Fix doc for windows uers (#2727)

PaddleCV/human_pose_estimation/README.md

 # Simple Baselines for Human Pose Estimation in Fluid
 
 ## Introduction
-This is a simple demonstration of re-implementation in [PaddlePaddle.Fluid](http://www.paddlepaddle.org/en) for the paper [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/abs/1804.06208) (ECCV'18) from MSRA. 
+This is a simple demonstration of re-implementation in [PaddlePaddle.Fluid](http://www.paddlepaddle.org/en) for the paper [Simple Baselines for Human Pose Estimation and Tracking](https://arxiv.org/abs/1804.06208) (ECCV'18) from MSRA.
 
 ![demo](demo.gif)
 
@@ -10,7 +10,7 @@ This is a simple demonstration of re-implementation in [PaddlePaddle.Fluid](http
 ## Requirements
 
   - Python == 2.7 or 3.6
-  - PaddlePaddle >= 1.1.0 
+  - PaddlePaddle >= 1.1.0
   - opencv-python >= 3.3
 
 ### Notes:
@@ -23,7 +23,7 @@ The code is developed and tested under 4 Tesla K40/P40 GPUS cards on CentOS with
 ## Results on MPII Val
 | Arch | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean | Mean@0.1| Models |
 | ---- |:----:|:--------:|:-----:|:-----:|:---:|:----:|:-----:|:----:|:-------:|:------:|
-| 256x256\_pose\_resnet\_50 in PyTorch | 96.351	| 95.329 | 88.989 | 83.176 | 88.420	| 83.960 | 79.594 | 88.532 | 33.911 | - |
+| 256x256\_pose\_resnet\_50 in PyTorch | 96.351    | 95.329 | 88.989 | 83.176 | 88.420    | 83.960 | 79.594 | 88.532 | 33.911 | - |
 | 256x256\_pose\_resnet\_50 in Fluid   | 96.385 | 95.363 | 89.211 | 84.084 | 88.454 | 84.182 | 79.546 | 88.748 | 33.750 | [`link`](https://paddlemodels.bj.bcebos.com/pose/pose-resnet50-mpii-256x256.tar.gz) |
 | 384x384\_pose\_resnet\_50 in PyTorch | 96.658 | 95.754 | 89.790 | 84.614 | 88.523 | 84.666 | 79.287 | 89.066 | 38.046 | - |
 | 384x384\_pose\_resnet\_50 in Fluid   | 96.862 | 95.635 | 90.046 | 85.557 | 88.818 | 84.948 | 78.484 | 89.235 | 38.093 | [`link`](https://paddlemodels.bj.bcebos.com/pose/pose-resnet50-mpii-384x384.tar.gz) |
@@ -88,13 +88,13 @@ python2 setup.py install --user
 Downloading the checkpoints of Pose-ResNet-50 trained on MPII dataset from [here](https://paddlemodels.bj.bcebos.com/pose/pose-resnet50-mpii-384x384.tar.gz). Extract it into the folder `checkpoints` under the directory root of this repo. Then run
 
 ```bash
-python val.py --dataset 'mpii' --checkpoint 'checkpoints/pose-resnet50-mpii-384x384' --data_root 'data/mpii'
+python val.py --dataset mpii --checkpoint checkpoints/pose-resnet50-mpii-384x384 --data_root data/mpii
 ```
 
 ### Perform Training
 
 ```bash
-python train.py --dataset 'mpii'
+python train.py --dataset mpii
 ```
 
 **Note**: Configurations for training are aggregated in the `lib/mpii_reader.py` and `lib/coco_reader.py`.
@@ -106,7 +106,7 @@ We also support to apply pre-trained models on customized images.
 Put the images into the folder `test` under the directory root of this repo. Then run
 
 ```bash
-python test.py --checkpoint 'checkpoints/pose-resnet-50-384x384-mpii'
+python test.py --checkpoint checkpoints/pose-resnet-50-384x384-mpii
 ```
 
 If there are multiple persons in images, detectors such as [Faster R-CNN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/rcnn), [SSD](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/object_detection) or others should be used first to crop them out. Because the simple baseline for human pose estimation is a top-down method.

PaddleCV/ssd/README.md

@@ -47,7 +47,7 @@ Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https
 
 `train.py` is the main caller of the training module. Examples of usage are shown below.
   ```bash
-  python -u train.py --batch_size=64 --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
+  python -u train.py --batch_size=64 --dataset=pascalvoc --pretrained_model=pretrained/ssd_mobilenet_v1_coco/
   ```
    - Set ```export CUDA_VISIBLE_DEVICES=0,1``` to specifiy the number of GPU you want to use.
    - For more help on arguments:
@@ -70,13 +70,13 @@ You can evaluate your trained model in different metrics like 11point, integral
 
 `eval.py` is the main caller of the evaluating module. Examples of usage are shown below.
 ```bash
-python eval.py --dataset='pascalvoc' --model_dir='train_pascal_model/best_model' --data_dir='data/pascalvoc' --test_list='test.txt' --ap_version='11point' --nms_threshold=0.45
+python eval.py --dataset=pascalvoc --model_dir=model/best_model --data_dir=data/pascalvoc --test_list=test.txt
 ```
 
 ### Infer and Visualize
 `infer.py` is the main caller of the inferring module. Examples of usage are shown below.
 ```bash
-python infer.py --dataset='pascalvoc' --nms_threshold=0.45 --model_dir='train_pascal_model/best_model' --image_path='./data/pascalvoc/VOCdevkit/VOC2007/JPEGImages/009963.jpg'
+python infer.py --dataset=pascalvoc --nms_threshold=0.45 --model_dir=model/best_model --image_path=./data/pascalvoc/VOCdevkit/VOC2007/JPEGImages/009963.jpg
 ```
 Below are the examples of running the inference and visualizing the model result.
 <p align="center">

PaddleCV/ssd/train.py

@@ -203,6 +203,7 @@ def train(args,
         fluid.io.save_persistables(exe, model_path, main_program=main_prog)
 
     best_map = 0.
+    test_map = None
     def test(epoc_id, best_map):
         _, accum_map = map_eval.get_map_var()
         map_eval.reset(exe)
@@ -329,4 +330,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()
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