add r18_fpn pretrained weights for rcnn

docs/appendix/model_zoo.md

@@ -36,6 +36,7 @@
 
 | 模型    | 模型大小    | 预测时间(毫秒) | BoxAP（%） |
 |:-------|:-----------|:-------------|:----------|
+|[FasterRCNN-ResNet18-FPN](https://bj.bcebos.com/paddlex/pretrained_weights/faster_rcnn_r18_fpn_1x.tar) | 173.2M | - | 32.6 |
 |[FasterRCNN-ResNet50](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar)|136.0MB| 197.715 | 35.2 |
 |[FasterRCNN-ResNet50_vd](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar)| 136.1MB | 475.700 | 36.4 |
 |[FasterRCNN-ResNet101](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar)| 212.5MB | 582.911 | 38.3 |
@@ -55,6 +56,7 @@
 
 | 模型    | 模型大小    | 预测时间(毫秒) | BoxAP (%) | MaskAP (%)  |
 |:-------|:-----------|:-------------|:----------|:----------|
+|[MaskRCNN-ResNet18-FPN](https://bj.bcebos.com/paddlex/pretrained_weights/mask_rcnn_r18_fpn_1x.tar) | 189.1MB | - | 33.6 | 30.5 |
 |[MaskRCNN-ResNet50](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) | 143.9MB | 87 | 38.2  | 33.4 |
 |[MaskRCNN-ResNet50-FPN](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar)| 177.7MB | 63.9 | 38.7 | 34.7 |
 |[MaskRCNN-ResNet50_vd-FPN](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | 177.7MB | 63.1 | 39.8 || 35.4 |

docs/train/instance_segmentation.md

@@ -10,9 +10,9 @@ PaddleX目前提供了MaskRCNN实例分割模型结构,多种backbone模型，
 
 | 模型(点击获取代码)               | Box MMAP/Seg MMAP | 模型大小 | GPU预测速度 | Arm预测速度 | 备注 |
 | :----------------  | :------- | :------- | :---------  | :---------  | :-----    |
-| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py)   |  38.7%/34.7%   |   170.0MB    |  160.185ms       |   -    | 模型精度高，适用于服务端部署   |
-| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py)   |  -/-   |   120.0MB    |  -       |   -    | 模型精度高，适用于服务端部署   |
-| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py)   |  38.7%/34.7%   |   116.MB    |  -       |   -    | 模型精度高，预测速度快，适用于服务端部署   |
+| [MaskRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r50_fpn.py)   |  38.7%/34.7%   |   177.7MB    |  160.185ms       |   -    | 模型精度高，适用于服务端部署   |
+| [MaskRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_r18_fpn.py)   |  33.6/30.5   |   189.1MB    |  -       |   -    | 模型精度高，适用于服务端部署   |
+| [MaskRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/instance_segmentation/mask_rcnn_hrnet_fpn.py)   |  38.7%/34.7%   |   120.7MB    |  -       |   -    | 模型精度高，预测速度快，适用于服务端部署   |
 
 
 ## 开始训练

docs/train/object_detection.md

@@ -13,8 +13,8 @@ PaddleX目前提供了FasterRCNN和YOLOv3两种检测结构,多种backbone模型
 | [YOLOv3-MobileNetV1](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv1.py) |  29.3%  |  99.2MB  |  15.442ms   | -  |  模型小，预测速度快，适用于低性能或移动端设备   |
 | [YOLOv3-MobileNetV3](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_mobilenetv3.py)        | 31.6%  | 100.7MB   |  143.322ms  | -  |  模型小，移动端上预测速度有优势   |
 | [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/yolov3_darknet53.py)     | 38.9  | 249.2MB   | 42.672ms   | -  |  模型较大，预测速度快，适用于服务端   |
-| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r50_fpn.py)   |  37.2%   |   136.0MB    |  197.715ms       |   -    | 模型精度高，适用于服务端部署   |
-| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r18_fpn.py)   |  -   |   -    |  -       |   -    | 模型精度高，适用于服务端部署   |
+| [FasterRCNN-ResNet50-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r50_fpn.py)   |  37.2%   |   167.7MB    |  197.715ms       |   -    | 模型精度高，适用于服务端部署   |
+| [FasterRCNN-ResNet18-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_r18_fpn.py)   |  32.6%   |   173.2MB    |  -       |   -    | 模型精度高，适用于服务端部署   |
 | [FasterRCNN-HRNet-FPN](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/train/object_detection/faster_rcnn_hrnet_fpn.py)   |  36.0%   |   115.MB    |  81.592ms       |   -    | 模型精度高，预测速度快，适用于服务端部署   |
 
 

paddlex/cv/models/utils/pretrain_weights.py

@@ -88,6 +88,8 @@ coco_pretrain = {
     'https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar',
     'YOLOv3_ResNet50_vd_COCO':
     'https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn.tar',
+    'FasterRCNN_ResNet18_COCO':
+    'https://bj.bcebos.com/paddlex/pretrained_weights/faster_rcnn_r18_fpn_1x.tar',
     'FasterRCNN_ResNet50_COCO':
     'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar',
     'FasterRCNN_ResNet50_vd_COCO':
@@ -98,6 +100,8 @@ coco_pretrain = {
     'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar',
     'FasterRCNN_HRNet_W18_COCO':
     'https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar',
+    'MaskRCNN_ResNet18_COCO':
+    'https://bj.bcebos.com/paddlex/pretrained_weights/mask_rcnn_r18_fpn_1x.tar',
     'MaskRCNN_ResNet50_COCO':
     'https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_2x.tar',
     'MaskRCNN_ResNet50_vd_COCO':
@@ -136,9 +140,10 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
         return flag
     warning_info = "{} does not support to be finetuned with weights pretrained on the {} dataset, so pretrain_weights is forced to be set to {}"
     if flag == 'COCO':
-        if class_name == "FasterRCNN" and backbone in ['ResNet18'] or \
-            class_name == "MaskRCNN" and backbone in ['ResNet18'] or \
-            class_name == 'DeepLabv3p' and backbone in ['Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5', 'MobileNetV2_x1.5', 'MobileNetV2_x2.0']:
+        if class_name == 'DeepLabv3p' and backbone in [
+                'Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5',
+                'MobileNetV2_x1.5', 'MobileNetV2_x2.0'
+        ]:
             model_name = '{}_{}'.format(class_name, backbone)
             logging.warning(warning_info.format(model_name, flag, 'IMAGENET'))
             flag = 'IMAGENET'