70,6 @@ FPNRoIAlign:
 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  num_convs: 4
  resolution: 28

@@ -80,7 +80,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
  resolution: 28
@@ -91,7 +90,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/configs/mask_rcnn_se154_vd_fpn_s1x.yml
+++ b/configs/mask_rcnn_se154_vd_fpn_s1x.yml
@@ -10,6 +10,7 @@ save_dir: output
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar
 weights: output/mask_rcnn_se154_vd_fpn_s1x/model_final/
 metric: COCO
+num_classes: 81

 MaskRCNN:
  backbone: SENet
@@ -71,7 +72,6 @@ FPNRoIAlign:
 MaskHead:
  dilation: 1
  num_chan_reduced: 256
-  num_classes: 81
  num_convs: 4
  resolution: 28

@@ -82,7 +82,6 @@ BBoxAssigner:
  bg_thresh_lo: 0.0
  fg_fraction: 0.25
  fg_thresh: 0.5
-  num_classes: 81

 MaskAssigner:
  resolution: 28
@@ -93,7 +92,6 @@ BBoxHead:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05
-  num_classes: 81

 TwoFCHead:
  num_chan: 1024

--- a/docs/CONFIG.md
+++ b/docs/CONFIG.md
@@ -188,3 +188,14 @@ A small utility (`tools/configure.py`) is included to simplify the configuration
    ```shell
    python tools/configure.py --minimal generate FasterRCNN BBoxHead
    ```
+
+
+# FAQ
+
+**Q:** There are some configuration options that are used by multiple modules (e.g., `num_classes`), how do I avoid duplication in config files?
+
+**A:** We provided a `__shared__` annotation for exactly this purpose, simply annotate like this `__shared__ = ['num_classes']`. It works as follows:
+
+1.  if `num_classes` is configured for a module in config file, it takes precedence.
+2.  if `num_classes` is not configured for a module but is present in the config file as a global key, its value will be used.
+3.  otherwise, the default value (`81`) will be used.
--- a/docs/CONFIG_cn.md
+++ b/docs/CONFIG_cn.md
@@ -180,3 +180,14 @@ pip install typeguard http://github.com/willthefrog/docstring_parser/tarball/mas
    ```shell
    python tools/configure.py --minimal generate FasterRCNN BBoxHead
    ```
+
+
+# FAQ
+
+**Q:** 某些配置项会在多个模块中用到(如 `num_classes`)，如何避免在配置文件中多次重复设置？
+
+**A:** 框架提供了 `__shared__` 标记来实现配置的共享，用户可以标记参数，如 `__shared__ = ['num_classes']` ，配置数值作用规则如下：
+
+1.  如果模块配置中提供了 `num_classes` ，会优先使用其数值。
+2.  如果模块配置中未提供 `num_classes` ，但配置文件中存在全局键值，那么会使用全局键值。
+3.  两者均为配置的情况下，将使用默认值(`81`)。
--- a/ppdet/core/config/schema.py
+++ b/ppdet/core/config/schema.py
@@ -43,13 +43,14 @@ except Exception:
        if not check_type.__warning_sent__:
            from ppdet.utils.cli import ColorTTY
            color_tty = ColorTTY()
-            message = "typeguard is not installed, type checking is not available"
+            message = "typeguard is not installed," \
+                + "type checking is not available"
            print(color_tty.yellow(message))
            check_type.__warning_sent__ = True

    check_type.__warning_sent__ = False

-__all__ = ['SchemaValue', 'SchemaDict', 'extract_schema']
+__all__ = ['SchemaValue', 'SchemaDict', 'SharedConfig', 'extract_schema']


 class SchemaValue(object):
@@ -160,6 +161,27 @@ class SchemaDict(dict):
                self.name, ", ".join(mismatch_keys)))


+class SharedConfig(object):
+    """
+    Representation class for `__shared__` annotations, which work as follows:
+
+    - if `key` is set for the module in config file, its value will take
+      precedence
+    - if `key` is not set for the module but present in the config file, its
+      value will be used
+    - otherwise, use the provided `default_value` as fallback
+
+    Args:
+        key: config[key] will be injected
+        default_value: fallback value
+    """
+
+    def __init__(self, key, default_value=None):
+        super(SharedConfig, self).__init__()
+        self.key = key
+        self.default_value = default_value
+
+
 def extract_schema(cls):
    """
    Extract schema from a given class
@@ -216,6 +238,7 @@ def extract_schema(cls):
    schema.strict = not has_kwargs
    schema.pymodule = importlib.import_module(cls.__module__)
    schema.inject = getattr(cls, '__inject__', [])
+    schema.shared = getattr(cls, '__shared__', [])
    for idx, name in enumerate(names):
        comment = name in comments and comments[name] or name
        if name in schema.inject:
@@ -223,8 +246,13 @@ def extract_schema(cls):
        else:
            type_ = name in annotations and annotations[name] or None
        value_schema = SchemaValue(name, comment, type_)
-        if idx >= num_required:
-            value_schema.set_default(defaults[idx - num_required])
+        if name in schema.shared:
+            assert idx >= num_required, "shared config must have default value"
+            default = defaults[idx - num_required]
+            value_schema.set_default(SharedConfig(name, default))
+        elif idx >= num_required:
+            default = defaults[idx - num_required]
+            value_schema.set_default(default)
        schema.set_schema(name, value_schema)

    return schema
--- a/ppdet/core/config/yaml_helpers.py
+++ b/ppdet/core/config/yaml_helpers.py
@@ -16,6 +16,7 @@ import importlib
 import inspect

 import yaml
+from .schema import SharedConfig

 __all__ = ['serializable', 'Callable']

@@ -59,7 +60,8 @@ def _make_python_representer(cls):

 def serializable(cls):
    """
-    Add loader and dumper for given class, which must be "trivially serializable"
+    Add loader and dumper for given class, which must be
+    "trivially serializable"

    Args:
        cls: class to be serialized
@@ -72,6 +74,10 @@ def serializable(cls):
    return cls


+yaml.add_representer(SharedConfig,
+                     lambda d, o: d.represent_data(o.default_value))
+
+
 @serializable
 class Callable(object):
    """

--- a/ppdet/core/workspace.py
+++ b/ppdet/core/workspace.py
@@ -23,7 +23,7 @@ import sys
 import yaml
 import copy

-from .config.schema import SchemaDict, extract_schema
+from .config.schema import SchemaDict, SharedConfig, extract_schema
 from .config.yaml_helpers import serializable

 __all__ = [
@@ -136,7 +136,8 @@ def create(cls_or_name, **kwargs):
    assert type(cls_or_name) in [type, str
                                 ], "should be a class or name of a class"
    name = type(cls_or_name) == str and cls_or_name or cls_or_name.__name__
-    assert name in global_config and isinstance(global_config[name], SchemaDict), \
+    assert name in global_config and \
+        isinstance(global_config[name], SchemaDict), \
        "the module {} is not registered".format(name)
    config = global_config[name]
    config.update(kwargs)
@@ -145,9 +146,26 @@ def create(cls_or_name, **kwargs):

    kwargs = {}
    kwargs.update(global_config[name])
+
+    # parse `shared` annoation of registered modules
+    if getattr(config, 'shared', None):
+        for k in config.shared:
+            target_key = config[k]
+            shared_conf = config.schema[k].default
+            assert isinstance(shared_conf, SharedConfig)
+            if target_key is not None and not isinstance(
+                    target_key, SharedConfig):
+                continue   # value is given for the module
+            elif shared_conf.key in global_config:
+                # `key` is present in config
+                kwargs[k] = global_config[shared_conf.key]
+            else:
+                kwargs[k] = shared_conf.default_value
+
+    # parse `inject` annoation of registered modules
    if getattr(config, 'inject', None):
        for k in config.inject:
-            target_key = global_config[name][k]
+            target_key = config[k]
            # optional dependency
            if target_key is None:
                continue

--- a/ppdet/data/data_feed.py
+++ b/ppdet/data/data_feed.py
@@ -181,7 +181,6 @@ class DataSet(object):
    Args:
        annotation (str): annotation file path
        image_dir (str): directory where image files are stored
-        num_classes (int): number of classes
        shuffle (bool): shuffle samples
    """
    __source__ = 'RoiDbSource'

--- a/ppdet/modeling/anchor_heads/retina_head.py
+++ b/ppdet/modeling/anchor_heads/retina_head.py
@@ -25,7 +25,7 @@ from paddle.fluid.regularizer import L2Decay
 from ppdet.modeling.ops import (AnchorGenerator, RetinaTargetAssign,
                                RetinaOutputDecoder)

-from ppdet.core.workspace import register, serializable
+from ppdet.core.workspace import register

 __all__ = ['RetinaHead']

@@ -52,6 +52,7 @@ class RetinaHead(object):
        sigma (float): The parameter in smooth l1 loss
    """
    __inject__ = ['anchor_generator', 'target_assign', 'output_decoder']
+    __shared__ = ['num_classes']

    def __init__(self,
                 anchor_generator=AnchorGenerator().__dict__,
@@ -333,7 +334,6 @@ class RetinaHead(object):
        cls_pred_reshape_list = output['cls_pred']
        bbox_pred_reshape_list = output['bbox_pred']
        anchor_reshape_list = output['anchor']
-        anchor_var_reshape_list = output['anchor_var']
        for i in range(self.max_level - self.min_level + 1):
            cls_pred_reshape_list[i] = fluid.layers.sigmoid(
                cls_pred_reshape_list[i])

--- a/ppdet/modeling/ops.py
+++ b/ppdet/modeling/ops.py
@@ -88,6 +88,7 @@ class GenerateProposals(object):
 class MaskAssigner(object):
    __op__ = fluid.layers.generate_mask_labels
    __append_doc__ = True
+    __shared__ = ['num_classes']

    def __init__(self, num_classes=81, resolution=14):
        super(MaskAssigner, self).__init__()
@@ -123,6 +124,7 @@ class MultiClassNMS(object):
 class BBoxAssigner(object):
    __op__ = fluid.layers.generate_proposal_labels
    __append_doc__ = True
+    __shared__ = ['num_classes']

    def __init__(self,
                 batch_size_per_im=512,

--- a/ppdet/modeling/roi_heads/bbox_head.py
+++ b/ppdet/modeling/roi_heads/bbox_head.py
@@ -92,12 +92,13 @@ class BBoxHead(object):
    RCNN bbox head

    Args:
-        head (object): the head module instance, e.g., `ResNetC5` or `TwoFCHead`
+        head (object): the head module instance, e.g., `ResNetC5`, `TwoFCHead`
        box_coder (object): `BoxCoder` instance
        nms (object): `MultiClassNMS` instance
        num_classes: number of output classes
    """
    __inject__ = ['head', 'box_coder', 'nms']
+    __shared__ = ['num_classes']

    def __init__(self,
                 head,

--- a/ppdet/modeling/roi_heads/cascade_head.py
+++ b/ppdet/modeling/roi_heads/cascade_head.py
@@ -37,6 +37,7 @@ class CascadeBBoxHead(object):
        num_classes: number of output classes
    """
    __inject__ = ['head', 'nms']
+    __shared__ = ['num_classes']

    def __init__(self, head, nms=MultiClassNMS().__dict__, num_classes=81):
        super(CascadeBBoxHead, self).__init__()

--- a/ppdet/modeling/roi_heads/mask_head.py
+++ b/ppdet/modeling/roi_heads/mask_head.py
@@ -38,6 +38,8 @@ class MaskHead(object):
        num_classes (int): number of output classes
    """

+    __shared__ = ['num_classes']
+
    def __init__(self,
                 num_convs=0,
                 num_chan_reduced=256,

--- a/ppdet/modeling/target_assigners.py
+++ b/ppdet/modeling/target_assigners.py
@@ -26,6 +26,8 @@ __all__ = ['BBoxAssigner', 'MaskAssigner', 'CascadeBBoxAssigner']

 @register
 class CascadeBBoxAssigner(object):
+    __shared__ = ['num_classes']
+
    def __init__(self,
                 batch_size_per_im=512,
                 fg_fraction=.25,