add group norm doc (#3221)

* add gn doc

PaddleCV/PaddleDetection/docs/DATA.md

@@ -100,7 +100,7 @@ like dataset, run this command:
 # --annotation: the path of file, which contains the name of annotation files
 # --save-dir: the save path
 # --samples: the number of samples (default is -1, which mean all datas in dataset)
-python ./tools/generate_data_for_training.py
+python ./ppdet/data/tools/generate_data_for_training.py
             --type=json \
             --annotation=./annotations/instances_val2017.json \
             --save-dir=./roidb \
@@ -181,7 +181,7 @@ whole data pipeline is fully customizable through the yaml configuration files.
 ```sh
  # a small utility (`tools/labelme2coco.py`) is provided to convert
  # Labelme-annotated dataset to COCO format.
- python ./tools/labelme2coco.py --json_input_dir ./labelme_annos/
+ python ./ppdet/data/tools/labelme2coco.py --json_input_dir ./labelme_annos/
                                 --image_input_dir ./labelme_imgs/
                                 --output_dir ./cocome/
                                 --train_proportion 0.8

PaddleCV/PaddleDetection/docs/DATA_cn.md

@@ -90,7 +90,7 @@
 # --annotation: 一个包含所需标注文件名的文件的路径
 # --save-dir: 保存路径
 # --samples: sample的个数（默认是-1，代表使用所有sample）
-python ./tools/generate_data_for_training.py
+python ./ppdet/data/tools/generate_data_for_training.py
             --type=json \
             --annotation=./annotations/instances_val2017.json \
             --save-dir=./roidb \
@@ -168,7 +168,7 @@ coco = Reader(ccfg.DATA, ccfg.TRANSFORM, maxiter=-1)
 - 选择1：将数据集转换为VOC格式或者COCO格式。
 ```
  # 在./tools/中提供了labelme2coco.py用于将labelme标注的数据集转换为COCO数据集
- python ./tools/labelme2coco.py --json_input_dir ./labelme_annos/
+ python ./ppdet/data/tools/labelme2coco.py --json_input_dir ./labelme_annos/
                                 --image_input_dir ./labelme_imgs/
                                 --output_dir ./cocome/
                                 --train_proportion 0.8

PaddleCV/PaddleDetection/docs/MODEL_ZOO.md

@@ -80,6 +80,16 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 - `c3-c5` means adding `dcn` in resnet stage 3 to 5.
 - Detailed configuration file in [configs/dcn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/dcn)
 
+### Group Normalization
+| Backbone             | Type           | Image/gpu | Lr schd | Box AP | Mask AP |                           Download                           |
+| :------------------- | :------------- | :-----: | :-----: | :----: | :-----: | :----------------------------------------------------------: |
+| ResNet50-FPN         | Faster         |    2    |   2x    |  39.7  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_gn_2x.tar) |
+| ResNet50-FPN         | Mask           |    1    |   2x    |  40.1  |   35.8  | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_gn_2x.tar) |
+
+#### Notes:
+- Group Normalization reference from [Group Normalization](https://arxiv.org/abs/1803.08494).
+- Detailed configuration file in [configs/gn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn)
+
 ### Yolo v3
 
 | Backbone     | Size | Image/gpu | Lr schd | Box AP | Download  |

PaddleCV/PaddleDetection/docs/MODEL_ZOO_cn.md

@@ -80,6 +80,16 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
 - `c3-c5`意思是在resnet模块的3到5阶段增加`dcn`.
 - 详细的配置文件在[configs/dcn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/dcn)
 
+### Group Normalization
+| 骨架网络             | 网络类型           | 每张GPU图片个数 | 学习率策略 | Box AP | Mask AP |                           下载                           |
+| :------------------- | :------------- |:--------: | :-----: | :----: | :-----: | :----------------------------------------------------------: |
+| ResNet50-FPN         | Faster         |    2      |   2x    |  39.7  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_gn_2x.tar) |
+| ResNet50-FPN         | Mask           |    1      |   2x    |  40.1  |   35.8  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_gn_2x.tar) |
+
+#### 注意事项:
+- Group Normalization参考论文[Group Normalization](https://arxiv.org/abs/1803.08494).
+- 详细的配置文件在[configs/gn](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/configs/gn)
+
 ### Yolo v3
 
 | 骨架网络     | 输入尺寸 | 每张GPU图片个数 | 学习率策略 | Box AP | 下载 |

PaddleCV/PaddleDetection/ppdet/utils/download.py

@@ -16,7 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-
 import os
 import os.path as osp
 import shutil
@@ -40,20 +39,26 @@ DATASET_HOME = osp.expanduser("~/.cache/paddle/dataset")
 # download info: (url, md5sum)
 DATASETS = {
     'coco': ([
-        ('http://images.cocodataset.org/zips/train2017.zip',
-         'cced6f7f71b7629ddf16f17bbcfab6b2', ),
-        ('http://images.cocodataset.org/zips/val2017.zip',
-         '442b8da7639aecaf257c1dceb8ba8c80', ),
-        ('http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
-         'f4bbac642086de4f52a3fdda2de5fa2c', ),
+        (
+            'http://images.cocodataset.org/zips/train2017.zip',
+            'cced6f7f71b7629ddf16f17bbcfab6b2', ),
+        (
+            'http://images.cocodataset.org/zips/val2017.zip',
+            '442b8da7639aecaf257c1dceb8ba8c80', ),
+        (
+            'http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
+            'f4bbac642086de4f52a3fdda2de5fa2c', ),
     ], ["annotations", "train2017", "val2017"]),
     'voc': ([
-        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
-         '6cd6e144f989b92b3379bac3b3de84fd', ),
-        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
-         'c52e279531787c972589f7e41ab4ae64', ),
-        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
-         'b6e924de25625d8de591ea690078ad9f', ),
+        (
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
+            '6cd6e144f989b92b3379bac3b3de84fd', ),
+        (
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
+            'c52e279531787c972589f7e41ab4ae64', ),
+        (
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
+            'b6e924de25625d8de591ea690078ad9f', ),
     ], ["VOCdevkit/VOC_all"]),
 }
 
@@ -112,8 +117,7 @@ def get_dataset_path(path, annotation, image_dir):
                 # dataset, VOC default label list should be used, 
                 # do not generate label_list.txt here. For default
                 # label, see ../data/source/voc_loader.py
-                merge_and_create_list(devkit_dir, years, 
-                                      output_tmp_dir)
+                merge_and_create_list(devkit_dir, years, output_tmp_dir)
                 shutil.move(output_tmp_dir, output_dir)
                 # remove source directory VOC2007 and VOC2012
                 shutil.rmtree(osp.join(devkit_dir, "VOC2007"))
@@ -121,7 +125,7 @@ def get_dataset_path(path, annotation, image_dir):
             return data_dir
 
     # not match any dataset in DATASETS
-    raise ValueError("{} not exists and unknow dataset type".format(path))
+    raise ValueError("{} not exists or unknow dataset type".format(path))
 
 
 def get_path(url, root_dir, md5sum=None):
@@ -174,13 +178,13 @@ def _dataset_exists(path, annotation, image_dir):
         annotation_path = osp.join(path, annotation)
         if not osp.isfile(annotation_path):
             logger.info("Config annotation {} is not a "
-                    "file".format(annotation_path))
+                        "file".format(annotation_path))
             return False
     if image_dir:
         image_path = osp.join(path, image_dir)
         if not osp.isdir(image_path):
             logger.info("Config image_dir {} is not a "
-                    "directory".format(image_path))
+                        "directory".format(image_path))
             return False
     return True