Fix doc for windows uers (#2727)

PaddleCV/human_pose_estimation/README.md

@@ -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)