Update Docs

README.md

@@ -126,7 +126,7 @@ PaddleSeg在AI Studio平台上提供了在线体验的教程，欢迎体验：
 * 2019.09.10
 
   **`v0.1.0`**
-  * PaddleSeg分割库初始版本发布，包含DeepLabv3+, U-Net, ICNet三类分割模型, 其中DeepLabv3+支持Xception, MobileNet两种可调节的骨干网络。
+  * PaddleSeg分割库初始版本发布，包含DeepLabv3+, U-Net, ICNet三类分割模型, 其中DeepLabv3+支持Xception, MobileNet v2两种可调节的骨干网络。
   * CVPR19 LIP人体部件分割比赛冠军预测模型发布[ACE2P](./contrib/ACE2P)
   * 预置基于DeepLabv3+网络的[人像分割](./contrib/HumanSeg/)和[车道线分割](./contrib/RoadLine)预测模型发布
 

configs/deepglobe_road_extraction.yaml

 EVAL_CROP_SIZE: (1025, 1025) # (width, height), for unpadding rangescaling and stepscaling
 TRAIN_CROP_SIZE: (769, 769) # (width, height), for unpadding rangescaling and stepscaling
-AUG: 
+AUG:
     AUG_METHOD: u"stepscaling" # choice unpadding rangescaling and stepscaling
     FIX_RESIZE_SIZE: (640, 640) # (width, height), for unpadding
     INF_RESIZE_VALUE: 500  # for rangescaling
@@ -9,8 +9,8 @@ AUG:
     MAX_SCALE_FACTOR: 2.0  # for stepscaling
     MIN_SCALE_FACTOR: 0.5  # for stepscaling
     SCALE_STEP_SIZE: 0.25  # for stepscaling
-BATCH_SIZE: 8 
-DATASET: 
+BATCH_SIZE: 8
+DATASET:
     DATA_DIR: "./dataset/MiniDeepGlobeRoadExtraction/"
     IMAGE_TYPE: "rgb"  # choice rgb or rgba
     NUM_CLASSES: 2
@@ -19,19 +19,19 @@ DATASET:
     VAL_FILE_LIST: "dataset/MiniDeepGlobeRoadExtraction/val.txt"
     IGNORE_INDEX: 255
     SEPARATOR: '|'
-FREEZE: 
+FREEZE:
     MODEL_FILENAME: "model"
     PARAMS_FILENAME: "params"
     SAVE_DIR: "freeze_model"
-MODEL: 
+MODEL:
     DEFAULT_NORM_TYPE: "bn"
     MODEL_NAME: "deeplabv3p"
     DEEPLAB:
-        BACKBONE: "mobilenet"
+        BACKBONE: "mobilenetv2"
         DEPTH_MULTIPLIER: 1.0
         ENCODER_WITH_ASPP: False
         ENABLE_DECODER: False
-TEST: 
+TEST:
     TEST_MODEL: "./saved_model/deeplabv3p_mobilenetv2-1-0_bn_deepglobe_road_extraction/final"
 TRAIN:
     MODEL_SAVE_DIR: "./saved_model/deeplabv3p_mobilenetv2-1-0_bn_deepglobe_road_extraction/"

configs/deeplabv3p_mobilenet-1-0_pet.yaml

@@ -30,7 +30,7 @@ MODEL:
     MODEL_NAME: "deeplabv3p"
     DEFAULT_NORM_TYPE: "bn"
     DEEPLAB:
-        BACKBONE: "mobilenet"
+        BACKBONE: "mobilenetv2"
         DEPTH_MULTIPLIER: 1.0
         ENCODER_WITH_ASPP: False
         ENABLE_DECODER: False

pdseg/utils/config.py

@@ -68,8 +68,8 @@ cfg.DATASET.DATA_DIM = 3
 cfg.DATASET.SEPARATOR = ' '
 # 忽略的像素标签值, 默认为255，一般无需改动
 cfg.DATASET.IGNORE_INDEX = 255
-# 数据增强是图像的padding值 
-cfg.DATASET.PADDING_VALUE = [127.5,127.5,127.5]
+# 数据增强是图像的padding值
+cfg.DATASET.PADDING_VALUE = [127.5, 127.5, 127.5]
 
 ########################### 数据增强配置 ######################################
 # 图像镜像左右翻转
@@ -182,11 +182,11 @@ cfg.MODEL.SCALE_LOSS = "DYNAMIC"
 cfg.MODEL.DEEPLAB.BACKBONE = "xception_65"
 # DeepLab output stride
 cfg.MODEL.DEEPLAB.OUTPUT_STRIDE = 16
-# MobileNet backbone scale 设置
+# MobileNet v2 backbone scale 设置
 cfg.MODEL.DEEPLAB.DEPTH_MULTIPLIER = 1.0
-# MobileNet backbone scale 设置
+# MobileNet v2 backbone scale 设置
 cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP = True
-# MobileNet backbone scale 设置
+# MobileNet v2 backbone scale 设置
 cfg.MODEL.DEEPLAB.ENABLE_DECODER = True
 # ASPP是否使用可分离卷积
 cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV = True

turtorial/finetune_deeplabv3plus.md

@@ -115,15 +115,15 @@ python pdseg/eval.py --use_gpu --cfg ./configs/deeplabv3p_xception65_pet.yaml
 
 |预训练模型名称|BackBone|Norm Type|数据集|配置|
 |-|-|-|-|-|
-|mobilenetv2-2-0_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 2.0 <br> MODEL.DEFAULT_NORM_TYPE: bn|
-|mobilenetv2-1-5_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.5 <br> MODEL.DEFAULT_NORM_TYPE: bn|
-|mobilenetv2-1-0_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEFAULT_NORM_TYPE: bn|
-|mobilenetv2-0-5_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 0.5 <br> MODEL.DEFAULT_NORM_TYPE: bn|
-|mobilenetv2-0-25_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 0.25 <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|mobilenetv2-2-0_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 2.0 <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|mobilenetv2-1-5_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.5 <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|mobilenetv2-1-0_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|mobilenetv2-0-5_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 0.5 <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|mobilenetv2-0-25_bn_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 0.25 <br> MODEL.DEFAULT_NORM_TYPE: bn|
 |xception41_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: xception_41 <br> MODEL.DEFAULT_NORM_TYPE: bn|
 |xception65_imagenet|-|bn|ImageNet|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: xception_65 <br> MODEL.DEFAULT_NORM_TYPE: bn|
-|deeplabv3p_mobilenetv2-1-0_bn_coco|MobileNet V2|bn|COCO|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEEPLAB.ENCODER_WITH_ASPP: False <br> MODEL.DEEPLAB.ENABLE_DECODER: False <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|deeplabv3p_mobilenetv2-1-0_bn_coco|MobileNet V2|bn|COCO|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEEPLAB.ENCODER_WITH_ASPP: False <br> MODEL.DEEPLAB.ENABLE_DECODER: False <br> MODEL.DEFAULT_NORM_TYPE: bn|
 |**deeplabv3p_xception65_bn_coco**|Xception|bn|COCO|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: xception_65 <br> MODEL.DEFAULT_NORM_TYPE: bn |
-|deeplabv3p_mobilenetv2-1-0_bn_cityscapes|MobileNet V2|bn|Cityscapes|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenet <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEEPLAB.ENCODER_WITH_ASPP: False <br> MODEL.DEEPLAB.ENABLE_DECODER: False <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|deeplabv3p_mobilenetv2-1-0_bn_cityscapes|MobileNet V2|bn|Cityscapes|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: mobilenetv2 <br> MODEL.DEEPLAB.DEPTH_MULTIPLIER: 1.0 <br> MODEL.DEEPLAB.ENCODER_WITH_ASPP: False <br> MODEL.DEEPLAB.ENABLE_DECODER: False <br> MODEL.DEFAULT_NORM_TYPE: bn|
 |deeplabv3p_xception65_gn_cityscapes|Xception|gn|Cityscapes|MODEL.MODEL_NAME: deeplabv3p <br>  MODEL.DEEPLAB.BACKBONE: xception_65 <br> MODEL.DEFAULT_NORM_TYPE: gn|
 |deeplabv3p_xception65_bn_cityscapes|Xception|bn|Cityscapes|MODEL.MODEL_NAME: deeplabv3p <br> MODEL.DEEPLAB.BACKBONE: xception_65 <br> MODEL.DEFAULT_NORM_TYPE: bn|